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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40,401 | On the hardness of data to learn | The notion of instance hardness may address what you are looking for. Instance hardness posits that each instance in a data set has a hardness property indicating the likelihood that it will be misclassified by a supervised learning algorithm. In a sense, instance hardness looks at the hardness of each individual rathe... | On the hardness of data to learn | The notion of instance hardness may address what you are looking for. Instance hardness posits that each instance in a data set has a hardness property indicating the likelihood that it will be miscla | On the hardness of data to learn
The notion of instance hardness may address what you are looking for. Instance hardness posits that each instance in a data set has a hardness property indicating the likelihood that it will be misclassified by a supervised learning algorithm. In a sense, instance hardness looks at the ... | On the hardness of data to learn
The notion of instance hardness may address what you are looking for. Instance hardness posits that each instance in a data set has a hardness property indicating the likelihood that it will be miscla |
40,402 | On the hardness of data to learn | Statistical learning theory typically deals with sample complexity, i.e., how many samples do I need to produce a hypothesis having low error with high probability. More concretely, if $S$ is a set of samples and $h_S$ is the hypothesis returned by some learning algorithm when given $S$ as input, then typically one loo... | On the hardness of data to learn | Statistical learning theory typically deals with sample complexity, i.e., how many samples do I need to produce a hypothesis having low error with high probability. More concretely, if $S$ is a set of | On the hardness of data to learn
Statistical learning theory typically deals with sample complexity, i.e., how many samples do I need to produce a hypothesis having low error with high probability. More concretely, if $S$ is a set of samples and $h_S$ is the hypothesis returned by some learning algorithm when given $S$... | On the hardness of data to learn
Statistical learning theory typically deals with sample complexity, i.e., how many samples do I need to produce a hypothesis having low error with high probability. More concretely, if $S$ is a set of |
40,403 | Goodness-of-Fit for continuous variables | What are some Goodness of Fit tests or indicies for continuous case?
Most goodness of fit tests are for the continuous case. There are, quite literally, hundreds of them. Besides the Kolmogorov-Smirnov test (for a fully specified distribution, based on maximum difference in ECDF) some commonly used ones include the An... | Goodness-of-Fit for continuous variables | What are some Goodness of Fit tests or indicies for continuous case?
Most goodness of fit tests are for the continuous case. There are, quite literally, hundreds of them. Besides the Kolmogorov-Smirn | Goodness-of-Fit for continuous variables
What are some Goodness of Fit tests or indicies for continuous case?
Most goodness of fit tests are for the continuous case. There are, quite literally, hundreds of them. Besides the Kolmogorov-Smirnov test (for a fully specified distribution, based on maximum difference in ECD... | Goodness-of-Fit for continuous variables
What are some Goodness of Fit tests or indicies for continuous case?
Most goodness of fit tests are for the continuous case. There are, quite literally, hundreds of them. Besides the Kolmogorov-Smirn |
40,404 | Goodness-of-Fit for continuous variables | The empirical CDF simply assigns a probability of $\frac{1}{N}$ to each sample point. Then you construct a CDF like you would for the discrete case.
Not sure why you are using KS in regression. If you assumed gaussian errors and did MLE, then you have effectively fitted a normal distribution to your residuals. You coul... | Goodness-of-Fit for continuous variables | The empirical CDF simply assigns a probability of $\frac{1}{N}$ to each sample point. Then you construct a CDF like you would for the discrete case.
Not sure why you are using KS in regression. If you | Goodness-of-Fit for continuous variables
The empirical CDF simply assigns a probability of $\frac{1}{N}$ to each sample point. Then you construct a CDF like you would for the discrete case.
Not sure why you are using KS in regression. If you assumed gaussian errors and did MLE, then you have effectively fitted a normal... | Goodness-of-Fit for continuous variables
The empirical CDF simply assigns a probability of $\frac{1}{N}$ to each sample point. Then you construct a CDF like you would for the discrete case.
Not sure why you are using KS in regression. If you |
40,405 | Why is standard error sometimes used for "error bands" in plots? | Mostly its that "it's been done that way in the past", but in some domains it is precisely because the authors are not drawing statistical inferences directly from the reported standard errors (even though, for the example paper
it might be reasonable to do so).
As an example, physics research papers often depict the ... | Why is standard error sometimes used for "error bands" in plots? | Mostly its that "it's been done that way in the past", but in some domains it is precisely because the authors are not drawing statistical inferences directly from the reported standard errors (even t | Why is standard error sometimes used for "error bands" in plots?
Mostly its that "it's been done that way in the past", but in some domains it is precisely because the authors are not drawing statistical inferences directly from the reported standard errors (even though, for the example paper
it might be reasonable to... | Why is standard error sometimes used for "error bands" in plots?
Mostly its that "it's been done that way in the past", but in some domains it is precisely because the authors are not drawing statistical inferences directly from the reported standard errors (even t |
40,406 | Why is standard error sometimes used for "error bands" in plots? | Whether from convention or otherwise, it is honest in the sense that it is easy for the reader to develop their own idea of significance, ie mentally the reader can consider a multiple of, say 2 or 3 times larger to get their own idea of significance. In a sense you are letting the data speak for itself rather than sp... | Why is standard error sometimes used for "error bands" in plots? | Whether from convention or otherwise, it is honest in the sense that it is easy for the reader to develop their own idea of significance, ie mentally the reader can consider a multiple of, say 2 or 3 | Why is standard error sometimes used for "error bands" in plots?
Whether from convention or otherwise, it is honest in the sense that it is easy for the reader to develop their own idea of significance, ie mentally the reader can consider a multiple of, say 2 or 3 times larger to get their own idea of significance. In... | Why is standard error sometimes used for "error bands" in plots?
Whether from convention or otherwise, it is honest in the sense that it is easy for the reader to develop their own idea of significance, ie mentally the reader can consider a multiple of, say 2 or 3 |
40,407 | Open source classification algorithms, preferably in C++ [closed] | dlib
http://dlib.net/
Machine Learning (including classification) http://dlib.net/ml.html
Examples: http://dlib.net/multiclass_classification_ex.cpp.html
Shark
http://image.diku.dk/shark/sphinx_pages/build/html/
http://image.diku.dk/shark/sphinx_pages/build/html/rest_sources/tutorials/tutorials.html
Shogun - A Large S... | Open source classification algorithms, preferably in C++ [closed] | dlib
http://dlib.net/
Machine Learning (including classification) http://dlib.net/ml.html
Examples: http://dlib.net/multiclass_classification_ex.cpp.html
Shark
http://image.diku.dk/shark/sphinx_pages/ | Open source classification algorithms, preferably in C++ [closed]
dlib
http://dlib.net/
Machine Learning (including classification) http://dlib.net/ml.html
Examples: http://dlib.net/multiclass_classification_ex.cpp.html
Shark
http://image.diku.dk/shark/sphinx_pages/build/html/
http://image.diku.dk/shark/sphinx_pages/bu... | Open source classification algorithms, preferably in C++ [closed]
dlib
http://dlib.net/
Machine Learning (including classification) http://dlib.net/ml.html
Examples: http://dlib.net/multiclass_classification_ex.cpp.html
Shark
http://image.diku.dk/shark/sphinx_pages/ |
40,408 | Open source classification algorithms, preferably in C++ [closed] | What you really need is OpenCV . OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. It has C++, C, Python and Java interfaces and supports Windows, Linux, Android and Mac OS.
You may find there lots of solutions for computer vision recognition problems. | Open source classification algorithms, preferably in C++ [closed] | What you really need is OpenCV . OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. It has C++, C, Python and Java interfaces and sup | Open source classification algorithms, preferably in C++ [closed]
What you really need is OpenCV . OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. It has C++, C, Python and Java interfaces and supports Windows, Linux, Android and Mac OS.
You may find... | Open source classification algorithms, preferably in C++ [closed]
What you really need is OpenCV . OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. It has C++, C, Python and Java interfaces and sup |
40,409 | Open source classification algorithms, preferably in C++ [closed] | LIBSVM, de facto standard for SVM learning (BSD license):
home page
implemented in C, so trivial to use in C++
interfaces in various languages available, including R (kernlab), Python (scikit-learn), ...
Shameless self advertisement: EnsembleSVM
home page
github
implemented in C++11
EnsembleSVM is a toolbox for fa... | Open source classification algorithms, preferably in C++ [closed] | LIBSVM, de facto standard for SVM learning (BSD license):
home page
implemented in C, so trivial to use in C++
interfaces in various languages available, including R (kernlab), Python (scikit-learn), | Open source classification algorithms, preferably in C++ [closed]
LIBSVM, de facto standard for SVM learning (BSD license):
home page
implemented in C, so trivial to use in C++
interfaces in various languages available, including R (kernlab), Python (scikit-learn), ...
Shameless self advertisement: EnsembleSVM
home... | Open source classification algorithms, preferably in C++ [closed]
LIBSVM, de facto standard for SVM learning (BSD license):
home page
implemented in C, so trivial to use in C++
interfaces in various languages available, including R (kernlab), Python (scikit-learn), |
40,410 | Open source classification algorithms, preferably in C++ [closed] | VlFeat http://www.vlfeat.org/ is great computer vision library written in pure C. | Open source classification algorithms, preferably in C++ [closed] | VlFeat http://www.vlfeat.org/ is great computer vision library written in pure C. | Open source classification algorithms, preferably in C++ [closed]
VlFeat http://www.vlfeat.org/ is great computer vision library written in pure C. | Open source classification algorithms, preferably in C++ [closed]
VlFeat http://www.vlfeat.org/ is great computer vision library written in pure C. |
40,411 | 3 way or 2 way anova? | This is clearly a designed experiment, & looks very much as if it was intended as a $3^{3-1}$ design for continuous predictors. The coding suggests that, & the names of the predictors suggest things that could be measured quantitatively, or at least treated as such with a pinch of salt. If so: the three main effects ar... | 3 way or 2 way anova? | This is clearly a designed experiment, & looks very much as if it was intended as a $3^{3-1}$ design for continuous predictors. The coding suggests that, & the names of the predictors suggest things t | 3 way or 2 way anova?
This is clearly a designed experiment, & looks very much as if it was intended as a $3^{3-1}$ design for continuous predictors. The coding suggests that, & the names of the predictors suggest things that could be measured quantitatively, or at least treated as such with a pinch of salt. If so: the... | 3 way or 2 way anova?
This is clearly a designed experiment, & looks very much as if it was intended as a $3^{3-1}$ design for continuous predictors. The coding suggests that, & the names of the predictors suggest things t |
40,412 | Continuous dependent variable between 0 and 1 fitted with sigmoidal function | I don't think beta regression, as suggested by @O_Devinyak, will work well for this case as there are exact 0s and 1s in the data and the beta distribution only works for values between, but not including, 0 and 1.
A solution that has become more popular in economics is the so-called fractional logit model, which econ... | Continuous dependent variable between 0 and 1 fitted with sigmoidal function | I don't think beta regression, as suggested by @O_Devinyak, will work well for this case as there are exact 0s and 1s in the data and the beta distribution only works for values between, but not inclu | Continuous dependent variable between 0 and 1 fitted with sigmoidal function
I don't think beta regression, as suggested by @O_Devinyak, will work well for this case as there are exact 0s and 1s in the data and the beta distribution only works for values between, but not including, 0 and 1.
A solution that has become ... | Continuous dependent variable between 0 and 1 fitted with sigmoidal function
I don't think beta regression, as suggested by @O_Devinyak, will work well for this case as there are exact 0s and 1s in the data and the beta distribution only works for values between, but not inclu |
40,413 | How to test whether linear models fit separately to two groups are better than a single model applied to both groups? | If you are conducting studies to deepen your theoretical understanding of some topic, this is a great thing to wonder about. Fortunately, there are well-developed statistical methods for assessing this question. What you do is fit both a full model that encompasses the possibility that the relationship differs betwee... | How to test whether linear models fit separately to two groups are better than a single model applie | If you are conducting studies to deepen your theoretical understanding of some topic, this is a great thing to wonder about. Fortunately, there are well-developed statistical methods for assessing th | How to test whether linear models fit separately to two groups are better than a single model applied to both groups?
