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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41,101 | R - moderated mediation using the lavaan package | I posted this question in another location and was provided the answer by Terry Jorgenson. Question and answer:
I would like to calculate the conditional indirect effects of X on Y given a set of values for the moderator W. Both X and the moderator are continuous. Could you provide some direction for structuring the... | R - moderated mediation using the lavaan package | I posted this question in another location and was provided the answer by Terry Jorgenson. Question and answer:
I would like to calculate the conditional indirect effects of X on Y given a set of val | R - moderated mediation using the lavaan package
I posted this question in another location and was provided the answer by Terry Jorgenson. Question and answer:
I would like to calculate the conditional indirect effects of X on Y given a set of values for the moderator W. Both X and the moderator are continuous. Cou... | R - moderated mediation using the lavaan package
I posted this question in another location and was provided the answer by Terry Jorgenson. Question and answer:
I would like to calculate the conditional indirect effects of X on Y given a set of val |
41,102 | Comparing a linear model with a spline model | I don't have time to answer all of your questions. To answer two of them: the $P$-value is for the test of all coefficients pertaining to a given predictor. In other words it is the global influence of all terms whether linear or nonlinear or interactions involving them. This is a test of association, where the null... | Comparing a linear model with a spline model | I don't have time to answer all of your questions. To answer two of them: the $P$-value is for the test of all coefficients pertaining to a given predictor. In other words it is the global influence | Comparing a linear model with a spline model
I don't have time to answer all of your questions. To answer two of them: the $P$-value is for the test of all coefficients pertaining to a given predictor. In other words it is the global influence of all terms whether linear or nonlinear or interactions involving them. ... | Comparing a linear model with a spline model
I don't have time to answer all of your questions. To answer two of them: the $P$-value is for the test of all coefficients pertaining to a given predictor. In other words it is the global influence |
41,103 | Comparing a linear model with a spline model | The "p-value" could be called an "unlikelihood statistics". It is supposed to be the extent to which the data disagrees with the Null or "alternative model". TYu are actually performing a "likelihood ratio test" when you compare two (nested) models. Although you have chosen to use the pspline function which defaults to... | Comparing a linear model with a spline model | The "p-value" could be called an "unlikelihood statistics". It is supposed to be the extent to which the data disagrees with the Null or "alternative model". TYu are actually performing a "likelihood | Comparing a linear model with a spline model
The "p-value" could be called an "unlikelihood statistics". It is supposed to be the extent to which the data disagrees with the Null or "alternative model". TYu are actually performing a "likelihood ratio test" when you compare two (nested) models. Although you have chosen ... | Comparing a linear model with a spline model
The "p-value" could be called an "unlikelihood statistics". It is supposed to be the extent to which the data disagrees with the Null or "alternative model". TYu are actually performing a "likelihood |
41,104 | Citation for ML vs. REML | Sigh.
Comparing models that are fitted with REML and differ in their fixed effects never makes sense.
Using AIC/BIC/p-values to compare the same model fitted with REML vs ML never makes sense; you need to make the decision which method to use on a priori, theoretical grounds.
Comparing models that are fitted with REM... | Citation for ML vs. REML | Sigh.
Comparing models that are fitted with REML and differ in their fixed effects never makes sense.
Using AIC/BIC/p-values to compare the same model fitted with REML vs ML never makes sense; you n | Citation for ML vs. REML
Sigh.
Comparing models that are fitted with REML and differ in their fixed effects never makes sense.
Using AIC/BIC/p-values to compare the same model fitted with REML vs ML never makes sense; you need to make the decision which method to use on a priori, theoretical grounds.
Comparing models... | Citation for ML vs. REML
Sigh.
Comparing models that are fitted with REML and differ in their fixed effects never makes sense.
Using AIC/BIC/p-values to compare the same model fitted with REML vs ML never makes sense; you n |
41,105 | Significant p-value but odds ratio confidence interval crosses 1 in logistic regression using restricted cubic splines | Nice work Dan. The inclusion of 1.0 in an odds ratio's confidence interval will be entirely consistent with the P-value from anova() if and only if the variable is linear or if it is categorical and the reference cell happened to be consistent with how summary sets up the contrasts. This is why I prefer anova for ove... | Significant p-value but odds ratio confidence interval crosses 1 in logistic regression using restri | Nice work Dan. The inclusion of 1.0 in an odds ratio's confidence interval will be entirely consistent with the P-value from anova() if and only if the variable is linear or if it is categorical and | Significant p-value but odds ratio confidence interval crosses 1 in logistic regression using restricted cubic splines
Nice work Dan. The inclusion of 1.0 in an odds ratio's confidence interval will be entirely consistent with the P-value from anova() if and only if the variable is linear or if it is categorical and t... | Significant p-value but odds ratio confidence interval crosses 1 in logistic regression using restri
Nice work Dan. The inclusion of 1.0 in an odds ratio's confidence interval will be entirely consistent with the P-value from anova() if and only if the variable is linear or if it is categorical and |
41,106 | What are some good (and fast) alternatives to dynamic time warping? | There are various approximations such as Wavelets and SAX that can be used to get a lower bound.
Wavelets on Wikipedia
Piecewise Aggregate Approximation (PAA)
Symbolic Aggregate approXimation (SAX) homepage
But first make sure that DTW is what you need. It would be a big waste of time if you spent a lot of effort to ... | What are some good (and fast) alternatives to dynamic time warping? | There are various approximations such as Wavelets and SAX that can be used to get a lower bound.
Wavelets on Wikipedia
Piecewise Aggregate Approximation (PAA)
Symbolic Aggregate approXimation (SAX) h | What are some good (and fast) alternatives to dynamic time warping?
There are various approximations such as Wavelets and SAX that can be used to get a lower bound.
Wavelets on Wikipedia
Piecewise Aggregate Approximation (PAA)
Symbolic Aggregate approXimation (SAX) homepage
But first make sure that DTW is what you ne... | What are some good (and fast) alternatives to dynamic time warping?
There are various approximations such as Wavelets and SAX that can be used to get a lower bound.
Wavelets on Wikipedia
Piecewise Aggregate Approximation (PAA)
Symbolic Aggregate approXimation (SAX) h |
41,107 | What are some good (and fast) alternatives to dynamic time warping? | Hmm.
