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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42,501 | Computing the Gini index | Gini index here ($G$, say) just calculates diversity or heterogeneity (or uncertainty if you will) from the sum of squared category probabilities. If every value is in the same category, then the measure is $1 - 1^2 = 0$. If every value of $n$ values is in a distinct category, then the measure is $1 - n(1/n)^2 = 1 - 1/... | Computing the Gini index | Gini index here ($G$, say) just calculates diversity or heterogeneity (or uncertainty if you will) from the sum of squared category probabilities. If every value is in the same category, then the meas | Computing the Gini index
Gini index here ($G$, say) just calculates diversity or heterogeneity (or uncertainty if you will) from the sum of squared category probabilities. If every value is in the same category, then the measure is $1 - 1^2 = 0$. If every value of $n$ values is in a distinct category, then the measure ... | Computing the Gini index
Gini index here ($G$, say) just calculates diversity or heterogeneity (or uncertainty if you will) from the sum of squared category probabilities. If every value is in the same category, then the meas |
42,502 | Computing the Gini index | I'm sorry to bring back a question from ages ago, but it came as reference in a newer one and it looks to me like it might cause some misunderstandings.
The calculations that Nick Cox gave are absolutely correct when computing the Gini index of the features, and help give us information about the features and their ho... | Computing the Gini index | I'm sorry to bring back a question from ages ago, but it came as reference in a newer one and it looks to me like it might cause some misunderstandings.
The calculations that Nick Cox gave are absolu | Computing the Gini index
I'm sorry to bring back a question from ages ago, but it came as reference in a newer one and it looks to me like it might cause some misunderstandings.
The calculations that Nick Cox gave are absolutely correct when computing the Gini index of the features, and help give us information about ... | Computing the Gini index
I'm sorry to bring back a question from ages ago, but it came as reference in a newer one and it looks to me like it might cause some misunderstandings.
The calculations that Nick Cox gave are absolu |
42,503 | Cronbach's Alpha with missing data | If data are MCAR, one would like to find an unbiased estimated of alpha. This could possibly be done via multiple imputation or listwise deletion. However, the latter might lead to severe loss of data. A third way is something like pairwise deletion which is implemented via an na.rm option in alpha() of the ltm package... | Cronbach's Alpha with missing data | If data are MCAR, one would like to find an unbiased estimated of alpha. This could possibly be done via multiple imputation or listwise deletion. However, the latter might lead to severe loss of data | Cronbach's Alpha with missing data
If data are MCAR, one would like to find an unbiased estimated of alpha. This could possibly be done via multiple imputation or listwise deletion. However, the latter might lead to severe loss of data. A third way is something like pairwise deletion which is implemented via an na.rm o... | Cronbach's Alpha with missing data
If data are MCAR, one would like to find an unbiased estimated of alpha. This could possibly be done via multiple imputation or listwise deletion. However, the latter might lead to severe loss of data |
42,504 | What diagnostics for random effects logistic regression? | It is true that there is not much information about diagnostics for random effects logistic models.
R package influence.ME provides tools for detecting influential data in mixed effects models, e.g. DFBETAS, Cook's Distance.
There is a similar function in Stata,
gllamm, including DFBETAS and Cook's Distance to detect... | What diagnostics for random effects logistic regression? | It is true that there is not much information about diagnostics for random effects logistic models.
R package influence.ME provides tools for detecting influential data in mixed effects models, e.g. | What diagnostics for random effects logistic regression?
It is true that there is not much information about diagnostics for random effects logistic models.
R package influence.ME provides tools for detecting influential data in mixed effects models, e.g. DFBETAS, Cook's Distance.
There is a similar function in Stata... | What diagnostics for random effects logistic regression?
It is true that there is not much information about diagnostics for random effects logistic models.
R package influence.ME provides tools for detecting influential data in mixed effects models, e.g. |
42,505 | Is Hoeffding's bound tight in any way? | A trivial example would be if $X_i$ is deterministic (say always equal to 0). The right hand side would then be the dirac mass at 0 (as seen in the proof of Hoeffding's inequality).
There can't be any other example as that would contradict the hypothesis that $\bar{X}$ is bounded, since
$$
0 < C \exp\left( -\frac{2n... | Is Hoeffding's bound tight in any way? | A trivial example would be if $X_i$ is deterministic (say always equal to 0). The right hand side would then be the dirac mass at 0 (as seen in the proof of Hoeffding's inequality).
There can't be an | Is Hoeffding's bound tight in any way?
A trivial example would be if $X_i$ is deterministic (say always equal to 0). The right hand side would then be the dirac mass at 0 (as seen in the proof of Hoeffding's inequality).
There can't be any other example as that would contradict the hypothesis that $\bar{X}$ is bounded... | Is Hoeffding's bound tight in any way?
A trivial example would be if $X_i$ is deterministic (say always equal to 0). The right hand side would then be the dirac mass at 0 (as seen in the proof of Hoeffding's inequality).
There can't be an |
42,506 | Is Hoeffding's bound tight in any way? | I'm not sure if you can say something for every $n$ (and all distributions) for Hoeffding precisely. For Chernoff you can say that the moment bound is tighter than the Chernoff bound. In the linked paper the author also notes (but defers the proof to the citation, that for large $t$, if $C(t)$ is the Chernoff bound the... | Is Hoeffding's bound tight in any way? | I'm not sure if you can say something for every $n$ (and all distributions) for Hoeffding precisely. For Chernoff you can say that the moment bound is tighter than the Chernoff bound. In the linked pa | Is Hoeffding's bound tight in any way?
I'm not sure if you can say something for every $n$ (and all distributions) for Hoeffding precisely. For Chernoff you can say that the moment bound is tighter than the Chernoff bound. In the linked paper the author also notes (but defers the proof to the citation, that for large $... | Is Hoeffding's bound tight in any way?
I'm not sure if you can say something for every $n$ (and all distributions) for Hoeffding precisely. For Chernoff you can say that the moment bound is tighter than the Chernoff bound. In the linked pa |
42,507 | Fitting a "sigmoid" function: Why is my fit so bad? | It appears that you have a misspecified functional form in your model. You are fitting a particular type of sigmoid function, but there are lots of types of sigmoid functions besides that one. Sigmoid functions are mostly discussed in the context of link functions for the regression of binary data. You can get some ... | Fitting a "sigmoid" function: Why is my fit so bad? | It appears that you have a misspecified functional form in your model. You are fitting a particular type of sigmoid function, but there are lots of types of sigmoid functions besides that one. Sigmo | Fitting a "sigmoid" function: Why is my fit so bad?
It appears that you have a misspecified functional form in your model. You are fitting a particular type of sigmoid function, but there are lots of types of sigmoid functions besides that one. Sigmoid functions are mostly discussed in the context of link functions f... | Fitting a "sigmoid" function: Why is my fit so bad?
It appears that you have a misspecified functional form in your model. You are fitting a particular type of sigmoid function, but there are lots of types of sigmoid functions besides that one. Sigmo |
42,508 | Simple linear regression with a random predictor | The answer to 1 is no which makes the answers to all the others not applicable.
Let me start with your last equation:
\begin{align}
y_i = \alpha + \beta w_i + \epsilon_i
\end{align}
Now, let's assume that your earlier equations for $y$ and $w$ are valid classical linear regression models, so that $Cov(x,\epsilon_1)=0$ ... | Simple linear regression with a random predictor | The answer to 1 is no which makes the answers to all the others not applicable.
Let me start with your last equation:
\begin{align}
y_i = \alpha + \beta w_i + \epsilon_i
\end{align}
Now, let's assume | Simple linear regression with a random predictor
The answer to 1 is no which makes the answers to all the others not applicable.
Let me start with your last equation:
\begin{align}
y_i = \alpha + \beta w_i + \epsilon_i
\end{align}
Now, let's assume that your earlier equations for $y$ and $w$ are valid classical linear ... | Simple linear regression with a random predictor
The answer to 1 is no which makes the answers to all the others not applicable.
Let me start with your last equation:
\begin{align}
y_i = \alpha + \beta w_i + \epsilon_i
\end{align}
Now, let's assume |
42,509 | Conditional Logit for recommender systems? | Preface
I work with recommender systems on daily basis and have also never heard of the application of such a model as recommender system. I can only speculate about the reasons though.
The main overall reason might be that recommender systems are often applied in a domain where the price/cost of an item is too small t... | Conditional Logit for recommender systems? | Preface
I work with recommender systems on daily basis and have also never heard of the application of such a model as recommender system. I can only speculate about the reasons though.
The main overa | Conditional Logit for recommender systems?
Preface
I work with recommender systems on daily basis and have also never heard of the application of such a model as recommender system. I can only speculate about the reasons though.
The main overall reason might be that recommender systems are often applied in a domain whe... | Conditional Logit for recommender systems?
Preface
I work with recommender systems on daily basis and have also never heard of the application of such a model as recommender system. I can only speculate about the reasons though.
The main overa |
42,510 | What are some examples of problems for which various techniques are well suited | This is an interesting question - in a lot of ways, it's really the question in Machine Learning.
The short version of the answer is something like this: your choice of model is very important, but there are no silver bullets or magic hammers to be had; with a few notable exceptions, it's worth thinking of all of the t... | What are some examples of problems for which various techniques are well suited | This is an interesting question - in a lot of ways, it's really the question in Machine Learning.
The short version of the answer is something like this: your choice of model is very important, but th | What are some examples of problems for which various techniques are well suited
This is an interesting question - in a lot of ways, it's really the question in Machine Learning.
The short version of the answer is something like this: your choice of model is very important, but there are no silver bullets or magic hamme... | What are some examples of problems for which various techniques are well suited
This is an interesting question - in a lot of ways, it's really the question in Machine Learning.
The short version of the answer is something like this: your choice of model is very important, but th |
42,511 | Finding the highest probability balls | While the problem is in general intractable, your motivating example assumes independent events, which provides more than enough structure to solve this problem. Let me restate what you are trying to show to make sure that I understand:
You have $n$ independent Binomial random variables $X_i\sim \mathrm{Binomial}(p_i)... | Finding the highest probability balls | While the problem is in general intractable, your motivating example assumes independent events, which provides more than enough structure to solve this problem. Let me restate what you are trying to | Finding the highest probability balls
While the problem is in general intractable, your motivating example assumes independent events, which provides more than enough structure to solve this problem. Let me restate what you are trying to show to make sure that I understand:
You have $n$ independent Binomial random var... | Finding the highest probability balls
While the problem is in general intractable, your motivating example assumes independent events, which provides more than enough structure to solve this problem. Let me restate what you are trying to |
42,512 | Weighted least squares to correct for heteroscedasticity | It appears you know the whole technique, so I will only deal with the specific question - and the answer is:
You should use the sample sizes that relate to the "explanatory" variables - I guess this is what you mean by "independent", i.e. of the regressors.
This comes out of the following:
In a regression setting, we... | Weighted least squares to correct for heteroscedasticity | It appears you know the whole technique, so I will only deal with the specific question - and the answer is:
You should use the sample sizes that relate to the "explanatory" variables - I guess this i | Weighted least squares to correct for heteroscedasticity
It appears you know the whole technique, so I will only deal with the specific question - and the answer is:
You should use the sample sizes that relate to the "explanatory" variables - I guess this is what you mean by "independent", i.e. of the regressors.
