idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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36,401 | Regression with rank order as dependent variable | I've heard of using an $L$ statistic calculated from $(N-1)r^2$, then compared to the chi-square table. (can anyone back me up on this?) All you'd have to do is convert all data into ranks, run it through a regular old multiple regression, then use the $L$ statistic to find your $p$-values.
However, I feel like inferen... | Regression with rank order as dependent variable | I've heard of using an $L$ statistic calculated from $(N-1)r^2$, then compared to the chi-square table. (can anyone back me up on this?) All you'd have to do is convert all data into ranks, run it thr | Regression with rank order as dependent variable
I've heard of using an $L$ statistic calculated from $(N-1)r^2$, then compared to the chi-square table. (can anyone back me up on this?) All you'd have to do is convert all data into ranks, run it through a regular old multiple regression, then use the $L$ statistic to f... | Regression with rank order as dependent variable
I've heard of using an $L$ statistic calculated from $(N-1)r^2$, then compared to the chi-square table. (can anyone back me up on this?) All you'd have to do is convert all data into ranks, run it thr |
36,402 | Variance-covariance matrix of the parameter estimates wrongly calculated? | Optim in default setting is doing minimization, see the manual:
By default optim performs minimization
So the output is already the negative hessian.
It should be further noted that:
Because the parameters in the call to the optimiser are pi, log(zeta),
log(delta), and mu, the delta method is used to obtain the st... | Variance-covariance matrix of the parameter estimates wrongly calculated? | Optim in default setting is doing minimization, see the manual:
By default optim performs minimization
So the output is already the negative hessian.
It should be further noted that:
Because the pa | Variance-covariance matrix of the parameter estimates wrongly calculated?
Optim in default setting is doing minimization, see the manual:
By default optim performs minimization
So the output is already the negative hessian.
It should be further noted that:
Because the parameters in the call to the optimiser are pi, ... | Variance-covariance matrix of the parameter estimates wrongly calculated?
Optim in default setting is doing minimization, see the manual:
By default optim performs minimization
So the output is already the negative hessian.
It should be further noted that:
Because the pa |
36,403 | Variance-covariance matrix of the parameter estimates wrongly calculated? | Seconded to @Jen 's answer. In fact the 5th line in the result of summary(hyperbfitalv) are SE's. They are indeed the square root of the diagonal elements of inverse-hessian solve(hyperbfitalv$hessian).
>>> sqrt(1.365591e-6)#for pi
0.0011685850418347824
>>> sqrt(5.113433e-3)#for mu
0.071508272248740568
>>> sqrt(1.5261... | Variance-covariance matrix of the parameter estimates wrongly calculated? | Seconded to @Jen 's answer. In fact the 5th line in the result of summary(hyperbfitalv) are SE's. They are indeed the square root of the diagonal elements of inverse-hessian solve(hyperbfitalv$hessian | Variance-covariance matrix of the parameter estimates wrongly calculated?
Seconded to @Jen 's answer. In fact the 5th line in the result of summary(hyperbfitalv) are SE's. They are indeed the square root of the diagonal elements of inverse-hessian solve(hyperbfitalv$hessian).
>>> sqrt(1.365591e-6)#for pi
0.00116858504... | Variance-covariance matrix of the parameter estimates wrongly calculated?
Seconded to @Jen 's answer. In fact the 5th line in the result of summary(hyperbfitalv) are SE's. They are indeed the square root of the diagonal elements of inverse-hessian solve(hyperbfitalv$hessian |
36,404 | Confusion related to predictive distribution of gaussian processes | $P(u*|x*,u) ~ N(u(x*)$, $\sigma^2$), directly from the definition of $u*$.
Notice that integration of two Gaussian pdf is normalized. It can be shown from the fact that
$$
\int_{-\infty}^{\infty}P(u^*|x^*, u)du^* =\int_{-\infty}^{\infty}\int_{u}P(u^*|x^*, u)P(u|s)dudu^*
=\int_{u}P(u|s)\int_{-\infty}^{\infty}P(u^*|x^*, ... | Confusion related to predictive distribution of gaussian processes | $P(u*|x*,u) ~ N(u(x*)$, $\sigma^2$), directly from the definition of $u*$.
Notice that integration of two Gaussian pdf is normalized. It can be shown from the fact that
$$
\int_{-\infty}^{\infty}P(u^* | Confusion related to predictive distribution of gaussian processes
$P(u*|x*,u) ~ N(u(x*)$, $\sigma^2$), directly from the definition of $u*$.
Notice that integration of two Gaussian pdf is normalized. It can be shown from the fact that
$$
\int_{-\infty}^{\infty}P(u^*|x^*, u)du^* =\int_{-\infty}^{\infty}\int_{u}P(u^*|x^... | Confusion related to predictive distribution of gaussian processes
$P(u*|x*,u) ~ N(u(x*)$, $\sigma^2$), directly from the definition of $u*$.
Notice that integration of two Gaussian pdf is normalized. It can be shown from the fact that
$$
\int_{-\infty}^{\infty}P(u^* |
36,405 | Confusion related to predictive distribution of gaussian processes | The detailed derivations of the equations for the conditional distribution of a Gaussian process can be found in chapter 2 and appendix A of the book [Rasmussen2005].
Take a look at (Eq. 2.23, 2.24) and above, which are based on the Gaussian identities (A.6) and the matrix property (A.11).
[Rasmussen2005] C. E. Rasmus... | Confusion related to predictive distribution of gaussian processes | The detailed derivations of the equations for the conditional distribution of a Gaussian process can be found in chapter 2 and appendix A of the book [Rasmussen2005].
Take a look at (Eq. 2.23, 2.24) a | Confusion related to predictive distribution of gaussian processes
The detailed derivations of the equations for the conditional distribution of a Gaussian process can be found in chapter 2 and appendix A of the book [Rasmussen2005].
Take a look at (Eq. 2.23, 2.24) and above, which are based on the Gaussian identities ... | Confusion related to predictive distribution of gaussian processes
The detailed derivations of the equations for the conditional distribution of a Gaussian process can be found in chapter 2 and appendix A of the book [Rasmussen2005].
Take a look at (Eq. 2.23, 2.24) a |
36,406 | Moment generating function of multinomial distribution | I will give the example with $k=2$ because it is more didactic, but you can generalize the solution. Before we start, let's remember that
$$ \sum_{x = 0}^n \frac{n!}{x!(n-x)!}a^xb^{n-x} = (a+b)^n. $$
By definition of the multinomial distribution we have
$$ P(X_1 = x_1, X_2 = x_2) = \frac{n!}{x_1!x_2!(n-x_1-x_2)!}p_1^{x... | Moment generating function of multinomial distribution | I will give the example with $k=2$ because it is more didactic, but you can generalize the solution. Before we start, let's remember that
$$ \sum_{x = 0}^n \frac{n!}{x!(n-x)!}a^xb^{n-x} = (a+b)^n. $$
| Moment generating function of multinomial distribution
I will give the example with $k=2$ because it is more didactic, but you can generalize the solution. Before we start, let's remember that
$$ \sum_{x = 0}^n \frac{n!}{x!(n-x)!}a^xb^{n-x} = (a+b)^n. $$
By definition of the multinomial distribution we have
$$ P(X_1 = ... | Moment generating function of multinomial distribution
I will give the example with $k=2$ because it is more didactic, but you can generalize the solution. Before we start, let's remember that
$$ \sum_{x = 0}^n \frac{n!}{x!(n-x)!}a^xb^{n-x} = (a+b)^n. $$
|
36,407 | Median + MAD for skewed data | If the uncontaminated data in your sample is drawn from an asymmetric distribution and the measure of scale you use to determine the width of the rejection region assumes that the good part of your data is symmetric, then, these rejection regions will be larger than they need to be. For illustration, if the distributio... | Median + MAD for skewed data | If the uncontaminated data in your sample is drawn from an asymmetric distribution and the measure of scale you use to determine the width of the rejection region assumes that the good part of your da | Median + MAD for skewed data
If the uncontaminated data in your sample is drawn from an asymmetric distribution and the measure of scale you use to determine the width of the rejection region assumes that the good part of your data is symmetric, then, these rejection regions will be larger than they need to be. For ill... | Median + MAD for skewed data
If the uncontaminated data in your sample is drawn from an asymmetric distribution and the measure of scale you use to determine the width of the rejection region assumes that the good part of your da |
36,408 | Median + MAD for skewed data | It seems to me that these rejection rules make most sense if you have grounds to believe that your data are drawn from some majority distribution PLUS a contaminating heavier-tailed distribution. That picture of a contaminated situation should ideally draw upon subject-matter knowledge of the real generating process (p... | Median + MAD for skewed data | It seems to me that these rejection rules make most sense if you have grounds to believe that your data are drawn from some majority distribution PLUS a contaminating heavier-tailed distribution. That | Median + MAD for skewed data
It seems to me that these rejection rules make most sense if you have grounds to believe that your data are drawn from some majority distribution PLUS a contaminating heavier-tailed distribution. That picture of a contaminated situation should ideally draw upon subject-matter knowledge of t... | Median + MAD for skewed data
It seems to me that these rejection rules make most sense if you have grounds to believe that your data are drawn from some majority distribution PLUS a contaminating heavier-tailed distribution. That |
36,409 | Path analysis or a full SEM? | 1) There are surely many questions for which a path analysis is sufficient.
2) Increasing complexity or increasing sample size required (etc.) are not by themselves valid reasons to avoid inclusion of latent variables.
3) Latent variables are not "treated as observed" in a path analysis - it's more accurate to say th... | Path analysis or a full SEM? | 1) There are surely many questions for which a path analysis is sufficient.
2) Increasing complexity or increasing sample size required (etc.) are not by themselves valid reasons to avoid inclusion o | Path analysis or a full SEM?
1) There are surely many questions for which a path analysis is sufficient.
2) Increasing complexity or increasing sample size required (etc.) are not by themselves valid reasons to avoid inclusion of latent variables.
3) Latent variables are not "treated as observed" in a path analysis -... | Path analysis or a full SEM?
1) There are surely many questions for which a path analysis is sufficient.
2) Increasing complexity or increasing sample size required (etc.) are not by themselves valid reasons to avoid inclusion o |
36,410 | Fat-shattering dimension | I was also looking for an explanation and this is the best one I got (Section 4.1.2). Apparently fat-shattering is a restrictive form of P-shattering that says for some fixed $r_x$ there is some $f$ that has a margin of atleast $\gamma$.
Check out the figure in reference for clearer explanation. | Fat-shattering dimension | I was also looking for an explanation and this is the best one I got (Section 4.1.2). Apparently fat-shattering is a restrictive form of P-shattering that says for some fixed $r_x$ there is some $f$ t | Fat-shattering dimension
I was also looking for an explanation and this is the best one I got (Section 4.1.2). Apparently fat-shattering is a restrictive form of P-shattering that says for some fixed $r_x$ there is some $f$ that has a margin of atleast $\gamma$.
Check out the figure in reference for clearer explanation... | Fat-shattering dimension
I was also looking for an explanation and this is the best one I got (Section 4.1.2). Apparently fat-shattering is a restrictive form of P-shattering that says for some fixed $r_x$ there is some $f$ t |
36,411 | How to perform an exponential regression with multiple variables in R | As a start:
f <- function(x1,x2,a,b1,b2) {a * (b1^x1) * (b2^x2) }
# generate some data
x1 <- 1:10
x2 <- c(2,3,5,4,6,7,8,10,9,11)
set.seed(44)
y <- 2*exp(x1/4) + rnorm(10)*2
dat <- data.frame(x1,x2, y)
# fit a nonlinear model
fm <- nls(y ~ f(x1,x2,a,b1,b2), data = dat, start = c(a=1, b1=1,b2=1))
# get estimates of a,... | How to perform an exponential regression with multiple variables in R | As a start:
f <- function(x1,x2,a,b1,b2) {a * (b1^x1) * (b2^x2) }
# generate some data
x1 <- 1:10
x2 <- c(2,3,5,4,6,7,8,10,9,11)
set.seed(44)
y <- 2*exp(x1/4) + rnorm(10)*2
dat <- data.frame(x1,x2, y | How to perform an exponential regression with multiple variables in R
As a start:
f <- function(x1,x2,a,b1,b2) {a * (b1^x1) * (b2^x2) }
# generate some data
x1 <- 1:10
x2 <- c(2,3,5,4,6,7,8,10,9,11)
set.seed(44)
y <- 2*exp(x1/4) + rnorm(10)*2
dat <- data.frame(x1,x2, y)
# fit a nonlinear model
fm <- nls(y ~ f(x1,x2,a... | How to perform an exponential regression with multiple variables in R
As a start:
f <- function(x1,x2,a,b1,b2) {a * (b1^x1) * (b2^x2) }
# generate some data
x1 <- 1:10
x2 <- c(2,3,5,4,6,7,8,10,9,11)
set.seed(44)
y <- 2*exp(x1/4) + rnorm(10)*2
dat <- data.frame(x1,x2, y |
36,412 | How to perform an exponential regression with multiple variables in R | Huub Hoofs' approach above worked! Thank you. Here is the technique I utilized to plot a visualization of the model:
# x1 is the variable we want to show on the x-axis
plot(x1, y)
# generate a range of values for x1 in small increments to create a smooth line
xRange <- seq(min(x1), max(x1), length.out = 1000)
# gene... | How to perform an exponential regression with multiple variables in R | Huub Hoofs' approach above worked! Thank you. Here is the technique I utilized to plot a visualization of the model:
# x1 is the variable we want to show on the x-axis
plot(x1, y)
# generate a range | How to perform an exponential regression with multiple variables in R
Huub Hoofs' approach above worked! Thank you. Here is the technique I utilized to plot a visualization of the model:
# x1 is the variable we want to show on the x-axis
plot(x1, y)
# generate a range of values for x1 in small increments to create a ... | How to perform an exponential regression with multiple variables in R
Huub Hoofs' approach above worked! Thank you. Here is the technique I utilized to plot a visualization of the model:
# x1 is the variable we want to show on the x-axis
plot(x1, y)
# generate a range |
36,413 | How to perform an exponential regression with multiple variables in R | If you really want to look at R2, it's best to linearize your model.