Lets say you data is richly sampled. If you downsample it by 1 in 2, the speedup will be a factor of 4.
If you downsample it by 1 in 10, the speedup will be a factor of 100.
So if your data is oversampled, this is the easiest way to get a speed up.
You can also use admissible lower bounding to speed things up [a]... | What are some good (and fast) alternatives to dynamic time warping? | Hmm.
Lets say you data is richly sampled. If you downsample it by 1 in 2, the speedup will be a factor of 4.
If you downsample it by 1 in 10, the speedup will be a factor of 100.
So if your data is o | What are some good (and fast) alternatives to dynamic time warping?
Hmm.
Lets say you data is richly sampled. If you downsample it by 1 in 2, the speedup will be a factor of 4.
If you downsample it by 1 in 10, the speedup will be a factor of 100.
So if your data is oversampled, this is the easiest way to get a speed u... | What are some good (and fast) alternatives to dynamic time warping?
Hmm.
Lets say you data is richly sampled. If you downsample it by 1 in 2, the speedup will be a factor of 4.
If you downsample it by 1 in 10, the speedup will be a factor of 100.
So if your data is o |
41,108 | Meta-analysis of trials given hazard ratios | Assuming that you have the log hazard ratios (collected in the vector yi) and the standard errors of the log hazard ratios (collected in the vector sei), then you can fit a fixed-effects model to these data with:
res <- rma(yi, sei=sei, data=dat, method="FE")
As with several other outcome measures typically used for m... | Meta-analysis of trials given hazard ratios | Assuming that you have the log hazard ratios (collected in the vector yi) and the standard errors of the log hazard ratios (collected in the vector sei), then you can fit a fixed-effects model to thes | Meta-analysis of trials given hazard ratios
Assuming that you have the log hazard ratios (collected in the vector yi) and the standard errors of the log hazard ratios (collected in the vector sei), then you can fit a fixed-effects model to these data with:
res <- rma(yi, sei=sei, data=dat, method="FE")
As with several... | Meta-analysis of trials given hazard ratios
Assuming that you have the log hazard ratios (collected in the vector yi) and the standard errors of the log hazard ratios (collected in the vector sei), then you can fit a fixed-effects model to thes |
41,109 | Confidence Interval of a Lognormal Random Variable | Is this correct?
No.
i) This isn't a confidence interval you're calculating (since those are for parameters or functions of them), nor is it really a prediction interval, a tolerance interval, or any of the more common statistical intervals ... since for starters it's based on known population values, not on a sample... | Confidence Interval of a Lognormal Random Variable | Is this correct?
No.
i) This isn't a confidence interval you're calculating (since those are for parameters or functions of them), nor is it really a prediction interval, a tolerance interval, or an | Confidence Interval of a Lognormal Random Variable
Is this correct?
No.
i) This isn't a confidence interval you're calculating (since those are for parameters or functions of them), nor is it really a prediction interval, a tolerance interval, or any of the more common statistical intervals ... since for starters it'... | Confidence Interval of a Lognormal Random Variable
Is this correct?
No.
i) This isn't a confidence interval you're calculating (since those are for parameters or functions of them), nor is it really a prediction interval, a tolerance interval, or an |
41,110 | Should predictive accuracy or, alternatively, minimizing the MSE, be reconsidered? | This is a nice and thoughtful post, and in my working life I have observed the things you outline to be correct - the successful statisticians and scientists at my workplace are those that can step back from raw predictive accuracy and deliver a model or analysis that is holistically appropriate for the problem at hand... | Should predictive accuracy or, alternatively, minimizing the MSE, be reconsidered? | This is a nice and thoughtful post, and in my working life I have observed the things you outline to be correct - the successful statisticians and scientists at my workplace are those that can step ba | Should predictive accuracy or, alternatively, minimizing the MSE, be reconsidered?
This is a nice and thoughtful post, and in my working life I have observed the things you outline to be correct - the successful statisticians and scientists at my workplace are those that can step back from raw predictive accuracy and d... | Should predictive accuracy or, alternatively, minimizing the MSE, be reconsidered?
This is a nice and thoughtful post, and in my working life I have observed the things you outline to be correct - the successful statisticians and scientists at my workplace are those that can step ba |
41,111 | How does R calculate prediction intervals in the forecast package? | Unless you are using the forecast function's bootstrap = TRUE option the forecast package's ARIMA intervals are calculated by passing an ARIMA object to predict(). The intervals assume that residuals are normally distributed . If you look at the R documentation for predict.Arima() you will see that it uses KalmanForeca... | How does R calculate prediction intervals in the forecast package? | Unless you are using the forecast function's bootstrap = TRUE option the forecast package's ARIMA intervals are calculated by passing an ARIMA object to predict(). The intervals assume that residuals | How does R calculate prediction intervals in the forecast package?
Unless you are using the forecast function's bootstrap = TRUE option the forecast package's ARIMA intervals are calculated by passing an ARIMA object to predict(). The intervals assume that residuals are normally distributed . If you look at the R docum... | How does R calculate prediction intervals in the forecast package?
Unless you are using the forecast function's bootstrap = TRUE option the forecast package's ARIMA intervals are calculated by passing an ARIMA object to predict(). The intervals assume that residuals |
41,112 | How does R calculate prediction intervals in the forecast package? | ERROR: type should be string, got "https://www.otexts.org/fpp/2/7 Goes into some further detail.\n95% prediction interval is $y^t \\pm 1.96 \\sigma$ \nand\n80% prediction interval is $y^t \\pm 1.28 \\sigma$\nWhere $y^t$ is a forecast value and $\\sigma$ \"is an estimate of the standard deviation of the forecast distribution\"." | How does R calculate prediction intervals in the forecast package? | ERROR: type should be string, got "https://www.otexts.org/fpp/2/7 Goes into some further detail.\n95% prediction interval is $y^t \\pm 1.96 \\sigma$ \nand\n80% prediction interval is $y^t \\pm 1.28 \\sigma$\nWhere $y^t$ is a forecast value and" | How does R calculate prediction intervals in the forecast package?
https://www.otexts.org/fpp/2/7 Goes into some further detail.