Thi... | Weighted least squares to correct for heteroscedasticity
It appears you know the whole technique, so I will only deal with the specific question - and the answer is:
You should use the sample sizes that relate to the "explanatory" variables - I guess this i |
42,513 | Performing "all-possible regressions" in R | After re-reading your question, I believe you mean to ask about model selection among your candidate predictor variables, and not actually running all possible regressions. Fitting all possible models from a given set of predictors is subject to a high degree of data-mining bias. Since many such sub-models will be high... | Performing "all-possible regressions" in R | After re-reading your question, I believe you mean to ask about model selection among your candidate predictor variables, and not actually running all possible regressions. Fitting all possible models | Performing "all-possible regressions" in R
After re-reading your question, I believe you mean to ask about model selection among your candidate predictor variables, and not actually running all possible regressions. Fitting all possible models from a given set of predictors is subject to a high degree of data-mining bi... | Performing "all-possible regressions" in R
After re-reading your question, I believe you mean to ask about model selection among your candidate predictor variables, and not actually running all possible regressions. Fitting all possible models |
42,514 | Performing "all-possible regressions" in R | I suggest you to use usual panel data analysis approach [in R this involves the use of plm package). There are three categories of explanatory variables (observed or unobserved) that this approach takes into account. First, the variable which is same across each stock but varies over time (economic fundamentals), secon... | Performing "all-possible regressions" in R | I suggest you to use usual panel data analysis approach [in R this involves the use of plm package). There are three categories of explanatory variables (observed or unobserved) that this approach tak | Performing "all-possible regressions" in R
I suggest you to use usual panel data analysis approach [in R this involves the use of plm package). There are three categories of explanatory variables (observed or unobserved) that this approach takes into account. First, the variable which is same across each stock but vari... | Performing "all-possible regressions" in R
I suggest you to use usual panel data analysis approach [in R this involves the use of plm package). There are three categories of explanatory variables (observed or unobserved) that this approach tak |
42,515 | How to include level-2 variables in a random intercept model when fitting HLM with lme4? | If they are level-2 predictors ("group characteristics" as you say), then they cannot have random effects. We would need to be able to compute separate slopes of the predictors for each group, but this doesn't make sense because the slope can only be computed across groups, not within a group. So the model that you hav... | How to include level-2 variables in a random intercept model when fitting HLM with lme4? | If they are level-2 predictors ("group characteristics" as you say), then they cannot have random effects. We would need to be able to compute separate slopes of the predictors for each group, but thi | How to include level-2 variables in a random intercept model when fitting HLM with lme4?
If they are level-2 predictors ("group characteristics" as you say), then they cannot have random effects. We would need to be able to compute separate slopes of the predictors for each group, but this doesn't make sense because th... | How to include level-2 variables in a random intercept model when fitting HLM with lme4?
If they are level-2 predictors ("group characteristics" as you say), then they cannot have random effects. We would need to be able to compute separate slopes of the predictors for each group, but thi |
42,516 | What is the $\mu^2$ squared effect size? | I can only think of this referring to $\eta^2$, computed as:
$\eta^2={SS_{effect} \over SS_{total}}$
This is the proportion of variance explained in the dependent variable by the grouping variable (in this case, a binary variable). This would be indeed the same value as the $R^2$ obtained if the difference between the ... | What is the $\mu^2$ squared effect size? | I can only think of this referring to $\eta^2$, computed as:
$\eta^2={SS_{effect} \over SS_{total}}$
This is the proportion of variance explained in the dependent variable by the grouping variable (in | What is the $\mu^2$ squared effect size?
I can only think of this referring to $\eta^2$, computed as:
$\eta^2={SS_{effect} \over SS_{total}}$
This is the proportion of variance explained in the dependent variable by the grouping variable (in this case, a binary variable). This would be indeed the same value as the $R^2... | What is the $\mu^2$ squared effect size?
I can only think of this referring to $\eta^2$, computed as:
$\eta^2={SS_{effect} \over SS_{total}}$
This is the proportion of variance explained in the dependent variable by the grouping variable (in |
42,517 | What is the $\mu^2$ squared effect size? | This is eta-squared and it is a fairly poor measure of effect size (partial eta-squared is often reported in statistical software such as SPSS when calculating ANOVA) | What is the $\mu^2$ squared effect size? | This is eta-squared and it is a fairly poor measure of effect size (partial eta-squared is often reported in statistical software such as SPSS when calculating ANOVA) | What is the $\mu^2$ squared effect size?
This is eta-squared and it is a fairly poor measure of effect size (partial eta-squared is often reported in statistical software such as SPSS when calculating ANOVA) | What is the $\mu^2$ squared effect size?
This is eta-squared and it is a fairly poor measure of effect size (partial eta-squared is often reported in statistical software such as SPSS when calculating ANOVA) |
42,518 | What is the $\mu^2$ squared effect size? | My apologies, I misread Patrick's response above; I now see he also indicated it was $_{partial}\eta^2$. I added the formulas for finding $_{partial}\eta^2$ should any need those.
I am posting on this old thread to clarify should others visit in the future. The value reported is $_{partial}\eta^2$, not $\eta^2$. Since... | What is the $\mu^2$ squared effect size? | My apologies, I misread Patrick's response above; I now see he also indicated it was $_{partial}\eta^2$. I added the formulas for finding $_{partial}\eta^2$ should any need those.
I am posting on thi | What is the $\mu^2$ squared effect size?
My apologies, I misread Patrick's response above; I now see he also indicated it was $_{partial}\eta^2$. I added the formulas for finding $_{partial}\eta^2$ should any need those.
I am posting on this old thread to clarify should others visit in the future. The value reported i... | What is the $\mu^2$ squared effect size?
My apologies, I misread Patrick's response above; I now see he also indicated it was $_{partial}\eta^2$. I added the formulas for finding $_{partial}\eta^2$ should any need those.
I am posting on thi |
42,519 | Interpretation of null and alternative hypotheses | If "the claim" is "that it takes at least 10 years", and you want to test this, then that is your alternative hypothesis. The null hypothesis negates the alternative. Thus:
$$
H_0: \mu\le10 \\
H_a: \mu>10
$$
A way to think about these things is that the alternative hypothesis is what the researcher really believes, ... | Interpretation of null and alternative hypotheses | If "the claim" is "that it takes at least 10 years", and you want to test this, then that is your alternative hypothesis. The null hypothesis negates the alternative. Thus:
$$
H_0: \mu\le10 \\
H_a: | Interpretation of null and alternative hypotheses
If "the claim" is "that it takes at least 10 years", and you want to test this, then that is your alternative hypothesis. The null hypothesis negates the alternative. Thus:
$$
H_0: \mu\le10 \\
H_a: \mu>10
$$
A way to think about these things is that the alternative h... | Interpretation of null and alternative hypotheses
If "the claim" is "that it takes at least 10 years", and you want to test this, then that is your alternative hypothesis. The null hypothesis negates the alternative. Thus:
$$
H_0: \mu\le10 \\
H_a: |
42,520 | Interpretation of null and alternative hypotheses | I am bit confused with gung's interpretation. Please correct me if I am wrong.
Since the claim is that it takes 'at least 10 years', then we should state this claim as mu>=10, and the other hypothesis should be the complementary of this one, so it is mu<10.
To my understanding, the claim of 'mu>=10' has to be the null... | Interpretation of null and alternative hypotheses | I am bit confused with gung's interpretation. Please correct me if I am wrong.
Since the claim is that it takes 'at least 10 years', then we should state this claim as mu>=10, and the other hypothesi | Interpretation of null and alternative hypotheses
I am bit confused with gung's interpretation. Please correct me if I am wrong.
Since the claim is that it takes 'at least 10 years', then we should state this claim as mu>=10, and the other hypothesis should be the complementary of this one, so it is mu<10.
To my under... | Interpretation of null and alternative hypotheses
I am bit confused with gung's interpretation. Please correct me if I am wrong.
Since the claim is that it takes 'at least 10 years', then we should state this claim as mu>=10, and the other hypothesi |
42,521 | Interpretation of null and alternative hypotheses | Ho : mu > = 10
Ha: mu < 10
Descriptive Statistics
Column 1
Sample Size, n: 10
Mean: 11.72
Variance, s^2: 8.179556
St Dev, s: 2.859992
test statistics , t = (11.72 - 10)/(2.86/sqrt(10)) = 1.9017
critical t (9, 0.05)= - 1.833.
hence, computed t (1.9017) > critical t (-1.833) .
Fail to reject Ho.
http://t... | Interpretation of null and alternative hypotheses | Ho : mu > = 10
Ha: mu < 10
Descriptive Statistics
Column 1
Sample Size, n: 10
Mean: 11.72
Variance, s^2: 8.179556
St Dev, s: 2.859992
test statistics , t = (11.72 - 10)/(2.86/sqrt(10) | Interpretation of null and alternative hypotheses
Ho : mu > = 10
Ha: mu < 10
Descriptive Statistics
Column 1
Sample Size, n: 10
Mean: 11.72
Variance, s^2: 8.179556
St Dev, s: 2.859992
test statistics , t = (11.72 - 10)/(2.86/sqrt(10)) = 1.9017
critical t (9, 0.05)= - 1.833.
hence, computed t (1.9017) >... | Interpretation of null and alternative hypotheses
Ho : mu > = 10
Ha: mu < 10
Descriptive Statistics
Column 1
Sample Size, n: 10
Mean: 11.72
Variance, s^2: 8.179556
St Dev, s: 2.859992
test statistics , t = (11.72 - 10)/(2.86/sqrt(10) |
42,522 | Does it make sense to use dynamic time warping when clustering time series that all have the same length and sampling interval? | It depends on what you mean by performance - in terms of computing power DTW suffers from computational lethargy and Euclidean distance method dosnt. DTW is superior when it comes to classification and clustering.
Euclidean distance, which assumes the ith point in one sequence is aligned with the ith point in the othe... | Does it make sense to use dynamic time warping when clustering time series that all have the same le | It depends on what you mean by performance - in terms of computing power DTW suffers from computational lethargy and Euclidean distance method dosnt. DTW is superior when it comes to classification an | Does it make sense to use dynamic time warping when clustering time series that all have the same length and sampling interval?
It depends on what you mean by performance - in terms of computing power DTW suffers from computational lethargy and Euclidean distance method dosnt. DTW is superior when it comes to classific... | Does it make sense to use dynamic time warping when clustering time series that all have the same le
It depends on what you mean by performance - in terms of computing power DTW suffers from computational lethargy and Euclidean distance method dosnt. DTW is superior when it comes to classification an |
42,523 | Does it make sense to use dynamic time warping when clustering time series that all have the same length and sampling interval? | It totally depends on if you want your clustering to be invariant to dilation in time. For example if you have a speech signal we often want to be invariant to the speed at which people speak. If you're clustering music maybe you wouldn't want to use DTW. | Does it make sense to use dynamic time warping when clustering time series that all have the same le | It totally depends on if you want your clustering to be invariant to dilation in time. For example if you have a speech signal we often want to be invariant to the speed at which people speak. If you' | Does it make sense to use dynamic time warping when clustering time series that all have the same length and sampling interval?
It totally depends on if you want your clustering to be invariant to dilation in time. For example if you have a speech signal we often want to be invariant to the speed at which people speak.... | Does it make sense to use dynamic time warping when clustering time series that all have the same le
It totally depends on if you want your clustering to be invariant to dilation in time. For example if you have a speech signal we often want to be invariant to the speed at which people speak. If you' |
42,524 | Does it make sense to use dynamic time warping when clustering time series that all have the same length and sampling interval? | You can check this great answer here: https://stats.stackexchange.com/a/22228/193114.
In general, it depends on the application you are working on. Euclidean distance is better than DTW when temporal alignment is not what you want. Euclidean distance is better than DTW when you wish to group time series that behave exa... | Does it make sense to use dynamic time warping when clustering time series that all have the same le | You can check this great answer here: https://stats.stackexchange.com/a/22228/193114.
In general, it depends on the application you are working on. Euclidean distance is better than DTW when temporal | Does it make sense to use dynamic time warping when clustering time series that all have the same length and sampling interval?
You can check this great answer here: https://stats.stackexchange.com/a/22228/193114.
In general, it depends on the application you are working on. Euclidean distance is better than DTW when t... | Does it make sense to use dynamic time warping when clustering time series that all have the same le
You can check this great answer here: https://stats.stackexchange.com/a/22228/193114.
In general, it depends on the application you are working on. Euclidean distance is better than DTW when temporal |
42,525 | Are sample statistics relevant when comparing census data across time periods? | The problem with studying the change of observables in time (such dataset is formally called "time series"), is that for each case observations in different points in time are dependent, which forces us at least to use tests that are valid for paired data (when comparing two data points), or VECM if you want to analyze... | Are sample statistics relevant when comparing census data across time periods? | The problem with studying the change of observables in time (such dataset is formally called "time series"), is that for each case observations in different points in time are dependent, which forces | Are sample statistics relevant when comparing census data across time periods?
The problem with studying the change of observables in time (such dataset is formally called "time series"), is that for each case observations in different points in time are dependent, which forces us at least to use tests that are valid f... | Are sample statistics relevant when comparing census data across time periods?
The problem with studying the change of observables in time (such dataset is formally called "time series"), is that for each case observations in different points in time are dependent, which forces |
42,526 | Are sample statistics relevant when comparing census data across time periods? | Sampling variability is only one possible source of error in the estimate of the quantity you are interested in. When you have a census, error due to sampling variability might be null, but there might be other sources of error, like measurement error. So if you compare two estimates from two censuses and the differenc... | Are sample statistics relevant when comparing census data across time periods? | Sampling variability is only one possible source of error in the estimate of the quantity you are interested in. When you have a census, error due to sampling variability might be null, but there migh | Are sample statistics relevant when comparing census data across time periods?