If you are conducting studies to deepen your theoretical understanding of some topic, this is a great thing to wonder about. Fortunately, there are well-developed statistical methods for assessing this... | How to test whether linear models fit separately to two groups are better than a single model applie
If you are conducting studies to deepen your theoretical understanding of some topic, this is a great thing to wonder about. Fortunately, there are well-developed statistical methods for assessing th |
40,414 | How to model variables, where instead of values we observe intervals? | In Stata, both cases can be handled with the interval regression command intreg, which is generalization of the Tobit. It can handle point, interval, or left/right censored data (or a mixture of them all). It does assume error term normality, but the log transformation can often work if your data require and permit it.... | How to model variables, where instead of values we observe intervals? | In Stata, both cases can be handled with the interval regression command intreg, which is generalization of the Tobit. It can handle point, interval, or left/right censored data (or a mixture of them | How to model variables, where instead of values we observe intervals?
In Stata, both cases can be handled with the interval regression command intreg, which is generalization of the Tobit. It can handle point, interval, or left/right censored data (or a mixture of them all). It does assume error term normality, but the... | How to model variables, where instead of values we observe intervals?
In Stata, both cases can be handled with the interval regression command intreg, which is generalization of the Tobit. It can handle point, interval, or left/right censored data (or a mixture of them |
40,415 | How to model variables, where instead of values we observe intervals? | You are looking for interval regression: In R you can use the survival package as explained here
Or you can try with the intReg package using the intReg function | How to model variables, where instead of values we observe intervals? | You are looking for interval regression: In R you can use the survival package as explained here
Or you can try with the intReg package using the intReg function | How to model variables, where instead of values we observe intervals?
You are looking for interval regression: In R you can use the survival package as explained here
Or you can try with the intReg package using the intReg function | How to model variables, where instead of values we observe intervals?
You are looking for interval regression: In R you can use the survival package as explained here
Or you can try with the intReg package using the intReg function |
40,416 | Is a post-hoc test supposed to be performed on transformed or original data? | Yes, you should use the same, transformed data throughout the analysis. Tukey's test makes the same assumptions as the ANOVA. | Is a post-hoc test supposed to be performed on transformed or original data? | Yes, you should use the same, transformed data throughout the analysis. Tukey's test makes the same assumptions as the ANOVA. | Is a post-hoc test supposed to be performed on transformed or original data?
Yes, you should use the same, transformed data throughout the analysis. Tukey's test makes the same assumptions as the ANOVA. | Is a post-hoc test supposed to be performed on transformed or original data?
Yes, you should use the same, transformed data throughout the analysis. Tukey's test makes the same assumptions as the ANOVA. |
40,417 | What is the way to represent factor variables in scikit-learn while using Random Forests? | It seems that you're comparing scikit-learn's Random Forest with randomForest package in R, where this package deals with categorical variables automatically.
However in scikit-learn you have to preprocess your data yourself. To do this, you could use DictVectorizer class, which would create new binary features for eve... | What is the way to represent factor variables in scikit-learn while using Random Forests? | It seems that you're comparing scikit-learn's Random Forest with randomForest package in R, where this package deals with categorical variables automatically.
However in scikit-learn you have to prepr | What is the way to represent factor variables in scikit-learn while using Random Forests?
It seems that you're comparing scikit-learn's Random Forest with randomForest package in R, where this package deals with categorical variables automatically.
However in scikit-learn you have to preprocess your data yourself. To d... | What is the way to represent factor variables in scikit-learn while using Random Forests?
It seems that you're comparing scikit-learn's Random Forest with randomForest package in R, where this package deals with categorical variables automatically.
However in scikit-learn you have to prepr |
40,418 | Limited Information Maximum Likelihood (LIML) estimation in R? | There are a number of ways to motivate the LIML estimator, the primary one being that it is a member of the family of IV estimators known as the $k$-class estimators.
Also, to my knowledge, a direct implementation of the LIML estimator is not available in R (see the sem package though).
$k$-class IV estimators
Consider... | Limited Information Maximum Likelihood (LIML) estimation in R? | There are a number of ways to motivate the LIML estimator, the primary one being that it is a member of the family of IV estimators known as the $k$-class estimators.
Also, to my knowledge, a direct i | Limited Information Maximum Likelihood (LIML) estimation in R?
There are a number of ways to motivate the LIML estimator, the primary one being that it is a member of the family of IV estimators known as the $k$-class estimators.
Also, to my knowledge, a direct implementation of the LIML estimator is not available in R... | Limited Information Maximum Likelihood (LIML) estimation in R?
There are a number of ways to motivate the LIML estimator, the primary one being that it is a member of the family of IV estimators known as the $k$-class estimators.
Also, to my knowledge, a direct i |
40,419 | Limited Information Maximum Likelihood (LIML) estimation in R? | I made an implementation of the k-class in a package, currently hosted on github, RCompAngrist (dedicated to replicate examples in Angrist and Pishke's book). The function kclass() uses the same interface than ivreg, and let to pre-specify k (obtaining OLS, 2SLS, any estim) or estimating it. It seeks to use slightly mo... | Limited Information Maximum Likelihood (LIML) estimation in R? | I made an implementation of the k-class in a package, currently hosted on github, RCompAngrist (dedicated to replicate examples in Angrist and Pishke's book). The function kclass() uses the same inter | Limited Information Maximum Likelihood (LIML) estimation in R?
I made an implementation of the k-class in a package, currently hosted on github, RCompAngrist (dedicated to replicate examples in Angrist and Pishke's book). The function kclass() uses the same interface than ivreg, and let to pre-specify k (obtaining OLS,... | Limited Information Maximum Likelihood (LIML) estimation in R?
I made an implementation of the k-class in a package, currently hosted on github, RCompAngrist (dedicated to replicate examples in Angrist and Pishke's book). The function kclass() uses the same inter |
40,420 | Limited Information Maximum Likelihood (LIML) estimation in R? | The R package ivmodel implements the LIML estimator (link here). | Limited Information Maximum Likelihood (LIML) estimation in R? | The R package ivmodel implements the LIML estimator (link here). | Limited Information Maximum Likelihood (LIML) estimation in R?
The R package ivmodel implements the LIML estimator (link here). | Limited Information Maximum Likelihood (LIML) estimation in R?
The R package ivmodel implements the LIML estimator (link here). |
40,421 | Is conditional logit a specific form of GLM? And what are its specificities? | Your reference says that clogit is a special form of Cox regression, not the GLMM. So you are probably mixing things up.
The conditional logit log-likelihood is (reverse engineering the LaTeX code from the Stata manual): conditional on $\sum_{j=1}^{n_i} y_{ij} = k_{1i}$,
$$
{\rm Pr}\Bigl[(y_{i1},\ldots,y_{i{n_i}})|\... | Is conditional logit a specific form of GLM? And what are its specificities? | Your reference says that clogit is a special form of Cox regression, not the GLMM. So you are probably mixing things up.
The conditional logit log-likelihood is (reverse engineering the LaTeX code fro | Is conditional logit a specific form of GLM? And what are its specificities?
Your reference says that clogit is a special form of Cox regression, not the GLMM. So you are probably mixing things up.
The conditional logit log-likelihood is (reverse engineering the LaTeX code from the Stata manual): conditional on $\sum_{... | Is conditional logit a specific form of GLM? And what are its specificities?
Your reference says that clogit is a special form of Cox regression, not the GLMM. So you are probably mixing things up.
The conditional logit log-likelihood is (reverse engineering the LaTeX code fro |
40,422 | Why does this criterion characterize the median of a continuous random variable? | When $a$ is a local minimum of $\mathbb{E}|X-a|$, that means for sufficiently small $\varepsilon$ the value of this expectation will not decrease when $a$ is changed to $a + \varepsilon$:
$$0 \le \mathbb{E}|X-(a+\varepsilon)| - \mathbb{E}|X-a| = \mathbb{E}[|X-(a+\varepsilon)| - |X-a|].$$
Consider the case where $\varep... | Why does this criterion characterize the median of a continuous random variable? | When $a$ is a local minimum of $\mathbb{E}|X-a|$, that means for sufficiently small $\varepsilon$ the value of this expectation will not decrease when $a$ is changed to $a + \varepsilon$:
$$0 \le \mat | Why does this criterion characterize the median of a continuous random variable?
When $a$ is a local minimum of $\mathbb{E}|X-a|$, that means for sufficiently small $\varepsilon$ the value of this expectation will not decrease when $a$ is changed to $a + \varepsilon$:
$$0 \le \mathbb{E}|X-(a+\varepsilon)| - \mathbb{E}|... | Why does this criterion characterize the median of a continuous random variable?
When $a$ is a local minimum of $\mathbb{E}|X-a|$, that means for sufficiently small $\varepsilon$ the value of this expectation will not decrease when $a$ is changed to $a + \varepsilon$:
$$0 \le \mat |
40,423 | Why does this criterion characterize the median of a continuous random variable? | Here's a simple conceptual explanation. You want to park your car and walk to two different stores. If you park anywhere on the line between them, your total walking distance will be minimized because if you move the car the distance to one store will increase by exactly the amount that the distance to the other decr... | Why does this criterion characterize the median of a continuous random variable? | Here's a simple conceptual explanation. You want to park your car and walk to two different stores. If you park anywhere on the line between them, your total walking distance will be minimized becau | Why does this criterion characterize the median of a continuous random variable?
Here's a simple conceptual explanation. You want to park your car and walk to two different stores. If you park anywhere on the line between them, your total walking distance will be minimized because if you move the car the distance to ... | Why does this criterion characterize the median of a continuous random variable?
Here's a simple conceptual explanation. You want to park your car and walk to two different stores. If you park anywhere on the line between them, your total walking distance will be minimized becau |
40,424 | Why does this criterion characterize the median of a continuous random variable? | A faster resolution of the minimisation is based on the representation (by an integration by part)
$$\int_0^\infty x \text{d}F(x)=\int_0^\infty (1-F(x))\text{d}x$$
which leads to
\begin{align*}\mathbb{E}[|X-a|]&=\int_{-\infty}^a (a-x) \text{d}F(x)+\int^{\infty}_a (x-a) \text{d}F(x)\\&=\int_{-\infty}^a F(x) \text{d}x+\i... | Why does this criterion characterize the median of a continuous random variable? | A faster resolution of the minimisation is based on the representation (by an integration by part)
$$\int_0^\infty x \text{d}F(x)=\int_0^\infty (1-F(x))\text{d}x$$
which leads to
\begin{align*}\mathbb | Why does this criterion characterize the median of a continuous random variable?
A faster resolution of the minimisation is based on the representation (by an integration by part)
$$\int_0^\infty x \text{d}F(x)=\int_0^\infty (1-F(x))\text{d}x$$
which leads to
\begin{align*}\mathbb{E}[|X-a|]&=\int_{-\infty}^a (a-x) \tex... | Why does this criterion characterize the median of a continuous random variable?
A faster resolution of the minimisation is based on the representation (by an integration by part)
$$\int_0^\infty x \text{d}F(x)=\int_0^\infty (1-F(x))\text{d}x$$
which leads to
\begin{align*}\mathbb |
40,425 | Handling poor inter-rater reliability while minimizing the loss of data | In a previous job I ran into this a lot. All sorts of inconsistencies. Like @rolando2 I don't think any general solution is going to be nearly as good as what you can come up with on your own; you would then just have to justify it to whoever your audience is.