Observe that R2 doesn't make sense for non linear general models, as discussed in another topic: https://stackoverflow.com/questions/14530770/calculating-r2-for-a-nonlinear-least-squares-fit
Note that the r squared is not defined for non-linear models... | How to perform an exponential regression with multiple variables in R | If you really want to look at R2, it's best to linearize your model.
Observe that R2 doesn't make sense for non linear general models, as discussed in another topic: https://stackoverflow.com/question | How to perform an exponential regression with multiple variables in R
If you really want to look at R2, it's best to linearize your model.
Observe that R2 doesn't make sense for non linear general models, as discussed in another topic: https://stackoverflow.com/questions/14530770/calculating-r2-for-a-nonlinear-least-sq... | How to perform an exponential regression with multiple variables in R
If you really want to look at R2, it's best to linearize your model.
Observe that R2 doesn't make sense for non linear general models, as discussed in another topic: https://stackoverflow.com/question |
36,414 | Why does the Phi coefficient approximates the Pearson's correlation? | By default, chisq.test() applies a continuity correction when computing the test statistic for 2x2 tables. If you switch off this behavior, then:
x = c(1, 1, 0, 0, 1, 0, 1, 1, 1)
y = c(1, 1, 0, 0, 0, 0, 1, 1, 1)
cor(x,y)
sqrt(chisq.test(table(x,y), correct=FALSE)$statistic/length(x)) # phi
will give yo... | Why does the Phi coefficient approximates the Pearson's correlation? | By default, chisq.test() applies a continuity correction when computing the test statistic for 2x2 tables. If you switch off this behavior, then:
x = c(1, 1, 0, 0, 1, 0, 1, 1, 1)
y = c(1, 1, | Why does the Phi coefficient approximates the Pearson's correlation?
By default, chisq.test() applies a continuity correction when computing the test statistic for 2x2 tables. If you switch off this behavior, then:
x = c(1, 1, 0, 0, 1, 0, 1, 1, 1)
y = c(1, 1, 0, 0, 0, 0, 1, 1, 1)
cor(x,y)
sqrt(chisq.tes... | Why does the Phi coefficient approximates the Pearson's correlation?
By default, chisq.test() applies a continuity correction when computing the test statistic for 2x2 tables. If you switch off this behavior, then:
x = c(1, 1, 0, 0, 1, 0, 1, 1, 1)
y = c(1, 1, |
36,415 | has anyone implemented an autoencoder with random forests | Maybe a little late but...
Ji Feng and Zhi-Hua Zhou (2017) have recently proposed an autoencoder model based on tree ensembles.
They learn a random forest or build a "completely-random forest" for the encoder part. To decode it, they follow tree branchs backward from leaves to the root which gives a series of rules fr... | has anyone implemented an autoencoder with random forests | Maybe a little late but...
Ji Feng and Zhi-Hua Zhou (2017) have recently proposed an autoencoder model based on tree ensembles.
They learn a random forest or build a "completely-random forest" for th | has anyone implemented an autoencoder with random forests
Maybe a little late but...
Ji Feng and Zhi-Hua Zhou (2017) have recently proposed an autoencoder model based on tree ensembles.
They learn a random forest or build a "completely-random forest" for the encoder part. To decode it, they follow tree branchs backwar... | has anyone implemented an autoencoder with random forests
Maybe a little late but...
Ji Feng and Zhi-Hua Zhou (2017) have recently proposed an autoencoder model based on tree ensembles.
They learn a random forest or build a "completely-random forest" for th |
36,416 | has anyone implemented an autoencoder with random forests | You can use the 1-hot encoding - for a single tree, each example is represented by a vector containing 1 with the selected leaf, and combine these vectors for a forest (either concatenated or OR'ed). This gives you an intermediate representation.
Another option is to use the proximity measure [1] to compute an unsuperv... | has anyone implemented an autoencoder with random forests | You can use the 1-hot encoding - for a single tree, each example is represented by a vector containing 1 with the selected leaf, and combine these vectors for a forest (either concatenated or OR'ed). | has anyone implemented an autoencoder with random forests
You can use the 1-hot encoding - for a single tree, each example is represented by a vector containing 1 with the selected leaf, and combine these vectors for a forest (either concatenated or OR'ed). This gives you an intermediate representation.
Another option ... | has anyone implemented an autoencoder with random forests
You can use the 1-hot encoding - for a single tree, each example is represented by a vector containing 1 with the selected leaf, and combine these vectors for a forest (either concatenated or OR'ed). |
36,417 | Nested/SplitModel - RepeatedMeasures/MixedModel ANOVA: levels of nesting & scripting in R | Tricky problem! Is location fixed or random? Is position fixed or random? I assume that sample is random.
Since treatment is assigned to location, location is the sampling unit. Basically, the comparison between treatments is done at that level. $n=8$.
The measurement unit is the observation you take on your "samples"... | Nested/SplitModel - RepeatedMeasures/MixedModel ANOVA: levels of nesting & scripting in R | Tricky problem! Is location fixed or random? Is position fixed or random? I assume that sample is random.
Since treatment is assigned to location, location is the sampling unit. Basically, the compar | Nested/SplitModel - RepeatedMeasures/MixedModel ANOVA: levels of nesting & scripting in R
Tricky problem! Is location fixed or random? Is position fixed or random? I assume that sample is random.
Since treatment is assigned to location, location is the sampling unit. Basically, the comparison between treatments is don... | Nested/SplitModel - RepeatedMeasures/MixedModel ANOVA: levels of nesting & scripting in R
Tricky problem! Is location fixed or random? Is position fixed or random? I assume that sample is random.
Since treatment is assigned to location, location is the sampling unit. Basically, the compar |
36,418 | How should I handle a left censored predictor variable in multiple regression? | One option is to include a variable that is 1 if symptom severity was not measured and 0 otherwise, then code all the symptom severities that were not measured as 0. The coefficient on the 0/1 variable will represent the average test score for those that did not have the severity measured and the slope for the severit... | How should I handle a left censored predictor variable in multiple regression? | One option is to include a variable that is 1 if symptom severity was not measured and 0 otherwise, then code all the symptom severities that were not measured as 0. The coefficient on the 0/1 variab | How should I handle a left censored predictor variable in multiple regression?
One option is to include a variable that is 1 if symptom severity was not measured and 0 otherwise, then code all the symptom severities that were not measured as 0. The coefficient on the 0/1 variable will represent the average test score ... | How should I handle a left censored predictor variable in multiple regression?
One option is to include a variable that is 1 if symptom severity was not measured and 0 otherwise, then code all the symptom severities that were not measured as 0. The coefficient on the 0/1 variab |
36,419 | In calculating the F-measure with precision and recall, why is the harmonic mean used? | The F-measure is often used in the natural language recognition field for means of evaluation. In particular, the F-measure was employed by the Message Understanding Conference (MUC), in order to evaluate named entity recognition (NER) tasks.
Directly quoted from A survey of named entity recognition and classification... | In calculating the F-measure with precision and recall, why is the harmonic mean used? | The F-measure is often used in the natural language recognition field for means of evaluation. In particular, the F-measure was employed by the Message Understanding Conference (MUC), in order to eval | In calculating the F-measure with precision and recall, why is the harmonic mean used?
The F-measure is often used in the natural language recognition field for means of evaluation. In particular, the F-measure was employed by the Message Understanding Conference (MUC), in order to evaluate named entity recognition (NE... | In calculating the F-measure with precision and recall, why is the harmonic mean used?
The F-measure is often used in the natural language recognition field for means of evaluation. In particular, the F-measure was employed by the Message Understanding Conference (MUC), in order to eval |
36,420 | Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set of data points | A typical way to test if two one-dimensional distribution functions are different is with the Kolmogorov-Smirnov test which is based on the statistic:
$\begin{align*}
\underset{x}{\operatorname{sup}}\:|F_1(x) - F_2(x)|
\end{align*}
$
The problem is that in higher dimensions there are $2^d-1$ ways to define a distributi... | Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set | A typical way to test if two one-dimensional distribution functions are different is with the Kolmogorov-Smirnov test which is based on the statistic:
$\begin{align*}
\underset{x}{\operatorname{sup}}\ | Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set of data points
A typical way to test if two one-dimensional distribution functions are different is with the Kolmogorov-Smirnov test which is based on the statistic:
$\begin{align*}
\underset{x}{\operatorname{sup}}\:|F_1... | Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set
A typical way to test if two one-dimensional distribution functions are different is with the Kolmogorov-Smirnov test which is based on the statistic:
$\begin{align*}
\underset{x}{\operatorname{sup}}\ |
36,421 | Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set of data points | A good test provides insight as well as a quantification of the apparent difference. A permutation test will do that, because you can plot the permutation distribution and it will show you just how and to what extent there is a difference in your data.
A natural test statistic would be the mean difference between the ... | Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set | A good test provides insight as well as a quantification of the apparent difference. A permutation test will do that, because you can plot the permutation distribution and it will show you just how a | Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set of data points
A good test provides insight as well as a quantification of the apparent difference. A permutation test will do that, because you can plot the permutation distribution and it will show you just how and to... | Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set
A good test provides insight as well as a quantification of the apparent difference. A permutation test will do that, because you can plot the permutation distribution and it will show you just how a |
36,422 | Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set of data points | Given the small sample size in your graph, the Wilcoxon rank-sum test seems appropriate to compare the y values in the red and blue groups. | Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set | Given the small sample size in your graph, the Wilcoxon rank-sum test seems appropriate to compare the y values in the red and blue groups. | Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set of data points
Given the small sample size in your graph, the Wilcoxon rank-sum test seems appropriate to compare the y values in the red and blue groups. | Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set
Given the small sample size in your graph, the Wilcoxon rank-sum test seems appropriate to compare the y values in the red and blue groups. |
36,423 | Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set of data points | I just read this and if you're trying to determine whether these points are from different clusters in 2D space I'd recommend simply taking a multivariate approach on this.
Using discriminant analysis (DA) on the x,y coordinates as Y (covariates) and using color as the X (categories). The area under the resulting ROC ... | Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set | I just read this and if you're trying to determine whether these points are from different clusters in 2D space I'd recommend simply taking a multivariate approach on this.
Using discriminant analysis | Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set of data points
I just read this and if you're trying to determine whether these points are from different clusters in 2D space I'd recommend simply taking a multivariate approach on this.
Using discriminant analysis (DA)... | Test whether (x,y) of one set of data points is significantly greater than the (x,y) of another set
I just read this and if you're trying to determine whether these points are from different clusters in 2D space I'd recommend simply taking a multivariate approach on this.
Using discriminant analysis |
36,424 | Which distributions on [0,1] other than the beta distribution form nice compounds with the binomial distribution? | If you just want to be able to write down the probability mass function, you have a lot of flexibility, basically because you can repeatedly use integration by parts. As long as you can integrate the distribution of $X$ repeatedly to get closed form expressions, you get at worst a double sum of closed form expressions ... | Which distributions on [0,1] other than the beta distribution form nice compounds with the binomial | If you just want to be able to write down the probability mass function, you have a lot of flexibility, basically because you can repeatedly use integration by parts. As long as you can integrate the | Which distributions on [0,1] other than the beta distribution form nice compounds with the binomial distribution?
If you just want to be able to write down the probability mass function, you have a lot of flexibility, basically because you can repeatedly use integration by parts. As long as you can integrate the distri... | Which distributions on [0,1] other than the beta distribution form nice compounds with the binomial
If you just want to be able to write down the probability mass function, you have a lot of flexibility, basically because you can repeatedly use integration by parts. As long as you can integrate the |
36,425 | $\chi^2$ test on user preferences | A psychologically meaningful model can guide us.