Sampling variability is only one possible source of error in the estimate of the quantity you are interested in. When you have a census, error due to sampling variability might be null, but there might be other sources of error, like measure... | Are sample statistics relevant when comparing census data across time periods?
Sampling variability is only one possible source of error in the estimate of the quantity you are interested in. When you have a census, error due to sampling variability might be null, but there migh |
42,527 | Interpreting matrices of SVD in practical applications [duplicate] | I think I have an answer. If you notice a mistake, let me know.
One interpretation explained in the book suggested by Cam.Davidson.Pilon is the factor interpretation. This tells you that a matrix $A$ with n rows and m columns can be decomposed into a matrix $C$ with $n$ rows and $r$ columns, a matrix $W$ with r rows a... | Interpreting matrices of SVD in practical applications [duplicate] | I think I have an answer. If you notice a mistake, let me know.
One interpretation explained in the book suggested by Cam.Davidson.Pilon is the factor interpretation. This tells you that a matrix $A$ | Interpreting matrices of SVD in practical applications [duplicate]
I think I have an answer. If you notice a mistake, let me know.
One interpretation explained in the book suggested by Cam.Davidson.Pilon is the factor interpretation. This tells you that a matrix $A$ with n rows and m columns can be decomposed into a m... | Interpreting matrices of SVD in practical applications [duplicate]
I think I have an answer. If you notice a mistake, let me know.
One interpretation explained in the book suggested by Cam.Davidson.Pilon is the factor interpretation. This tells you that a matrix $A$ |
42,528 | How to do 4-parametric regression for ELISA data in R | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
The 2nd answer to a Google search for 4 parameter logi... | How to do 4-parametric regression for ELISA data in R | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| How to do 4-parametric regression for ELISA data in R
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
... | How to do 4-parametric regression for ELISA data in R
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42,529 | How to do 4-parametric regression for ELISA data in R | After days, I record my found here:
http://www.bioassay.dk/index-filer/start/DraftDrcManual.pdf gives me
the current manual of drc package in R. For example:
library(drc) model1 <- drm(SLOPE~DOSE, CURVE,
fct=LL.4(names=c("Slope", "Lower", "Upper", "ED50")),data=spinach)
summary(model1) plot(model1)
If I wanna ... | How to do 4-parametric regression for ELISA data in R | After days, I record my found here:
http://www.bioassay.dk/index-filer/start/DraftDrcManual.pdf gives me
the current manual of drc package in R. For example:
library(drc) model1 <- drm(SLOPE~DOSE, C | How to do 4-parametric regression for ELISA data in R
After days, I record my found here:
http://www.bioassay.dk/index-filer/start/DraftDrcManual.pdf gives me
the current manual of drc package in R. For example:
library(drc) model1 <- drm(SLOPE~DOSE, CURVE,
fct=LL.4(names=c("Slope", "Lower", "Upper", "ED50")),data=... | How to do 4-parametric regression for ELISA data in R
After days, I record my found here:
http://www.bioassay.dk/index-filer/start/DraftDrcManual.pdf gives me
the current manual of drc package in R. For example:
library(drc) model1 <- drm(SLOPE~DOSE, C |
42,530 | How to do 4-parametric regression for ELISA data in R | You can find the least-square estimate of the parameters using nonlinear regression. Example:
f=function(B,x)
(B[1]-B[4])/(1+(x/B[3])^B[2])+B[4]
LS=function(B,y,x)
sum((y-f(B,x))^2)
x=runif(100,0,5)
B=c(1,5,2.5,5)
y=f(B,x)
plot(x,y)
### Estimate should be very close to B
nlm(LS,c(1,1,1,1),x=x,y=y) | How to do 4-parametric regression for ELISA data in R | You can find the least-square estimate of the parameters using nonlinear regression. Example:
f=function(B,x)
(B[1]-B[4])/(1+(x/B[3])^B[2])+B[4]
LS=function(B,y,x)
sum((y-f(B,x))^2)
x=runif(100 | How to do 4-parametric regression for ELISA data in R
You can find the least-square estimate of the parameters using nonlinear regression. Example:
f=function(B,x)
(B[1]-B[4])/(1+(x/B[3])^B[2])+B[4]
LS=function(B,y,x)
sum((y-f(B,x))^2)
x=runif(100,0,5)
B=c(1,5,2.5,5)
y=f(B,x)
plot(x,y)
### Estimate should be v... | How to do 4-parametric regression for ELISA data in R
You can find the least-square estimate of the parameters using nonlinear regression. Example:
f=function(B,x)
(B[1]-B[4])/(1+(x/B[3])^B[2])+B[4]
LS=function(B,y,x)
sum((y-f(B,x))^2)
x=runif(100 |
42,531 | How to do 4-parametric regression for ELISA data in R | There is an excellent R tutorial on fitting the 4 parameter logistic model for calibration purposes (e.g. on ELISA data) here:
http://weightinginbayesianmodels.github.io/poctcalibration/over_tutorials.html
http://weightinginbayesianmodels.github.io/poctcalibration/calib_tut4_curve_ocon.html
http://weightinginbayesianmo... | How to do 4-parametric regression for ELISA data in R | There is an excellent R tutorial on fitting the 4 parameter logistic model for calibration purposes (e.g. on ELISA data) here:
http://weightinginbayesianmodels.github.io/poctcalibration/over_tutorials | How to do 4-parametric regression for ELISA data in R
There is an excellent R tutorial on fitting the 4 parameter logistic model for calibration purposes (e.g. on ELISA data) here:
http://weightinginbayesianmodels.github.io/poctcalibration/over_tutorials.html
http://weightinginbayesianmodels.github.io/poctcalibration/c... | How to do 4-parametric regression for ELISA data in R
There is an excellent R tutorial on fitting the 4 parameter logistic model for calibration purposes (e.g. on ELISA data) here:
http://weightinginbayesianmodels.github.io/poctcalibration/over_tutorials |
42,532 | How to do 4-parametric regression for ELISA data in R | After days, I record my found here:
http://www.bioassay.dk/index-filer/start/DraftDrcManual.pdf gives me the current manual of drc package in R. For example:
library(drc)
model1 <- drm(SLOPE~DOSE, CURVE, fct=LL.4(names=c("Slope", "Lower", "Upper", "ED50")),data=spinach)
summary(model1)
plot(model1)
If I wanna predict ... | How to do 4-parametric regression for ELISA data in R | After days, I record my found here:
http://www.bioassay.dk/index-filer/start/DraftDrcManual.pdf gives me the current manual of drc package in R. For example:
library(drc)
model1 <- drm(SLOPE~DOSE, CUR | How to do 4-parametric regression for ELISA data in R
After days, I record my found here:
http://www.bioassay.dk/index-filer/start/DraftDrcManual.pdf gives me the current manual of drc package in R. For example:
library(drc)
model1 <- drm(SLOPE~DOSE, CURVE, fct=LL.4(names=c("Slope", "Lower", "Upper", "ED50")),data=spin... | How to do 4-parametric regression for ELISA data in R
After days, I record my found here:
http://www.bioassay.dk/index-filer/start/DraftDrcManual.pdf gives me the current manual of drc package in R. For example:
library(drc)
model1 <- drm(SLOPE~DOSE, CUR |
42,533 | How to do 4-parametric regression for ELISA data in R | For what it's worth, below is an example comparing drc::drm() and gnlm::gnlr():
library(drc)
spinach1 <- subset(spinach, CURVE==1)
model.drm <- drm(SLOPE~DOSE, CURVE,
fct=LL.4(names=c("B", "D", "A","C")),data=spinach1)
summary(model.drm)
library(gnlm)
attach(spinach1)
model.gnlr <- gnlr(y = SLOPE,
... | How to do 4-parametric regression for ELISA data in R | For what it's worth, below is an example comparing drc::drm() and gnlm::gnlr():
library(drc)
spinach1 <- subset(spinach, CURVE==1)
model.drm <- drm(SLOPE~DOSE, CURVE,
fct=LL.4(names=c("B | How to do 4-parametric regression for ELISA data in R
For what it's worth, below is an example comparing drc::drm() and gnlm::gnlr():
library(drc)
spinach1 <- subset(spinach, CURVE==1)
model.drm <- drm(SLOPE~DOSE, CURVE,
fct=LL.4(names=c("B", "D", "A","C")),data=spinach1)
summary(model.drm)
library(gnlm... | How to do 4-parametric regression for ELISA data in R
For what it's worth, below is an example comparing drc::drm() and gnlm::gnlr():
library(drc)
spinach1 <- subset(spinach, CURVE==1)
model.drm <- drm(SLOPE~DOSE, CURVE,
fct=LL.4(names=c("B |
42,534 | What's the problem with model identifiability? | I recommend you read Andrew Gelman's blog post Think identifiability Bayesian inference.
Right off the bat, I can tell you that identifiability does not have to do with a model by itself (as in "an unidentifiable model"), rather than with the combination of this model with some data. That is to say, it has to do with ... | What's the problem with model identifiability? | I recommend you read Andrew Gelman's blog post Think identifiability Bayesian inference.
Right off the bat, I can tell you that identifiability does not have to do with a model by itself (as in "an u | What's the problem with model identifiability?
I recommend you read Andrew Gelman's blog post Think identifiability Bayesian inference.
Right off the bat, I can tell you that identifiability does not have to do with a model by itself (as in "an unidentifiable model"), rather than with the combination of this model wit... | What's the problem with model identifiability?
I recommend you read Andrew Gelman's blog post Think identifiability Bayesian inference.
Right off the bat, I can tell you that identifiability does not have to do with a model by itself (as in "an u |
42,535 | Difference between "estimated" and "fitted"? | I think the term "fitted" is mostly used in regression frameworks where you fit a line through data. For example: $\hat{y}=\beta_{0}+\beta_{1}x$. The value of the fitted line at the data point $x_{i}$ is then called the fitted value or the predicted value at this point ($\hat{y_{i}}$). The term estimate is in generally... | Difference between "estimated" and "fitted"? | I think the term "fitted" is mostly used in regression frameworks where you fit a line through data. For example: $\hat{y}=\beta_{0}+\beta_{1}x$. The value of the fitted line at the data point $x_{i}$ | Difference between "estimated" and "fitted"?
I think the term "fitted" is mostly used in regression frameworks where you fit a line through data. For example: $\hat{y}=\beta_{0}+\beta_{1}x$. The value of the fitted line at the data point $x_{i}$ is then called the fitted value or the predicted value at this point ($\ha... | Difference between "estimated" and "fitted"?
I think the term "fitted" is mostly used in regression frameworks where you fit a line through data. For example: $\hat{y}=\beta_{0}+\beta_{1}x$. The value of the fitted line at the data point $x_{i}$ |
42,536 | Difference between "estimated" and "fitted"? | Often you want to fit a model, not values. The estimate of parameters of this model are then estimated using an estimator, that is a specific estimation technique. For example in Tsay, the standard deviation (volatility) is a parameter that is estimated. It sounds clear to me. In the R manual, "fitted values" sounds ve... | Difference between "estimated" and "fitted"? | Often you want to fit a model, not values. The estimate of parameters of this model are then estimated using an estimator, that is a specific estimation technique. For example in Tsay, the standard de | Difference between "estimated" and "fitted"?
Often you want to fit a model, not values. The estimate of parameters of this model are then estimated using an estimator, that is a specific estimation technique. For example in Tsay, the standard deviation (volatility) is a parameter that is estimated. It sounds clear to m... | Difference between "estimated" and "fitted"?
Often you want to fit a model, not values. The estimate of parameters of this model are then estimated using an estimator, that is a specific estimation technique. For example in Tsay, the standard de |
42,537 | What does the Semivariance tell me? | The context is a stationary spatial random process ("random field"). Imagine--quite hypothetically--that this process is infinite in extent and that you can take arbitrarily large samples of the point locations and observe the field's values there. Consider such a sample where all points are further apart than the ra... | What does the Semivariance tell me? | The context is a stationary spatial random process ("random field"). Imagine--quite hypothetically--that this process is infinite in extent and that you can take arbitrarily large samples of the poin | What does the Semivariance tell me?