95% prediction interval is $y^t \pm 1.96 \sigma$
and
80% prediction interval is $y^t \pm 1.28 \sigma$
Where $y^t$ is a forecast value and $\sigma$ "is an estimate of the standard deviation o... | How does R calculate prediction intervals in the forecast package?
https://www.otexts.org/fpp/2/7 Goes into some further detail.
95% prediction interval is $y^t \pm 1.96 \sigma$
and
80% prediction interval is $y^t \pm 1.28 \sigma$
Where $y^t$ is a forecast value and |
41,113 | Is Theano using back-propagation? | Theano creates a symbolic graph. This graph allows it to compute derivatives based on the connected inputs, the Op implemented on the Variables, and the output(created by the Apply Node).
import theano.tensor as T
x = T.dmatrix('x')
y = T.dmatrix('y')
z = x + y
The Apply nodes are blue, Variables are red, Op is green... | Is Theano using back-propagation? | Theano creates a symbolic graph. This graph allows it to compute derivatives based on the connected inputs, the Op implemented on the Variables, and the output(created by the Apply Node).
import thean | Is Theano using back-propagation?
Theano creates a symbolic graph. This graph allows it to compute derivatives based on the connected inputs, the Op implemented on the Variables, and the output(created by the Apply Node).
import theano.tensor as T
x = T.dmatrix('x')
y = T.dmatrix('y')
z = x + y
The Apply nodes are bl... | Is Theano using back-propagation?
Theano creates a symbolic graph. This graph allows it to compute derivatives based on the connected inputs, the Op implemented on the Variables, and the output(created by the Apply Node).
import thean |
41,114 | Is Theano using back-propagation? | I think that when you refer to "backpropagation" here you're really meaning "automatic differentiation". The alternative would be "symbolic differentiation", where you find a formula for the derivative of some loss wrt some parameter and compute gradients according to that formula.
Theano sort of combines both. Each ... | Is Theano using back-propagation? | I think that when you refer to "backpropagation" here you're really meaning "automatic differentiation". The alternative would be "symbolic differentiation", where you find a formula for the derivati | Is Theano using back-propagation?
I think that when you refer to "backpropagation" here you're really meaning "automatic differentiation". The alternative would be "symbolic differentiation", where you find a formula for the derivative of some loss wrt some parameter and compute gradients according to that formula.
Th... | Is Theano using back-propagation?
I think that when you refer to "backpropagation" here you're really meaning "automatic differentiation". The alternative would be "symbolic differentiation", where you find a formula for the derivati |
41,115 | projecting survival curves estimates into the future | As far as I am aware, there is no way to extrapolate beyond that point with standard R-software.
And with good reason too: the Kaplan Meier curves do not make assumptions about the parametric distribution of the data. Because of this, they are complete indifferent to the assignment of probability mass beyond the last ... | projecting survival curves estimates into the future | As far as I am aware, there is no way to extrapolate beyond that point with standard R-software.
And with good reason too: the Kaplan Meier curves do not make assumptions about the parametric distrib | projecting survival curves estimates into the future
As far as I am aware, there is no way to extrapolate beyond that point with standard R-software.
And with good reason too: the Kaplan Meier curves do not make assumptions about the parametric distribution of the data. Because of this, they are complete indifferent t... | projecting survival curves estimates into the future
As far as I am aware, there is no way to extrapolate beyond that point with standard R-software.
And with good reason too: the Kaplan Meier curves do not make assumptions about the parametric distrib |
41,116 | Suppose $X_1 X_2, ..., X_n$ are $n$ independent variables, is their Covariance matrix, $\Sigma$, diagonal? | Independence implies zero correlation (but the converse doesn't hold):
$\:\:E(XY)=\int\int \,x\,y\, f(x,y)\, dy \,dx$
$\qquad\qquad=\int\,\int x\,y \,f(x)\,f(y)\, dy\, dx\quad$ (independence)
$\qquad\qquad=\int\, y\, f(y)\, dy\,\cdot\,\int\, x \,f(x)\, dx$
$\qquad\qquad=E(X)\,E(Y)$
Hence $\text{Cov}(X,Y)=E(XY)-E(X)E(Y... | Suppose $X_1 X_2, ..., X_n$ are $n$ independent variables, is their Covariance matrix, $\Sigma$, dia | Independence implies zero correlation (but the converse doesn't hold):
$\:\:E(XY)=\int\int \,x\,y\, f(x,y)\, dy \,dx$
$\qquad\qquad=\int\,\int x\,y \,f(x)\,f(y)\, dy\, dx\quad$ (independence)
$\qquad\ | Suppose $X_1 X_2, ..., X_n$ are $n$ independent variables, is their Covariance matrix, $\Sigma$, diagonal?