However, one thing you can do is a series of sensitivity an... | Handling poor inter-rater reliability while minimizing the loss of data | In a previous job I ran into this a lot. All sorts of inconsistencies. Like @rolando2 I don't think any general solution is going to be nearly as good as what you can come up with on your own; you wou | Handling poor inter-rater reliability while minimizing the loss of data
In a previous job I ran into this a lot. All sorts of inconsistencies. Like @rolando2 I don't think any general solution is going to be nearly as good as what you can come up with on your own; you would then just have to justify it to whoever your ... | Handling poor inter-rater reliability while minimizing the loss of data
In a previous job I ran into this a lot. All sorts of inconsistencies. Like @rolando2 I don't think any general solution is going to be nearly as good as what you can come up with on your own; you wou |
40,426 | Handling poor inter-rater reliability while minimizing the loss of data | Structural equation modeling may help here, particularly confirmatory factor analysis, which allows you to test whether your hypothesized measurement model fits the data and if not, how you might adjust it to do so, for example by dropping items.
Like exploratory factor analysis, CFA models survey responses (or other i... | Handling poor inter-rater reliability while minimizing the loss of data | Structural equation modeling may help here, particularly confirmatory factor analysis, which allows you to test whether your hypothesized measurement model fits the data and if not, how you might adju | Handling poor inter-rater reliability while minimizing the loss of data
Structural equation modeling may help here, particularly confirmatory factor analysis, which allows you to test whether your hypothesized measurement model fits the data and if not, how you might adjust it to do so, for example by dropping items.
L... | Handling poor inter-rater reliability while minimizing the loss of data
Structural equation modeling may help here, particularly confirmatory factor analysis, which allows you to test whether your hypothesized measurement model fits the data and if not, how you might adju |
40,427 | Replicating simulation results from a paper | Note: In the original post, I noted that (aside from a minor error which has since been corrected) this was actually a problem with plotting the density, not with the simulation. At the time I wasn't sure what was going on, but I found that truncating the data seemed to solve the problem, so I recommended that. I have ... | Replicating simulation results from a paper | Note: In the original post, I noted that (aside from a minor error which has since been corrected) this was actually a problem with plotting the density, not with the simulation. At the time I wasn't | Replicating simulation results from a paper
Note: In the original post, I noted that (aside from a minor error which has since been corrected) this was actually a problem with plotting the density, not with the simulation. At the time I wasn't sure what was going on, but I found that truncating the data seemed to solve... | Replicating simulation results from a paper
Note: In the original post, I noted that (aside from a minor error which has since been corrected) this was actually a problem with plotting the density, not with the simulation. At the time I wasn't |
40,428 | How do I create and interpret an interaction plot in ggplot2? | The original suggestion for displaying an interaction via box-plot does not quite make sense in this instance, since both of your variables that define the interaction are continuous. You could dichotomize either G or P, but you do not have much data to work with. Because of this, I would suggest coplots (a description... | How do I create and interpret an interaction plot in ggplot2? | The original suggestion for displaying an interaction via box-plot does not quite make sense in this instance, since both of your variables that define the interaction are continuous. You could dichot | How do I create and interpret an interaction plot in ggplot2?
The original suggestion for displaying an interaction via box-plot does not quite make sense in this instance, since both of your variables that define the interaction are continuous. You could dichotomize either G or P, but you do not have much data to work... | How do I create and interpret an interaction plot in ggplot2?
The original suggestion for displaying an interaction via box-plot does not quite make sense in this instance, since both of your variables that define the interaction are continuous. You could dichot |
40,429 | Why are RBMs symmetric? | Well, RBM is an energy based model, and as such it has undirected edges, and thus you could say "symmetric weights".
The probability distribution over visible and hidden units, defined by the RBM is based on the Energy function:
$$E = -\sum_{i,j} w_{ij} \, v_i \, h_j -\sum_i \alpha_i \, v_i - \sum_i \beta_i \, h_i$$
As... | Why are RBMs symmetric? | Well, RBM is an energy based model, and as such it has undirected edges, and thus you could say "symmetric weights".
The probability distribution over visible and hidden units, defined by the RBM is b | Why are RBMs symmetric?
Well, RBM is an energy based model, and as such it has undirected edges, and thus you could say "symmetric weights".
The probability distribution over visible and hidden units, defined by the RBM is based on the Energy function:
$$E = -\sum_{i,j} w_{ij} \, v_i \, h_j -\sum_i \alpha_i \, v_i - \s... | Why are RBMs symmetric?
Well, RBM is an energy based model, and as such it has undirected edges, and thus you could say "symmetric weights".
The probability distribution over visible and hidden units, defined by the RBM is b |
40,430 | Why are RBMs symmetric? | Back propagation Neural Network works in a "inductive/causal" way, that is, the i-th layer induces the (i+1)-th layer. It is one way directional, not bi-directional. As a result, we get a "deterministic" result rather than a stochastic result.
On the other hand, the RBM, as said, is energy based. The transition is bi-... | Why are RBMs symmetric? | Back propagation Neural Network works in a "inductive/causal" way, that is, the i-th layer induces the (i+1)-th layer. It is one way directional, not bi-directional. As a result, we get a "determinist | Why are RBMs symmetric?
Back propagation Neural Network works in a "inductive/causal" way, that is, the i-th layer induces the (i+1)-th layer. It is one way directional, not bi-directional. As a result, we get a "deterministic" result rather than a stochastic result.
On the other hand, the RBM, as said, is energy base... | Why are RBMs symmetric?
Back propagation Neural Network works in a "inductive/causal" way, that is, the i-th layer induces the (i+1)-th layer. It is one way directional, not bi-directional. As a result, we get a "determinist |
40,431 | Why does log likelihood function for a model use SSE/n and not SSE/df? | To expand on a very good answer that Placidia gave:
Unbiasedness is not necessarily the best possible property for an estimator. Shrinkage estimators applied in situations of multiple collinearity or with many possible regressors (lasso) are intentionally biased, and this is done to improve their other properties (eas... | Why does log likelihood function for a model use SSE/n and not SSE/df? | To expand on a very good answer that Placidia gave:
Unbiasedness is not necessarily the best possible property for an estimator. Shrinkage estimators applied in situations of multiple collinearity or | Why does log likelihood function for a model use SSE/n and not SSE/df?
To expand on a very good answer that Placidia gave:
Unbiasedness is not necessarily the best possible property for an estimator. Shrinkage estimators applied in situations of multiple collinearity or with many possible regressors (lasso) are intent... | Why does log likelihood function for a model use SSE/n and not SSE/df?
To expand on a very good answer that Placidia gave:
Unbiasedness is not necessarily the best possible property for an estimator. Shrinkage estimators applied in situations of multiple collinearity or |
40,432 | Why does log likelihood function for a model use SSE/n and not SSE/df? | The same situation happens in the simplest Normal means model: $Y=\mu + \epsilon$, with $\epsilon$ ~ N(0,$\sigma^2$).
The MLE of $\sigma^2$ is the sum of squares about the mean divided by n: $$S^2=\sum \frac{(Y_i-\bar{Y})^2}{n}$$.
However, this quantity is a biased estimator: $E(S^2) \neq \sigma^2$. Dividing the sum of... | Why does log likelihood function for a model use SSE/n and not SSE/df? | The same situation happens in the simplest Normal means model: $Y=\mu + \epsilon$, with $\epsilon$ ~ N(0,$\sigma^2$).
The MLE of $\sigma^2$ is the sum of squares about the mean divided by n: $$S^2=\su | Why does log likelihood function for a model use SSE/n and not SSE/df?
The same situation happens in the simplest Normal means model: $Y=\mu + \epsilon$, with $\epsilon$ ~ N(0,$\sigma^2$).
The MLE of $\sigma^2$ is the sum of squares about the mean divided by n: $$S^2=\sum \frac{(Y_i-\bar{Y})^2}{n}$$.
However, this quan... | Why does log likelihood function for a model use SSE/n and not SSE/df?
The same situation happens in the simplest Normal means model: $Y=\mu + \epsilon$, with $\epsilon$ ~ N(0,$\sigma^2$).
The MLE of $\sigma^2$ is the sum of squares about the mean divided by n: $$S^2=\su |
40,433 | Box-Ljung test on white noise series | Let us do a simulation where we apply the test 1000 times for time series of length 120.
> fun <- function(n) {Box.test(rnorm(n),type="Ljung-Box",lag=log(n))$p.value}
> set.seed(111)
> mc <- sapply(rep(120,1000),fun)
> sum(mc<0.05)/length(mc)
[1] 0.039
So you get 39 cases out of 1000 for which the null is rejected if ... | Box-Ljung test on white noise series | Let us do a simulation where we apply the test 1000 times for time series of length 120.
> fun <- function(n) {Box.test(rnorm(n),type="Ljung-Box",lag=log(n))$p.value}
> set.seed(111)
> mc <- sapply(re | Box-Ljung test on white noise series
Let us do a simulation where we apply the test 1000 times for time series of length 120.
> fun <- function(n) {Box.test(rnorm(n),type="Ljung-Box",lag=log(n))$p.value}
> set.seed(111)
> mc <- sapply(rep(120,1000),fun)
> sum(mc<0.05)/length(mc)
[1] 0.039
So you get 39 cases out of 10... | Box-Ljung test on white noise series
Let us do a simulation where we apply the test 1000 times for time series of length 120.
> fun <- function(n) {Box.test(rnorm(n),type="Ljung-Box",lag=log(n))$p.value}
> set.seed(111)
> mc <- sapply(re |
40,434 | Box-Ljung test on white noise series | This subsequence testing is actually related to the locally stationary extension of the Box test to detect time-varying dependencies (paper).
Under the null hypothesis of independent white noise you can compute the Box test on $N$ non-overlapping subwindows over your time series, add them all together, and then test th... | Box-Ljung test on white noise series | This subsequence testing is actually related to the locally stationary extension of the Box test to detect time-varying dependencies (paper).
Under the null hypothesis of independent white noise you c | Box-Ljung test on white noise series
This subsequence testing is actually related to the locally stationary extension of the Box test to detect time-varying dependencies (paper).
Under the null hypothesis of independent white noise you can compute the Box test on $N$ non-overlapping subwindows over your time series, ad... | Box-Ljung test on white noise series
This subsequence testing is actually related to the locally stationary extension of the Box test to detect time-varying dependencies (paper).
Under the null hypothesis of independent white noise you c |
40,435 | Visualization of binned frequency distribution in R | This kind of plot could be generated with geom_rect.
Your data:
names <- read.csv("http://samswift.org/files/app_c.csv")
sum50 <- tapply(names$count, (seq_along(names$count)-1) %/% 50, sum)
First, we need additional variables:
The cumulative sum:
cum <- rev(cumsum(rev(sum50)))
Put all into a data frame. The variable... | Visualization of binned frequency distribution in R | This kind of plot could be generated with geom_rect.
Your data:
names <- read.csv("http://samswift.org/files/app_c.csv")
sum50 <- tapply(names$count, (seq_along(names$count)-1) %/% 50, sum)
First, w | Visualization of binned frequency distribution in R
This kind of plot could be generated with geom_rect.
Your data:
names <- read.csv("http://samswift.org/files/app_c.csv")
sum50 <- tapply(names$count, (seq_along(names$count)-1) %/% 50, sum)
First, we need additional variables:
The cumulative sum:
cum <- rev(cumsum(r... | Visualization of binned frequency distribution in R
This kind of plot could be generated with geom_rect.
Your data:
names <- read.csv("http://samswift.org/files/app_c.csv")
sum50 <- tapply(names$count, (seq_along(names$count)-1) %/% 50, sum)
First, w |
40,436 | What is better for time series prediction: AR or ARIMA? | ARIMA is more general. It allows fitting certain nonstationary time series and even stationary series that cannot be fit by low order autoregressive models. | What is better for time series prediction: AR or ARIMA? | ARIMA is more general. It allows fitting certain nonstationary time series and even stationary series that cannot be fit by low order autoregressive models. | What is better for time series prediction: AR or ARIMA?
ARIMA is more general. It allows fitting certain nonstationary time series and even stationary series that cannot be fit by low order autoregressive models. | What is better for time series prediction: AR or ARIMA?
ARIMA is more general. It allows fitting certain nonstationary time series and even stationary series that cannot be fit by low order autoregressive models. |
40,437 | What is better for time series prediction: AR or ARIMA? | At the heart of ARIMA modelling is the concept of:
letting the data speak for itself.