The context is a stationary spatial random process ("random field"). Imagine--quite hypothetically--that this process is infinite in extent and that you can take arbitrarily large samples of the point locations and observe the field's values there. Consider such a sample where all ... | What does the Semivariance tell me?
The context is a stationary spatial random process ("random field"). Imagine--quite hypothetically--that this process is infinite in extent and that you can take arbitrarily large samples of the poin |
42,538 | What does the Semivariance tell me? | from Wikipedia:
$\hat{\gamma}(h):=\frac{1}{|N(h)|}\sum_{(i,j)\in N(h)} |z_i-z_j|^2$
where $N(h)$ denotes the set of pairs of observations $i,\;j$ such that $|x_i-x_j| = h$, and $|N(h)|$ is the number of pairs in the set.
Your sill is the limit for $\hat{\gamma} (h)$ you observe. According to the formula, it i... | What does the Semivariance tell me? | from Wikipedia:
$\hat{\gamma}(h):=\frac{1}{|N(h)|}\sum_{(i,j)\in N(h)} |z_i-z_j|^2$
where $N(h)$ denotes the set of pairs of observations $i,\;j$ such that $|x_i-x_j| = h$, and $|N(h)|$ is th | What does the Semivariance tell me?
from Wikipedia:
$\hat{\gamma}(h):=\frac{1}{|N(h)|}\sum_{(i,j)\in N(h)} |z_i-z_j|^2$
where $N(h)$ denotes the set of pairs of observations $i,\;j$ such that $|x_i-x_j| = h$, and $|N(h)|$ is the number of pairs in the set.
Your sill is the limit for $\hat{\gamma} (h)$ you obs... | What does the Semivariance tell me?
from Wikipedia:
$\hat{\gamma}(h):=\frac{1}{|N(h)|}\sum_{(i,j)\in N(h)} |z_i-z_j|^2$
where $N(h)$ denotes the set of pairs of observations $i,\;j$ such that $|x_i-x_j| = h$, and $|N(h)|$ is th |
42,539 | Confidence intervals based on the CLT: ever useful? | The speed of convergence towards Gaussian depends on the exact laws, and in particular on the values of the cumulants:
if there are no cumulant of order 1 or 2, i.e. no mean or no variance, you cannnot expect convergence to the normal;
if cumulants of order 1 and 2 exist, it all depends on the cumulants of higher orde... | Confidence intervals based on the CLT: ever useful? | The speed of convergence towards Gaussian depends on the exact laws, and in particular on the values of the cumulants:
if there are no cumulant of order 1 or 2, i.e. no mean or no variance, you cannn | Confidence intervals based on the CLT: ever useful?
The speed of convergence towards Gaussian depends on the exact laws, and in particular on the values of the cumulants:
if there are no cumulant of order 1 or 2, i.e. no mean or no variance, you cannnot expect convergence to the normal;
if cumulants of order 1 and 2 e... | Confidence intervals based on the CLT: ever useful?
The speed of convergence towards Gaussian depends on the exact laws, and in particular on the values of the cumulants:
if there are no cumulant of order 1 or 2, i.e. no mean or no variance, you cannn |
42,540 | Which performance measure for unbalanced binary classification without an 'active' class? | Have a look at the Matthews Correlation Coefficient
$$MCC = \frac{TP \cdot TN - FP \cdot FN}{\sqrt{ (TP + FP)(TP + FN)(TN + FP)(TN + FN) }}$$
I have seen it pretty often as performance metrix in classification of SNPs dataset. Have a look at this link as well, they discuss the difference between AUC and MCC
Otherwise ... | Which performance measure for unbalanced binary classification without an 'active' class? | Have a look at the Matthews Correlation Coefficient
$$MCC = \frac{TP \cdot TN - FP \cdot FN}{\sqrt{ (TP + FP)(TP + FN)(TN + FP)(TN + FN) }}$$
I have seen it pretty often as performance metrix in clas | Which performance measure for unbalanced binary classification without an 'active' class?
Have a look at the Matthews Correlation Coefficient
$$MCC = \frac{TP \cdot TN - FP \cdot FN}{\sqrt{ (TP + FP)(TP + FN)(TN + FP)(TN + FN) }}$$
I have seen it pretty often as performance metrix in classification of SNPs dataset. Ha... | Which performance measure for unbalanced binary classification without an 'active' class?
Have a look at the Matthews Correlation Coefficient
$$MCC = \frac{TP \cdot TN - FP \cdot FN}{\sqrt{ (TP + FP)(TP + FN)(TN + FP)(TN + FN) }}$$
I have seen it pretty often as performance metrix in clas |
42,541 | What is better to transform doing linear regression, response or explanatory variable(s)? | It depends on the situation. When you have multiple independent variables, sometimes only one of them has a nonlinear relationship - in this case, transforming the dependent variable may cause problems with the other variables. In some cases, a log transform makes more substantive sense for one or the other variable. I... | What is better to transform doing linear regression, response or explanatory variable(s)? | It depends on the situation. When you have multiple independent variables, sometimes only one of them has a nonlinear relationship - in this case, transforming the dependent variable may cause problem | What is better to transform doing linear regression, response or explanatory variable(s)?
It depends on the situation. When you have multiple independent variables, sometimes only one of them has a nonlinear relationship - in this case, transforming the dependent variable may cause problems with the other variables. In... | What is better to transform doing linear regression, response or explanatory variable(s)?
It depends on the situation. When you have multiple independent variables, sometimes only one of them has a nonlinear relationship - in this case, transforming the dependent variable may cause problem |
42,542 | What is better to transform doing linear regression, response or explanatory variable(s)? | Doing linear reg. its better to tranform the independent (explanatory ) variable and use the tranformation methods according to your data and cheak r-square for the model and MSE(mean square of error) for the modle if r-square is higher and MSE is mininmum you can say tranformation is appropriate.. | What is better to transform doing linear regression, response or explanatory variable(s)? | Doing linear reg. its better to tranform the independent (explanatory ) variable and use the tranformation methods according to your data and cheak r-square for the model and MSE(mean square of error) | What is better to transform doing linear regression, response or explanatory variable(s)?
Doing linear reg. its better to tranform the independent (explanatory ) variable and use the tranformation methods according to your data and cheak r-square for the model and MSE(mean square of error) for the modle if r-square is ... | What is better to transform doing linear regression, response or explanatory variable(s)?
Doing linear reg. its better to tranform the independent (explanatory ) variable and use the tranformation methods according to your data and cheak r-square for the model and MSE(mean square of error) |
42,543 | Confusion about scatter matrices | What @whuber and I were saying to you in the comments are equivalent things. @whuber pointed out that the text you were reading makes points column vectors. I stuck to your own original notation where points are row vectors (this way of presenting is more common). When points are columns, thansposed ("T", or just ' in ... | Confusion about scatter matrices | What @whuber and I were saying to you in the comments are equivalent things. @whuber pointed out that the text you were reading makes points column vectors. I stuck to your own original notation where | Confusion about scatter matrices
What @whuber and I were saying to you in the comments are equivalent things. @whuber pointed out that the text you were reading makes points column vectors. I stuck to your own original notation where points are row vectors (this way of presenting is more common). When points are column... | Confusion about scatter matrices
What @whuber and I were saying to you in the comments are equivalent things. @whuber pointed out that the text you were reading makes points column vectors. I stuck to your own original notation where |
42,544 | How bad is considering a random effect as a fixed effect? | Doug Bates (author of R package lme4) writes in his unpublished book (available online) that 6 levels are the minimum number required for obtaining reasonable estimates of variance components. By treating the technician effect as fixed, one loses the interpretation of the regression generalizing to a population of tech... | How bad is considering a random effect as a fixed effect? | Doug Bates (author of R package lme4) writes in his unpublished book (available online) that 6 levels are the minimum number required for obtaining reasonable estimates of variance components. By trea | How bad is considering a random effect as a fixed effect?
Doug Bates (author of R package lme4) writes in his unpublished book (available online) that 6 levels are the minimum number required for obtaining reasonable estimates of variance components. By treating the technician effect as fixed, one loses the interpretat... | How bad is considering a random effect as a fixed effect?
Doug Bates (author of R package lme4) writes in his unpublished book (available online) that 6 levels are the minimum number required for obtaining reasonable estimates of variance components. By trea |
42,545 | Nearest Neighbor Algorithm for Circular dimensions | How many circular dimensions are there? Two of the tricks described below trade off resources for representational convenience, unfortunately exponentially on the number of circular dimensions.
There are at least three tricks you can use:
Use coordinate patches: create many small kd-trees, overlapping so that they ar... | Nearest Neighbor Algorithm for Circular dimensions | How many circular dimensions are there? Two of the tricks described below trade off resources for representational convenience, unfortunately exponentially on the number of circular dimensions.
There | Nearest Neighbor Algorithm for Circular dimensions
How many circular dimensions are there? Two of the tricks described below trade off resources for representational convenience, unfortunately exponentially on the number of circular dimensions.
There are at least three tricks you can use:
Use coordinate patches: crea... | Nearest Neighbor Algorithm for Circular dimensions
How many circular dimensions are there? Two of the tricks described below trade off resources for representational convenience, unfortunately exponentially on the number of circular dimensions.
There |
42,546 | Nearest Neighbor Algorithm for Circular dimensions | Try a clustering algorithm like DBScan. R and Weka both have this algo. It's density based. So if the clusters are continuous in nature for the day, it should do a good job of making a cluster for a day. | Nearest Neighbor Algorithm for Circular dimensions | Try a clustering algorithm like DBScan. R and Weka both have this algo. It's density based. So if the clusters are continuous in nature for the day, it should do a good job of making a cluster for a | Nearest Neighbor Algorithm for Circular dimensions
Try a clustering algorithm like DBScan. R and Weka both have this algo. It's density based. So if the clusters are continuous in nature for the day, it should do a good job of making a cluster for a day. | Nearest Neighbor Algorithm for Circular dimensions
Try a clustering algorithm like DBScan. R and Weka both have this algo. It's density based. So if the clusters are continuous in nature for the day, it should do a good job of making a cluster for a |
42,547 | Can one compute confidence intervals for a census with high nonresponse rates? | In general, no, you cannot do this.
More detail: The problem is that the 32% may be different from the 68%. If all you know is that they are missing then you have no way of saying in what way they may be different. But it's complex. With any missing data problem, even one with a census, one key thing is to determine (i... | Can one compute confidence intervals for a census with high nonresponse rates? | In general, no, you cannot do this.
More detail: The problem is that the 32% may be different from the 68%. If all you know is that they are missing then you have no way of saying in what way they may | Can one compute confidence intervals for a census with high nonresponse rates?
In general, no, you cannot do this.
More detail: The problem is that the 32% may be different from the 68%. If all you know is that they are missing then you have no way of saying in what way they may be different. But it's complex. With any... | Can one compute confidence intervals for a census with high nonresponse rates?
In general, no, you cannot do this.
More detail: The problem is that the 32% may be different from the 68%. If all you know is that they are missing then you have no way of saying in what way they may |
42,548 | Method of p value correction for a set of dependent p values | You should definitely correct for multiple comparisons! There are a huge number of strategies for this, many of which are suitable for dependent data. The Benjamini–Hochberg–Yekutieli procedure is computationally simple approach to setting the false discovery rate and works under arbitrary dependence assumptions; your ... | Method of p value correction for a set of dependent p values | You should definitely correct for multiple comparisons! There are a huge number of strategies for this, many of which are suitable for dependent data. The Benjamini–Hochberg–Yekutieli procedure is com | Method of p value correction for a set of dependent p values
You should definitely correct for multiple comparisons! There are a huge number of strategies for this, many of which are suitable for dependent data. The Benjamini–Hochberg–Yekutieli procedure is computationally simple approach to setting the false discovery... | Method of p value correction for a set of dependent p values
You should definitely correct for multiple comparisons! There are a huge number of strategies for this, many of which are suitable for dependent data. The Benjamini–Hochberg–Yekutieli procedure is com |
42,549 | When is blocked Metropolis sampling more efficient? | I don't have references on hand, unfortunately, but generally the more dependence there is between $X$ and $Y$, the more efficient it will be to update them jointly. In your example $X$ and $Y$ are independent; if, for example, $X$ and $Y$ were still jointly Gaussian but had a correlation of .99, updating them separate... | When is blocked Metropolis sampling more efficient? | I don't have references on hand, unfortunately, but generally the more dependence there is between $X$ and $Y$, the more efficient it will be to update them jointly. In your example $X$ and $Y$ are in | When is blocked Metropolis sampling more efficient?