Derivation of a useful test
Any variation in the observations can be attributed to variations among the subjects. We might imagine that each subject, at some level, comes up with a numeric value for the result of method 1 and a numeric value for the result of method 2. T... | $\chi^2$ test on user preferences | A psychologically meaningful model can guide us.
Derivation of a useful test
Any variation in the observations can be attributed to variations among the subjects. We might imagine that each subject, a | $\chi^2$ test on user preferences
A psychologically meaningful model can guide us.
Derivation of a useful test
Any variation in the observations can be attributed to variations among the subjects. We might imagine that each subject, at some level, comes up with a numeric value for the result of method 1 and a numeric v... | $\chi^2$ test on user preferences
A psychologically meaningful model can guide us.
Derivation of a useful test
Any variation in the observations can be attributed to variations among the subjects. We might imagine that each subject, a |
36,426 | $\chi^2$ test on user preferences | I suspect whuber's answer is (as usual) more replete than what I am about to type. I admit, I may not fully understand whuber's answer... so what I am saying may not be unique or useful. However, I did not notice where in whuber's answer the nesting of preferences under individuals as well as the nesting of preferenc... | $\chi^2$ test on user preferences | I suspect whuber's answer is (as usual) more replete than what I am about to type. I admit, I may not fully understand whuber's answer... so what I am saying may not be unique or useful. However, I | $\chi^2$ test on user preferences
I suspect whuber's answer is (as usual) more replete than what I am about to type. I admit, I may not fully understand whuber's answer... so what I am saying may not be unique or useful. However, I did not notice where in whuber's answer the nesting of preferences under individuals a... | $\chi^2$ test on user preferences
I suspect whuber's answer is (as usual) more replete than what I am about to type. I admit, I may not fully understand whuber's answer... so what I am saying may not be unique or useful. However, I |
36,427 | Using kriging with very sparse data | From ten points you are going to have 45 (10*(10-1)/2) points in your variogram cloud from the distances between each pair of points. Once the system has binned that, or even without binning, its going to be dominated by noise, I reckon. Get a plot of the variogram cloud to see what I mean.
If autokrige can't fit a nic... | Using kriging with very sparse data | From ten points you are going to have 45 (10*(10-1)/2) points in your variogram cloud from the distances between each pair of points. Once the system has binned that, or even without binning, its goin | Using kriging with very sparse data
From ten points you are going to have 45 (10*(10-1)/2) points in your variogram cloud from the distances between each pair of points. Once the system has binned that, or even without binning, its going to be dominated by noise, I reckon. Get a plot of the variogram cloud to see what ... | Using kriging with very sparse data
From ten points you are going to have 45 (10*(10-1)/2) points in your variogram cloud from the distances between each pair of points. Once the system has binned that, or even without binning, its goin |
36,428 | Is the overlap between two gene expression samples significant? | The table looks like this
37 deg C
42 deg C yes no
yes 38 97
no 10 4855
yes and no refer to cases overexpressed or not
I ran Fisher's exact test in SAS
The output is pasted below:
Laura Gene expression data
The FREQ Procedure
Statistics for Table of Group by ... | Is the overlap between two gene expression samples significant? | The table looks like this
37 deg C
42 deg C yes no
yes 38 97
no 10 4855
yes and no refer to cases overexpressed or not
I ran Fisher's exact test | Is the overlap between two gene expression samples significant?
The table looks like this
37 deg C
42 deg C yes no
yes 38 97
no 10 4855
yes and no refer to cases overexpressed or not
I ran Fisher's exact test in SAS
The output is pasted below:
Laura Gene expressio... | Is the overlap between two gene expression samples significant?
The table looks like this
37 deg C
42 deg C yes no
yes 38 97
no 10 4855
yes and no refer to cases overexpressed or not
I ran Fisher's exact test |
36,429 | Is the overlap between two gene expression samples significant? | The exact test referred to by Michael is probably the way I would recommend using to solve the problem (fewest assumptions). For reference, the corresponding common statistical test would be a $\chi^2$ test of independence. | Is the overlap between two gene expression samples significant? | The exact test referred to by Michael is probably the way I would recommend using to solve the problem (fewest assumptions). For reference, the corresponding common statistical test would be a $\chi^ | Is the overlap between two gene expression samples significant?
The exact test referred to by Michael is probably the way I would recommend using to solve the problem (fewest assumptions). For reference, the corresponding common statistical test would be a $\chi^2$ test of independence. | Is the overlap between two gene expression samples significant?
The exact test referred to by Michael is probably the way I would recommend using to solve the problem (fewest assumptions). For reference, the corresponding common statistical test would be a $\chi^ |
36,430 | Some questions about two-sample comparisons | I will answer your bullets with bullets of my own in the same order:
I think the sentence is referring to the large sample (asymptotic) distribution of the test statistic, not the data. As you can see here, the Mann-Whitney U test statistic has an approximate normal distribution when the sample size is large.
In ord... | Some questions about two-sample comparisons | I will answer your bullets with bullets of my own in the same order:
I think the sentence is referring to the large sample (asymptotic) distribution of the test statistic, not the data. As you can s | Some questions about two-sample comparisons
I will answer your bullets with bullets of my own in the same order:
I think the sentence is referring to the large sample (asymptotic) distribution of the test statistic, not the data. As you can see here, the Mann-Whitney U test statistic has an approximate normal distrib... | Some questions about two-sample comparisons
I will answer your bullets with bullets of my own in the same order:
I think the sentence is referring to the large sample (asymptotic) distribution of the test statistic, not the data. As you can s |
36,431 | Is it acceptable to use Cronbach's alpha to assess reliability of questionnaire composed of categorical and conditional items? | Some quick rules
If you have unordered categorical data (i.e., three or more unordered categories; which you do), then you don't use Cronbach's alpha.
If you have binary data (e.g., incorrect/correct data), then many people do use Cronbach's alpha, but see the Sjitsma reference given by @Momo.
If you have conditional... | Is it acceptable to use Cronbach's alpha to assess reliability of questionnaire composed of categori | Some quick rules
If you have unordered categorical data (i.e., three or more unordered categories; which you do), then you don't use Cronbach's alpha.
If you have binary data (e.g., incorrect/correc | Is it acceptable to use Cronbach's alpha to assess reliability of questionnaire composed of categorical and conditional items?
Some quick rules
If you have unordered categorical data (i.e., three or more unordered categories; which you do), then you don't use Cronbach's alpha.
If you have binary data (e.g., incorrect... | Is it acceptable to use Cronbach's alpha to assess reliability of questionnaire composed of categori
Some quick rules
If you have unordered categorical data (i.e., three or more unordered categories; which you do), then you don't use Cronbach's alpha.
If you have binary data (e.g., incorrect/correc |
36,432 | Is it acceptable to use Cronbach's alpha to assess reliability of questionnaire composed of categorical and conditional items? | Generally Cronbach's coefficient $\alpha$ should not be used if you want a measure of reliability or internal consistency (which is what you need it for, I presume). See the OA Psychometrika article by Sjitsma.
An easily available alternative is the GLB statistic (e.g. in R psych::glb).
Edit
Based on the comments by @... | Is it acceptable to use Cronbach's alpha to assess reliability of questionnaire composed of categori | Generally Cronbach's coefficient $\alpha$ should not be used if you want a measure of reliability or internal consistency (which is what you need it for, I presume). See the OA Psychometrika article b | Is it acceptable to use Cronbach's alpha to assess reliability of questionnaire composed of categorical and conditional items?
Generally Cronbach's coefficient $\alpha$ should not be used if you want a measure of reliability or internal consistency (which is what you need it for, I presume). See the OA Psychometrika ar... | Is it acceptable to use Cronbach's alpha to assess reliability of questionnaire composed of categori
Generally Cronbach's coefficient $\alpha$ should not be used if you want a measure of reliability or internal consistency (which is what you need it for, I presume). See the OA Psychometrika article b |
36,433 | Is it acceptable to use Cronbach's alpha to assess reliability of questionnaire composed of categorical and conditional items? | If you are looking at Yes/No items or items coded of 0's and 1's, I have used Guttman's split Lambda 4 coefficient, which can be done in SPSS easily. | Is it acceptable to use Cronbach's alpha to assess reliability of questionnaire composed of categori | If you are looking at Yes/No items or items coded of 0's and 1's, I have used Guttman's split Lambda 4 coefficient, which can be done in SPSS easily. | Is it acceptable to use Cronbach's alpha to assess reliability of questionnaire composed of categorical and conditional items?
If you are looking at Yes/No items or items coded of 0's and 1's, I have used Guttman's split Lambda 4 coefficient, which can be done in SPSS easily. | Is it acceptable to use Cronbach's alpha to assess reliability of questionnaire composed of categori
If you are looking at Yes/No items or items coded of 0's and 1's, I have used Guttman's split Lambda 4 coefficient, which can be done in SPSS easily. |
36,434 | OLS vs. logistic regression for exploratory analysis with a binary outcome | If the explanatory variables have values over the entire real line it makes little sense to express an expectation that is a proportion in $[0,1]$ as a linear function of variable defined over the entire real line. If the sigmoid shape of the logit transformation doesn't describe the shape then perhaps it is best to s... | OLS vs. logistic regression for exploratory analysis with a binary outcome | If the explanatory variables have values over the entire real line it makes little sense to express an expectation that is a proportion in $[0,1]$ as a linear function of variable defined over the ent | OLS vs. logistic regression for exploratory analysis with a binary outcome
If the explanatory variables have values over the entire real line it makes little sense to express an expectation that is a proportion in $[0,1]$ as a linear function of variable defined over the entire real line. If the sigmoid shape of the l... | OLS vs. logistic regression for exploratory analysis with a binary outcome
If the explanatory variables have values over the entire real line it makes little sense to express an expectation that is a proportion in $[0,1]$ as a linear function of variable defined over the ent |
36,435 | logistic regression. How to get dual function? | Tom Minka gives the derivation in this excellent paper "A comparison of numerical optimizers for logistic regression" pdf, section 9 | logistic regression. How to get dual function? | Tom Minka gives the derivation in this excellent paper "A comparison of numerical optimizers for logistic regression" pdf, section 9 | logistic regression. How to get dual function?
Tom Minka gives the derivation in this excellent paper "A comparison of numerical optimizers for logistic regression" pdf, section 9 | logistic regression. How to get dual function?
Tom Minka gives the derivation in this excellent paper "A comparison of numerical optimizers for logistic regression" pdf, section 9 |
36,436 | logistic regression. How to get dual function? | LIBLINEAR supports $\ell_2$-regularized logistic regression. According to the authors, the package implements the "trust region Newton method". Here, you can find the slides to learn more, but note that it is not based on the dual formulation.
@whuber I am explaining here, because there wasn't space in the comments...
... | logistic regression. How to get dual function? | LIBLINEAR supports $\ell_2$-regularized logistic regression. According to the authors, the package implements the "trust region Newton method". Here, you can find the slides to learn more, but note th | logistic regression. How to get dual function?
LIBLINEAR supports $\ell_2$-regularized logistic regression. According to the authors, the package implements the "trust region Newton method". Here, you can find the slides to learn more, but note that it is not based on the dual formulation.
@whuber I am explaining here,... | logistic regression. How to get dual function?
LIBLINEAR supports $\ell_2$-regularized logistic regression. According to the authors, the package implements the "trust region Newton method". Here, you can find the slides to learn more, but note th |
36,437 | logistic regression. How to get dual function? | Why not just take partial derivative of minimization function with respect to unknown parameters? You can find a lot of material on the web. | logistic regression. How to get dual function? | Why not just take partial derivative of minimization function with respect to unknown parameters? You can find a lot of material on the web. | logistic regression. How to get dual function?
Why not just take partial derivative of minimization function with respect to unknown parameters? You can find a lot of material on the web. | logistic regression. How to get dual function?
Why not just take partial derivative of minimization function with respect to unknown parameters? You can find a lot of material on the web. |
36,438 | logistic regression. How to get dual function? | Use the lower bound of the convex function $\log(1+x)$:
$$
\log (1+x) \ge \log(1+x_1) +(x-x_1)*(`d(\log(1+x_1))` )
$$
where $d(f)$ is the differentiation of $f$.
Note that the factor of $x$ on the right is your dual variable, which is in between $[0,1]$; now the problem is quadratic in $w$ and can be solved. | logistic regression. How to get dual function? | Use the lower bound of the convex function $\log(1+x)$:
$$
\log (1+x) \ge \log(1+x_1) +(x-x_1)*(`d(\log(1+x_1))` )
$$
where $d(f)$ is the differentiation of $f$.