Independence implies zero correlation (but the converse doesn't hold):
$\:\:E(XY)=\int\int \,x\,y\, f(x,y)\, dy \,dx$
$\qquad\qquad=\int\,\int x\,y \,f(x)\,f(y)\, dy\, dx\quad$ (independence)
$\qquad\qquad=\int\, ... | Suppose $X_1 X_2, ..., X_n$ are $n$ independent variables, is their Covariance matrix, $\Sigma$, dia
Independence implies zero correlation (but the converse doesn't hold):
$\:\:E(XY)=\int\int \,x\,y\, f(x,y)\, dy \,dx$
$\qquad\qquad=\int\,\int x\,y \,f(x)\,f(y)\, dy\, dx\quad$ (independence)
$\qquad\ |
41,117 | Computing Dvoretzky–Kiefer–Wolfowitz bounds in MATLAB | It sounds like you want to compute a confidence band: a region that contains the whole CDF with probability $1-\alpha$. Doing this with the Dvoretzky–Kiefer–Wolfowitz inequality involves three steps:
Generate the CDF (i.e., sort and count your values--trivial in matlab)
The inequality itself says that
$$ P\bigg(\sup_... | Computing Dvoretzky–Kiefer–Wolfowitz bounds in MATLAB | It sounds like you want to compute a confidence band: a region that contains the whole CDF with probability $1-\alpha$. Doing this with the Dvoretzky–Kiefer–Wolfowitz inequality involves three steps:
| Computing Dvoretzky–Kiefer–Wolfowitz bounds in MATLAB
It sounds like you want to compute a confidence band: a region that contains the whole CDF with probability $1-\alpha$. Doing this with the Dvoretzky–Kiefer–Wolfowitz inequality involves three steps:
Generate the CDF (i.e., sort and count your values--trivial in ma... | Computing Dvoretzky–Kiefer–Wolfowitz bounds in MATLAB
It sounds like you want to compute a confidence band: a region that contains the whole CDF with probability $1-\alpha$. Doing this with the Dvoretzky–Kiefer–Wolfowitz inequality involves three steps:
|
41,118 | Discrepancy between metafor and weighted lm() standard errors | A while ago, I wrote up an extensive comparison between the rma() function from the metafor package and the lm() and lme() functions (the latter from the nlme package) for fitting fixed- and random/mixed-effects models. You can find this on the metafor package website:
http://www.metafor-project.org/doku.php/tips:rma_v... | Discrepancy between metafor and weighted lm() standard errors | A while ago, I wrote up an extensive comparison between the rma() function from the metafor package and the lm() and lme() functions (the latter from the nlme package) for fitting fixed- and random/mi | Discrepancy between metafor and weighted lm() standard errors
A while ago, I wrote up an extensive comparison between the rma() function from the metafor package and the lm() and lme() functions (the latter from the nlme package) for fitting fixed- and random/mixed-effects models. You can find this on the metafor packa... | Discrepancy between metafor and weighted lm() standard errors
A while ago, I wrote up an extensive comparison between the rma() function from the metafor package and the lm() and lme() functions (the latter from the nlme package) for fitting fixed- and random/mi |
41,119 | What is a good method to identify outliers in exam data? | Have you considered Item Response Theory-based methods? IRT is designed especially for this kind of purposes.
Simple example is Rasch model that lets you compute both the student abilities and question difficulty in a single generalized linear model (or generalized linear mixed model). With binary answer format the Ras... | What is a good method to identify outliers in exam data? | Have you considered Item Response Theory-based methods? IRT is designed especially for this kind of purposes.
Simple example is Rasch model that lets you compute both the student abilities and questio | What is a good method to identify outliers in exam data?
Have you considered Item Response Theory-based methods? IRT is designed especially for this kind of purposes.
Simple example is Rasch model that lets you compute both the student abilities and question difficulty in a single generalized linear model (or generaliz... | What is a good method to identify outliers in exam data?
Have you considered Item Response Theory-based methods? IRT is designed especially for this kind of purposes.
Simple example is Rasch model that lets you compute both the student abilities and questio |
41,120 | What is a good method to identify outliers in exam data? | You might try fitting a logistic model. Response is a whether the answer was correct or not. You could add a (random?) effect for the student and a fixed effect for each question. You could develop a ranking based on the coefficients for each question. | What is a good method to identify outliers in exam data? | You might try fitting a logistic model. Response is a whether the answer was correct or not. You could add a (random?) effect for the student and a fixed effect for each question. You could develop a | What is a good method to identify outliers in exam data?
You might try fitting a logistic model. Response is a whether the answer was correct or not. You could add a (random?) effect for the student and a fixed effect for each question. You could develop a ranking based on the coefficients for each question. | What is a good method to identify outliers in exam data?
You might try fitting a logistic model. Response is a whether the answer was correct or not. You could add a (random?) effect for the student and a fixed effect for each question. You could develop a |
41,121 | How to interpret R VIF function in CAR package? | The vif function in the car package works like this:
Run an OLS regression that has for example $X_1$ as a dependent variable on the left hand side and all your other independent variables on the right hand side.
Take the $R^2$ from the regression in 1 and stick it into this equation: ${\rm VIF} = \frac{1}{1-R_i^2}$.
... | How to interpret R VIF function in CAR package? | The vif function in the car package works like this:
Run an OLS regression that has for example $X_1$ as a dependent variable on the left hand side and all your other independent variables on the rig | How to interpret R VIF function in CAR package?
The vif function in the car package works like this:
Run an OLS regression that has for example $X_1$ as a dependent variable on the left hand side and all your other independent variables on the right hand side.
Take the $R^2$ from the regression in 1 and stick it into ... | How to interpret R VIF function in CAR package?
The vif function in the car package works like this:
Run an OLS regression that has for example $X_1$ as a dependent variable on the left hand side and all your other independent variables on the rig |
41,122 | Intuitive meaning of error-in-variables | For the intuition you can imagine the signal from your favorite radio station that you receive in your car. That's the variable. Sometimes the weather isn't good and then the signal is disturbed such that you hear noise during the song and the bigger the disturbance the more noise will interfere with your song until yo... | Intuitive meaning of error-in-variables | For the intuition you can imagine the signal from your favorite radio station that you receive in your car. That's the variable. Sometimes the weather isn't good and then the signal is disturbed such | Intuitive meaning of error-in-variables
For the intuition you can imagine the signal from your favorite radio station that you receive in your car. That's the variable. Sometimes the weather isn't good and then the signal is disturbed such that you hear noise during the song and the bigger the disturbance the more nois... | Intuitive meaning of error-in-variables
For the intuition you can imagine the signal from your favorite radio station that you receive in your car. That's the variable. Sometimes the weather isn't good and then the signal is disturbed such |
41,123 | Rebuilding a signal using the fast fourier transform [closed] | Sine and Cosine are the same wave shifted by $\pi/2$ in phase $$\cos(x)=\sin(x+\pi/2)$$. Fourier transform returns you a complex number for each frequency. This number has the amplitude and the angle (phase). It's basically a set of Sine waves with amplitudes and phases. Equivalently, you can re-write them as a sum of ... | Rebuilding a signal using the fast fourier transform [closed] | Sine and Cosine are the same wave shifted by $\pi/2$ in phase $$\cos(x)=\sin(x+\pi/2)$$. Fourier transform returns you a complex number for each frequency. This number has the amplitude and the angle | Rebuilding a signal using the fast fourier transform [closed]
Sine and Cosine are the same wave shifted by $\pi/2$ in phase $$\cos(x)=\sin(x+\pi/2)$$. Fourier transform returns you a complex number for each frequency. This number has the amplitude and the angle (phase). It's basically a set of Sine waves with amplitude... | Rebuilding a signal using the fast fourier transform [closed]
Sine and Cosine are the same wave shifted by $\pi/2$ in phase $$\cos(x)=\sin(x+\pi/2)$$. Fourier transform returns you a complex number for each frequency. This number has the amplitude and the angle |
41,124 | Can 'selection bias' refer to bias in the intervention as well as in the sampling? | I think these two are actually very similar. In the experimental settings, you are comparing some mean outcome for treated versus control and you are worried that there might be individuals with some observed characteristics who appear only among participants or non-participants (or appear more often) and/or that this ... | Can 'selection bias' refer to bias in the intervention as well as in the sampling? | I think these two are actually very similar. In the experimental settings, you are comparing some mean outcome for treated versus control and you are worried that there might be individuals with some | Can 'selection bias' refer to bias in the intervention as well as in the sampling?