I don't have references on hand, unfortunately, but generally the more dependence there is between $X$ and $Y$, the more efficient it will be to update them jointly. In your example $X$ and $Y$ are independent; if, for example, $X$ and $Y$ were still jointly Gaussian ... | When is blocked Metropolis sampling more efficient?
I don't have references on hand, unfortunately, but generally the more dependence there is between $X$ and $Y$, the more efficient it will be to update them jointly. In your example $X$ and $Y$ are in |
42,550 | Exponent for non-linear regression (in R)? | In order that this receive an answer (though it may not add a heck of a lot to whuber's comment):
I want to find the best way to determine the value for the exponent $\gamma$ in the following regression: $y = \beta x^\gamma$
The observed $y$ will not actually be $\beta x^\gamma$; if it were there'd be no need for sta... | Exponent for non-linear regression (in R)? | In order that this receive an answer (though it may not add a heck of a lot to whuber's comment):
I want to find the best way to determine the value for the exponent $\gamma$ in the following regress | Exponent for non-linear regression (in R)?
In order that this receive an answer (though it may not add a heck of a lot to whuber's comment):
I want to find the best way to determine the value for the exponent $\gamma$ in the following regression: $y = \beta x^\gamma$
The observed $y$ will not actually be $\beta x^\ga... | Exponent for non-linear regression (in R)?
In order that this receive an answer (though it may not add a heck of a lot to whuber's comment):
I want to find the best way to determine the value for the exponent $\gamma$ in the following regress |
42,551 | Is significance of the p-value reliable with extremely small sample sizes? [duplicate] | If you know that they are distributed normally you are right (with the addition, as @gung points out, that the variances need to be qual). The use of the t statistic with the appropriate degrees of freedom means that this approach is alright despite the tiny sample size.
On your secondary question about what to do if ... | Is significance of the p-value reliable with extremely small sample sizes? [duplicate] | If you know that they are distributed normally you are right (with the addition, as @gung points out, that the variances need to be qual). The use of the t statistic with the appropriate degrees of f | Is significance of the p-value reliable with extremely small sample sizes? [duplicate]
If you know that they are distributed normally you are right (with the addition, as @gung points out, that the variances need to be qual). The use of the t statistic with the appropriate degrees of freedom means that this approach i... | Is significance of the p-value reliable with extremely small sample sizes? [duplicate]
If you know that they are distributed normally you are right (with the addition, as @gung points out, that the variances need to be qual). The use of the t statistic with the appropriate degrees of f |
42,552 | Definition of "optimal" instruments | I haven't read the particular page in the book, but here is my take on that.
$\bar X = E(X|\Omega)$ $\rightarrow$ that's your 'ideal' first stage regression here. $\bar X$ is the set of all variables that are informative after controlling for everything else ($\Omega$) that determines Y.
$E(V|\Omega) = 0$ $\rightarrow$... | Definition of "optimal" instruments | I haven't read the particular page in the book, but here is my take on that.
$\bar X = E(X|\Omega)$ $\rightarrow$ that's your 'ideal' first stage regression here. $\bar X$ is the set of all variables | Definition of "optimal" instruments
I haven't read the particular page in the book, but here is my take on that.
$\bar X = E(X|\Omega)$ $\rightarrow$ that's your 'ideal' first stage regression here. $\bar X$ is the set of all variables that are informative after controlling for everything else ($\Omega$) that determine... | Definition of "optimal" instruments
I haven't read the particular page in the book, but here is my take on that.
$\bar X = E(X|\Omega)$ $\rightarrow$ that's your 'ideal' first stage regression here. $\bar X$ is the set of all variables |
42,553 | Optimal sampling to estimate a quadratic function | Let $y_i$ denote the random variable $\hat{f}(x_i)$ and $Y_n$ denote $\{y_i\}_{i \leq n}$,
In the case where you have to fix all your points $\{x_i\}_{i \leq n}$ before you get any observation $y_i$, I suggest you to consider points which maximize some optimal design criterion. Like StasK said, D-optimality is a common... | Optimal sampling to estimate a quadratic function | Let $y_i$ denote the random variable $\hat{f}(x_i)$ and $Y_n$ denote $\{y_i\}_{i \leq n}$,
In the case where you have to fix all your points $\{x_i\}_{i \leq n}$ before you get any observation $y_i$, | Optimal sampling to estimate a quadratic function
Let $y_i$ denote the random variable $\hat{f}(x_i)$ and $Y_n$ denote $\{y_i\}_{i \leq n}$,
In the case where you have to fix all your points $\{x_i\}_{i \leq n}$ before you get any observation $y_i$, I suggest you to consider points which maximize some optimal design cr... | Optimal sampling to estimate a quadratic function
Let $y_i$ denote the random variable $\hat{f}(x_i)$ and $Y_n$ denote $\{y_i\}_{i \leq n}$,
In the case where you have to fix all your points $\{x_i\}_{i \leq n}$ before you get any observation $y_i$, |
42,554 | A "Gambler's Loss function"? | I imagine it would depend on the payout scheme. For example if being wrong isn't a continuum but a binary either/or, then you would probably want to pick a loss function like mean absolute error or accuracy so that all incorrect answers are treated equally. On the other hand, if being overconfident -- saying something ... | A "Gambler's Loss function"? | I imagine it would depend on the payout scheme. For example if being wrong isn't a continuum but a binary either/or, then you would probably want to pick a loss function like mean absolute error or ac | A "Gambler's Loss function"?
I imagine it would depend on the payout scheme. For example if being wrong isn't a continuum but a binary either/or, then you would probably want to pick a loss function like mean absolute error or accuracy so that all incorrect answers are treated equally. On the other hand, if being overc... | A "Gambler's Loss function"?
I imagine it would depend on the payout scheme. For example if being wrong isn't a continuum but a binary either/or, then you would probably want to pick a loss function like mean absolute error or ac |
42,555 | A "Gambler's Loss function"? | First a comment on the previous answer and then the answer. On derivatives/options it is more complicated than that. You would likely not sell short or sell a call without a stop loss order and/or a hedge. Let's say you sold an option to buy (a call) at 100, and bought a call at 200. A good loss function might be expon... | A "Gambler's Loss function"? | First a comment on the previous answer and then the answer. On derivatives/options it is more complicated than that. You would likely not sell short or sell a call without a stop loss order and/or a h | A "Gambler's Loss function"?
First a comment on the previous answer and then the answer. On derivatives/options it is more complicated than that. You would likely not sell short or sell a call without a stop loss order and/or a hedge. Let's say you sold an option to buy (a call) at 100, and bought a call at 200. A good... | A "Gambler's Loss function"?
First a comment on the previous answer and then the answer. On derivatives/options it is more complicated than that. You would likely not sell short or sell a call without a stop loss order and/or a h |
42,556 | Small n: non-parametric or parametric tests? | The data were collected using Likert scales in a questionnaire.
From the moment you decided to use Likert scales, the issue of whether or not the data were actually from a normal distribution was decided (in the negative). It's pointless to test (and answer with chance of error) a question to which you already know th... | Small n: non-parametric or parametric tests? | The data were collected using Likert scales in a questionnaire.
From the moment you decided to use Likert scales, the issue of whether or not the data were actually from a normal distribution was dec | Small n: non-parametric or parametric tests?
The data were collected using Likert scales in a questionnaire.
From the moment you decided to use Likert scales, the issue of whether or not the data were actually from a normal distribution was decided (in the negative). It's pointless to test (and answer with chance of e... | Small n: non-parametric or parametric tests?
The data were collected using Likert scales in a questionnaire.
From the moment you decided to use Likert scales, the issue of whether or not the data were actually from a normal distribution was dec |
42,557 | Small n: non-parametric or parametric tests? | The sample size is not an issue here. You can only use nonparametric procedures (depending on the particular question Wilcoxon test, rank correlation, Kruskal-Wallis test or others) with Likert scale data due to their ordinal scale.
Parametric procedures use the spaceing between different levels. But on a Likert scale... | Small n: non-parametric or parametric tests? | The sample size is not an issue here. You can only use nonparametric procedures (depending on the particular question Wilcoxon test, rank correlation, Kruskal-Wallis test or others) with Likert scale | Small n: non-parametric or parametric tests?
The sample size is not an issue here. You can only use nonparametric procedures (depending on the particular question Wilcoxon test, rank correlation, Kruskal-Wallis test or others) with Likert scale data due to their ordinal scale.
Parametric procedures use the spaceing be... | Small n: non-parametric or parametric tests?
The sample size is not an issue here. You can only use nonparametric procedures (depending on the particular question Wilcoxon test, rank correlation, Kruskal-Wallis test or others) with Likert scale |
42,558 | Permutation test to test significance of skewness/kurtosis of two distributions? | Want to improve this post? Add citations from reputable sources by editing the post. Posts with unsourced content may be edited or deleted.
Yes, this method is correct, assuming you do not care about the direction of the difference. | Permutation test to test significance of skewness/kurtosis of two distributions? | Want to improve this post? Add citations from reputable sources by editing the post. Posts with unsourced content may be edited or deleted.
| Permutation test to test significance of skewness/kurtosis of two distributions?
Want to improve this post? Add citations from reputable sources by editing the post. Posts with unsourced content may be edited or deleted.
Yes, this method is correct, ... | Permutation test to test significance of skewness/kurtosis of two distributions?
Want to improve this post? Add citations from reputable sources by editing the post. Posts with unsourced content may be edited or deleted.
|
42,559 | How do you estimate correlations between ordinal and binary data in SPSS? | If you're comparing the correlation of two variables, just report the Spearman correlation coefficient. The Spearman correlation is recommended over Pearson correlation for this type of data: How to choose between Pearson and Spearman correlation?.
If you want to know how multiple variables impact the answer to one of... | How do you estimate correlations between ordinal and binary data in SPSS? | If you're comparing the correlation of two variables, just report the Spearman correlation coefficient. The Spearman correlation is recommended over Pearson correlation for this type of data: How to c | How do you estimate correlations between ordinal and binary data in SPSS?
If you're comparing the correlation of two variables, just report the Spearman correlation coefficient. The Spearman correlation is recommended over Pearson correlation for this type of data: How to choose between Pearson and Spearman correlation... | How do you estimate correlations between ordinal and binary data in SPSS?
If you're comparing the correlation of two variables, just report the Spearman correlation coefficient. The Spearman correlation is recommended over Pearson correlation for this type of data: How to c |
42,560 | How do you estimate correlations between ordinal and binary data in SPSS? | When one of the variables is binary (such as group membership) just any kind of correlation (whether the other variable is continuous, likert , ...) is not much more than some rescaled version of some difference of means between the two groups defined by the binary variable. So normally it is better to just focus on... | How do you estimate correlations between ordinal and binary data in SPSS? | When one of the variables is binary (such as group membership) just any kind of correlation (whether the other variable is continuous, likert , ...) is not much more than some rescaled version of som | How do you estimate correlations between ordinal and binary data in SPSS?
When one of the variables is binary (such as group membership) just any kind of correlation (whether the other variable is continuous, likert , ...) is not much more than some rescaled version of some difference of means between the two groups d... | How do you estimate correlations between ordinal and binary data in SPSS?
When one of the variables is binary (such as group membership) just any kind of correlation (whether the other variable is continuous, likert , ...) is not much more than some rescaled version of som |
42,561 | Why is tf-idf used in conjunction with SVMs for classifying documents? | If you only multiply each feature by some weight that correspond to the term's rarity (i.e. $log(\frac{M}{a_i})$ where $M$ is the total number of documents and $a_i$ is the number of documents with the considered term), and then use SVM, then the feature scaling you have performed is useless (as you have observed).
How... | Why is tf-idf used in conjunction with SVMs for classifying documents? | If you only multiply each feature by some weight that correspond to the term's rarity (i.e. $log(\frac{M}{a_i})$ where $M$ is the total number of documents and $a_i$ is the number of documents with th | Why is tf-idf used in conjunction with SVMs for classifying documents?
If you only multiply each feature by some weight that correspond to the term's rarity (i.e. $log(\frac{M}{a_i})$ where $M$ is the total number of documents and $a_i$ is the number of documents with the considered term), and then use SVM, then the fe... | Why is tf-idf used in conjunction with SVMs for classifying documents?