As gung has already said, you should use a model that is consistent with the data.
Sometimes an AR model provides an adequate representation of the data generating mechanism. Other times an ARIMA model is more appropriate. The key... | What is better for time series prediction: AR or ARIMA? | At the heart of ARIMA modelling is the concept of:
letting the data speak for itself.
As gung has already said, you should use a model that is consistent with the data.
Sometimes an AR model provi | What is better for time series prediction: AR or ARIMA?
At the heart of ARIMA modelling is the concept of:
letting the data speak for itself.
As gung has already said, you should use a model that is consistent with the data.
Sometimes an AR model provides an adequate representation of the data generating mechanism.... | What is better for time series prediction: AR or ARIMA?
At the heart of ARIMA modelling is the concept of:
letting the data speak for itself.
As gung has already said, you should use a model that is consistent with the data.
Sometimes an AR model provi |
40,438 | In a neural network with N covariates, are >N hidden units useless? | No, there is no need to worry about this, because the non-linear transformation means that the feature space generated by the hidden layer neurons can be of higher dimension than the input space without being linearly dependent.
Consider Ripley's synthetic benchmark dataset, which consists of two classes, each of which... | In a neural network with N covariates, are >N hidden units useless? | No, there is no need to worry about this, because the non-linear transformation means that the feature space generated by the hidden layer neurons can be of higher dimension than the input space witho | In a neural network with N covariates, are >N hidden units useless?
No, there is no need to worry about this, because the non-linear transformation means that the feature space generated by the hidden layer neurons can be of higher dimension than the input space without being linearly dependent.
Consider Ripley's synth... | In a neural network with N covariates, are >N hidden units useless?
No, there is no need to worry about this, because the non-linear transformation means that the feature space generated by the hidden layer neurons can be of higher dimension than the input space witho |
40,439 | Laplacian-Beltrami approximation based on an empirical sample | This is only a hint on how to approach this problem, where I try to point out a number of questions that have to be clarified before a complete answer can be given. We assume that $\nu$ is a probability measure.
First, as an operator, $L^t$ needs a domain. We assume initially that the domain is the Hilbert space $H = L... | Laplacian-Beltrami approximation based on an empirical sample | This is only a hint on how to approach this problem, where I try to point out a number of questions that have to be clarified before a complete answer can be given. We assume that $\nu$ is a probabili | Laplacian-Beltrami approximation based on an empirical sample
This is only a hint on how to approach this problem, where I try to point out a number of questions that have to be clarified before a complete answer can be given. We assume that $\nu$ is a probability measure.
First, as an operator, $L^t$ needs a domain. W... | Laplacian-Beltrami approximation based on an empirical sample
This is only a hint on how to approach this problem, where I try to point out a number of questions that have to be clarified before a complete answer can be given. We assume that $\nu$ is a probabili |
40,440 | Reference on factor analysis with categorical variables | I think the best method, in your case, is to factor analyze the polychoric correlation matrix. In R, the 'psych' package allows you to perform the polychoric factor analysis (by the fa.poly command) and also to compute the factor scores. Here the documentation and this web page may be useful.
Moreover, the 'psych' pack... | Reference on factor analysis with categorical variables | I think the best method, in your case, is to factor analyze the polychoric correlation matrix. In R, the 'psych' package allows you to perform the polychoric factor analysis (by the fa.poly command) a | Reference on factor analysis with categorical variables
I think the best method, in your case, is to factor analyze the polychoric correlation matrix. In R, the 'psych' package allows you to perform the polychoric factor analysis (by the fa.poly command) and also to compute the factor scores. Here the documentation and... | Reference on factor analysis with categorical variables
I think the best method, in your case, is to factor analyze the polychoric correlation matrix. In R, the 'psych' package allows you to perform the polychoric factor analysis (by the fa.poly command) a |
40,441 | Gradient descent and elastic-net logistic regression | It took me a while to figure it out, but I thought I share my results here.
The algorithms used for optimization by Friedman et al. and Genkin et al. are different. The latter use an Majorization-Minimization algorithm and Friedman et al. use iteratively re-weighted least squares (IRLS). Both use coordinate descent ins... | Gradient descent and elastic-net logistic regression | It took me a while to figure it out, but I thought I share my results here.
The algorithms used for optimization by Friedman et al. and Genkin et al. are different. The latter use an Majorization-Mini | Gradient descent and elastic-net logistic regression
It took me a while to figure it out, but I thought I share my results here.
The algorithms used for optimization by Friedman et al. and Genkin et al. are different. The latter use an Majorization-Minimization algorithm and Friedman et al. use iteratively re-weighted ... | Gradient descent and elastic-net logistic regression
It took me a while to figure it out, but I thought I share my results here.
The algorithms used for optimization by Friedman et al. and Genkin et al. are different. The latter use an Majorization-Mini |
40,442 | Factor analysis for ordinal variables that have different categories | I feel, from your description of the task, that it can be solved by means of quantifying (turning ordinal-level into scale-level) variables the way that the predictions or associatiations are maximized. This is known as optimal scaling and is implemented in SPSS (and, of course, in R, I believe). You might choose betwe... | Factor analysis for ordinal variables that have different categories | I feel, from your description of the task, that it can be solved by means of quantifying (turning ordinal-level into scale-level) variables the way that the predictions or associatiations are maximize | Factor analysis for ordinal variables that have different categories
I feel, from your description of the task, that it can be solved by means of quantifying (turning ordinal-level into scale-level) variables the way that the predictions or associatiations are maximized. This is known as optimal scaling and is implemen... | Factor analysis for ordinal variables that have different categories
I feel, from your description of the task, that it can be solved by means of quantifying (turning ordinal-level into scale-level) variables the way that the predictions or associatiations are maximize |
40,443 | Factor analysis for ordinal variables that have different categories | A kind of PCA applied to categorical data is MCA (Multiple Correspondence analysis) that let you detect underlying structures in a data set. | Factor analysis for ordinal variables that have different categories | A kind of PCA applied to categorical data is MCA (Multiple Correspondence analysis) that let you detect underlying structures in a data set. | Factor analysis for ordinal variables that have different categories
A kind of PCA applied to categorical data is MCA (Multiple Correspondence analysis) that let you detect underlying structures in a data set. | Factor analysis for ordinal variables that have different categories
A kind of PCA applied to categorical data is MCA (Multiple Correspondence analysis) that let you detect underlying structures in a data set. |
40,444 | Suitable number of classes for SVM in text categorization | Following answer is based on my own personal insights of doing text analysis.
Of course, an increase in the number of categories will increase the time significantly since you have bigger matrix dimensions and so on. But it's not necessary a bad approach. Moreover first strategy looks somehow strange to me since the re... | Suitable number of classes for SVM in text categorization | Following answer is based on my own personal insights of doing text analysis.
Of course, an increase in the number of categories will increase the time significantly since you have bigger matrix dimen | Suitable number of classes for SVM in text categorization
Following answer is based on my own personal insights of doing text analysis.
Of course, an increase in the number of categories will increase the time significantly since you have bigger matrix dimensions and so on. But it's not necessary a bad approach. Moreov... | Suitable number of classes for SVM in text categorization
Following answer is based on my own personal insights of doing text analysis.
Of course, an increase in the number of categories will increase the time significantly since you have bigger matrix dimen |
40,445 | Suitable number of classes for SVM in text categorization | You might want to consider using LibLINEAR instead of (Lib)SVM. It is said to run faster than SVM for cases like document classification, though I'm not sure how it effects memory usage (see section 'When to use LIBLINEAR but not LIBSVM'). Here is the package for R. | Suitable number of classes for SVM in text categorization | You might want to consider using LibLINEAR instead of (Lib)SVM. It is said to run faster than SVM for cases like document classification, though I'm not sure how it effects memory usage (see section ' | Suitable number of classes for SVM in text categorization
You might want to consider using LibLINEAR instead of (Lib)SVM. It is said to run faster than SVM for cases like document classification, though I'm not sure how it effects memory usage (see section 'When to use LIBLINEAR but not LIBSVM'). Here is the package fo... | Suitable number of classes for SVM in text categorization
You might want to consider using LibLINEAR instead of (Lib)SVM. It is said to run faster than SVM for cases like document classification, though I'm not sure how it effects memory usage (see section ' |
40,446 | Suitable number of classes for SVM in text categorization | I work as a project manager & software engineer at a small, research oriented software company. We have recently completed a text categorization project and made many experiments on the way.
We work with a Lineer SVM implementation I coded in C# based on Platt's "Sequential Minimal Optimization" Algorithm and later im... | Suitable number of classes for SVM in text categorization | I work as a project manager & software engineer at a small, research oriented software company. We have recently completed a text categorization project and made many experiments on the way.
We work | Suitable number of classes for SVM in text categorization
I work as a project manager & software engineer at a small, research oriented software company. We have recently completed a text categorization project and made many experiments on the way.
We work with a Lineer SVM implementation I coded in C# based on Platt'... | Suitable number of classes for SVM in text categorization
I work as a project manager & software engineer at a small, research oriented software company. We have recently completed a text categorization project and made many experiments on the way.
We work |
40,447 | Limit of a convolution and sum of distribution functions | I have found some info on this problem. This question is about the proof of a theorem due to Feller, to be found on volume 2 of his "Introduction to Probability Theory and its Applications" (p. 278-279). Here is a restatement.
$\mathbf{Theorem.}$ Let $X_1,\dots,X_n$ be independent random variables with distribution fun... | Limit of a convolution and sum of distribution functions | I have found some info on this problem. This question is about the proof of a theorem due to Feller, to be found on volume 2 of his "Introduction to Probability Theory and its Applications" (p. 278-27 | Limit of a convolution and sum of distribution functions
I have found some info on this problem. This question is about the proof of a theorem due to Feller, to be found on volume 2 of his "Introduction to Probability Theory and its Applications" (p. 278-279). Here is a restatement.
$\mathbf{Theorem.}$ Let $X_1,\dots,X... | Limit of a convolution and sum of distribution functions
I have found some info on this problem. This question is about the proof of a theorem due to Feller, to be found on volume 2 of his "Introduction to Probability Theory and its Applications" (p. 278-27 |
40,448 | Limit of a convolution and sum of distribution functions | You would use induction. Assume it is true for n and then show for n+1. Also write
P{X1+X2+...+Xn +Xn=1>x] as P{X1+X2+...+Xn>x-Xn+1] which equals ∫P{X1+X2+...+Xn>x-y]f(y) dy where fis the density for Xn+1. Then try to express the ratio as a factor times the ratio in the form of the induction hypothesis for n. Then ... | Limit of a convolution and sum of distribution functions | You would use induction. Assume it is true for n and then show for n+1. Also write
P{X1+X2+...+Xn +Xn=1>x] as P{X1+X2+...+Xn>x-Xn+1] which equals ∫P{X1+X2+...+Xn>x-y]f(y) dy where fis the density f | Limit of a convolution and sum of distribution functions
You would use induction. Assume it is true for n and then show for n+1. Also write
P{X1+X2+...+Xn +Xn=1>x] as P{X1+X2+...+Xn>x-Xn+1] which equals ∫P{X1+X2+...+Xn>x-y]f(y) dy where fis the density for Xn+1. Then try to express the ratio as a factor times the ra... | Limit of a convolution and sum of distribution functions
You would use induction. Assume it is true for n and then show for n+1. Also write
P{X1+X2+...+Xn +Xn=1>x] as P{X1+X2+...+Xn>x-Xn+1] which equals ∫P{X1+X2+...+Xn>x-y]f(y) dy where fis the density f |
40,449 | What if an overall ANOVA is not significant, but specific contrasts are? | The answer to your question reflects your view of overall (or experimentwise) alpha. If, before you collected your data, you set forth planned comparisons (and it sounds like you did that), then after you collect the data you do those and only those comparisons, and there is no reason to look at any other comparisons ... | What if an overall ANOVA is not significant, but specific contrasts are? | The answer to your question reflects your view of overall (or experimentwise) alpha. If, before you collected your data, you set forth planned comparisons (and it sounds like you did that), then afte | What if an overall ANOVA is not significant, but specific contrasts are?