I think these two are actually very similar. In the experimental settings, you are comparing some mean outcome for treated versus control and you are worried that there might be individuals with some observed characteristics who appear o... | Can 'selection bias' refer to bias in the intervention as well as in the sampling?
I think these two are actually very similar. In the experimental settings, you are comparing some mean outcome for treated versus control and you are worried that there might be individuals with some |
41,125 | Can 'selection bias' refer to bias in the intervention as well as in the sampling? | I like @DimitriyV.Masterov's answer (+1); they are very similar and you could probably use "selection bias" for "differential probability of selection to the programme treatment group". However, I am somewhat uncomfortable with that usage and think it would probably be better to use different phrasing.
You don't rea... | Can 'selection bias' refer to bias in the intervention as well as in the sampling? | I like @DimitriyV.Masterov's answer (+1); they are very similar and you could probably use "selection bias" for "differential probability of selection to the programme treatment group". However, I am | Can 'selection bias' refer to bias in the intervention as well as in the sampling?
I like @DimitriyV.Masterov's answer (+1); they are very similar and you could probably use "selection bias" for "differential probability of selection to the programme treatment group". However, I am somewhat uncomfortable with that usa... | Can 'selection bias' refer to bias in the intervention as well as in the sampling?
I like @DimitriyV.Masterov's answer (+1); they are very similar and you could probably use "selection bias" for "differential probability of selection to the programme treatment group". However, I am |
41,126 | Can 'selection bias' refer to bias in the intervention as well as in the sampling? | Selection Bias occurs when there is no appropriate randomization achieved while selecting individuals, groups or data to be analysed.
Selection bias implies that the obtained sample does not exactly represent the population that was actually intended to be analyzed. | Can 'selection bias' refer to bias in the intervention as well as in the sampling? | Selection Bias occurs when there is no appropriate randomization achieved while selecting individuals, groups or data to be analysed.
Selection bias implies that the obtained sample does not exactly r | Can 'selection bias' refer to bias in the intervention as well as in the sampling?
Selection Bias occurs when there is no appropriate randomization achieved while selecting individuals, groups or data to be analysed.
Selection bias implies that the obtained sample does not exactly represent the population that was actu... | Can 'selection bias' refer to bias in the intervention as well as in the sampling?
Selection Bias occurs when there is no appropriate randomization achieved while selecting individuals, groups or data to be analysed.
Selection bias implies that the obtained sample does not exactly r |
41,127 | Handling outliers in Bayesian linear regression | Consider a set of data with no outlying observations, at a suitable value of the parameters. Now consider moving an observation far into the tail (keeping parameter values and the remaining data constant)
If the density has thin tails, an observation far away is very unlikely (has low relative probability given the par... | Handling outliers in Bayesian linear regression | Consider a set of data with no outlying observations, at a suitable value of the parameters. Now consider moving an observation far into the tail (keeping parameter values and the remaining data const | Handling outliers in Bayesian linear regression
Consider a set of data with no outlying observations, at a suitable value of the parameters. Now consider moving an observation far into the tail (keeping parameter values and the remaining data constant)
If the density has thin tails, an observation far away is very unli... | Handling outliers in Bayesian linear regression
Consider a set of data with no outlying observations, at a suitable value of the parameters. Now consider moving an observation far into the tail (keeping parameter values and the remaining data const |
41,128 | Proportion of variance in dependent variable accounted for by predictors in a mixed effects model | You can use MuMIn package and its r.squaredGLMM() function which will give you 2 approximated r-squared values based on Nakagawa & Schielzeth (2012) and Johnson (2014):
Marginal R^2 is the proportion of variance explained by the fixed effects alone.
Conditional R^2 is the proportion of variance explained by the fixed a... | Proportion of variance in dependent variable accounted for by predictors in a mixed effects model | You can use MuMIn package and its r.squaredGLMM() function which will give you 2 approximated r-squared values based on Nakagawa & Schielzeth (2012) and Johnson (2014):
Marginal R^2 is the proportion | Proportion of variance in dependent variable accounted for by predictors in a mixed effects model
You can use MuMIn package and its r.squaredGLMM() function which will give you 2 approximated r-squared values based on Nakagawa & Schielzeth (2012) and Johnson (2014):
Marginal R^2 is the proportion of variance explained ... | Proportion of variance in dependent variable accounted for by predictors in a mixed effects model
You can use MuMIn package and its r.squaredGLMM() function which will give you 2 approximated r-squared values based on Nakagawa & Schielzeth (2012) and Johnson (2014):
Marginal R^2 is the proportion |
41,129 | Proportion of variance in dependent variable accounted for by predictors in a mixed effects model | "Variance explained" by the model is described by $R^2$ statistic. For LMM's Nakagawa and Schielzeth (2013) suggested that $R^2$ could be decomposed into variance explained by fixed effects ($R^2_m$) and random effects ($R^2_c$) (see here). In this approach, variance explained by the fixed effects is defined as:
$$R^2... | Proportion of variance in dependent variable accounted for by predictors in a mixed effects model | "Variance explained" by the model is described by $R^2$ statistic. For LMM's Nakagawa and Schielzeth (2013) suggested that $R^2$ could be decomposed into variance explained by fixed effects ($R^2_m$) | Proportion of variance in dependent variable accounted for by predictors in a mixed effects model
"Variance explained" by the model is described by $R^2$ statistic. For LMM's Nakagawa and Schielzeth (2013) suggested that $R^2$ could be decomposed into variance explained by fixed effects ($R^2_m$) and random effects ($... | Proportion of variance in dependent variable accounted for by predictors in a mixed effects model
"Variance explained" by the model is described by $R^2$ statistic. For LMM's Nakagawa and Schielzeth (2013) suggested that $R^2$ could be decomposed into variance explained by fixed effects ($R^2_m$) |
41,130 | Negative Binomial Regression? | Assuming LOS is intended as a DV and not a covariate, "Length of stay" is not really a count (in the required sense), but a (possibly discretized) duration. You wouldn't normally use a count model for that.