Note that the factor of $x$ on the | logistic regression. How to get dual function?
Use the lower bound of the convex function $\log(1+x)$:
$$
\log (1+x) \ge \log(1+x_1) +(x-x_1)*(`d(\log(1+x_1))` )
$$
where $d(f)$ is the differentiation of $f$.
Note that the factor of $x$ on the right is your dual variable, which is in between $[0,1]$; now the problem... | logistic regression. How to get dual function?
Use the lower bound of the convex function $\log(1+x)$:
$$
\log (1+x) \ge \log(1+x_1) +(x-x_1)*(`d(\log(1+x_1))` )
$$
where $d(f)$ is the differentiation of $f$.
Note that the factor of $x$ on the |
36,439 | Binomial regression asymptotes | Interesting question. A possibility that comes to my mind is including an additional parameter $p\in[0,1]$ in order to control the upper bound of the 'link' function.
Let $\{{\bf x}_j,y_j,n_j\}$, $j=1,...,n$ be independent observations, where $y_j\sim \text{Binomial}\{n_i,pF({\bf x}_j^T\beta)\}$, $p\in[0,1]$, ${\bf x}_... | Binomial regression asymptotes | Interesting question. A possibility that comes to my mind is including an additional parameter $p\in[0,1]$ in order to control the upper bound of the 'link' function.
Let $\{{\bf x}_j,y_j,n_j\}$, $j=1 | Binomial regression asymptotes
Interesting question. A possibility that comes to my mind is including an additional parameter $p\in[0,1]$ in order to control the upper bound of the 'link' function.
Let $\{{\bf x}_j,y_j,n_j\}$, $j=1,...,n$ be independent observations, where $y_j\sim \text{Binomial}\{n_i,pF({\bf x}_j^T\b... | Binomial regression asymptotes
Interesting question. A possibility that comes to my mind is including an additional parameter $p\in[0,1]$ in order to control the upper bound of the 'link' function.
Let $\{{\bf x}_j,y_j,n_j\}$, $j=1 |
36,440 | Binomial regression asymptotes | I would use the maximum of the X vector as the total possible number of successes. (This is a biased estimate of the true maximum number of successes, but it should work fairly well if you have enough data).
accuracy <- c(0, 0, 0, 0, 0, 1, 3, 5, 9, 13, 14, 15, 14, 15, 16,
15, 14, 14, 15)
x <- 1:length(ac... | Binomial regression asymptotes | I would use the maximum of the X vector as the total possible number of successes. (This is a biased estimate of the true maximum number of successes, but it should work fairly well if you have enough | Binomial regression asymptotes
I would use the maximum of the X vector as the total possible number of successes. (This is a biased estimate of the true maximum number of successes, but it should work fairly well if you have enough data).
accuracy <- c(0, 0, 0, 0, 0, 1, 3, 5, 9, 13, 14, 15, 14, 15, 16,
1... | Binomial regression asymptotes
I would use the maximum of the X vector as the total possible number of successes. (This is a biased estimate of the true maximum number of successes, but it should work fairly well if you have enough |
36,441 | Binomial regression asymptotes | Note that binomial regression is based on having a binary response for each individual case. each individual response has to be able to take one of two values. If there is some limit to the proportion then there must also have been some cases which could only take one value.
It sounds like you are not dealing with bi... | Binomial regression asymptotes | Note that binomial regression is based on having a binary response for each individual case. each individual response has to be able to take one of two values. If there is some limit to the proporti | Binomial regression asymptotes
Note that binomial regression is based on having a binary response for each individual case. each individual response has to be able to take one of two values. If there is some limit to the proportion then there must also have been some cases which could only take one value.
It sounds l... | Binomial regression asymptotes
Note that binomial regression is based on having a binary response for each individual case. each individual response has to be able to take one of two values. If there is some limit to the proporti |
36,442 | What are the primary differences between Taxometric analyses (e.g., MAXCOV, MAXEIG) and Latent Class analyses? | See Tueller (2010), Tueller and Lubke (2010), and [Ruscio et al.'l book][3] for complete detail on what is summarized below. Taxometric procedures generally work by computing simple statistics on subset of sorted data. MAMBAC uses the mean, MAXCOV uses the covariance, and MAXEIG using the eigen value. Latent class anal... | What are the primary differences between Taxometric analyses (e.g., MAXCOV, MAXEIG) and Latent Class | See Tueller (2010), Tueller and Lubke (2010), and [Ruscio et al.'l book][3] for complete detail on what is summarized below. Taxometric procedures generally work by computing simple statistics on subs | What are the primary differences between Taxometric analyses (e.g., MAXCOV, MAXEIG) and Latent Class analyses?
See Tueller (2010), Tueller and Lubke (2010), and [Ruscio et al.'l book][3] for complete detail on what is summarized below. Taxometric procedures generally work by computing simple statistics on subset of sor... | What are the primary differences between Taxometric analyses (e.g., MAXCOV, MAXEIG) and Latent Class
See Tueller (2010), Tueller and Lubke (2010), and [Ruscio et al.'l book][3] for complete detail on what is summarized below. Taxometric procedures generally work by computing simple statistics on subs |
36,443 | Does it make sense to consider non-binary logit? | There are situations where this sort of thing makes sense, for instance say you are trying to determine if someone is likely to like some particular icecream as a function of the ingredients, then you could get a sample of say 100 people and get them to taste each icecream and say whether they like it or not. If you a... | Does it make sense to consider non-binary logit? | There are situations where this sort of thing makes sense, for instance say you are trying to determine if someone is likely to like some particular icecream as a function of the ingredients, then you | Does it make sense to consider non-binary logit?
There are situations where this sort of thing makes sense, for instance say you are trying to determine if someone is likely to like some particular icecream as a function of the ingredients, then you could get a sample of say 100 people and get them to taste each icecre... | Does it make sense to consider non-binary logit?
There are situations where this sort of thing makes sense, for instance say you are trying to determine if someone is likely to like some particular icecream as a function of the ingredients, then you |
36,444 | Does it make sense to consider non-binary logit? | It definitely makes sense in the case of values between 0 and 1. Consider if you have training data with identical X but different Y. If you average the Ys for those X (and keep the proportion of those samples the original data set unchanged), you'll arrive at the same optimal solution.
Another way of thinking about ... | Does it make sense to consider non-binary logit? | It definitely makes sense in the case of values between 0 and 1. Consider if you have training data with identical X but different Y. If you average the Ys for those X (and keep the proportion of th | Does it make sense to consider non-binary logit?
It definitely makes sense in the case of values between 0 and 1. Consider if you have training data with identical X but different Y. If you average the Ys for those X (and keep the proportion of those samples the original data set unchanged), you'll arrive at the same... | Does it make sense to consider non-binary logit?
It definitely makes sense in the case of values between 0 and 1. Consider if you have training data with identical X but different Y. If you average the Ys for those X (and keep the proportion of th |
36,445 | When is half normal distribution useful? | In Bayesian statistics the half normal,with a sufficiently large variation parameter, can be used as a noninformative prior distribution on the SD of a standard distribution. This is suggested in, for example:
Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models. Bayesian analysis, 1, 5... | When is half normal distribution useful? | In Bayesian statistics the half normal,with a sufficiently large variation parameter, can be used as a noninformative prior distribution on the SD of a standard distribution. This is suggested in, for | When is half normal distribution useful?
In Bayesian statistics the half normal,with a sufficiently large variation parameter, can be used as a noninformative prior distribution on the SD of a standard distribution. This is suggested in, for example:
Gelman, A. (2006). Prior distributions for variance parameters in hie... | When is half normal distribution useful?
In Bayesian statistics the half normal,with a sufficiently large variation parameter, can be used as a noninformative prior distribution on the SD of a standard distribution. This is suggested in, for |
36,446 | When is half normal distribution useful? | In quality control, there is something called a moving-range statistic which is the absolute value of successive differences. The half-normal serves as the basis of the chart as discussed in the following article:
http://www.tandfonline.com/doi/abs/10.1080/08982119508904612#preview | When is half normal distribution useful? | In quality control, there is something called a moving-range statistic which is the absolute value of successive differences. The half-normal serves as the basis of the chart as discussed in the foll | When is half normal distribution useful?
In quality control, there is something called a moving-range statistic which is the absolute value of successive differences. The half-normal serves as the basis of the chart as discussed in the following article:
http://www.tandfonline.com/doi/abs/10.1080/08982119508904612#pre... | When is half normal distribution useful?
In quality control, there is something called a moving-range statistic which is the absolute value of successive differences. The half-normal serves as the basis of the chart as discussed in the foll |
36,447 | Multi-dimensional goodness of fit | You can always bin/discretize your data- even in higher dimensions- and use a Chi-square test.
A review of several adaptations of the KS for two dimensions, can be found here. | Multi-dimensional goodness of fit | You can always bin/discretize your data- even in higher dimensions- and use a Chi-square test.
A review of several adaptations of the KS for two dimensions, can be found here. | Multi-dimensional goodness of fit
You can always bin/discretize your data- even in higher dimensions- and use a Chi-square test.
A review of several adaptations of the KS for two dimensions, can be found here. | Multi-dimensional goodness of fit
You can always bin/discretize your data- even in higher dimensions- and use a Chi-square test.
A review of several adaptations of the KS for two dimensions, can be found here. |
36,448 | Show series of exponential decay distributions in one chart | One simple adjustment to your current graphic production would be, instead of producing all of the elements of the box-plot (even the minimalist Tufte style one), would be to produce a line chart connecting the summary statistics that the box plot is displaying (median, quartiles, mean, outer hinges, whatever). Below i... | Show series of exponential decay distributions in one chart | One simple adjustment to your current graphic production would be, instead of producing all of the elements of the box-plot (even the minimalist Tufte style one), would be to produce a line chart conn | Show series of exponential decay distributions in one chart
One simple adjustment to your current graphic production would be, instead of producing all of the elements of the box-plot (even the minimalist Tufte style one), would be to produce a line chart connecting the summary statistics that the box plot is displayin... | Show series of exponential decay distributions in one chart
One simple adjustment to your current graphic production would be, instead of producing all of the elements of the box-plot (even the minimalist Tufte style one), would be to produce a line chart conn |
36,449 | Show series of exponential decay distributions in one chart | Have you considered perhaps focusing on having a 2D plot of number of tickets on the x-axis vs number of people that have submitted that many tickets on the y-axis, and showing each cohort on a plot by itself? Then you could flip from cohort to cohort (like a Rolodex) to get a sense of change over time using animation... | Show series of exponential decay distributions in one chart | Have you considered perhaps focusing on having a 2D plot of number of tickets on the x-axis vs number of people that have submitted that many tickets on the y-axis, and showing each cohort on a plot b | Show series of exponential decay distributions in one chart
Have you considered perhaps focusing on having a 2D plot of number of tickets on the x-axis vs number of people that have submitted that many tickets on the y-axis, and showing each cohort on a plot by itself? Then you could flip from cohort to cohort (like a... | Show series of exponential decay distributions in one chart
Have you considered perhaps focusing on having a 2D plot of number of tickets on the x-axis vs number of people that have submitted that many tickets on the y-axis, and showing each cohort on a plot b |
36,450 | What does "case-control" and "cross-sectional" mean in the context of logistic modeling? | First, the definitions, then a slight twist on the statement you posted, then hopefully an illuminating answer.
Cross-Sectional Study: A study where you take a "snapshot" of a population at a single point in time. You're not following anyone, it's simply a "At this point, do you have or not have a disease" - along with... | What does "case-control" and "cross-sectional" mean in the context of logistic modeling? | First, the definitions, then a slight twist on the statement you posted, then hopefully an illuminating answer.
Cross-Sectional Study: A study where you take a "snapshot" of a population at a single p | What does "case-control" and "cross-sectional" mean in the context of logistic modeling?
First, the definitions, then a slight twist on the statement you posted, then hopefully an illuminating answer.
Cross-Sectional Study: A study where you take a "snapshot" of a population at a single point in time. You're not follow... | What does "case-control" and "cross-sectional" mean in the context of logistic modeling?
First, the definitions, then a slight twist on the statement you posted, then hopefully an illuminating answer.