The answer to your question reflects your view of overall (or experimentwise) alpha. If, before you collected your data, you set forth planned comparisons (and it sounds like you did that), then after you collect the data you do those and only th... | What if an overall ANOVA is not significant, but specific contrasts are?
The answer to your question reflects your view of overall (or experimentwise) alpha. If, before you collected your data, you set forth planned comparisons (and it sounds like you did that), then afte |
40,450 | What if an overall ANOVA is not significant, but specific contrasts are? | Yes you need to adjust alpha, especially if you do planned comparisons that are non-orthogonal. If you have set the contrasts in an ANOVA and tested the orthogonality between them, you can descompose the SC and df of the ANOVA. The planned comparison are always more powerful than traditional post hoc test, but in cases... | What if an overall ANOVA is not significant, but specific contrasts are? | Yes you need to adjust alpha, especially if you do planned comparisons that are non-orthogonal. If you have set the contrasts in an ANOVA and tested the orthogonality between them, you can descompose | What if an overall ANOVA is not significant, but specific contrasts are?
Yes you need to adjust alpha, especially if you do planned comparisons that are non-orthogonal. If you have set the contrasts in an ANOVA and tested the orthogonality between them, you can descompose the SC and df of the ANOVA. The planned compari... | What if an overall ANOVA is not significant, but specific contrasts are?
Yes you need to adjust alpha, especially if you do planned comparisons that are non-orthogonal. If you have set the contrasts in an ANOVA and tested the orthogonality between them, you can descompose |
40,451 | Variable ordering using PCA | It is described in Michael Friendly's American Statistician paper on corrgrams, Preprint PDF here. See section on correlation ordering. Also if you look at the source of the corrgram library you will see some other potential ways to order the data as well.
To describe what the code is doing in a nut-shell, the variable... | Variable ordering using PCA | It is described in Michael Friendly's American Statistician paper on corrgrams, Preprint PDF here. See section on correlation ordering. Also if you look at the source of the corrgram library you will | Variable ordering using PCA
It is described in Michael Friendly's American Statistician paper on corrgrams, Preprint PDF here. See section on correlation ordering. Also if you look at the source of the corrgram library you will see some other potential ways to order the data as well.
To describe what the code is doing ... | Variable ordering using PCA
It is described in Michael Friendly's American Statistician paper on corrgrams, Preprint PDF here. See section on correlation ordering. Also if you look at the source of the corrgram library you will |
40,452 | Simple regression models for data with a breakpoint | This will be an R-centric answer. One approach is to wrap the call to lm in a function which is passed the breakpoint and constructs a regression conditional upon that breakpoint, then minimize the deviance of the fitted model conditional upon the breakpoint by just iterating over the possible values for the breakpoin... | Simple regression models for data with a breakpoint | This will be an R-centric answer. One approach is to wrap the call to lm in a function which is passed the breakpoint and constructs a regression conditional upon that breakpoint, then minimize the d | Simple regression models for data with a breakpoint
This will be an R-centric answer. One approach is to wrap the call to lm in a function which is passed the breakpoint and constructs a regression conditional upon that breakpoint, then minimize the deviance of the fitted model conditional upon the breakpoint by just ... | Simple regression models for data with a breakpoint
This will be an R-centric answer. One approach is to wrap the call to lm in a function which is passed the breakpoint and constructs a regression conditional upon that breakpoint, then minimize the d |
40,453 | Simple regression models for data with a breakpoint | This is an example of detecting a change in intercept (B0 in your notation ) or sometimes referred to a level or step shift. This often occurs in time series data where a variable in the model is impacted by a 0,0,0,0,0,0,1,1,1,1,1,1 ..... phenomenon at or around some unknown arbitrary point. It is referred to as Inter... | Simple regression models for data with a breakpoint | This is an example of detecting a change in intercept (B0 in your notation ) or sometimes referred to a level or step shift. This often occurs in time series data where a variable in the model is impa | Simple regression models for data with a breakpoint
This is an example of detecting a change in intercept (B0 in your notation ) or sometimes referred to a level or step shift. This often occurs in time series data where a variable in the model is impacted by a 0,0,0,0,0,0,1,1,1,1,1,1 ..... phenomenon at or around some... | Simple regression models for data with a breakpoint
This is an example of detecting a change in intercept (B0 in your notation ) or sometimes referred to a level or step shift. This often occurs in time series data where a variable in the model is impa |
40,454 | Simple regression models for data with a breakpoint | Sounds like you want a spline regression with a single knot.
In SAS see PROC TRANSREG. In R see (e.g) the Splines package. | Simple regression models for data with a breakpoint | Sounds like you want a spline regression with a single knot.
In SAS see PROC TRANSREG. In R see (e.g) the Splines package. | Simple regression models for data with a breakpoint
Sounds like you want a spline regression with a single knot.
In SAS see PROC TRANSREG. In R see (e.g) the Splines package. | Simple regression models for data with a breakpoint
Sounds like you want a spline regression with a single knot.
In SAS see PROC TRANSREG. In R see (e.g) the Splines package. |
40,455 | Benchmark dataset for decision tree algorithm | I would recommend the UCI repository you're mentioning. It has been around for quite some time, contains many data sets and is frequently referenced in scientific publications. | Benchmark dataset for decision tree algorithm | I would recommend the UCI repository you're mentioning. It has been around for quite some time, contains many data sets and is frequently referenced in scientific publications. | Benchmark dataset for decision tree algorithm
I would recommend the UCI repository you're mentioning. It has been around for quite some time, contains many data sets and is frequently referenced in scientific publications. | Benchmark dataset for decision tree algorithm
I would recommend the UCI repository you're mentioning. It has been around for quite some time, contains many data sets and is frequently referenced in scientific publications. |
40,456 | Benchmark dataset for decision tree algorithm | You're on the right track with the UCI repository. MLcomp is another great resource, and will automatically score your algorithm on multiple datasets. | Benchmark dataset for decision tree algorithm | You're on the right track with the UCI repository. MLcomp is another great resource, and will automatically score your algorithm on multiple datasets. | Benchmark dataset for decision tree algorithm
You're on the right track with the UCI repository. MLcomp is another great resource, and will automatically score your algorithm on multiple datasets. | Benchmark dataset for decision tree algorithm
You're on the right track with the UCI repository. MLcomp is another great resource, and will automatically score your algorithm on multiple datasets. |
40,457 | Benchmark dataset for decision tree algorithm | You could try having a look at the datasets from Kaggle competitions at kaggle.com. Some require a fair degree of pre-processing, but there are some relatively 'clean' datasets there. You can see how your algorithm performs by submitting predictions to either current or past competitions and see how well it performs re... | Benchmark dataset for decision tree algorithm | You could try having a look at the datasets from Kaggle competitions at kaggle.com. Some require a fair degree of pre-processing, but there are some relatively 'clean' datasets there. You can see how | Benchmark dataset for decision tree algorithm
You could try having a look at the datasets from Kaggle competitions at kaggle.com. Some require a fair degree of pre-processing, but there are some relatively 'clean' datasets there. You can see how your algorithm performs by submitting predictions to either current or pas... | Benchmark dataset for decision tree algorithm
You could try having a look at the datasets from Kaggle competitions at kaggle.com. Some require a fair degree of pre-processing, but there are some relatively 'clean' datasets there. You can see how |
40,458 | Benchmark dataset for decision tree algorithm | Thought I should mention milksets, a Python wrapper around some UCI datasets. It appears to have 7 of the datasets, and produces them as a simple 2D Numpy array. | Benchmark dataset for decision tree algorithm | Thought I should mention milksets, a Python wrapper around some UCI datasets. It appears to have 7 of the datasets, and produces them as a simple 2D Numpy array. | Benchmark dataset for decision tree algorithm
Thought I should mention milksets, a Python wrapper around some UCI datasets. It appears to have 7 of the datasets, and produces them as a simple 2D Numpy array. | Benchmark dataset for decision tree algorithm
Thought I should mention milksets, a Python wrapper around some UCI datasets. It appears to have 7 of the datasets, and produces them as a simple 2D Numpy array. |
40,459 | Problem with character vectors and linear regression in R | You can build a formula from character vectors using standard R functions and as.formula(). The trick is to note that you need to have a full formula (containing at least a ~) for R to create a formula object for you. Here is an example
## predictors
vars <- "x1 + x2 + x3 + x4 + x5"
## dummy data for example
dat <- da... | Problem with character vectors and linear regression in R | You can build a formula from character vectors using standard R functions and as.formula(). The trick is to note that you need to have a full formula (containing at least a ~) for R to create a formul | Problem with character vectors and linear regression in R
You can build a formula from character vectors using standard R functions and as.formula(). The trick is to note that you need to have a full formula (containing at least a ~) for R to create a formula object for you. Here is an example
## predictors
vars <- "x1... | Problem with character vectors and linear regression in R
You can build a formula from character vectors using standard R functions and as.formula(). The trick is to note that you need to have a full formula (containing at least a ~) for R to create a formul |
40,460 | Probability of winning a tournament | The model described in the links is not the diffusion model. The model you are trying to implement is called the Independent Chip Model or ICM. They give different estimates for your expected share of second and lower place prizes. Here are two ways to describe the ICM:
(1) Determine the winner so that each player's ch... | Probability of winning a tournament | The model described in the links is not the diffusion model. The model you are trying to implement is called the Independent Chip Model or ICM. They give different estimates for your expected share of | Probability of winning a tournament
The model described in the links is not the diffusion model. The model you are trying to implement is called the Independent Chip Model or ICM. They give different estimates for your expected share of second and lower place prizes. Here are two ways to describe the ICM:
(1) Determine... | Probability of winning a tournament
The model described in the links is not the diffusion model. The model you are trying to implement is called the Independent Chip Model or ICM. They give different estimates for your expected share of |
40,461 | Probability of winning a tournament | Do players keep playing until they run out of chips?
Suppose we assume players A B C each have some number of chips and keep playing until they have 0. Suppose we further assume odds of winning a hand are either 1/3, 1/3, 1/3 or some other value (per Michelle's comment about skill). Suppose we further assume each pot ... | Probability of winning a tournament | Do players keep playing until they run out of chips?
Suppose we assume players A B C each have some number of chips and keep playing until they have 0. Suppose we further assume odds of winning a han | Probability of winning a tournament
Do players keep playing until they run out of chips?
Suppose we assume players A B C each have some number of chips and keep playing until they have 0. Suppose we further assume odds of winning a hand are either 1/3, 1/3, 1/3 or some other value (per Michelle's comment about skill).... | Probability of winning a tournament
Do players keep playing until they run out of chips?
Suppose we assume players A B C each have some number of chips and keep playing until they have 0. Suppose we further assume odds of winning a han |
40,462 | Probability of winning a tournament | I suspect you are making this more complex than it needs to be. If there are 27 million players (or even 27 thousand - I think you have a typo) and the game is pure chance and we can reasonably assume that no player has a significant number of chips compared to the total number of chips...
Then your chance of winnin... | Probability of winning a tournament | I suspect you are making this more complex than it needs to be. If there are 27 million players (or even 27 thousand - I think you have a typo) and the game is pure chance and we can reasonably assum | Probability of winning a tournament
I suspect you are making this more complex than it needs to be. If there are 27 million players (or even 27 thousand - I think you have a typo) and the game is pure chance and we can reasonably assume that no player has a significant number of chips compared to the total number of c... | Probability of winning a tournament
I suspect you are making this more complex than it needs to be. If there are 27 million players (or even 27 thousand - I think you have a typo) and the game is pure chance and we can reasonably assum |
40,463 | Oversampling in logistic regression | If you are thinking about oversampling based on the outcome then you have to be quite careful.
In general this is not ok. Endogenous sampling schemes will give you biased results. See Wooldridge chapter 17.