I'd be inclined toward using a survival model; that will also enable you to cope with the likely censoring (for p... | Negative Binomial Regression? | Assuming LOS is intended as a DV and not a covariate, "Length of stay" is not really a count (in the required sense), but a (possibly discretized) duration. You wouldn't normally use a count model for | Negative Binomial Regression?
Assuming LOS is intended as a DV and not a covariate, "Length of stay" is not really a count (in the required sense), but a (possibly discretized) duration. You wouldn't normally use a count model for that.
I'd be inclined toward using a survival model; that will also enable you to cope wi... | Negative Binomial Regression?
Assuming LOS is intended as a DV and not a covariate, "Length of stay" is not really a count (in the required sense), but a (possibly discretized) duration. You wouldn't normally use a count model for |
41,131 | Negative Binomial Regression? | The advice you refers (that the count need to refer to the same length time intervals) seems to be irrelevant here. That is meant for a situation where you are counting number of point events within some time interval. But your response variable is a duration, so a completely different situation. So I think you cou... | Negative Binomial Regression? | The advice you refers (that the count need to refer to the same length time intervals) seems to be irrelevant here. That is meant for a situation where you are counting number of point events within | Negative Binomial Regression?
The advice you refers (that the count need to refer to the same length time intervals) seems to be irrelevant here. That is meant for a situation where you are counting number of point events within some time interval. But your response variable is a duration, so a completely different ... | Negative Binomial Regression?
The advice you refers (that the count need to refer to the same length time intervals) seems to be irrelevant here. That is meant for a situation where you are counting number of point events within |
41,132 | Negative Binomial Regression? | Negative binomial would still be appropriate. Poisson would be as well, if your data meets the equidispersion assumption. (If it does not, stick with nbreg.) | Negative Binomial Regression? | Negative binomial would still be appropriate. Poisson would be as well, if your data meets the equidispersion assumption. (If it does not, stick with nbreg.) | Negative Binomial Regression?
Negative binomial would still be appropriate. Poisson would be as well, if your data meets the equidispersion assumption. (If it does not, stick with nbreg.) | Negative Binomial Regression?
Negative binomial would still be appropriate. Poisson would be as well, if your data meets the equidispersion assumption. (If it does not, stick with nbreg.) |
41,133 | Is the mean of equal length subsets always equal to the mean of the set? | Would it be accurate to say the mean of equal length subsets is always equal to the mean of the set?
It's always true!
Consider $n=mk$ observations, where you take $k$ mutually-exclusive groups of size $m$.
Label the observations in group $i$ as $x_{ij},\: j=1,2,...,m$.
The individual means are $\bar{x}_i = \frac{1}{... | Is the mean of equal length subsets always equal to the mean of the set? | Would it be accurate to say the mean of equal length subsets is always equal to the mean of the set?
It's always true!
Consider $n=mk$ observations, where you take $k$ mutually-exclusive groups of s | Is the mean of equal length subsets always equal to the mean of the set?
Would it be accurate to say the mean of equal length subsets is always equal to the mean of the set?
It's always true!
Consider $n=mk$ observations, where you take $k$ mutually-exclusive groups of size $m$.
Label the observations in group $i$ as... | Is the mean of equal length subsets always equal to the mean of the set?
Would it be accurate to say the mean of equal length subsets is always equal to the mean of the set?
It's always true!
Consider $n=mk$ observations, where you take $k$ mutually-exclusive groups of s |
41,134 | Conditional independence iff joint factorizes | Since $X$ and $Y$ are independent given $Z$ iff $p(x,y|z)=p(x|z)p(y|z)$, all you need to prove is that, if $p(x,y|z)=g(x,z)h(y,z)$, then $p(x,y|z)=p(x|z)p(y|z)$.
Starting from the equality $p(x,y|z)=g(x,z)h(y,z)$. one can integrate both sides in x: $$
\int_\text{X}p(x,y|z)\text{d}x=\int_\text{X}g(x,z)h(y,z)\text{d}x
$$... | Conditional independence iff joint factorizes | Since $X$ and $Y$ are independent given $Z$ iff $p(x,y|z)=p(x|z)p(y|z)$, all you need to prove is that, if $p(x,y|z)=g(x,z)h(y,z)$, then $p(x,y|z)=p(x|z)p(y|z)$.
Starting from the equality $p(x,y|z)=g | Conditional independence iff joint factorizes
Since $X$ and $Y$ are independent given $Z$ iff $p(x,y|z)=p(x|z)p(y|z)$, all you need to prove is that, if $p(x,y|z)=g(x,z)h(y,z)$, then $p(x,y|z)=p(x|z)p(y|z)$.
Starting from the equality $p(x,y|z)=g(x,z)h(y,z)$. one can integrate both sides in x: $$
\int_\text{X}p(x,y|z)\... | Conditional independence iff joint factorizes
Since $X$ and $Y$ are independent given $Z$ iff $p(x,y|z)=p(x|z)p(y|z)$, all you need to prove is that, if $p(x,y|z)=g(x,z)h(y,z)$, then $p(x,y|z)=p(x|z)p(y|z)$.
Starting from the equality $p(x,y|z)=g |
41,135 | Data transformation using copulas | This page by MathWorks has a detailed description of using
copulas for various tasks with a lot of examples within MATLAB: Probability Distributions Used for Multivariate Modeling. This is helpful for just seeing the nuts and bolts of how copulas can be used in some simple cases.