Cross-Sectional Study: A study where you take a "snapshot" of a population at a single p |
36,451 | Matching loss function for tanh units in a neural net | I think I've derived something that'll work:
$$-\frac{1}{2}((1-x_0)log|1-tanh(x)| + (1+x_0)log|1+tanh(x)|)$$
The derivative of this quantity w/respect to $x$ is $tanh(x) - x_0$, which is precisely what I need. | Matching loss function for tanh units in a neural net | I think I've derived something that'll work:
$$-\frac{1}{2}((1-x_0)log|1-tanh(x)| + (1+x_0)log|1+tanh(x)|)$$
The derivative of this quantity w/respect to $x$ is $tanh(x) - x_0$, which is precisely wha | Matching loss function for tanh units in a neural net
I think I've derived something that'll work:
$$-\frac{1}{2}((1-x_0)log|1-tanh(x)| + (1+x_0)log|1+tanh(x)|)$$
The derivative of this quantity w/respect to $x$ is $tanh(x) - x_0$, which is precisely what I need. | Matching loss function for tanh units in a neural net
I think I've derived something that'll work:
$$-\frac{1}{2}((1-x_0)log|1-tanh(x)| + (1+x_0)log|1+tanh(x)|)$$
The derivative of this quantity w/respect to $x$ is $tanh(x) - x_0$, which is precisely wha |
36,452 | Matching loss function for tanh units in a neural net | The loss function is chosen according to the noise process assumed to contaminate the data, not the output layer activation function. The purpose of the output layer activation function is to apply whatever constraints ought to apply on the output of the model. There is a correspondance between loss function and acti... | Matching loss function for tanh units in a neural net | The loss function is chosen according to the noise process assumed to contaminate the data, not the output layer activation function. The purpose of the output layer activation function is to apply w | Matching loss function for tanh units in a neural net
The loss function is chosen according to the noise process assumed to contaminate the data, not the output layer activation function. The purpose of the output layer activation function is to apply whatever constraints ought to apply on the output of the model. Th... | Matching loss function for tanh units in a neural net
The loss function is chosen according to the noise process assumed to contaminate the data, not the output layer activation function. The purpose of the output layer activation function is to apply w |
36,453 | Significance and credibility intervals for interaction term in logistic regression | No, your calculation isn't correct, because:
a) $b_1$ and $b_3$ are probably correlated in the posterior distribution, and
b) even if they weren't, that isn't how you would calculate it (think of the law of large numbers).
But never fear, there is a really easy way to do this in WinBUGS. Just define a new variable:
b1b... | Significance and credibility intervals for interaction term in logistic regression | No, your calculation isn't correct, because:
a) $b_1$ and $b_3$ are probably correlated in the posterior distribution, and
b) even if they weren't, that isn't how you would calculate it (think of the | Significance and credibility intervals for interaction term in logistic regression
No, your calculation isn't correct, because:
a) $b_1$ and $b_3$ are probably correlated in the posterior distribution, and
b) even if they weren't, that isn't how you would calculate it (think of the law of large numbers).
But never fear... | Significance and credibility intervals for interaction term in logistic regression
No, your calculation isn't correct, because:
a) $b_1$ and $b_3$ are probably correlated in the posterior distribution, and
b) even if they weren't, that isn't how you would calculate it (think of the |
36,454 | Significance and credibility intervals for interaction term in logistic regression | A few thoughts:
1) I'm not sure whether the fact that this is Bayesian matters.
2) I think your approach is correct
3) Interactions in logistic regression are tricky. I wrote about this in a paper that is about SAS PROC LOGISTIC, but the general idea holds. That paper is on my blog and is available here | Significance and credibility intervals for interaction term in logistic regression | A few thoughts:
1) I'm not sure whether the fact that this is Bayesian matters.
2) I think your approach is correct
3) Interactions in logistic regression are tricky. I wrote about this in a paper th | Significance and credibility intervals for interaction term in logistic regression
A few thoughts:
1) I'm not sure whether the fact that this is Bayesian matters.
2) I think your approach is correct
3) Interactions in logistic regression are tricky. I wrote about this in a paper that is about SAS PROC LOGISTIC, but th... | Significance and credibility intervals for interaction term in logistic regression
A few thoughts:
1) I'm not sure whether the fact that this is Bayesian matters.
2) I think your approach is correct
3) Interactions in logistic regression are tricky. I wrote about this in a paper th |
36,455 | Significance and credibility intervals for interaction term in logistic regression | I'm currently having a similar problem. I also believe that the approach to calculate the total effect of w is correct. I believe this can be tested via
h0: b2 + b3 * mean(x) = 0;
ha: b2 + b3 * mean(x) != 0
However, I stumbled upon a paper by Ai/Norton, who claim that "the magnitude of the interaction effet in nonlinea... | Significance and credibility intervals for interaction term in logistic regression | I'm currently having a similar problem. I also believe that the approach to calculate the total effect of w is correct. I believe this can be tested via
h0: b2 + b3 * mean(x) = 0;
ha: b2 + b3 * mean(x | Significance and credibility intervals for interaction term in logistic regression
I'm currently having a similar problem. I also believe that the approach to calculate the total effect of w is correct. I believe this can be tested via
h0: b2 + b3 * mean(x) = 0;
ha: b2 + b3 * mean(x) != 0
However, I stumbled upon a pap... | Significance and credibility intervals for interaction term in logistic regression
I'm currently having a similar problem. I also believe that the approach to calculate the total effect of w is correct. I believe this can be tested via
h0: b2 + b3 * mean(x) = 0;
ha: b2 + b3 * mean(x |
36,456 | Fitting a generalized least squares model with correlated data; use ML or REML? | Your intuition is correct, the same principles apply. I looked in Pinheiro/Bates section 5.4, where gls is introduced, but it doesn't say so explicitly, so you'll just have to trust me, I guess. :)
In Chapter 2 they go through the theory of REML and ML and you'll notice that none of the theory depends on there being... | Fitting a generalized least squares model with correlated data; use ML or REML? | Your intuition is correct, the same principles apply. I looked in Pinheiro/Bates section 5.4, where gls is introduced, but it doesn't say so explicitly, so you'll just have to trust me, I guess. :) | Fitting a generalized least squares model with correlated data; use ML or REML?
Your intuition is correct, the same principles apply. I looked in Pinheiro/Bates section 5.4, where gls is introduced, but it doesn't say so explicitly, so you'll just have to trust me, I guess. :)
In Chapter 2 they go through the theory... | Fitting a generalized least squares model with correlated data; use ML or REML?
Your intuition is correct, the same principles apply. I looked in Pinheiro/Bates section 5.4, where gls is introduced, but it doesn't say so explicitly, so you'll just have to trust me, I guess. :) |
36,457 | Linear regression, heteroscedasticity, White's test interpretation? | The original White paper where the test statistic was proposed is an enlightening read. This excerpt I think is of interest here:
...the null hypothesis maintains not
only that the errors are
homoskedastic, but also that they are
independent of the regressors, and
that the model is correctly
specified... Fai... | Linear regression, heteroscedasticity, White's test interpretation? | The original White paper where the test statistic was proposed is an enlightening read. This excerpt I think is of interest here:
...the null hypothesis maintains not
only that the errors are
hom | Linear regression, heteroscedasticity, White's test interpretation?
The original White paper where the test statistic was proposed is an enlightening read. This excerpt I think is of interest here:
...the null hypothesis maintains not
only that the errors are
homoskedastic, but also that they are
independent of ... | Linear regression, heteroscedasticity, White's test interpretation?
The original White paper where the test statistic was proposed is an enlightening read. This excerpt I think is of interest here:
...the null hypothesis maintains not
only that the errors are
hom |
36,458 | Linear regression, heteroscedasticity, White's test interpretation? | This does not answer the question of how to use the test. However, you should know that most economists generally never run those tests -- especially, applied microeconomists. Instead, you just use the Huber-White adjusted standard errors which corrects for various misspecifications in the distribution of your error te... | Linear regression, heteroscedasticity, White's test interpretation? | This does not answer the question of how to use the test. However, you should know that most economists generally never run those tests -- especially, applied microeconomists. Instead, you just use th | Linear regression, heteroscedasticity, White's test interpretation?
This does not answer the question of how to use the test. However, you should know that most economists generally never run those tests -- especially, applied microeconomists. Instead, you just use the Huber-White adjusted standard errors which correct... | Linear regression, heteroscedasticity, White's test interpretation?
This does not answer the question of how to use the test. However, you should know that most economists generally never run those tests -- especially, applied microeconomists. Instead, you just use th |
36,459 | What method is used in Google's correlate? | As chl points out, the Google Correlate tutorial states that Google Correlate uses Pearson's product-moment correlation coefficient.
They don't mention which language this is implemented in, although Google does use R for some applications, so I'd be guessing that. | What method is used in Google's correlate? | As chl points out, the Google Correlate tutorial states that Google Correlate uses Pearson's product-moment correlation coefficient.
They don't mention which language this is implemented in, although | What method is used in Google's correlate?
As chl points out, the Google Correlate tutorial states that Google Correlate uses Pearson's product-moment correlation coefficient.
They don't mention which language this is implemented in, although Google does use R for some applications, so I'd be guessing that. | What method is used in Google's correlate?
As chl points out, the Google Correlate tutorial states that Google Correlate uses Pearson's product-moment correlation coefficient.
They don't mention which language this is implemented in, although |
36,460 | Central limit theorem for sum from varied distributions | The theorem 3.1 in this book answers your first question. The key restriction in central limit theorem is not different distributions but the independence. The result is a very nice one, since it says that for interesting sums of independent random variables the limiting distribution has to have certain property, name... | Central limit theorem for sum from varied distributions | The theorem 3.1 in this book answers your first question. The key restriction in central limit theorem is not different distributions but the independence. The result is a very nice one, since it says | Central limit theorem for sum from varied distributions
The theorem 3.1 in this book answers your first question. The key restriction in central limit theorem is not different distributions but the independence. The result is a very nice one, since it says that for interesting sums of independent random variables the ... | Central limit theorem for sum from varied distributions
The theorem 3.1 in this book answers your first question. The key restriction in central limit theorem is not different distributions but the independence. The result is a very nice one, since it says |
36,461 | Central limit theorem for sum from varied distributions | mpiktas gave a very good technical answer.
There's a nice simulation of this here:
http://onlinestatbook.com/stat_sim/sampling_dist/index.html
You can manipulate the distribution at the top of this demo to show a combination of different distributions (such as bimodal), and the distribution of sample means will still ... | Central limit theorem for sum from varied distributions | mpiktas gave a very good technical answer.
There's a nice simulation of this here:
http://onlinestatbook.com/stat_sim/sampling_dist/index.html
You can manipulate the distribution at the top of this d | Central limit theorem for sum from varied distributions
mpiktas gave a very good technical answer.
There's a nice simulation of this here:
http://onlinestatbook.com/stat_sim/sampling_dist/index.html
You can manipulate the distribution at the top of this demo to show a combination of different distributions (such as bi... | Central limit theorem for sum from varied distributions
mpiktas gave a very good technical answer.
There's a nice simulation of this here:
http://onlinestatbook.com/stat_sim/sampling_dist/index.html
You can manipulate the distribution at the top of this d |
36,462 | How are zero values handled in lm()? | The problem you described here is known as limited dependent variable problem usually represented by truncated or censored data (the former could be seen as a special case of the later). In this case application of lm() function would not be the best choice, since it in general will produce biased and inconsistent esti... | How are zero values handled in lm()? | The problem you described here is known as limited dependent variable problem usually represented by truncated or censored data (the former could be seen as a special case of the later). In this case | How are zero values handled in lm()?
The problem you described here is known as limited dependent variable problem usually represented by truncated or censored data (the former could be seen as a special case of the later). In this case application of lm() function would not be the best choice, since it in general will... | How are zero values handled in lm()?
The problem you described here is known as limited dependent variable problem usually represented by truncated or censored data (the former could be seen as a special case of the later). In this case |
36,463 | How are zero values handled in lm()? | What % of the predictor is 0, and what other values does it take on?
The concern is whether a predictor with such little variation (vast majority being the value of 0) would be useful in a regression model.
To approach this, you can first stratify and do one analysis with the subset of the data where predictor is 0, a... | How are zero values handled in lm()? | What % of the predictor is 0, and what other values does it take on?
The concern is whether a predictor with such little variation (vast majority being the value of 0) would be useful in a regression | How are zero values handled in lm()?
What % of the predictor is 0, and what other values does it take on?
The concern is whether a predictor with such little variation (vast majority being the value of 0) would be useful in a regression model.
To approach this, you can first stratify and do one analysis with the subse... | How are zero values handled in lm()?
What % of the predictor is 0, and what other values does it take on?
The concern is whether a predictor with such little variation (vast majority being the value of 0) would be useful in a regression |
36,464 | How to model zero inflated, over dispersed poisson time series? | You may want to check out hurdle() from the pscl package in R. It specifies two-component models, one that handles the zero counts and one that handles the positive counts. Check out the hurdle help page here.
EDIT: I just found this post in R help that describes the zeroinf() function in R (also from the pscl package)... | How to model zero inflated, over dispersed poisson time series? | You may want to check out hurdle() from the pscl package in R. It specifies two-component models, one that handles the zero counts and one that handles the positive counts. Check out the hurdle help p | How to model zero inflated, over dispersed poisson time series?
You may want to check out hurdle() from the pscl package in R. It specifies two-component models, one that handles the zero counts and one that handles the positive counts. Check out the hurdle help page here.