In the case of the Logit model things are somewhat different. Borrowing from Manski the thing is that in general... | Oversampling in logistic regression | If you are thinking about oversampling based on the outcome then you have to be quite careful.
In general this is not ok. Endogenous sampling schemes will give you biased results. See Wooldridge chap | Oversampling in logistic regression
If you are thinking about oversampling based on the outcome then you have to be quite careful.
In general this is not ok. Endogenous sampling schemes will give you biased results. See Wooldridge chapter 17.
In the case of the Logit model things are somewhat different. Borrowing from... | Oversampling in logistic regression
If you are thinking about oversampling based on the outcome then you have to be quite careful.
In general this is not ok. Endogenous sampling schemes will give you biased results. See Wooldridge chap |
40,464 | What do you do with your testing data? | I would retrain the model using the training and test partitions; there doesn't seem much to lose from doing so, and the final model will probably perform slightly better than the pessimistic (as it is based on a smaller training set) performance estimate.
If the dataset is small, a better solution would be to combine ... | What do you do with your testing data? | I would retrain the model using the training and test partitions; there doesn't seem much to lose from doing so, and the final model will probably perform slightly better than the pessimistic (as it i | What do you do with your testing data?
I would retrain the model using the training and test partitions; there doesn't seem much to lose from doing so, and the final model will probably perform slightly better than the pessimistic (as it is based on a smaller training set) performance estimate.
If the dataset is small,... | What do you do with your testing data?
I would retrain the model using the training and test partitions; there doesn't seem much to lose from doing so, and the final model will probably perform slightly better than the pessimistic (as it i |
40,465 | How to use segmented package to fit a piecewise linear regression with one breakpoint? | It appears that what is happening is that one of the estimates for a breakpoint is moving outside the range of offer during the fitting procedure. (You can deduce this by typing seg.lm.fit at the prompt and fighting your way through the code.) You can work around this by specifying PSI, the starting values for the br... | How to use segmented package to fit a piecewise linear regression with one breakpoint? | It appears that what is happening is that one of the estimates for a breakpoint is moving outside the range of offer during the fitting procedure. (You can deduce this by typing seg.lm.fit at the pro | How to use segmented package to fit a piecewise linear regression with one breakpoint?
It appears that what is happening is that one of the estimates for a breakpoint is moving outside the range of offer during the fitting procedure. (You can deduce this by typing seg.lm.fit at the prompt and fighting your way through... | How to use segmented package to fit a piecewise linear regression with one breakpoint?
It appears that what is happening is that one of the estimates for a breakpoint is moving outside the range of offer during the fitting procedure. (You can deduce this by typing seg.lm.fit at the pro |
40,466 | Predicting Y using X for the following data | This is really an illustrated comment.
It's not at all clear that the relationship is heteroscedastic. It may appear so in large part due to the decreasing number of observations with larger $x$. Take this simulation, for example:
This kernel density plot shows 20,000 iid draws from $(X,Y)$ where $X$ has a $B(1,5)$ ... | Predicting Y using X for the following data | This is really an illustrated comment.
It's not at all clear that the relationship is heteroscedastic. It may appear so in large part due to the decreasing number of observations with larger $x$. Ta | Predicting Y using X for the following data
This is really an illustrated comment.
It's not at all clear that the relationship is heteroscedastic. It may appear so in large part due to the decreasing number of observations with larger $x$. Take this simulation, for example:
This kernel density plot shows 20,000 iid ... | Predicting Y using X for the following data
This is really an illustrated comment.
It's not at all clear that the relationship is heteroscedastic. It may appear so in large part due to the decreasing number of observations with larger $x$. Ta |
40,467 | Predicting Y using X for the following data | If you are merely interested in modelling the mean of the above relationship, the solution to address the heteroskedasticity should not be complicated (there is a chance though that I am completely mistaken with what I say here).
OLS estimators are not biased from heteroskedasticity and so you only have to worry about ... | Predicting Y using X for the following data | If you are merely interested in modelling the mean of the above relationship, the solution to address the heteroskedasticity should not be complicated (there is a chance though that I am completely mi | Predicting Y using X for the following data
If you are merely interested in modelling the mean of the above relationship, the solution to address the heteroskedasticity should not be complicated (there is a chance though that I am completely mistaken with what I say here).
OLS estimators are not biased from heteroskeda... | Predicting Y using X for the following data
If you are merely interested in modelling the mean of the above relationship, the solution to address the heteroskedasticity should not be complicated (there is a chance though that I am completely mi |
40,468 | Testing for confounding | First, it isn't terribly complicated to check for the association of Z on X or Y, even in survival analysis. Propensity scores and Inverse Probability of Treatment Weights (both common methods for adjusting for confounding in a survival context), along with other somewhat more esoteric methods are based on estimating t... | Testing for confounding | First, it isn't terribly complicated to check for the association of Z on X or Y, even in survival analysis. Propensity scores and Inverse Probability of Treatment Weights (both common methods for adj | Testing for confounding
First, it isn't terribly complicated to check for the association of Z on X or Y, even in survival analysis. Propensity scores and Inverse Probability of Treatment Weights (both common methods for adjusting for confounding in a survival context), along with other somewhat more esoteric methods a... | Testing for confounding
First, it isn't terribly complicated to check for the association of Z on X or Y, even in survival analysis. Propensity scores and Inverse Probability of Treatment Weights (both common methods for adj |
40,469 | Testing for confounding | There is no test for confounding, unfortunately. If you think Z is a confounder, adjust for it.
It's not sufficient to look at changes (of size 10% or otherwise) because, even when Z isn't a confounder, when using non-collapsible measures of effect - like odds ratios - the parameter estimated in the adjusted analysis ... | Testing for confounding | There is no test for confounding, unfortunately. If you think Z is a confounder, adjust for it.
It's not sufficient to look at changes (of size 10% or otherwise) because, even when Z isn't a confound | Testing for confounding
There is no test for confounding, unfortunately. If you think Z is a confounder, adjust for it.
It's not sufficient to look at changes (of size 10% or otherwise) because, even when Z isn't a confounder, when using non-collapsible measures of effect - like odds ratios - the parameter estimated i... | Testing for confounding
There is no test for confounding, unfortunately. If you think Z is a confounder, adjust for it.
It's not sufficient to look at changes (of size 10% or otherwise) because, even when Z isn't a confound |
40,470 | Contrasts to ANOVA in R | Look at ?contrasts and ?lm. The default contrasts in R are different than those of SAS, but the difference is explained and you can specify the alternate form. (Am I correct in thinking you wanted to compare output with SAS?) I didn't keep your ":" in the formula since I thought it would be clearer to include a full i... | Contrasts to ANOVA in R | Look at ?contrasts and ?lm. The default contrasts in R are different than those of SAS, but the difference is explained and you can specify the alternate form. (Am I correct in thinking you wanted to | Contrasts to ANOVA in R
Look at ?contrasts and ?lm. The default contrasts in R are different than those of SAS, but the difference is explained and you can specify the alternate form. (Am I correct in thinking you wanted to compare output with SAS?) I didn't keep your ":" in the formula since I thought it would be cle... | Contrasts to ANOVA in R
Look at ?contrasts and ?lm. The default contrasts in R are different than those of SAS, but the difference is explained and you can specify the alternate form. (Am I correct in thinking you wanted to |
40,471 | Contrasts to ANOVA in R | Have a look on An R companion to "Experimental Design" by Vikneswaran. See chapter 5 in particular on "Orthogonality, Orthogonal Decomposition, and their Role in Modern Experimental Design". | Contrasts to ANOVA in R | Have a look on An R companion to "Experimental Design" by Vikneswaran. See chapter 5 in particular on "Orthogonality, Orthogonal Decomposition, and their Role in Modern Experimental Design". | Contrasts to ANOVA in R
Have a look on An R companion to "Experimental Design" by Vikneswaran. See chapter 5 in particular on "Orthogonality, Orthogonal Decomposition, and their Role in Modern Experimental Design". | Contrasts to ANOVA in R
Have a look on An R companion to "Experimental Design" by Vikneswaran. See chapter 5 in particular on "Orthogonality, Orthogonal Decomposition, and their Role in Modern Experimental Design". |
40,472 | Can categorical data only take finitely or countably infinitely many values? | "Categorical" is not a well-defined mathematical term, so to answer this question we have to look to how this word is intended to be used. It is employed in contrast to "ordinal," "interval," and "ratio." One way to understand the primary distinctions is in terms of the groups of allowable re-expressions of the value... | Can categorical data only take finitely or countably infinitely many values? | "Categorical" is not a well-defined mathematical term, so to answer this question we have to look to how this word is intended to be used. It is employed in contrast to "ordinal," "interval," and "ra | Can categorical data only take finitely or countably infinitely many values?
"Categorical" is not a well-defined mathematical term, so to answer this question we have to look to how this word is intended to be used. It is employed in contrast to "ordinal," "interval," and "ratio." One way to understand the primary di... | Can categorical data only take finitely or countably infinitely many values?
"Categorical" is not a well-defined mathematical term, so to answer this question we have to look to how this word is intended to be used. It is employed in contrast to "ordinal," "interval," and "ra |
40,473 | Can categorical data only take finitely or countably infinitely many values? | Alright, here's my attempt at an answer, from my (admittedly imperfect) understanding of your question.
"Categorical data" is something of a fuzzy and problematic term to begin with. Similar to the "I know it when I see it" definition of obscene material. There are some very clear cases of categorical data, where the v... | Can categorical data only take finitely or countably infinitely many values? | Alright, here's my attempt at an answer, from my (admittedly imperfect) understanding of your question.
"Categorical data" is something of a fuzzy and problematic term to begin with. Similar to the "I | Can categorical data only take finitely or countably infinitely many values?
Alright, here's my attempt at an answer, from my (admittedly imperfect) understanding of your question.
"Categorical data" is something of a fuzzy and problematic term to begin with. Similar to the "I know it when I see it" definition of obsce... | Can categorical data only take finitely or countably infinitely many values?
Alright, here's my attempt at an answer, from my (admittedly imperfect) understanding of your question.
"Categorical data" is something of a fuzzy and problematic term to begin with. Similar to the "I |
40,474 | Can categorical data only take finitely or countably infinitely many values? | Categorical data is discrete - otherwise it would be hard to assign categories to the data.
My take is this: the natural numbers are discrete and thus categorical. They are also ordinal and interval data, but also categorical. Since the natural numbers are countably infinite we see that there are categorical variables ... | Can categorical data only take finitely or countably infinitely many values? | Categorical data is discrete - otherwise it would be hard to assign categories to the data.
My take is this: the natural numbers are discrete and thus categorical. They are also ordinal and interval d | Can categorical data only take finitely or countably infinitely many values?
Categorical data is discrete - otherwise it would be hard to assign categories to the data.
My take is this: the natural numbers are discrete and thus categorical. They are also ordinal and interval data, but also categorical. Since the natura... | Can categorical data only take finitely or countably infinitely many values?
Categorical data is discrete - otherwise it would be hard to assign categories to the data.
My take is this: the natural numbers are discrete and thus categorical. They are also ordinal and interval d |
40,475 | How to deal with outliers? | I've recommended two methods in the past. They depend on the nature of the data in a general sense.
If the outliers are part of a well known distribution of data with a well known problem with outliers then, if others haven't done it already, analyze the distribution with and without outliers, using a variety of way... | How to deal with outliers? | I've recommended two methods in the past. They depend on the nature of the data in a general sense.
If the outliers are part of a well known distribution of data with a well known problem with outl | How to deal with outliers?
I've recommended two methods in the past. They depend on the nature of the data in a general sense.
If the outliers are part of a well known distribution of data with a well known problem with outliers then, if others haven't done it already, analyze the distribution with and without outli... | How to deal with outliers?
I've recommended two methods in the past. They depend on the nature of the data in a general sense.