For transformation of a data matrix $X... | Data transformation using copulas | This page by MathWorks has a detailed description of using
copulas for various tasks with a lot of examples within MATLAB: Probability Distributions Used for Multivariate Modeling. This is helpful fo | Data transformation using copulas
This page by MathWorks has a detailed description of using
copulas for various tasks with a lot of examples within MATLAB: Probability Distributions Used for Multivariate Modeling. This is helpful for just seeing the nuts and bolts of how copulas can be used in some simple cases.
For ... | Data transformation using copulas
This page by MathWorks has a detailed description of using
copulas for various tasks with a lot of examples within MATLAB: Probability Distributions Used for Multivariate Modeling. This is helpful fo |
41,136 | Data transformation using copulas | Copulas used to recover the joint distribution from marginal distributions. This application is based on Sklar's theorem, which states that if you have two marginal distributions then they're linked through a copula to a joint distribution. A copula is simply a function itself. If you have a bi-variate distribution, th... | Data transformation using copulas | Copulas used to recover the joint distribution from marginal distributions. This application is based on Sklar's theorem, which states that if you have two marginal distributions then they're linked t | Data transformation using copulas
Copulas used to recover the joint distribution from marginal distributions. This application is based on Sklar's theorem, which states that if you have two marginal distributions then they're linked through a copula to a joint distribution. A copula is simply a function itself. If you ... | Data transformation using copulas
Copulas used to recover the joint distribution from marginal distributions. This application is based on Sklar's theorem, which states that if you have two marginal distributions then they're linked t |
41,137 | Fitting parametric CDF to ecdf | I wouldn't use MSE simply because $\hat{F}$ doesn't have constant variance. If you used a weighted MSE that should do better ... but then $\hat{F}_i$ and $\hat{F}_j$ aren't independent, either.
One possibility if you're trying to make $\hat F$ as close to $F$ in a MSE-like sense as possible is you might try minimizing ... | Fitting parametric CDF to ecdf | I wouldn't use MSE simply because $\hat{F}$ doesn't have constant variance. If you used a weighted MSE that should do better ... but then $\hat{F}_i$ and $\hat{F}_j$ aren't independent, either.
One po | Fitting parametric CDF to ecdf
I wouldn't use MSE simply because $\hat{F}$ doesn't have constant variance. If you used a weighted MSE that should do better ... but then $\hat{F}_i$ and $\hat{F}_j$ aren't independent, either.
One possibility if you're trying to make $\hat F$ as close to $F$ in a MSE-like sense as possib... | Fitting parametric CDF to ecdf
I wouldn't use MSE simply because $\hat{F}$ doesn't have constant variance. If you used a weighted MSE that should do better ... but then $\hat{F}_i$ and $\hat{F}_j$ aren't independent, either.
One po |
41,138 | Fitting parametric CDF to ecdf | You asked about relevant litterature.
If your model is a power law, log-normal, poisson or binomial you should check the work of Clauset et al. "PowerLaw Distribution in Empirical Data" http://arxiv.org/pdf/0706.1062.pdf They describe how to do so by bootstraping, performing fits on the ecdf of the actual data and gene... | Fitting parametric CDF to ecdf | You asked about relevant litterature.
If your model is a power law, log-normal, poisson or binomial you should check the work of Clauset et al. "PowerLaw Distribution in Empirical Data" http://arxiv.o | Fitting parametric CDF to ecdf
You asked about relevant litterature.
If your model is a power law, log-normal, poisson or binomial you should check the work of Clauset et al. "PowerLaw Distribution in Empirical Data" http://arxiv.org/pdf/0706.1062.pdf They describe how to do so by bootstraping, performing fits on the e... | Fitting parametric CDF to ecdf
You asked about relevant litterature.
If your model is a power law, log-normal, poisson or binomial you should check the work of Clauset et al. "PowerLaw Distribution in Empirical Data" http://arxiv.o |
41,139 | Is the harmonic mean the maximum likelihood estimator for some common continous distribution's parameter? | This will happen for the recirpocal of a random variable following an Exponential distribution.
Let $X$ follow an Exponential distribution with probability density function
$$f_X(x) = \alpha e^{-\alpha x}$$
Consider $$ Y = \frac 1{X} \Rightarrow X = 1/Y \Rightarrow \frac{\partial X}{\partial Y} = -Y^{-2}$$
Then
$$f_Y(... | Is the harmonic mean the maximum likelihood estimator for some common continous distribution's param | This will happen for the recirpocal of a random variable following an Exponential distribution.
Let $X$ follow an Exponential distribution with probability density function
$$f_X(x) = \alpha e^{-\alp | Is the harmonic mean the maximum likelihood estimator for some common continous distribution's parameter?
This will happen for the recirpocal of a random variable following an Exponential distribution.
Let $X$ follow an Exponential distribution with probability density function
$$f_X(x) = \alpha e^{-\alpha x}$$
Consid... | Is the harmonic mean the maximum likelihood estimator for some common continous distribution's param
This will happen for the recirpocal of a random variable following an Exponential distribution.