If you only multiply each feature by some weight that correspond to the term's rarity (i.e. $log(\frac{M}{a_i})$ where $M$ is the total number of documents and $a_i$ is the number of documents with th |
42,562 | Is there any test for a null hypothesis of non-normality? | I think the question is the same whether normality is the null hypothesis or the alternative. the test is a measure of goodness of fit. If the test is based on the traditional approach then you look for how large the test statistic should be to reject normality. If the you make normality the alternative then you are... | Is there any test for a null hypothesis of non-normality? | I think the question is the same whether normality is the null hypothesis or the alternative. the test is a measure of goodness of fit. If the test is based on the traditional approach then you look | Is there any test for a null hypothesis of non-normality?
I think the question is the same whether normality is the null hypothesis or the alternative. the test is a measure of goodness of fit. If the test is based on the traditional approach then you look for how large the test statistic should be to reject normalit... | Is there any test for a null hypothesis of non-normality?
I think the question is the same whether normality is the null hypothesis or the alternative. the test is a measure of goodness of fit. If the test is based on the traditional approach then you look |
42,563 | Is there any test for a null hypothesis of non-normality? | I think the problem is not well defined! You want to proove the data "do not come from a normal distribution". That is easy, accept! NO data "comes from a normal distribution", the normal distribution is at best an approximation.
But you could, of course, have goodness-of-fit tests for other distributions, say the Poi... | Is there any test for a null hypothesis of non-normality? | I think the problem is not well defined! You want to proove the data "do not come from a normal distribution". That is easy, accept! NO data "comes from a normal distribution", the normal distribution | Is there any test for a null hypothesis of non-normality?
I think the problem is not well defined! You want to proove the data "do not come from a normal distribution". That is easy, accept! NO data "comes from a normal distribution", the normal distribution is at best an approximation.
But you could, of course, have ... | Is there any test for a null hypothesis of non-normality?
I think the problem is not well defined! You want to proove the data "do not come from a normal distribution". That is easy, accept! NO data "comes from a normal distribution", the normal distribution |
42,564 | Is there any test for a null hypothesis of non-normality? | The answer is Chi square Goodness of fit test for normal distribution
Here you can see the example http://www.stat.yale.edu/Courses/1997-98/101/chigf.htm | Is there any test for a null hypothesis of non-normality? | The answer is Chi square Goodness of fit test for normal distribution
Here you can see the example http://www.stat.yale.edu/Courses/1997-98/101/chigf.htm | Is there any test for a null hypothesis of non-normality?
The answer is Chi square Goodness of fit test for normal distribution
Here you can see the example http://www.stat.yale.edu/Courses/1997-98/101/chigf.htm | Is there any test for a null hypothesis of non-normality?
The answer is Chi square Goodness of fit test for normal distribution
Here you can see the example http://www.stat.yale.edu/Courses/1997-98/101/chigf.htm |
42,565 | Is Ye Shiwen's 400m IM performance in Olympics "anomalous" statistically? | In the 1968 games in Mexico City Bob Beamon shattered the world record in the long jump with a jump of 29 ft 2 inches topping a world record that was under 28 ft at the time. It took decades for any one to come close and finally break Beamon's record. What explains it? The high altitude meant thin air and less air r... | Is Ye Shiwen's 400m IM performance in Olympics "anomalous" statistically? | In the 1968 games in Mexico City Bob Beamon shattered the world record in the long jump with a jump of 29 ft 2 inches topping a world record that was under 28 ft at the time. It took decades for any | Is Ye Shiwen's 400m IM performance in Olympics "anomalous" statistically?
In the 1968 games in Mexico City Bob Beamon shattered the world record in the long jump with a jump of 29 ft 2 inches topping a world record that was under 28 ft at the time. It took decades for any one to come close and finally break Beamon's r... | Is Ye Shiwen's 400m IM performance in Olympics "anomalous" statistically?
In the 1968 games in Mexico City Bob Beamon shattered the world record in the long jump with a jump of 29 ft 2 inches topping a world record that was under 28 ft at the time. It took decades for any |
42,566 | Logistic regression with an log transformed variable, how to determine economic significance | The interpretation you want to put on the covariate change makes sense in a simple model with one covariate. If you have two or more there could be some interaction and the best you can say in general is that x% is the magnitude of change when you change variable U by one standard deviation with the others held fixed... | Logistic regression with an log transformed variable, how to determine economic significance | The interpretation you want to put on the covariate change makes sense in a simple model with one covariate. If you have two or more there could be some interaction and the best you can say in gener | Logistic regression with an log transformed variable, how to determine economic significance
The interpretation you want to put on the covariate change makes sense in a simple model with one covariate. If you have two or more there could be some interaction and the best you can say in general is that x% is the magnit... | Logistic regression with an log transformed variable, how to determine economic significance
The interpretation you want to put on the covariate change makes sense in a simple model with one covariate. If you have two or more there could be some interaction and the best you can say in gener |
42,567 | Logistic regression with an log transformed variable, how to determine economic significance | Please disregard my answer if it repeats what it has been suggested to you in linked posts...
With log variables, your interpretation of the mfx $dy/dx$ becomes:
$\frac{dy}{d\log x}$ which is equivalent to $\frac{dy}{dx/x}$
so the interpretation implies that 1% variation in $X$ causes a $\frac{dy}{d\log x}$ variatio... | Logistic regression with an log transformed variable, how to determine economic significance | Please disregard my answer if it repeats what it has been suggested to you in linked posts...
With log variables, your interpretation of the mfx $dy/dx$ becomes:
$\frac{dy}{d\log x}$ which is equival | Logistic regression with an log transformed variable, how to determine economic significance
Please disregard my answer if it repeats what it has been suggested to you in linked posts...
With log variables, your interpretation of the mfx $dy/dx$ becomes:
$\frac{dy}{d\log x}$ which is equivalent to $\frac{dy}{dx/x}$
... | Logistic regression with an log transformed variable, how to determine economic significance
Please disregard my answer if it repeats what it has been suggested to you in linked posts...
With log variables, your interpretation of the mfx $dy/dx$ becomes:
$\frac{dy}{d\log x}$ which is equival |
42,568 | How to statistically test whether there is any nesting effect in the data? | Goal: To calculate ICC and to test whether the resulted ICC is big enough to account for the clustering effect in the study.
My Study: All students within 52 schools followed longitudinally over three years. Schools are randomly assigned to Treatment or control. My question is do we need to account for school clusterin... | How to statistically test whether there is any nesting effect in the data? | Goal: To calculate ICC and to test whether the resulted ICC is big enough to account for the clustering effect in the study.
My Study: All students within 52 schools followed longitudinally over three | How to statistically test whether there is any nesting effect in the data?
Goal: To calculate ICC and to test whether the resulted ICC is big enough to account for the clustering effect in the study.
My Study: All students within 52 schools followed longitudinally over three years. Schools are randomly assigned to Trea... | How to statistically test whether there is any nesting effect in the data?
Goal: To calculate ICC and to test whether the resulted ICC is big enough to account for the clustering effect in the study.
My Study: All students within 52 schools followed longitudinally over three |
42,569 | How to calculate precision and recall when some of the test data remains unclassified | It's useful to keep in mind that precision/recall are inherently tied to a particular state or label of interest. In information retrieval that label might be "relevant" as opposed to "not relevant," whereas in cancer that label might be "malignant" as opposed to "benign."
As @Thomas Jungblut mentions, it would be val... | How to calculate precision and recall when some of the test data remains unclassified | It's useful to keep in mind that precision/recall are inherently tied to a particular state or label of interest. In information retrieval that label might be "relevant" as opposed to "not relevant," | How to calculate precision and recall when some of the test data remains unclassified
It's useful to keep in mind that precision/recall are inherently tied to a particular state or label of interest. In information retrieval that label might be "relevant" as opposed to "not relevant," whereas in cancer that label migh... | How to calculate precision and recall when some of the test data remains unclassified
It's useful to keep in mind that precision/recall are inherently tied to a particular state or label of interest. In information retrieval that label might be "relevant" as opposed to "not relevant," |
42,570 | How to compare loess models from related datasets? | Unfortunately loess fits are not comparable. A loess (or lowess) curve is not like one based on a linear or quadratic or cubic equation. I know of no statistial package that will provide an equation that defines a loess curve, nor a fit statistic such as R-squared for it. Such a curve is completely opportunistic and... | How to compare loess models from related datasets? | Unfortunately loess fits are not comparable. A loess (or lowess) curve is not like one based on a linear or quadratic or cubic equation. I know of no statistial package that will provide an equation | How to compare loess models from related datasets?
Unfortunately loess fits are not comparable. A loess (or lowess) curve is not like one based on a linear or quadratic or cubic equation. I know of no statistial package that will provide an equation that defines a loess curve, nor a fit statistic such as R-squared fo... | How to compare loess models from related datasets?
Unfortunately loess fits are not comparable. A loess (or lowess) curve is not like one based on a linear or quadratic or cubic equation. I know of no statistial package that will provide an equation |
42,571 | How to compare loess models from related datasets? | Is the variance between the data and the curve bigger than the variance between the two curves, averaged over patients? If you want a p-value I suppose you could assume normality of errors and use the F-test on ratio of variances. The loess fit isn't a formal fit, so this is not a formal test. Could you use a formal fi... | How to compare loess models from related datasets? | Is the variance between the data and the curve bigger than the variance between the two curves, averaged over patients? If you want a p-value I suppose you could assume normality of errors and use the | How to compare loess models from related datasets?
Is the variance between the data and the curve bigger than the variance between the two curves, averaged over patients? If you want a p-value I suppose you could assume normality of errors and use the F-test on ratio of variances. The loess fit isn't a formal fit, so t... | How to compare loess models from related datasets?
Is the variance between the data and the curve bigger than the variance between the two curves, averaged over patients? If you want a p-value I suppose you could assume normality of errors and use the |
42,572 | How to compare loess models from related datasets? | predict.loess in R has an se parameter which if TRUE returns standard errors for all predicted points. These are the usual standard errors of residuals. One can, as well, simply take the absolute value of difference between predicted values and originals and so get a sequence of absolute errors. These are often more... | How to compare loess models from related datasets? | predict.loess in R has an se parameter which if TRUE returns standard errors for all predicted points. These are the usual standard errors of residuals. One can, as well, simply take the absolute va | How to compare loess models from related datasets?
predict.loess in R has an se parameter which if TRUE returns standard errors for all predicted points. These are the usual standard errors of residuals. One can, as well, simply take the absolute value of difference between predicted values and originals and so get a... | How to compare loess models from related datasets?
predict.loess in R has an se parameter which if TRUE returns standard errors for all predicted points. These are the usual standard errors of residuals. One can, as well, simply take the absolute va |
42,573 | How to compare loess models from related datasets? | In general when comparing two different fitting curves the way to go is a performance measure.
It does not allow you to draw strong conclusions, but if you have two options you can calculate the R squared for example and at least have an idea of how good your fit can be. | How to compare loess models from related datasets? | In general when comparing two different fitting curves the way to go is a performance measure.
It does not allow you to draw strong conclusions, but if you have two options you can calculate the R squ | How to compare loess models from related datasets?
In general when comparing two different fitting curves the way to go is a performance measure.
It does not allow you to draw strong conclusions, but if you have two options you can calculate the R squared for example and at least have an idea of how good your fit can b... | How to compare loess models from related datasets?
In general when comparing two different fitting curves the way to go is a performance measure.
It does not allow you to draw strong conclusions, but if you have two options you can calculate the R squ |
42,574 | What to conclude when you fail to find an association in an epidemiological study? | Yes, the weaknesses of associations found in epidemiological studies also apply to a failure to find an association. You've already eliminated the first go-to problem, that of a study being underpowered, so at the moment we're just talking about bias. Two issues that may mean your study is failing to find a true associ... | What to conclude when you fail to find an association in an epidemiological study? | Yes, the weaknesses of associations found in epidemiological studies also apply to a failure to find an association. You've already eliminated the first go-to problem, that of a study being underpower | What to conclude when you fail to find an association in an epidemiological study?
Yes, the weaknesses of associations found in epidemiological studies also apply to a failure to find an association. You've already eliminated the first go-to problem, that of a study being underpowered, so at the moment we're just talki... | What to conclude when you fail to find an association in an epidemiological study?