EDIT: I just found this post in R help that de... | How to model zero inflated, over dispersed poisson time series?
You may want to check out hurdle() from the pscl package in R. It specifies two-component models, one that handles the zero counts and one that handles the positive counts. Check out the hurdle help p |
36,465 | How to model zero inflated, over dispersed poisson time series? | Another option for negative binomial regression in R is the excellent MASS package's glm.nb() function. UCLA's statistical consulting group has a pretty clear vignette, which unfortunately does not seem to provide any obvious insights into your autocorrelation issues, but maybe searching these various nb-regression opt... | How to model zero inflated, over dispersed poisson time series? | Another option for negative binomial regression in R is the excellent MASS package's glm.nb() function. UCLA's statistical consulting group has a pretty clear vignette, which unfortunately does not se | How to model zero inflated, over dispersed poisson time series?
Another option for negative binomial regression in R is the excellent MASS package's glm.nb() function. UCLA's statistical consulting group has a pretty clear vignette, which unfortunately does not seem to provide any obvious insights into your autocorrela... | How to model zero inflated, over dispersed poisson time series?
Another option for negative binomial regression in R is the excellent MASS package's glm.nb() function. UCLA's statistical consulting group has a pretty clear vignette, which unfortunately does not se |
36,466 | How to model zero inflated, over dispersed poisson time series? | If you have access to SAS 9.2 you could use PROC COUNTREG. It's a fairly new procedure and if you poke around the SAS site you can find out about it in the SAS/ETS(R) 9.2 User's Guide. COUNTREG does count modeling with or without zero inflation, has a "by" clause to split analyses, and allows both categorical and conti... | How to model zero inflated, over dispersed poisson time series? | If you have access to SAS 9.2 you could use PROC COUNTREG. It's a fairly new procedure and if you poke around the SAS site you can find out about it in the SAS/ETS(R) 9.2 User's Guide. COUNTREG does c | How to model zero inflated, over dispersed poisson time series?
If you have access to SAS 9.2 you could use PROC COUNTREG. It's a fairly new procedure and if you poke around the SAS site you can find out about it in the SAS/ETS(R) 9.2 User's Guide. COUNTREG does count modeling with or without zero inflation, has a "by"... | How to model zero inflated, over dispersed poisson time series?
If you have access to SAS 9.2 you could use PROC COUNTREG. It's a fairly new procedure and if you poke around the SAS site you can find out about it in the SAS/ETS(R) 9.2 User's Guide. COUNTREG does c |
36,467 | When do you consider a variable is a latent variable? | The relevant section of the classical typology distinguishes between (observed) variables, latent variables, and parameters.
Regular variables are observed and have a distribution. Latent variables are not observed and have a distribution. Parameters are not observed and do not have a distribution.
Parameters vs lat... | When do you consider a variable is a latent variable? | The relevant section of the classical typology distinguishes between (observed) variables, latent variables, and parameters.
Regular variables are observed and have a distribution. Latent variables ar | When do you consider a variable is a latent variable?
The relevant section of the classical typology distinguishes between (observed) variables, latent variables, and parameters.
Regular variables are observed and have a distribution. Latent variables are not observed and have a distribution. Parameters are not observ... | When do you consider a variable is a latent variable?
The relevant section of the classical typology distinguishes between (observed) variables, latent variables, and parameters.
Regular variables are observed and have a distribution. Latent variables ar |
36,468 | When do you consider a variable is a latent variable? | That is a modeling decision. One way to look at it can be illustrated by the following example.
A couple of hundreds electrodes are attached to the head to measure brain activity. Electricity, blood flow, whatever and you get lots of signals. These measurements that you get are observables. They are mixed in probably ... | When do you consider a variable is a latent variable? | That is a modeling decision. One way to look at it can be illustrated by the following example.
A couple of hundreds electrodes are attached to the head to measure brain activity. Electricity, blood | When do you consider a variable is a latent variable?
That is a modeling decision. One way to look at it can be illustrated by the following example.
A couple of hundreds electrodes are attached to the head to measure brain activity. Electricity, blood flow, whatever and you get lots of signals. These measurements tha... | When do you consider a variable is a latent variable?
That is a modeling decision. One way to look at it can be illustrated by the following example.
A couple of hundreds electrodes are attached to the head to measure brain activity. Electricity, blood |
36,469 | Gaussian state space forecasting with regression effects | Here's the solution I came up with: The trick is to add NAs to the end of the observation data. When seeing NA as a response variable the Kalman filter algorithm will simply predict the next value and not update the state vector. This is exactly what we want to make our forecast.
nAhead <- 12
mod <- dlmModSeas(4)+dlmMo... | Gaussian state space forecasting with regression effects | Here's the solution I came up with: The trick is to add NAs to the end of the observation data. When seeing NA as a response variable the Kalman filter algorithm will simply predict the next value and | Gaussian state space forecasting with regression effects
Here's the solution I came up with: The trick is to add NAs to the end of the observation data. When seeing NA as a response variable the Kalman filter algorithm will simply predict the next value and not update the state vector. This is exactly what we want to m... | Gaussian state space forecasting with regression effects
Here's the solution I came up with: The trick is to add NAs to the end of the observation data. When seeing NA as a response variable the Kalman filter algorithm will simply predict the next value and |
36,470 | Alternative to the Wilcoxon test when the distribution isn't continuous? | I have found that the Wilcoxon statistic is still fine for this purpose and that small simulations do a good job of estimating the size and the power of the test. I suspect this is more powerful than just comparing the two medians. The main concern is lack of power due to extensive numbers of ties, but that concern a... | Alternative to the Wilcoxon test when the distribution isn't continuous? | I have found that the Wilcoxon statistic is still fine for this purpose and that small simulations do a good job of estimating the size and the power of the test. I suspect this is more powerful than | Alternative to the Wilcoxon test when the distribution isn't continuous?
I have found that the Wilcoxon statistic is still fine for this purpose and that small simulations do a good job of estimating the size and the power of the test. I suspect this is more powerful than just comparing the two medians. The main conc... | Alternative to the Wilcoxon test when the distribution isn't continuous?
I have found that the Wilcoxon statistic is still fine for this purpose and that small simulations do a good job of estimating the size and the power of the test. I suspect this is more powerful than |
36,471 | Hypothesis testing that one time-series of a measure of entropy doesn't belong to a population | I have a relatively simple solution to propose, Hugo. Because you're forthright about not being a statistician (often a plus ;-) but obviously can handle technical language, I'll take some pains to be technically clear but avoid statistical jargon.
Let's start by checking my understanding: you have six series of data... | Hypothesis testing that one time-series of a measure of entropy doesn't belong to a population | I have a relatively simple solution to propose, Hugo. Because you're forthright about not being a statistician (often a plus ;-) but obviously can handle technical language, I'll take some pains to | Hypothesis testing that one time-series of a measure of entropy doesn't belong to a population
I have a relatively simple solution to propose, Hugo. Because you're forthright about not being a statistician (often a plus ;-) but obviously can handle technical language, I'll take some pains to be technically clear but ... | Hypothesis testing that one time-series of a measure of entropy doesn't belong to a population
I have a relatively simple solution to propose, Hugo. Because you're forthright about not being a statistician (often a plus ;-) but obviously can handle technical language, I'll take some pains to |
36,472 | Hypothesis testing that one time-series of a measure of entropy doesn't belong to a population | You can do it all on Excel.
Plotting the six time series should give you a hint of the shapes of the curves. Let's say that, as you mentioned, five of the curves look like they're exponential and the sixth looks like it grows sub-linearly.
Insert a trendline for each curve. If you are right, five of them will provide t... | Hypothesis testing that one time-series of a measure of entropy doesn't belong to a population | You can do it all on Excel.
Plotting the six time series should give you a hint of the shapes of the curves. Let's say that, as you mentioned, five of the curves look like they're exponential and the | Hypothesis testing that one time-series of a measure of entropy doesn't belong to a population
You can do it all on Excel.
Plotting the six time series should give you a hint of the shapes of the curves. Let's say that, as you mentioned, five of the curves look like they're exponential and the sixth looks like it grows... | Hypothesis testing that one time-series of a measure of entropy doesn't belong to a population
You can do it all on Excel.
Plotting the six time series should give you a hint of the shapes of the curves. Let's say that, as you mentioned, five of the curves look like they're exponential and the |
36,473 | How to start an analysis of keywords from a bibliography and detect correlations? | I'm also outside my area of expertise, but assuming that you want to use R, here are a few thoughts.
There is a bibtex package in R for importing bibtex files.
Various character functions could be used to extract the key words.
The data sounds a little like a two-mode network, which might mean packages like sna and ig... | How to start an analysis of keywords from a bibliography and detect correlations? | I'm also outside my area of expertise, but assuming that you want to use R, here are a few thoughts.
There is a bibtex package in R for importing bibtex files.
Various character functions could be us | How to start an analysis of keywords from a bibliography and detect correlations?
I'm also outside my area of expertise, but assuming that you want to use R, here are a few thoughts.
There is a bibtex package in R for importing bibtex files.
Various character functions could be used to extract the key words.
The data ... | How to start an analysis of keywords from a bibliography and detect correlations?
I'm also outside my area of expertise, but assuming that you want to use R, here are a few thoughts.
There is a bibtex package in R for importing bibtex files.
Various character functions could be us |
36,474 | How to start an analysis of keywords from a bibliography and detect correlations? | so you have a document x keyword matrix which basically represents a bipartite graph (or two-mode network depending on your cultural background) with edges between documents and tags. If you're not interested in individual documents - as I understand you -, you can create a network of keywords by counting the number of... | How to start an analysis of keywords from a bibliography and detect correlations? | so you have a document x keyword matrix which basically represents a bipartite graph (or two-mode network depending on your cultural background) with edges between documents and tags. If you're not in | How to start an analysis of keywords from a bibliography and detect correlations?
so you have a document x keyword matrix which basically represents a bipartite graph (or two-mode network depending on your cultural background) with edges between documents and tags. If you're not interested in individual documents - as ... | How to start an analysis of keywords from a bibliography and detect correlations?
so you have a document x keyword matrix which basically represents a bipartite graph (or two-mode network depending on your cultural background) with edges between documents and tags. If you're not in |
36,475 | How to start an analysis of keywords from a bibliography and detect correlations? | I would recommend using Association Rule Learning for this. It allows you to find words that often co-occur.
If you have a lot of data, it will be much faster than calculating a correlation matrix.
See my video series on text mining here. Includes a tutorial on Association Rules for text. | How to start an analysis of keywords from a bibliography and detect correlations? | I would recommend using Association Rule Learning for this. It allows you to find words that often co-occur.
If you have a lot of data, it will be much faster than calculating a correlation matrix.
| How to start an analysis of keywords from a bibliography and detect correlations?
I would recommend using Association Rule Learning for this. It allows you to find words that often co-occur.
If you have a lot of data, it will be much faster than calculating a correlation matrix.
See my video series on text mining her... | How to start an analysis of keywords from a bibliography and detect correlations?
I would recommend using Association Rule Learning for this. It allows you to find words that often co-occur.
If you have a lot of data, it will be much faster than calculating a correlation matrix.
|
36,476 | How to start an analysis of keywords from a bibliography and detect correlations? | You may want to take a look at the phi coefficient which is a measure of association for nominal variables. | How to start an analysis of keywords from a bibliography and detect correlations? | You may want to take a look at the phi coefficient which is a measure of association for nominal variables. | How to start an analysis of keywords from a bibliography and detect correlations?
You may want to take a look at the phi coefficient which is a measure of association for nominal variables. | How to start an analysis of keywords from a bibliography and detect correlations?
You may want to take a look at the phi coefficient which is a measure of association for nominal variables. |
36,477 | How to start an analysis of keywords from a bibliography and detect correlations? | You could try to employ the theory and praxis of association analysis or market basket analysis to your problem (just read "items" as "keywords" / "cited reference" and "market basket" as "journal article").
Disclaimer - this is just an idea, I did not do anything like that myself. Just my 2Cents. | How to start an analysis of keywords from a bibliography and detect correlations? | You could try to employ the theory and praxis of association analysis or market basket analysis to your problem (just read "items" as "keywords" / "cited reference" and "market basket" as "journal art | How to start an analysis of keywords from a bibliography and detect correlations?
You could try to employ the theory and praxis of association analysis or market basket analysis to your problem (just read "items" as "keywords" / "cited reference" and "market basket" as "journal article").
Disclaimer - this is just an i... | How to start an analysis of keywords from a bibliography and detect correlations?