If the outliers are part of a well known distribution of data with a well known problem with outl |
40,476 | Hidden Markov model (forward algorithm) in R | Let $\mathbf{X}$ be an observation sequence and $\lambda$ be a Hidden Markov Model (HMM). Then the forward algorithm determines $\textrm{Pr}(\mathbf{X}|\lambda)$, the likelihood of realizing sequence $\mathbf{X}$ from HMM $\lambda$.
In more plain English terms... Let's say you trained up your HMM $\lambda$ and you'd ... | Hidden Markov model (forward algorithm) in R | Let $\mathbf{X}$ be an observation sequence and $\lambda$ be a Hidden Markov Model (HMM). Then the forward algorithm determines $\textrm{Pr}(\mathbf{X}|\lambda)$, the likelihood of realizing sequence | Hidden Markov model (forward algorithm) in R
Let $\mathbf{X}$ be an observation sequence and $\lambda$ be a Hidden Markov Model (HMM). Then the forward algorithm determines $\textrm{Pr}(\mathbf{X}|\lambda)$, the likelihood of realizing sequence $\mathbf{X}$ from HMM $\lambda$.
In more plain English terms... Let's say... | Hidden Markov model (forward algorithm) in R
Let $\mathbf{X}$ be an observation sequence and $\lambda$ be a Hidden Markov Model (HMM). Then the forward algorithm determines $\textrm{Pr}(\mathbf{X}|\lambda)$, the likelihood of realizing sequence |
40,477 | Higher order generalization of the multivariate normal distribution | I don't think there is a positive answer to your question. The beauty of the normal distribution, univariate or multivariate, is that it is easily defined by the cumulants of order higher than two being zero. (The cumulant of order $k$ is the normalized $k$-th derivative of the characteristic function: $\kappa_k = i^{-... | Higher order generalization of the multivariate normal distribution | I don't think there is a positive answer to your question. The beauty of the normal distribution, univariate or multivariate, is that it is easily defined by the cumulants of order higher than two bei | Higher order generalization of the multivariate normal distribution
I don't think there is a positive answer to your question. The beauty of the normal distribution, univariate or multivariate, is that it is easily defined by the cumulants of order higher than two being zero. (The cumulant of order $k$ is the normalize... | Higher order generalization of the multivariate normal distribution
I don't think there is a positive answer to your question. The beauty of the normal distribution, univariate or multivariate, is that it is easily defined by the cumulants of order higher than two bei |
40,478 | Model errors, residuals and heteroscedasticity | Conclusions
It makes no sense to model the variance of the residuals explicitly, because the variance depends on the fitting procedure, which is not part of the model. We must model the variance of the errors and then, for our chosen fitting procedure, we may determine the distribution of the residuals.
Assumptions
Th... | Model errors, residuals and heteroscedasticity | Conclusions
It makes no sense to model the variance of the residuals explicitly, because the variance depends on the fitting procedure, which is not part of the model. We must model the variance of t | Model errors, residuals and heteroscedasticity
Conclusions
It makes no sense to model the variance of the residuals explicitly, because the variance depends on the fitting procedure, which is not part of the model. We must model the variance of the errors and then, for our chosen fitting procedure, we may determine th... | Model errors, residuals and heteroscedasticity
Conclusions
It makes no sense to model the variance of the residuals explicitly, because the variance depends on the fitting procedure, which is not part of the model. We must model the variance of t |
40,479 | Model errors, residuals and heteroscedasticity | A generative model for heteroskedastic regression would be to say that the responses were drawn from a normal distribution where the mean and variance are functions of the explanatory variables, i.e.
$y_i \sim N(f(x_i;\beta_\mu), g(x_i;\beta_\sigma))$
where $\beta_\mu$ and $\beta_\sigma$ are the parameters of the two c... | Model errors, residuals and heteroscedasticity | A generative model for heteroskedastic regression would be to say that the responses were drawn from a normal distribution where the mean and variance are functions of the explanatory variables, i.e.
| Model errors, residuals and heteroscedasticity
A generative model for heteroskedastic regression would be to say that the responses were drawn from a normal distribution where the mean and variance are functions of the explanatory variables, i.e.
$y_i \sim N(f(x_i;\beta_\mu), g(x_i;\beta_\sigma))$
where $\beta_\mu$ and... | Model errors, residuals and heteroscedasticity
A generative model for heteroskedastic regression would be to say that the responses were drawn from a normal distribution where the mean and variance are functions of the explanatory variables, i.e.
|
40,480 | Fit between two curves | This is kind of like the analysis of animal tracking data that I have to do in lab. My reflex would be something like this:
First define a linearized track as @Iterator suggested. I do this by defining a spline with somewhere between 3 and 20 control points. You can either fit the spline through all of your data or ... | Fit between two curves | This is kind of like the analysis of animal tracking data that I have to do in lab. My reflex would be something like this:
First define a linearized track as @Iterator suggested. I do this by defin | Fit between two curves
This is kind of like the analysis of animal tracking data that I have to do in lab. My reflex would be something like this:
First define a linearized track as @Iterator suggested. I do this by defining a spline with somewhere between 3 and 20 control points. You can either fit the spline throu... | Fit between two curves
This is kind of like the analysis of animal tracking data that I have to do in lab. My reflex would be something like this:
First define a linearized track as @Iterator suggested. I do this by defin |
40,481 | Fit between two curves | My suggestion is to rescale the points to be proportions of the length of the arc (the curve). At that point, you can look at interpolations, such as (0,0.01, ..., 1.00) of the arc length and compare the correlation, or whatnot.
However, a more interesting question might be the relationships between the gradient of mo... | Fit between two curves | My suggestion is to rescale the points to be proportions of the length of the arc (the curve). At that point, you can look at interpolations, such as (0,0.01, ..., 1.00) of the arc length and compare | Fit between two curves
My suggestion is to rescale the points to be proportions of the length of the arc (the curve). At that point, you can look at interpolations, such as (0,0.01, ..., 1.00) of the arc length and compare the correlation, or whatnot.
However, a more interesting question might be the relationships bet... | Fit between two curves
My suggestion is to rescale the points to be proportions of the length of the arc (the curve). At that point, you can look at interpolations, such as (0,0.01, ..., 1.00) of the arc length and compare |
40,482 | Feature selection for low probability event prediction | My first advice would be that unless identifying the informative features is a goal of the analysis, don't bother with feature selection and just use a regularised model, such a penalised logistic regression, ridge regression or SVM, and let the regularisation handle the over-fitting. It is often said that feature sel... | Feature selection for low probability event prediction | My first advice would be that unless identifying the informative features is a goal of the analysis, don't bother with feature selection and just use a regularised model, such a penalised logistic reg | Feature selection for low probability event prediction
My first advice would be that unless identifying the informative features is a goal of the analysis, don't bother with feature selection and just use a regularised model, such a penalised logistic regression, ridge regression or SVM, and let the regularisation hand... | Feature selection for low probability event prediction
My first advice would be that unless identifying the informative features is a goal of the analysis, don't bother with feature selection and just use a regularised model, such a penalised logistic reg |
40,483 | Feature selection for low probability event prediction | The problem of estimating probabilities falls under the category of "regression," since the probability is a conditional mean. Classical methods for feature selection (AKA "subset selection" or "model selection") methods for regression include best-k, forward- and backward- stepwise, and forward stagewise, all describ... | Feature selection for low probability event prediction | The problem of estimating probabilities falls under the category of "regression," since the probability is a conditional mean. Classical methods for feature selection (AKA "subset selection" or "mode | Feature selection for low probability event prediction
The problem of estimating probabilities falls under the category of "regression," since the probability is a conditional mean. Classical methods for feature selection (AKA "subset selection" or "model selection") methods for regression include best-k, forward- and... | Feature selection for low probability event prediction
The problem of estimating probabilities falls under the category of "regression," since the probability is a conditional mean. Classical methods for feature selection (AKA "subset selection" or "mode |
40,484 | Mediation model with linear regression | You can run regression models separately, if you follow the Baron-Kenny approach. As far as I know, there are two general approaches to test for mediation: (1) path models (and, SEM, of course) and (2) the Baron-and-Kenny approach (see item (a)). I use Mplus to run my mediation models which is very handy (+ bootstrappe... | Mediation model with linear regression | You can run regression models separately, if you follow the Baron-Kenny approach. As far as I know, there are two general approaches to test for mediation: (1) path models (and, SEM, of course) and (2 | Mediation model with linear regression
You can run regression models separately, if you follow the Baron-Kenny approach. As far as I know, there are two general approaches to test for mediation: (1) path models (and, SEM, of course) and (2) the Baron-and-Kenny approach (see item (a)). I use Mplus to run my mediation mo... | Mediation model with linear regression
You can run regression models separately, if you follow the Baron-Kenny approach. As far as I know, there are two general approaches to test for mediation: (1) path models (and, SEM, of course) and (2 |
40,485 | Mediation model with linear regression | Whether you control for A depends on what you are trying to accomplish. Including A in the model will increase your R-squared.
But if A is uncorrelated with X or M (as your diagram indicates) then inclusion/non-Inclusion of A will not affect coefficients or p-values for X or M. | Mediation model with linear regression | Whether you control for A depends on what you are trying to accomplish. Including A in the model will increase your R-squared.
But if A is uncorrelated with X or M (as your diagram indicates) then inc | Mediation model with linear regression
Whether you control for A depends on what you are trying to accomplish. Including A in the model will increase your R-squared.
But if A is uncorrelated with X or M (as your diagram indicates) then inclusion/non-Inclusion of A will not affect coefficients or p-values for X or M. | Mediation model with linear regression
Whether you control for A depends on what you are trying to accomplish. Including A in the model will increase your R-squared.
But if A is uncorrelated with X or M (as your diagram indicates) then inc |
40,486 | Training multiple models for classification using the same dataset | Just to make sure that we are on the same page, I take it from your description that you consider a supervised learning problem where you know the Good/Bad status of your objects and where you have a vector of features for each object that you want to use to classify the object as either Good or Bad. Moreover, the resu... | Training multiple models for classification using the same dataset | Just to make sure that we are on the same page, I take it from your description that you consider a supervised learning problem where you know the Good/Bad status of your objects and where you have a | Training multiple models for classification using the same dataset
Just to make sure that we are on the same page, I take it from your description that you consider a supervised learning problem where you know the Good/Bad status of your objects and where you have a vector of features for each object that you want to u... | Training multiple models for classification using the same dataset
Just to make sure that we are on the same page, I take it from your description that you consider a supervised learning problem where you know the Good/Bad status of your objects and where you have a |
40,487 | Training multiple models for classification using the same dataset | I would use the same training dataset for both models, and use the same CV-folds for tuning. Don't use ANY of the 25% hold-out for training or tuning. Once you've fit your 2 models on the 75% training sample, evaluate your performance using the holdout.
If you are using R, the caret package has functions for creating... | Training multiple models for classification using the same dataset | I would use the same training dataset for both models, and use the same CV-folds for tuning. Don't use ANY of the 25% hold-out for training or tuning. Once you've fit your 2 models on the 75% traini | Training multiple models for classification using the same dataset
I would use the same training dataset for both models, and use the same CV-folds for tuning. Don't use ANY of the 25% hold-out for training or tuning. Once you've fit your 2 models on the 75% training sample, evaluate your performance using the holdou... | Training multiple models for classification using the same dataset
I would use the same training dataset for both models, and use the same CV-folds for tuning. Don't use ANY of the 25% hold-out for training or tuning. Once you've fit your 2 models on the 75% traini |
40,488 | Weird residuals in linear regression | What is the value of the residual that shows such a high count? It does not appear to be zero (slightly to the right of 0), so maybe 1? In any case, there may be something about that value that may provide you with some insight about the underlying mechanism. For example, if X and Y are measurements taken by observers,... | Weird residuals in linear regression | What is the value of the residual that shows such a high count? It does not appear to be zero (slightly to the right of 0), so maybe 1? In any case, there may be something about that value that may pr | Weird residuals in linear regression
What is the value of the residual that shows such a high count? It does not appear to be zero (slightly to the right of 0), so maybe 1? In any case, there may be something about that value that may provide you with some insight about the underlying mechanism. For example, if X and Y... | Weird residuals in linear regression
What is the value of the residual that shows such a high count? It does not appear to be zero (slightly to the right of 0), so maybe 1? In any case, there may be something about that value that may pr |
40,489 | How to get generalisation performance from nnet in R using k-fold cross-validation? | If you are planning to tune the network (e.g. select a value for the learning rate) on your training data and determine the error generalization on that same data set, you need to use a nested cross validation, where in each fold, you are tuning the model on that 9/10 of the data set (using 10 fold cv). See this post w... | How to get generalisation performance from nnet in R using k-fold cross-validation? | If you are planning to tune the network (e.g. select a value for the learning rate) on your training data and determine the error generalization on that same data set, you need to use a nested cross v | How to get generalisation performance from nnet in R using k-fold cross-validation?