Yes, the weaknesses of associations found in epidemiological studies also apply to a failure to find an association. You've already eliminated the first go-to problem, that of a study being underpower |
42,575 | What to conclude when you fail to find an association in an epidemiological study? | I think it's worth distinguishing a few aspects of the problem:
Precision: If you have a bigger sample size, you will typically be able to estimate parameters with greater precision whether you define this in a frequentist sense in terms of smaller confidence intervals or in a Bayesian sense in terms of smaller credib... | What to conclude when you fail to find an association in an epidemiological study? | I think it's worth distinguishing a few aspects of the problem:
Precision: If you have a bigger sample size, you will typically be able to estimate parameters with greater precision whether you defin | What to conclude when you fail to find an association in an epidemiological study?
I think it's worth distinguishing a few aspects of the problem:
Precision: If you have a bigger sample size, you will typically be able to estimate parameters with greater precision whether you define this in a frequentist sense in term... | What to conclude when you fail to find an association in an epidemiological study?
I think it's worth distinguishing a few aspects of the problem:
Precision: If you have a bigger sample size, you will typically be able to estimate parameters with greater precision whether you defin |
42,576 | What to conclude when you fail to find an association in an epidemiological study? | Well, in physics we are very used to what you are saying and, to me at least, it is in fact far more exciting when this happens that when some researcher confirms theory. In these cases you just report that: you've found no evidence of what the theory predicts.
After checking your results and the possibe flaws of your... | What to conclude when you fail to find an association in an epidemiological study? | Well, in physics we are very used to what you are saying and, to me at least, it is in fact far more exciting when this happens that when some researcher confirms theory. In these cases you just repor | What to conclude when you fail to find an association in an epidemiological study?
Well, in physics we are very used to what you are saying and, to me at least, it is in fact far more exciting when this happens that when some researcher confirms theory. In these cases you just report that: you've found no evidence of w... | What to conclude when you fail to find an association in an epidemiological study?
Well, in physics we are very used to what you are saying and, to me at least, it is in fact far more exciting when this happens that when some researcher confirms theory. In these cases you just repor |
42,577 | Hyperparameter estimation in Gaussian process | It would be a good idea to get the optimisation code to print out the hyper-parameters each time it performs a function evaluation. Usually you can work out what is going wrong once you know what the model decides about the hyper-parameter values.
I suspect what is happening is that the model has decided that an essen... | Hyperparameter estimation in Gaussian process | It would be a good idea to get the optimisation code to print out the hyper-parameters each time it performs a function evaluation. Usually you can work out what is going wrong once you know what the | Hyperparameter estimation in Gaussian process
It would be a good idea to get the optimisation code to print out the hyper-parameters each time it performs a function evaluation. Usually you can work out what is going wrong once you know what the model decides about the hyper-parameter values.
I suspect what is happeni... | Hyperparameter estimation in Gaussian process
It would be a good idea to get the optimisation code to print out the hyper-parameters each time it performs a function evaluation. Usually you can work out what is going wrong once you know what the |
42,578 | Variance-gamma distribution: parameter estimation | 1. In pp. 16 they mention that the package uses "BFGS" or "Nelder-Mead". The second one is the default option. Please, take a look at these links to see their differences. Optimisation is actually made on the likelihood function. This is, the fitted parameters in the output of vgFit are actually the maximum likelihood ... | Variance-gamma distribution: parameter estimation | 1. In pp. 16 they mention that the package uses "BFGS" or "Nelder-Mead". The second one is the default option. Please, take a look at these links to see their differences. Optimisation is actually mad | Variance-gamma distribution: parameter estimation
1. In pp. 16 they mention that the package uses "BFGS" or "Nelder-Mead". The second one is the default option. Please, take a look at these links to see their differences. Optimisation is actually made on the likelihood function. This is, the fitted parameters in the ou... | Variance-gamma distribution: parameter estimation
1. In pp. 16 they mention that the package uses "BFGS" or "Nelder-Mead". The second one is the default option. Please, take a look at these links to see their differences. Optimisation is actually mad |
42,579 | Appropriateness of PCA to visualize clusters in genetic data | I do share your concern about the matter of visual rendering of clusters by means of PCA. If the 1st 2 or 3 principal components account for only small portion of multidimensional variability they are likely to miss most of the directions along which the clusters differentiate. Moreover, even when components are strong... | Appropriateness of PCA to visualize clusters in genetic data | I do share your concern about the matter of visual rendering of clusters by means of PCA. If the 1st 2 or 3 principal components account for only small portion of multidimensional variability they are | Appropriateness of PCA to visualize clusters in genetic data
I do share your concern about the matter of visual rendering of clusters by means of PCA. If the 1st 2 or 3 principal components account for only small portion of multidimensional variability they are likely to miss most of the directions along which the clus... | Appropriateness of PCA to visualize clusters in genetic data
I do share your concern about the matter of visual rendering of clusters by means of PCA. If the 1st 2 or 3 principal components account for only small portion of multidimensional variability they are |
42,580 | Appropriateness of PCA to visualize clusters in genetic data | As your data likely is very noisy, you can try to improve the PCA performance using a robust variation of it, see Wikipedia for details.
But in general I do share your concern. Because in complex data sets such as genetic data, different clusters may show different correlations that cannot be adequately represented by ... | Appropriateness of PCA to visualize clusters in genetic data | As your data likely is very noisy, you can try to improve the PCA performance using a robust variation of it, see Wikipedia for details.
But in general I do share your concern. Because in complex data | Appropriateness of PCA to visualize clusters in genetic data
As your data likely is very noisy, you can try to improve the PCA performance using a robust variation of it, see Wikipedia for details.
But in general I do share your concern. Because in complex data sets such as genetic data, different clusters may show dif... | Appropriateness of PCA to visualize clusters in genetic data
As your data likely is very noisy, you can try to improve the PCA performance using a robust variation of it, see Wikipedia for details.
But in general I do share your concern. Because in complex data |
42,581 | Appropriateness of PCA to visualize clusters in genetic data | One theory. For human, the proportion of variation within groups (i.e. same ethnic group) account for ~85% of the genetic variation within individuals. Conversely, the variation within populations (i.e. continental scale) account for only ~15% of the genetic variation within individuals.
Even though the first three com... | Appropriateness of PCA to visualize clusters in genetic data | One theory. For human, the proportion of variation within groups (i.e. same ethnic group) account for ~85% of the genetic variation within individuals. Conversely, the variation within populations (i. | Appropriateness of PCA to visualize clusters in genetic data
One theory. For human, the proportion of variation within groups (i.e. same ethnic group) account for ~85% of the genetic variation within individuals. Conversely, the variation within populations (i.e. continental scale) account for only ~15% of the genetic ... | Appropriateness of PCA to visualize clusters in genetic data
One theory. For human, the proportion of variation within groups (i.e. same ethnic group) account for ~85% of the genetic variation within individuals. Conversely, the variation within populations (i. |
42,582 | Support vector regression in R | For selecting parameters (such as the C parameter) I have no great advice. I have found I just need to try different values and using bootstrap or cross-validation to select the best ones.
One useful nugget I picked up along the way though is the concept of calibrating the predictions from the SVM output for regression... | Support vector regression in R | For selecting parameters (such as the C parameter) I have no great advice. I have found I just need to try different values and using bootstrap or cross-validation to select the best ones.
One useful | Support vector regression in R
For selecting parameters (such as the C parameter) I have no great advice. I have found I just need to try different values and using bootstrap or cross-validation to select the best ones.
One useful nugget I picked up along the way though is the concept of calibrating the predictions fro... | Support vector regression in R
For selecting parameters (such as the C parameter) I have no great advice. I have found I just need to try different values and using bootstrap or cross-validation to select the best ones.
One useful |
42,583 | Support vector regression in R | Try using the kernlab package, you can use ksvm(...,type='eps-svr') to get regression. It's smart enough to automatically select regression if given a continuous variable.
A grid search for parameters usually works reasonably well. Choice of kernel can have a big impact on performance. One rules of thumb I've found ... | Support vector regression in R | Try using the kernlab package, you can use ksvm(...,type='eps-svr') to get regression. It's smart enough to automatically select regression if given a continuous variable.
A grid search for parameter | Support vector regression in R
Try using the kernlab package, you can use ksvm(...,type='eps-svr') to get regression. It's smart enough to automatically select regression if given a continuous variable.
A grid search for parameters usually works reasonably well. Choice of kernel can have a big impact on performance. ... | Support vector regression in R
Try using the kernlab package, you can use ksvm(...,type='eps-svr') to get regression. It's smart enough to automatically select regression if given a continuous variable.
A grid search for parameter |
42,584 | Mean squared error for data with skewed distribution | One possible pragmatic response is inspired by the motivation behind the logit transformation used in logistic regression. In that situation, the original response is constrained between 0 and 1 and hence treating it as though it has a normal distribution causes all sorts of problems. Part of the response is to trans... | Mean squared error for data with skewed distribution | One possible pragmatic response is inspired by the motivation behind the logit transformation used in logistic regression. In that situation, the original response is constrained between 0 and 1 and | Mean squared error for data with skewed distribution
One possible pragmatic response is inspired by the motivation behind the logit transformation used in logistic regression. In that situation, the original response is constrained between 0 and 1 and hence treating it as though it has a normal distribution causes all... | Mean squared error for data with skewed distribution
One possible pragmatic response is inspired by the motivation behind the logit transformation used in logistic regression. In that situation, the original response is constrained between 0 and 1 and |
42,585 | Mean squared error for data with skewed distribution | If I understand correctly, the problem you are facing is that by using MSE you are developing a poor predictor. This is a common issue on skewed-population problems. For example, if you were trying to predict if a person has cancer or not (binary) in a popluation where 99% of people dont have cancer, by analyzing a rou... | Mean squared error for data with skewed distribution | If I understand correctly, the problem you are facing is that by using MSE you are developing a poor predictor. This is a common issue on skewed-population problems. For example, if you were trying to | Mean squared error for data with skewed distribution
If I understand correctly, the problem you are facing is that by using MSE you are developing a poor predictor. This is a common issue on skewed-population problems. For example, if you were trying to predict if a person has cancer or not (binary) in a popluation whe... | Mean squared error for data with skewed distribution
If I understand correctly, the problem you are facing is that by using MSE you are developing a poor predictor. This is a common issue on skewed-population problems. For example, if you were trying to |
42,586 | Looking for an efficient algorithm to detect Tomek links | The problem of detecting Tomek links is the same as identifying any nearest neighbor (the class label does not help here, you have to check every data point no matter which label it has).
My personal preference is to go for kd-trees (as long as the dimension is not too high). The stackoverflow - question Nearest neighb... | Looking for an efficient algorithm to detect Tomek links | The problem of detecting Tomek links is the same as identifying any nearest neighbor (the class label does not help here, you have to check every data point no matter which label it has).
My personal | Looking for an efficient algorithm to detect Tomek links
The problem of detecting Tomek links is the same as identifying any nearest neighbor (the class label does not help here, you have to check every data point no matter which label it has).
My personal preference is to go for kd-trees (as long as the dimension is n... | Looking for an efficient algorithm to detect Tomek links
The problem of detecting Tomek links is the same as identifying any nearest neighbor (the class label does not help here, you have to check every data point no matter which label it has).
My personal |
42,587 | Looking for an efficient algorithm to detect Tomek links | If you can accept approximate answers (up to a controllable error rate) you can also have a look at approximate nearest neighbors search methods such as Locality Sensitive Hashing and more advanced methods implemented in the flann library. | Looking for an efficient algorithm to detect Tomek links | If you can accept approximate answers (up to a controllable error rate) you can also have a look at approximate nearest neighbors search methods such as Locality Sensitive Hashing and more advanced me | Looking for an efficient algorithm to detect Tomek links
If you can accept approximate answers (up to a controllable error rate) you can also have a look at approximate nearest neighbors search methods such as Locality Sensitive Hashing and more advanced methods implemented in the flann library. | Looking for an efficient algorithm to detect Tomek links
If you can accept approximate answers (up to a controllable error rate) you can also have a look at approximate nearest neighbors search methods such as Locality Sensitive Hashing and more advanced me |
42,588 | Looking for an efficient algorithm to detect Tomek links | The imbalanced-learn package provides many re-sampling techniques for working with imbalanced datasets: https://github.com/scikit-learn-contrib/imbalanced-learn
See this example for an implementation of the Tomek links re-sampling. | Looking for an efficient algorithm to detect Tomek links | The imbalanced-learn package provides many re-sampling techniques for working with imbalanced datasets: https://github.com/scikit-learn-contrib/imbalanced-learn
See this example for an implementation | Looking for an efficient algorithm to detect Tomek links
The imbalanced-learn package provides many re-sampling techniques for working with imbalanced datasets: https://github.com/scikit-learn-contrib/imbalanced-learn
See this example for an implementation of the Tomek links re-sampling. | Looking for an efficient algorithm to detect Tomek links
The imbalanced-learn package provides many re-sampling techniques for working with imbalanced datasets: https://github.com/scikit-learn-contrib/imbalanced-learn
See this example for an implementation |
42,589 | How do I estimate the parameters of a log-normal distribution from the sample mean and sample variance | Suppose that $X = \ln(Y)$ follows a Normal distribuion with mean $\mu$ and variance $\sigma^2$ ($N(\mu,\sigma^2)$).