You could try to employ the theory and praxis of association analysis or market basket analysis to your problem (just read "items" as "keywords" / "cited reference" and "market basket" as "journal art |
36,478 | Constructing smoothing splines with cross-validation | Nonparametric Regression and Spline Smoothing by Eubank is a good book. You probably want to start with Chapters 2 and 5 which cover goodness of fit and the theory and construction of smoothing splines. I've heard good things about Generalized Additive Models: An Introduction with R, which might be better if you're l... | Constructing smoothing splines with cross-validation | Nonparametric Regression and Spline Smoothing by Eubank is a good book. You probably want to start with Chapters 2 and 5 which cover goodness of fit and the theory and construction of smoothing splin | Constructing smoothing splines with cross-validation
Nonparametric Regression and Spline Smoothing by Eubank is a good book. You probably want to start with Chapters 2 and 5 which cover goodness of fit and the theory and construction of smoothing splines. I've heard good things about Generalized Additive Models: An I... | Constructing smoothing splines with cross-validation
Nonparametric Regression and Spline Smoothing by Eubank is a good book. You probably want to start with Chapters 2 and 5 which cover goodness of fit and the theory and construction of smoothing splin |
36,479 | How can I obtain some of all possible combinations in R? | If you wish to trade processing speed for memory (which I think you do), I would suggest the following algorithm:
Set up a loop from 1 to N Choose K, indexed by i
Each i can be considered an index to a combinadic, decode as such
Use the combination to perform your test statistic, store the result, discard the combinat... | How can I obtain some of all possible combinations in R? | If you wish to trade processing speed for memory (which I think you do), I would suggest the following algorithm:
Set up a loop from 1 to N Choose K, indexed by i
Each i can be considered an index to | How can I obtain some of all possible combinations in R?
If you wish to trade processing speed for memory (which I think you do), I would suggest the following algorithm:
Set up a loop from 1 to N Choose K, indexed by i
Each i can be considered an index to a combinadic, decode as such
Use the combination to perform yo... | How can I obtain some of all possible combinations in R?
If you wish to trade processing speed for memory (which I think you do), I would suggest the following algorithm:
Set up a loop from 1 to N Choose K, indexed by i
Each i can be considered an index to |
36,480 | How can I obtain some of all possible combinations in R? | Generating combinations is pretty easy, see for instance this; write this code in R and then process each combination at a time it appears. | How can I obtain some of all possible combinations in R? | Generating combinations is pretty easy, see for instance this; write this code in R and then process each combination at a time it appears. | How can I obtain some of all possible combinations in R?
Generating combinations is pretty easy, see for instance this; write this code in R and then process each combination at a time it appears. | How can I obtain some of all possible combinations in R?
Generating combinations is pretty easy, see for instance this; write this code in R and then process each combination at a time it appears. |
36,481 | How to manage categorical variable with MANY categories | Until Breiman and the 21st c the historic barrier for working with massively categorical features was computational, for example in ANOVA, inverting a cross-products matrix with too many categories was infeasible.
That said, it's useful to distinguish between massively categorical features vs targets. It's true that ra... | How to manage categorical variable with MANY categories | Until Breiman and the 21st c the historic barrier for working with massively categorical features was computational, for example in ANOVA, inverting a cross-products matrix with too many categories wa | How to manage categorical variable with MANY categories
Until Breiman and the 21st c the historic barrier for working with massively categorical features was computational, for example in ANOVA, inverting a cross-products matrix with too many categories was infeasible.
That said, it's useful to distinguish between mass... | How to manage categorical variable with MANY categories
Until Breiman and the 21st c the historic barrier for working with massively categorical features was computational, for example in ANOVA, inverting a cross-products matrix with too many categories wa |
36,482 | Generating uniformly distributed random solutions of a linear equation | The intersection of the $n+1$ simplex
$$\{\mathbf{p}\in\mathbb R_+^{n+1};\ \mathbf{p}^\top\mathbf 1_{n+1}=1\}$$
when $\mathbf 1_{n+1}=(1,\ldots1)^\top$ and of the constrained hyperplane
$$\{\mathbf{p}\in\mathbb R_+^{n+1};\ \mathbf{p}^\top\mathbf \iota_{n+1}=x\}$$
when $\iota_{n+1}=(0,1,\ldots n)^\top$ is within a $(n−1... | Generating uniformly distributed random solutions of a linear equation | The intersection of the $n+1$ simplex
$$\{\mathbf{p}\in\mathbb R_+^{n+1};\ \mathbf{p}^\top\mathbf 1_{n+1}=1\}$$
when $\mathbf 1_{n+1}=(1,\ldots1)^\top$ and of the constrained hyperplane
$$\{\mathbf{p} | Generating uniformly distributed random solutions of a linear equation
The intersection of the $n+1$ simplex
$$\{\mathbf{p}\in\mathbb R_+^{n+1};\ \mathbf{p}^\top\mathbf 1_{n+1}=1\}$$
when $\mathbf 1_{n+1}=(1,\ldots1)^\top$ and of the constrained hyperplane
$$\{\mathbf{p}\in\mathbb R_+^{n+1};\ \mathbf{p}^\top\mathbf \io... | Generating uniformly distributed random solutions of a linear equation
The intersection of the $n+1$ simplex
$$\{\mathbf{p}\in\mathbb R_+^{n+1};\ \mathbf{p}^\top\mathbf 1_{n+1}=1\}$$
when $\mathbf 1_{n+1}=(1,\ldots1)^\top$ and of the constrained hyperplane
$$\{\mathbf{p} |
36,483 | Generating uniformly distributed random solutions of a linear equation | Here's on possible approach, in R. It takes random samples from a simplex as starting points, and then uses optimisation to find nearby points that meet your criteria. I'm not sure how to verify that the resulting values are still "uniformly" distributed in the admissible parameter space.
find_solution = function(n, x)... | Generating uniformly distributed random solutions of a linear equation | Here's on possible approach, in R. It takes random samples from a simplex as starting points, and then uses optimisation to find nearby points that meet your criteria. I'm not sure how to verify that | Generating uniformly distributed random solutions of a linear equation
Here's on possible approach, in R. It takes random samples from a simplex as starting points, and then uses optimisation to find nearby points that meet your criteria. I'm not sure how to verify that the resulting values are still "uniformly" distri... | Generating uniformly distributed random solutions of a linear equation
Here's on possible approach, in R. It takes random samples from a simplex as starting points, and then uses optimisation to find nearby points that meet your criteria. I'm not sure how to verify that |
36,484 | Formal Definition of Identification | Yes, it needs to hold for all $z$ (subject to some technical caveats)
The idea of non-identifiability is that two different parameter values give the same sampling distribution, making them impossible to distinguish based on the data. Subject to some technical caveats which you can usually ignore,$^\dagger$ this means... | Formal Definition of Identification | Yes, it needs to hold for all $z$ (subject to some technical caveats)
The idea of non-identifiability is that two different parameter values give the same sampling distribution, making them impossible | Formal Definition of Identification
Yes, it needs to hold for all $z$ (subject to some technical caveats)
The idea of non-identifiability is that two different parameter values give the same sampling distribution, making them impossible to distinguish based on the data. Subject to some technical caveats which you can ... | Formal Definition of Identification
Yes, it needs to hold for all $z$ (subject to some technical caveats)
The idea of non-identifiability is that two different parameter values give the same sampling distribution, making them impossible |
36,485 | Formal Definition of Identification | I see no problem with how identification is defined. The bracketed part is saying that $\theta_0$ is identified if whenever $\theta \neq \theta_0$ and $\theta \in \Theta$, then it must be that $f(z|\theta) \neq f(z|\theta_0)$.
Here, the $\neq$ is for two functions. Two functions are not equal if there is some $z$ for w... | Formal Definition of Identification | I see no problem with how identification is defined. The bracketed part is saying that $\theta_0$ is identified if whenever $\theta \neq \theta_0$ and $\theta \in \Theta$, then it must be that $f(z|\t | Formal Definition of Identification
I see no problem with how identification is defined. The bracketed part is saying that $\theta_0$ is identified if whenever $\theta \neq \theta_0$ and $\theta \in \Theta$, then it must be that $f(z|\theta) \neq f(z|\theta_0)$.
Here, the $\neq$ is for two functions. Two functions are ... | Formal Definition of Identification
I see no problem with how identification is defined. The bracketed part is saying that $\theta_0$ is identified if whenever $\theta \neq \theta_0$ and $\theta \in \Theta$, then it must be that $f(z|\t |
36,486 | Formal Definition of Identification | Below I quote the parameter identifiability definition from Section 4.6, Statistical Models, by A. C. Davison:
There must be a 1-1 mapping between models and elements of the parameter space, otherwise there may be no unique value of $\theta$ for $\hat{\theta}$ to converge to. A model in which each $\theta$ generates a... | Formal Definition of Identification | Below I quote the parameter identifiability definition from Section 4.6, Statistical Models, by A. C. Davison:
There must be a 1-1 mapping between models and elements of the parameter space, otherwis | Formal Definition of Identification
Below I quote the parameter identifiability definition from Section 4.6, Statistical Models, by A. C. Davison:
There must be a 1-1 mapping between models and elements of the parameter space, otherwise there may be no unique value of $\theta$ for $\hat{\theta}$ to converge to. A mode... | Formal Definition of Identification
Below I quote the parameter identifiability definition from Section 4.6, Statistical Models, by A. C. Davison:
There must be a 1-1 mapping between models and elements of the parameter space, otherwis |
36,487 | Are mixed models necessary if random effects estimates are close to zero? | To summarize the useful information provided in comments: you don't necessarily have to use a mixed model, but you do have to take the lack of independence among the measurements into account in some way. Robust standard errors and generalized estimating equations (GEE), as discussed by Mc Neish et al. (linked from a c... | Are mixed models necessary if random effects estimates are close to zero? | To summarize the useful information provided in comments: you don't necessarily have to use a mixed model, but you do have to take the lack of independence among the measurements into account in some | Are mixed models necessary if random effects estimates are close to zero?
To summarize the useful information provided in comments: you don't necessarily have to use a mixed model, but you do have to take the lack of independence among the measurements into account in some way. Robust standard errors and generalized es... | Are mixed models necessary if random effects estimates are close to zero?
To summarize the useful information provided in comments: you don't necessarily have to use a mixed model, but you do have to take the lack of independence among the measurements into account in some |
36,488 | Sample uniformly from unit square conditioned on sum and product | Let's solve a generalization, so that we can obtain both solutions at once.
Let $h:[0,1]^2\to\mathbb{R}$ be differentiable with derivative $\nabla h=(D_1h, D_2h).$ To avoid technical complications in the analysis, further suppose the level curve of $h$ of height $a$ determines a differentiable partial function of eit... | Sample uniformly from unit square conditioned on sum and product | Let's solve a generalization, so that we can obtain both solutions at once.
Let $h:[0,1]^2\to\mathbb{R}$ be differentiable with derivative $\nabla h=(D_1h, D_2h).$ To avoid technical complications in | Sample uniformly from unit square conditioned on sum and product
Let's solve a generalization, so that we can obtain both solutions at once.
Let $h:[0,1]^2\to\mathbb{R}$ be differentiable with derivative $\nabla h=(D_1h, D_2h).$ To avoid technical complications in the analysis, further suppose the level curve of $h$ ... | Sample uniformly from unit square conditioned on sum and product
Let's solve a generalization, so that we can obtain both solutions at once.
Let $h:[0,1]^2\to\mathbb{R}$ be differentiable with derivative $\nabla h=(D_1h, D_2h).$ To avoid technical complications in |
36,489 | Why does log-linear analysis seem to ignore the Poisson regression equidispersion assumption? | Quoting from Section 4.3.3 of the second edition of Agresti's "Categorical Data Analysis":
Overdispersion is common in the modeling of counts. When the model for the mean is correct but the true distribution is not Poisson, the ML estimates of model parameters are still consistent but standard errors are incorrect.
H... | Why does log-linear analysis seem to ignore the Poisson regression equidispersion assumption? | Quoting from Section 4.3.3 of the second edition of Agresti's "Categorical Data Analysis":
Overdispersion is common in the modeling of counts. When the model for the mean is correct but the true dist | Why does log-linear analysis seem to ignore the Poisson regression equidispersion assumption?
Quoting from Section 4.3.3 of the second edition of Agresti's "Categorical Data Analysis":
Overdispersion is common in the modeling of counts. When the model for the mean is correct but the true distribution is not Poisson, t... | Why does log-linear analysis seem to ignore the Poisson regression equidispersion assumption?
Quoting from Section 4.3.3 of the second edition of Agresti's "Categorical Data Analysis":
Overdispersion is common in the modeling of counts. When the model for the mean is correct but the true dist |
36,490 | Who first coined the phrase "correlation does not imply causation"? | tl;dr: A book reviewer with the initials F.A.D in a 1900 issue of Nature appears to be the first to publish the phrase "correlation does not imply causation."