If you are planning to tune the network (e.g. select a value for the learning rate) on your training data and determine the error generalization on that same data set, you need to use a nested cross validation, where in each fold, you a... | How to get generalisation performance from nnet in R using k-fold cross-validation?
If you are planning to tune the network (e.g. select a value for the learning rate) on your training data and determine the error generalization on that same data set, you need to use a nested cross v |
40,490 | How to get generalisation performance from nnet in R using k-fold cross-validation? | Implementing k-fold CV (with or without nesting) is relatively straightforward in R; and stratified sampling (wrt. class membership or subjects' characteristics, e.g. age or gender) is not that difficult.
About the way to assess one's classifier performance, you can directly look at the R code for the tune() function.... | How to get generalisation performance from nnet in R using k-fold cross-validation? | Implementing k-fold CV (with or without nesting) is relatively straightforward in R; and stratified sampling (wrt. class membership or subjects' characteristics, e.g. age or gender) is not that diffic | How to get generalisation performance from nnet in R using k-fold cross-validation?
Implementing k-fold CV (with or without nesting) is relatively straightforward in R; and stratified sampling (wrt. class membership or subjects' characteristics, e.g. age or gender) is not that difficult.
About the way to assess one's ... | How to get generalisation performance from nnet in R using k-fold cross-validation?
Implementing k-fold CV (with or without nesting) is relatively straightforward in R; and stratified sampling (wrt. class membership or subjects' characteristics, e.g. age or gender) is not that diffic |
40,491 | Two-way clustering in R | Generally speaking, you should always find useful pointers by looking at the relevant CRAN TAsk Views, in this case the one that deals with Cluster packages, or maybe Quick-R.
It's not clear to me whether the link you gave referenced standard clustering techniques for $n$ (individuals) by $k$ (variables) matrix of meas... | Two-way clustering in R | Generally speaking, you should always find useful pointers by looking at the relevant CRAN TAsk Views, in this case the one that deals with Cluster packages, or maybe Quick-R.
It's not clear to me whe | Two-way clustering in R
Generally speaking, you should always find useful pointers by looking at the relevant CRAN TAsk Views, in this case the one that deals with Cluster packages, or maybe Quick-R.
It's not clear to me whether the link you gave referenced standard clustering techniques for $n$ (individuals) by $k$ (v... | Two-way clustering in R
Generally speaking, you should always find useful pointers by looking at the relevant CRAN TAsk Views, in this case the one that deals with Cluster packages, or maybe Quick-R.
It's not clear to me whe |
40,492 | Validating an existing questionnaire into another language | I don't know what your questionnaire aims to assess. In Health-related Quality-of-Life studies, for example, there are a certain number of recommendations for translation issues that were discussed in the following papers (among others):
Marquis et al., Translating and evaluating questionnaires: Cultural issues for in... | Validating an existing questionnaire into another language | I don't know what your questionnaire aims to assess. In Health-related Quality-of-Life studies, for example, there are a certain number of recommendations for translation issues that were discussed in | Validating an existing questionnaire into another language
I don't know what your questionnaire aims to assess. In Health-related Quality-of-Life studies, for example, there are a certain number of recommendations for translation issues that were discussed in the following papers (among others):
Marquis et al., Transl... | Validating an existing questionnaire into another language
I don't know what your questionnaire aims to assess. In Health-related Quality-of-Life studies, for example, there are a certain number of recommendations for translation issues that were discussed in |
40,493 | Validating an existing questionnaire into another language | I found some good ideas in the short Sage book, Translating Questionnaires and Other Research Instruments, at
http://www.uk.sagepub.com/books/Book5861 . I wouldn't call it the most systematic or the most entertaining read, but it was helpful and it's fairly up-to-date and inexpensive. | Validating an existing questionnaire into another language | I found some good ideas in the short Sage book, Translating Questionnaires and Other Research Instruments, at
http://www.uk.sagepub.com/books/Book5861 . I wouldn't call it the most systematic or the | Validating an existing questionnaire into another language
I found some good ideas in the short Sage book, Translating Questionnaires and Other Research Instruments, at
http://www.uk.sagepub.com/books/Book5861 . I wouldn't call it the most systematic or the most entertaining read, but it was helpful and it's fairly up... | Validating an existing questionnaire into another language
I found some good ideas in the short Sage book, Translating Questionnaires and Other Research Instruments, at
http://www.uk.sagepub.com/books/Book5861 . I wouldn't call it the most systematic or the |
40,494 | Validating an existing questionnaire into another language | The procedure i have normally seen followed is to translate the questionnaire from english, and then have it back translated by someone else. If the two english translations match up, then you are good to go, otherwise repeat until they do. | Validating an existing questionnaire into another language | The procedure i have normally seen followed is to translate the questionnaire from english, and then have it back translated by someone else. If the two english translations match up, then you are goo | Validating an existing questionnaire into another language
The procedure i have normally seen followed is to translate the questionnaire from english, and then have it back translated by someone else. If the two english translations match up, then you are good to go, otherwise repeat until they do. | Validating an existing questionnaire into another language
The procedure i have normally seen followed is to translate the questionnaire from english, and then have it back translated by someone else. If the two english translations match up, then you are goo |
40,495 | Validating an existing questionnaire into another language | Wakening up an old question I just wanted to add two sources concerning guidelines for translating questionnaires.
The first one would be EORTC's Guidelines and the second PROMIS's Guidelines. Reading these will help you grasp the concept of translating according to every possible detail. From there, you can make your ... | Validating an existing questionnaire into another language | Wakening up an old question I just wanted to add two sources concerning guidelines for translating questionnaires.
The first one would be EORTC's Guidelines and the second PROMIS's Guidelines. Reading | Validating an existing questionnaire into another language
Wakening up an old question I just wanted to add two sources concerning guidelines for translating questionnaires.
The first one would be EORTC's Guidelines and the second PROMIS's Guidelines. Reading these will help you grasp the concept of translating accordi... | Validating an existing questionnaire into another language
Wakening up an old question I just wanted to add two sources concerning guidelines for translating questionnaires.
The first one would be EORTC's Guidelines and the second PROMIS's Guidelines. Reading |
40,496 | Is principal components analysis valid if the distribution(s) are Zipf like? What would be similar to PCA but suited to non gaussian data? | The truth is PCA contains an inherent assumption of linearity, i.e. that changing the basis can reframe the problem to provide a more discriminating view on the data. Does it have to be true when working with Zipf/power law following data? It depends on whether all your variables are of the same distribution. If so, yo... | Is principal components analysis valid if the distribution(s) are Zipf like? What would be similar t | The truth is PCA contains an inherent assumption of linearity, i.e. that changing the basis can reframe the problem to provide a more discriminating view on the data. Does it have to be true when work | Is principal components analysis valid if the distribution(s) are Zipf like? What would be similar to PCA but suited to non gaussian data?
The truth is PCA contains an inherent assumption of linearity, i.e. that changing the basis can reframe the problem to provide a more discriminating view on the data. Does it have t... | Is principal components analysis valid if the distribution(s) are Zipf like? What would be similar t
The truth is PCA contains an inherent assumption of linearity, i.e. that changing the basis can reframe the problem to provide a more discriminating view on the data. Does it have to be true when work |
40,497 | Is principal components analysis valid if the distribution(s) are Zipf like? What would be similar to PCA but suited to non gaussian data? | PCA is probably fine to use on your data, as it appears that it does not make any assumptions about the structure of the data. See link for a good introduction.
The note leads to a short PDF tutorial on PCA.
Hope this helps. | Is principal components analysis valid if the distribution(s) are Zipf like? What would be similar t | PCA is probably fine to use on your data, as it appears that it does not make any assumptions about the structure of the data. See link for a good introduction.
The note leads to a short PDF tutorial | Is principal components analysis valid if the distribution(s) are Zipf like? What would be similar to PCA but suited to non gaussian data?
PCA is probably fine to use on your data, as it appears that it does not make any assumptions about the structure of the data. See link for a good introduction.
The note leads to a ... | Is principal components analysis valid if the distribution(s) are Zipf like? What would be similar t
PCA is probably fine to use on your data, as it appears that it does not make any assumptions about the structure of the data. See link for a good introduction.
The note leads to a short PDF tutorial |
40,498 | Is principal components analysis valid if the distribution(s) are Zipf like? What would be similar to PCA but suited to non gaussian data? | There's an excellent typology and discussion of different types of input to PCA as well as Matlab routines for PCA with extreme valued information in this paper by Xie and Xeng, Cauchy Principal Component Analysis.
Basically, their approach involves shifting to probabilistic assumptions. They explicitly compare the loc... | Is principal components analysis valid if the distribution(s) are Zipf like? What would be similar t | There's an excellent typology and discussion of different types of input to PCA as well as Matlab routines for PCA with extreme valued information in this paper by Xie and Xeng, Cauchy Principal Compo | Is principal components analysis valid if the distribution(s) are Zipf like? What would be similar to PCA but suited to non gaussian data?
There's an excellent typology and discussion of different types of input to PCA as well as Matlab routines for PCA with extreme valued information in this paper by Xie and Xeng, Cau... | Is principal components analysis valid if the distribution(s) are Zipf like? What would be similar t
There's an excellent typology and discussion of different types of input to PCA as well as Matlab routines for PCA with extreme valued information in this paper by Xie and Xeng, Cauchy Principal Compo |
40,499 | Randomized SVD and singular values | We have implemented this (along with a power iteration refinement) in the scikit-learn python package.
Our implementation is able to find the exact same singular values and vectors if k + p > rank(M) as demonstrated in the tests.
If you cut (k + p) before reaching near zero singular values (i.e. in the k + p < rank(M) ... | Randomized SVD and singular values | We have implemented this (along with a power iteration refinement) in the scikit-learn python package.
Our implementation is able to find the exact same singular values and vectors if k + p > rank(M) | Randomized SVD and singular values
We have implemented this (along with a power iteration refinement) in the scikit-learn python package.
Our implementation is able to find the exact same singular values and vectors if k + p > rank(M) as demonstrated in the tests.
If you cut (k + p) before reaching near zero singular v... | Randomized SVD and singular values
We have implemented this (along with a power iteration refinement) in the scikit-learn python package.
Our implementation is able to find the exact same singular values and vectors if k + p > rank(M) |
40,500 | Randomized SVD and singular values | I do not think the singular values should match those of the full matrix. You are computing an approximation of the input matrix by projection onto $k+p$ random vectors. For a rank $k+p$ matrix to approximate a rank $n \gg k+p$ matrix, the trace should probably be the same, but then if the first $k$ singular values are... | Randomized SVD and singular values | I do not think the singular values should match those of the full matrix. You are computing an approximation of the input matrix by projection onto $k+p$ random vectors. For a rank $k+p$ matrix to app | Randomized SVD and singular values
I do not think the singular values should match those of the full matrix. You are computing an approximation of the input matrix by projection onto $k+p$ random vectors. For a rank $k+p$ matrix to approximate a rank $n \gg k+p$ matrix, the trace should probably be the same, but then i... | Randomized SVD and singular values
I do not think the singular values should match those of the full matrix. You are computing an approximation of the input matrix by projection onto $k+p$ random vectors. For a rank $k+p$ matrix to app |
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