Using $$ E(g(X)) = \int_{-\infty}^{+\infty}g(x)p(x)dx$$ (where $p(x)$ is the pdf of the Normal distribution), we have that
$$E(Y) = E(\exp(X)) = \int_{-\infty}^{+\infty}\exp(x)p(x)dx=\exp(\sigma^2/2+\mu)... | How do I estimate the parameters of a log-normal distribution from the sample mean and sample varian | Suppose that $X = \ln(Y)$ follows a Normal distribuion with mean $\mu$ and variance $\sigma^2$ ($N(\mu,\sigma^2)$).
Using $$ E(g(X)) = \int_{-\infty}^{+\infty}g(x)p(x)dx$$ (where $p(x)$ is the pdf of | How do I estimate the parameters of a log-normal distribution from the sample mean and sample variance
Suppose that $X = \ln(Y)$ follows a Normal distribuion with mean $\mu$ and variance $\sigma^2$ ($N(\mu,\sigma^2)$).
Using $$ E(g(X)) = \int_{-\infty}^{+\infty}g(x)p(x)dx$$ (where $p(x)$ is the pdf of the Normal distri... | How do I estimate the parameters of a log-normal distribution from the sample mean and sample varian
Suppose that $X = \ln(Y)$ follows a Normal distribuion with mean $\mu$ and variance $\sigma^2$ ($N(\mu,\sigma^2)$).
Using $$ E(g(X)) = \int_{-\infty}^{+\infty}g(x)p(x)dx$$ (where $p(x)$ is the pdf of |
42,590 | How do I estimate the parameters of a log-normal distribution from the sample mean and sample variance | This is not directly an answer to your question:
But if you can instead get the mean and standard deviation of log X then you should be able to reuse the existing estimators for the normal distribution.
That seems to be the most simple method, actually, unless there is additionally a location parameter. | How do I estimate the parameters of a log-normal distribution from the sample mean and sample varian | This is not directly an answer to your question:
But if you can instead get the mean and standard deviation of log X then you should be able to reuse the existing estimators for the normal distributio | How do I estimate the parameters of a log-normal distribution from the sample mean and sample variance
This is not directly an answer to your question:
But if you can instead get the mean and standard deviation of log X then you should be able to reuse the existing estimators for the normal distribution.
That seems to ... | How do I estimate the parameters of a log-normal distribution from the sample mean and sample varian
This is not directly an answer to your question:
But if you can instead get the mean and standard deviation of log X then you should be able to reuse the existing estimators for the normal distributio |
42,591 | Combined odds ratio | It sounds like you only have p-values for each SNP. In this case you can use Fisher's method, but it is a rather crude analysis. Not only will meta-analyzing betas and SEs give you better power, but it also permits estimation of an overall effect, which will be useful in many ways, not least planning replication studie... | Combined odds ratio | It sounds like you only have p-values for each SNP. In this case you can use Fisher's method, but it is a rather crude analysis. Not only will meta-analyzing betas and SEs give you better power, but i | Combined odds ratio
It sounds like you only have p-values for each SNP. In this case you can use Fisher's method, but it is a rather crude analysis. Not only will meta-analyzing betas and SEs give you better power, but it also permits estimation of an overall effect, which will be useful in many ways, not least plannin... | Combined odds ratio
It sounds like you only have p-values for each SNP. In this case you can use Fisher's method, but it is a rather crude analysis. Not only will meta-analyzing betas and SEs give you better power, but i |
42,592 | Where do I find large face datasets? | If you can handle unconstrained imaging conditions you should perhaps look at LFW and PubFig.
If you need controlled imaging conditions you should perhaps look at MultiPIE. | Where do I find large face datasets? | If you can handle unconstrained imaging conditions you should perhaps look at LFW and PubFig.
If you need controlled imaging conditions you should perhaps look at MultiPIE. | Where do I find large face datasets?
If you can handle unconstrained imaging conditions you should perhaps look at LFW and PubFig.
If you need controlled imaging conditions you should perhaps look at MultiPIE. | Where do I find large face datasets?
If you can handle unconstrained imaging conditions you should perhaps look at LFW and PubFig.
If you need controlled imaging conditions you should perhaps look at MultiPIE. |
42,593 | Survival analysis with categorical variable | You can split each of your patients up into multiple records. For example, if Patient Joe is followed for 5 years, switching from A to B two years in, and B to C two years after that, he'd be three records. Joe # 1 who entered at Year 0, and left at year 2. Joe #2 who enters at year 2 and leaves at year 4, and Joe #3, ... | Survival analysis with categorical variable | You can split each of your patients up into multiple records. For example, if Patient Joe is followed for 5 years, switching from A to B two years in, and B to C two years after that, he'd be three re | Survival analysis with categorical variable
You can split each of your patients up into multiple records. For example, if Patient Joe is followed for 5 years, switching from A to B two years in, and B to C two years after that, he'd be three records. Joe # 1 who entered at Year 0, and left at year 2. Joe #2 who enters ... | Survival analysis with categorical variable
You can split each of your patients up into multiple records. For example, if Patient Joe is followed for 5 years, switching from A to B two years in, and B to C two years after that, he'd be three re |
42,594 | Survival analysis with categorical variable | The simplest approach is to break up each subject into multiple person-years with each year associated with only one category and an event yes/no indicator. You can get yearly probabilities from this without difficulties. Note that this would assume that the probabilities are constant over time. Poisson regression can ... | Survival analysis with categorical variable | The simplest approach is to break up each subject into multiple person-years with each year associated with only one category and an event yes/no indicator. You can get yearly probabilities from this | Survival analysis with categorical variable
The simplest approach is to break up each subject into multiple person-years with each year associated with only one category and an event yes/no indicator. You can get yearly probabilities from this without difficulties. Note that this would assume that the probabilities are... | Survival analysis with categorical variable
The simplest approach is to break up each subject into multiple person-years with each year associated with only one category and an event yes/no indicator. You can get yearly probabilities from this |
42,595 | Survival analysis with categorical variable | If I understand you description correctly, you case falls within discrete time survival/hazard analysis (discrete time in the sense of discretized intervals of a continuous process, not in the sense that events only happen after fixed intervals).
In that case I would follow Aniko's suggestion and use a logistic regress... | Survival analysis with categorical variable | If I understand you description correctly, you case falls within discrete time survival/hazard analysis (discrete time in the sense of discretized intervals of a continuous process, not in the sense t | Survival analysis with categorical variable
If I understand you description correctly, you case falls within discrete time survival/hazard analysis (discrete time in the sense of discretized intervals of a continuous process, not in the sense that events only happen after fixed intervals).
In that case I would follow A... | Survival analysis with categorical variable
If I understand you description correctly, you case falls within discrete time survival/hazard analysis (discrete time in the sense of discretized intervals of a continuous process, not in the sense t |
42,596 | Mixed model with repeated measures and both of two treatments with lme or lmer in R | Many years late, but for completeness' sake, I will attempt to answer this.
As noted in the question, the main complication is that there is variance in the data explainable by the fact that there are multiple measurements on each individual. You can account for this with a random effect. The simplest model that you m... | Mixed model with repeated measures and both of two treatments with lme or lmer in R | Many years late, but for completeness' sake, I will attempt to answer this.
As noted in the question, the main complication is that there is variance in the data explainable by the fact that there ar | Mixed model with repeated measures and both of two treatments with lme or lmer in R
Many years late, but for completeness' sake, I will attempt to answer this.
As noted in the question, the main complication is that there is variance in the data explainable by the fact that there are multiple measurements on each indi... | Mixed model with repeated measures and both of two treatments with lme or lmer in R
Many years late, but for completeness' sake, I will attempt to answer this.
As noted in the question, the main complication is that there is variance in the data explainable by the fact that there ar |
42,597 | Readdressing the semantics of multivariate and multivariable analysis | I found this to be the most useful response:
The distinction is important, but we have no way of imposing a terminology on "the world" (except, perhaps, by writing seminal papers). Just make sure that when you read a paper, you know which interpretation of the terms the author means, and make sure to explain to your ow... | Readdressing the semantics of multivariate and multivariable analysis | I found this to be the most useful response:
The distinction is important, but we have no way of imposing a terminology on "the world" (except, perhaps, by writing seminal papers). Just make sure that | Readdressing the semantics of multivariate and multivariable analysis
I found this to be the most useful response:
The distinction is important, but we have no way of imposing a terminology on "the world" (except, perhaps, by writing seminal papers). Just make sure that when you read a paper, you know which interpretat... | Readdressing the semantics of multivariate and multivariable analysis
I found this to be the most useful response:
The distinction is important, but we have no way of imposing a terminology on "the world" (except, perhaps, by writing seminal papers). Just make sure that |
42,598 | In spatial regression, what is a spherical autocorrelation structure? | I'll make a leap of faith, and assume that you are referring to a spherical spatial correlation structure.
A spherical spatial correlation structure has two parameters: $n$, the "nugget" effect, which acts to reduce all the correlations between two observations more than 0 distance apart, and $d$, the range (distance)... | In spatial regression, what is a spherical autocorrelation structure? | I'll make a leap of faith, and assume that you are referring to a spherical spatial correlation structure.
A spherical spatial correlation structure has two parameters: $n$, the "nugget" effect, whic | In spatial regression, what is a spherical autocorrelation structure?
I'll make a leap of faith, and assume that you are referring to a spherical spatial correlation structure.
A spherical spatial correlation structure has two parameters: $n$, the "nugget" effect, which acts to reduce all the correlations between two ... | In spatial regression, what is a spherical autocorrelation structure?
I'll make a leap of faith, and assume that you are referring to a spherical spatial correlation structure.
A spherical spatial correlation structure has two parameters: $n$, the "nugget" effect, whic |
42,599 | What is a good source to learn about multidimensional scaling? | A good textbook on multivariate data analysis, mixing introductory material and more advanced theory, is Modern Multivariate Statistical Techniques, by Alan J. Izenman (Springer, 2008). A review by John Maindonald was published in the JSS.
It features a complete chapter dedicated to MDS (chapter 13), with a lot of illu... | What is a good source to learn about multidimensional scaling? | A good textbook on multivariate data analysis, mixing introductory material and more advanced theory, is Modern Multivariate Statistical Techniques, by Alan J. Izenman (Springer, 2008). A review by Jo | What is a good source to learn about multidimensional scaling?
A good textbook on multivariate data analysis, mixing introductory material and more advanced theory, is Modern Multivariate Statistical Techniques, by Alan J. Izenman (Springer, 2008). A review by John Maindonald was published in the JSS.
It features a com... | What is a good source to learn about multidimensional scaling?
A good textbook on multivariate data analysis, mixing introductory material and more advanced theory, is Modern Multivariate Statistical Techniques, by Alan J. Izenman (Springer, 2008). A review by Jo |
42,600 | Why are degrees of freedom so high in repeated measures mixed models? | Unless I'm reading you incorrectly you would have approximately this many degrees of freedom in a standard repeated measures ANOVA. The ANOVA error is the interaction between subjects and the effect and requires degrees of freedom from both. However, if you are making multiple measures at each time interval then yes,... | Why are degrees of freedom so high in repeated measures mixed models? | Unless I'm reading you incorrectly you would have approximately this many degrees of freedom in a standard repeated measures ANOVA. The ANOVA error is the interaction between subjects and the effect | Why are degrees of freedom so high in repeated measures mixed models?
Unless I'm reading you incorrectly you would have approximately this many degrees of freedom in a standard repeated measures ANOVA. The ANOVA error is the interaction between subjects and the effect and requires degrees of freedom from both. Howeve... | Why are degrees of freedom so high in repeated measures mixed models?
Unless I'm reading you incorrectly you would have approximately this many degrees of freedom in a standard repeated measures ANOVA. The ANOVA error is the interaction between subjects and the effect |
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