Long form answer
Depending on whether you are looking for the exact words "correlation does not imply causation" (note also "correlation is not causation"), or j... | Who first coined the phrase "correlation does not imply causation"? | tl;dr: A book reviewer with the initials F.A.D in a 1900 issue of Nature appears to be the first to publish the phrase "correlation does not imply causation."
Long form answer
Depending on whether you | Who first coined the phrase "correlation does not imply causation"?
tl;dr: A book reviewer with the initials F.A.D in a 1900 issue of Nature appears to be the first to publish the phrase "correlation does not imply causation."
Long form answer
Depending on whether you are looking for the exact words "correlation does n... | Who first coined the phrase "correlation does not imply causation"?
tl;dr: A book reviewer with the initials F.A.D in a 1900 issue of Nature appears to be the first to publish the phrase "correlation does not imply causation."
Long form answer
Depending on whether you |
36,491 | If $X$ and $Y$ are uncorrelated random variables, then under what condition is $E[X \mid Y] \approx E[X]?$ | One situation where this is interestingly almost true is when $X$ and $Y$ are both projections from the same high-dimensional distribution, ie, $X=a^TZ$, $Y=b^TZ$ for high-dimensional $Z$. Hall & Li (and earlier work by various people) showed that for 'most' distributions for $Z$ on a high-dimensional sphere and most ... | If $X$ and $Y$ are uncorrelated random variables, then under what condition is $E[X \mid Y] \approx | One situation where this is interestingly almost true is when $X$ and $Y$ are both projections from the same high-dimensional distribution, ie, $X=a^TZ$, $Y=b^TZ$ for high-dimensional $Z$. Hall & Li | If $X$ and $Y$ are uncorrelated random variables, then under what condition is $E[X \mid Y] \approx E[X]?$
One situation where this is interestingly almost true is when $X$ and $Y$ are both projections from the same high-dimensional distribution, ie, $X=a^TZ$, $Y=b^TZ$ for high-dimensional $Z$. Hall & Li (and earlier ... | If $X$ and $Y$ are uncorrelated random variables, then under what condition is $E[X \mid Y] \approx
One situation where this is interestingly almost true is when $X$ and $Y$ are both projections from the same high-dimensional distribution, ie, $X=a^TZ$, $Y=b^TZ$ for high-dimensional $Z$. Hall & Li |
36,492 | If $X$ and $Y$ are uncorrelated random variables, then under what condition is $E[X \mid Y] \approx E[X]?$ | No, they cannot be said to be approximately equal in general unless they are exactly equal. To see this, consider:
$$\mathbb{E}[X|Y] - \mathbb{E}[X] = \delta$$
for any $\delta$ that is near the boundary of what you consider to be "approximately equal". Now, multiply $X$ by $10^9$:
$$\mathbb{E}[10^9X|Y] - \mathbb{E}[... | If $X$ and $Y$ are uncorrelated random variables, then under what condition is $E[X \mid Y] \approx | No, they cannot be said to be approximately equal in general unless they are exactly equal. To see this, consider:
$$\mathbb{E}[X|Y] - \mathbb{E}[X] = \delta$$
for any $\delta$ that is near the boun | If $X$ and $Y$ are uncorrelated random variables, then under what condition is $E[X \mid Y] \approx E[X]?$
No, they cannot be said to be approximately equal in general unless they are exactly equal. To see this, consider:
$$\mathbb{E}[X|Y] - \mathbb{E}[X] = \delta$$
for any $\delta$ that is near the boundary of what ... | If $X$ and $Y$ are uncorrelated random variables, then under what condition is $E[X \mid Y] \approx
No, they cannot be said to be approximately equal in general unless they are exactly equal. To see this, consider:
$$\mathbb{E}[X|Y] - \mathbb{E}[X] = \delta$$
for any $\delta$ that is near the boun |
36,493 | If $X$ and $Y$ are uncorrelated random variables, then under what condition is $E[X \mid Y] \approx E[X]?$ | Now, uncorrelated does not imply independence, so $E[X \mid Y] \ne E[X]$.
I find the conclusion 'so $E[X \mid Y] \ne E[X]$' in this sentence a bit confusing.
If variables are uncorrelated then it does not follow $E[X \mid Y] \ne E[X]$.
In addition independence does neither mean $E[X \mid Y] \ne E[X]$. You can have in... | If $X$ and $Y$ are uncorrelated random variables, then under what condition is $E[X \mid Y] \approx | Now, uncorrelated does not imply independence, so $E[X \mid Y] \ne E[X]$.
I find the conclusion 'so $E[X \mid Y] \ne E[X]$' in this sentence a bit confusing.
If variables are uncorrelated then it do | If $X$ and $Y$ are uncorrelated random variables, then under what condition is $E[X \mid Y] \approx E[X]?$
Now, uncorrelated does not imply independence, so $E[X \mid Y] \ne E[X]$.
I find the conclusion 'so $E[X \mid Y] \ne E[X]$' in this sentence a bit confusing.
If variables are uncorrelated then it does not follow... | If $X$ and $Y$ are uncorrelated random variables, then under what condition is $E[X \mid Y] \approx
Now, uncorrelated does not imply independence, so $E[X \mid Y] \ne E[X]$.
I find the conclusion 'so $E[X \mid Y] \ne E[X]$' in this sentence a bit confusing.
If variables are uncorrelated then it do |
36,494 | Finding unbiased estimator for Truncated Poisson Distribution | Your answer looks correct to me, but it can be further simplified. You should have:
$$\begin{align}
\mathbb{E}(T)
&= 2 \Bigg[ p_X(1) + p_X(3) + p_X(5) + \cdots \Bigg] \\[6pt]
&= 2 \Bigg[ \frac{e^{-\theta} \theta}{1! (1-e^{-\theta})} + \frac{e^{-\theta} \theta^3}{3! (1-e^{-\theta})} + \frac{e^{-\theta} \theta^5}{5! (1-... | Finding unbiased estimator for Truncated Poisson Distribution | Your answer looks correct to me, but it can be further simplified. You should have:
$$\begin{align}
\mathbb{E}(T)
&= 2 \Bigg[ p_X(1) + p_X(3) + p_X(5) + \cdots \Bigg] \\[6pt]
&= 2 \Bigg[ \frac{e^{-\t | Finding unbiased estimator for Truncated Poisson Distribution
Your answer looks correct to me, but it can be further simplified. You should have:
$$\begin{align}
\mathbb{E}(T)
&= 2 \Bigg[ p_X(1) + p_X(3) + p_X(5) + \cdots \Bigg] \\[6pt]
&= 2 \Bigg[ \frac{e^{-\theta} \theta}{1! (1-e^{-\theta})} + \frac{e^{-\theta} \the... | Finding unbiased estimator for Truncated Poisson Distribution
Your answer looks correct to me, but it can be further simplified. You should have:
$$\begin{align}
\mathbb{E}(T)
&= 2 \Bigg[ p_X(1) + p_X(3) + p_X(5) + \cdots \Bigg] \\[6pt]
&= 2 \Bigg[ \frac{e^{-\t |
36,495 | How to tell which variable is more meaningful when modeling the relationship between several predictors and outcome variable? | I believe you're looking for a metric of (relative) variable importance (see also this thread). Many available methods rely on the decomposition of the $R^2$ to assign ranks or relative importance to each predictor in a multiple linear regression model. A certain approach in this family is better known under the term "... | How to tell which variable is more meaningful when modeling the relationship between several predict | I believe you're looking for a metric of (relative) variable importance (see also this thread). Many available methods rely on the decomposition of the $R^2$ to assign ranks or relative importance to | How to tell which variable is more meaningful when modeling the relationship between several predictors and outcome variable?
I believe you're looking for a metric of (relative) variable importance (see also this thread). Many available methods rely on the decomposition of the $R^2$ to assign ranks or relative importan... | How to tell which variable is more meaningful when modeling the relationship between several predict
I believe you're looking for a metric of (relative) variable importance (see also this thread). Many available methods rely on the decomposition of the $R^2$ to assign ranks or relative importance to |
36,496 | How to tell which variable is more meaningful when modeling the relationship between several predictors and outcome variable? | The following answer is some sort of an ignorant attempt to use {rms} package, following @JTH's suggestion. I got to say that this is the first time I'm using this package, and I have very minimal understanding of what I'm doing. Hence, I ask that anybody who can -- please provide feedback!
I've followed the procedure ... | How to tell which variable is more meaningful when modeling the relationship between several predict | The following answer is some sort of an ignorant attempt to use {rms} package, following @JTH's suggestion. I got to say that this is the first time I'm using this package, and I have very minimal und | How to tell which variable is more meaningful when modeling the relationship between several predictors and outcome variable?
The following answer is some sort of an ignorant attempt to use {rms} package, following @JTH's suggestion. I got to say that this is the first time I'm using this package, and I have very minim... | How to tell which variable is more meaningful when modeling the relationship between several predict
The following answer is some sort of an ignorant attempt to use {rms} package, following @JTH's suggestion. I got to say that this is the first time I'm using this package, and I have very minimal und |
36,497 | How to tell which variable is more meaningful when modeling the relationship between several predictors and outcome variable? | As all your variables are numeric and limited to the same range of values, directly comparing the absolute values of the coefficients (as you did in your answer) is one possible way to estimate effect size.
It might be, however, that for one variable the answers are only in the range 1-3, while they are for another var... | How to tell which variable is more meaningful when modeling the relationship between several predict | As all your variables are numeric and limited to the same range of values, directly comparing the absolute values of the coefficients (as you did in your answer) is one possible way to estimate effect | How to tell which variable is more meaningful when modeling the relationship between several predictors and outcome variable?
As all your variables are numeric and limited to the same range of values, directly comparing the absolute values of the coefficients (as you did in your answer) is one possible way to estimate ... | How to tell which variable is more meaningful when modeling the relationship between several predict
As all your variables are numeric and limited to the same range of values, directly comparing the absolute values of the coefficients (as you did in your answer) is one possible way to estimate effect |
36,498 | How to tell which variable is more meaningful when modeling the relationship between several predictors and outcome variable? | I'd look at the residuals for determining the effectiveness of a model. Plots of residuals versus other quantities are used to find failures of assumptions. The most common plot, especially useful in simple regression, is the plot of residuals versus the fitted values. A null plot would indicate no failure of assumptio... | How to tell which variable is more meaningful when modeling the relationship between several predict | I'd look at the residuals for determining the effectiveness of a model. Plots of residuals versus other quantities are used to find failures of assumptions. The most common plot, especially useful in | How to tell which variable is more meaningful when modeling the relationship between several predictors and outcome variable?
I'd look at the residuals for determining the effectiveness of a model. Plots of residuals versus other quantities are used to find failures of assumptions. The most common plot, especially usef... | How to tell which variable is more meaningful when modeling the relationship between several predict
I'd look at the residuals for determining the effectiveness of a model. Plots of residuals versus other quantities are used to find failures of assumptions. The most common plot, especially useful in |
36,499 | Which link function could be used for a glm where the response is per cent (0 - 100%)? | Nice question!
You can start your modelling efforts by converting your cover variable so that it is expressed as a proportion rather than a percentage (i.e., simply divide the values of your original cover variable by 100).
Once you get the converted variable, look to see how many 0 and 1 values you have, if any, in ad... | Which link function could be used for a glm where the response is per cent (0 - 100%)? | Nice question!
You can start your modelling efforts by converting your cover variable so that it is expressed as a proportion rather than a percentage (i.e., simply divide the values of your original | Which link function could be used for a glm where the response is per cent (0 - 100%)?
Nice question!
You can start your modelling efforts by converting your cover variable so that it is expressed as a proportion rather than a percentage (i.e., simply divide the values of your original cover variable by 100).
Once you ... | Which link function could be used for a glm where the response is per cent (0 - 100%)?
Nice question!
You can start your modelling efforts by converting your cover variable so that it is expressed as a proportion rather than a percentage (i.e., simply divide the values of your original |
36,500 | How to systematically choose which interactions to include in a multiple regression model? | I think a lot depends on what the purpose of the model is. Inference or prediction ?
If it is inference then you really need to incorporate some domain knowledge into the process, otherwise you risk identifying completely spurious associations, where an interaction may appear to be meaningful but in reality is either a... | How to systematically choose which interactions to include in a multiple regression model? | I think a lot depends on what the purpose of the model is. Inference or prediction ?
If it is inference then you really need to incorporate some domain knowledge into the process, otherwise you risk i | How to systematically choose which interactions to include in a multiple regression model?
I think a lot depends on what the purpose of the model is. Inference or prediction ?
If it is inference then you really need to incorporate some domain knowledge into the process, otherwise you risk identifying completely spuriou... | How to systematically choose which interactions to include in a multiple regression model?
I think a lot depends on what the purpose of the model is. Inference or prediction ?
If it is inference then you really need to incorporate some domain knowledge into the process, otherwise you risk i |
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