Let $X$ follow an Exponential distribution with probability density function
$$f_X(x) = \alpha e^{-\alp |
41,140 | 5 defectives rule of thumb | This reference on Lean Six Sigma, p. 159, provides a formula to calculate the minimum sample size, and mentions the "$5$ defectives" rule of thumb, a formula that relates to the normal distribution, and it is perhaps more useful to the OP than my contested (see comments) reasoning. I cannot argue about the reference's ... | 5 defectives rule of thumb | This reference on Lean Six Sigma, p. 159, provides a formula to calculate the minimum sample size, and mentions the "$5$ defectives" rule of thumb, a formula that relates to the normal distribution, a | 5 defectives rule of thumb
This reference on Lean Six Sigma, p. 159, provides a formula to calculate the minimum sample size, and mentions the "$5$ defectives" rule of thumb, a formula that relates to the normal distribution, and it is perhaps more useful to the OP than my contested (see comments) reasoning. I cannot a... | 5 defectives rule of thumb
This reference on Lean Six Sigma, p. 159, provides a formula to calculate the minimum sample size, and mentions the "$5$ defectives" rule of thumb, a formula that relates to the normal distribution, a |
41,141 | 5 defectives rule of thumb | I think this comes from the rule of thumb for using the normal approximation for a confidence interval (cf. @AlecosPapadopoulos' answer). In short, it is recommended that [the smaller of] $np$ [or $n(1-p)$] be greater than $5$ for the normal approximation to be used. If this condition holds, it is often considered th... | 5 defectives rule of thumb | I think this comes from the rule of thumb for using the normal approximation for a confidence interval (cf. @AlecosPapadopoulos' answer). In short, it is recommended that [the smaller of] $np$ [or $n | 5 defectives rule of thumb
I think this comes from the rule of thumb for using the normal approximation for a confidence interval (cf. @AlecosPapadopoulos' answer). In short, it is recommended that [the smaller of] $np$ [or $n(1-p)$] be greater than $5$ for the normal approximation to be used. If this condition holds... | 5 defectives rule of thumb
I think this comes from the rule of thumb for using the normal approximation for a confidence interval (cf. @AlecosPapadopoulos' answer). In short, it is recommended that [the smaller of] $np$ [or $n |
41,142 | k-means cluster, How to re-calculate centroid when using cosine similarity? | You can normalize the vectors in each cluster by their lengths and add them up, then normalize the sum. The result will be a unit vector in the direction of the centroid (a.k.a. prototype) vector. As far as the spherical k-means algorithm is concerned, the length of the centroid vector does not matter and is not used. ... | k-means cluster, How to re-calculate centroid when using cosine similarity? | You can normalize the vectors in each cluster by their lengths and add them up, then normalize the sum. The result will be a unit vector in the direction of the centroid (a.k.a. prototype) vector. As | k-means cluster, How to re-calculate centroid when using cosine similarity?
You can normalize the vectors in each cluster by their lengths and add them up, then normalize the sum. The result will be a unit vector in the direction of the centroid (a.k.a. prototype) vector. As far as the spherical k-means algorithm is co... | k-means cluster, How to re-calculate centroid when using cosine similarity?
You can normalize the vectors in each cluster by their lengths and add them up, then normalize the sum. The result will be a unit vector in the direction of the centroid (a.k.a. prototype) vector. As |
41,143 | k-means cluster, How to re-calculate centroid when using cosine similarity? | It should be safe to use the regular means of computing the mean with cosine - at least if your data is positive and does not include a zero vector.
Spherical k-means (that is the proper search term) IIRC normalizes the mean vectors to unit length.
Beware of corner cases: if your clustering degenerates and a cluster be... | k-means cluster, How to re-calculate centroid when using cosine similarity? | It should be safe to use the regular means of computing the mean with cosine - at least if your data is positive and does not include a zero vector.
Spherical k-means (that is the proper search term) | k-means cluster, How to re-calculate centroid when using cosine similarity?
It should be safe to use the regular means of computing the mean with cosine - at least if your data is positive and does not include a zero vector.
Spherical k-means (that is the proper search term) IIRC normalizes the mean vectors to unit len... | k-means cluster, How to re-calculate centroid when using cosine similarity?
It should be safe to use the regular means of computing the mean with cosine - at least if your data is positive and does not include a zero vector.
Spherical k-means (that is the proper search term) |
41,144 | k-means cluster, How to re-calculate centroid when using cosine similarity? | There are a few implementations of k-means (one k-means in R) which allows you to just input a distance matrix instead of actual data. There is package called 'proxy' on cran, that you use to find a cosine similarity formula based distance matrix of the data.
You can directly use this distance matrix in k-means. | k-means cluster, How to re-calculate centroid when using cosine similarity? | There are a few implementations of k-means (one k-means in R) which allows you to just input a distance matrix instead of actual data. There is package called 'proxy' on cran, that you use to find a c | k-means cluster, How to re-calculate centroid when using cosine similarity?
There are a few implementations of k-means (one k-means in R) which allows you to just input a distance matrix instead of actual data. There is package called 'proxy' on cran, that you use to find a cosine similarity formula based distance matr... | k-means cluster, How to re-calculate centroid when using cosine similarity?
There are a few implementations of k-means (one k-means in R) which allows you to just input a distance matrix instead of actual data. There is package called 'proxy' on cran, that you use to find a c |
41,145 | k-means cluster, How to re-calculate centroid when using cosine similarity? | As per http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.31.7900&rep=rep1&type=pdf it says that you could add up all the vectors and then divide each vector element by the number of vectors.
See image
I personally don't like this formula. The reason is that this does NOT BISECT the angle between 2 Vectors and ... | k-means cluster, How to re-calculate centroid when using cosine similarity? | As per http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.31.7900&rep=rep1&type=pdf it says that you could add up all the vectors and then divide each vector element by the number of vectors.
S | k-means cluster, How to re-calculate centroid when using cosine similarity?
As per http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.31.7900&rep=rep1&type=pdf it says that you could add up all the vectors and then divide each vector element by the number of vectors.
See image
I personally don't like this formu... | k-means cluster, How to re-calculate centroid when using cosine similarity?
As per http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.31.7900&rep=rep1&type=pdf it says that you could add up all the vectors and then divide each vector element by the number of vectors.
S |
41,146 | How to get coefficients of gradient boosting models? | I use R "in industry". GBM's and other tree-based methods don't have "coefficients" so it's pointless to try to extract them.
What you CAN do is encode each tree as a SQL query. It take a little effort, but once you can do it for a single tree, you can loop over all the trees in a model, generate ~500 SQL queries, an... | How to get coefficients of gradient boosting models? | I use R "in industry". GBM's and other tree-based methods don't have "coefficients" so it's pointless to try to extract them.
What you CAN do is encode each tree as a SQL query. It take a little eff | How to get coefficients of gradient boosting models?
I use R "in industry". GBM's and other tree-based methods don't have "coefficients" so it's pointless to try to extract them.
What you CAN do is encode each tree as a SQL query. It take a little effort, but once you can do it for a single tree, you can loop over al... | How to get coefficients of gradient boosting models?
I use R "in industry". GBM's and other tree-based methods don't have "coefficients" so it's pointless to try to extract them.
What you CAN do is encode each tree as a SQL query. It take a little eff |
41,147 | How to get coefficients of gradient boosting models? | Like Zach mentioned earlier, "coefficients" don't really apply for a GBM. I'm not sure how you're implementing it, but in a package like CARET (for R) you can look at variable importance during model building. You can also see something similar in the vignette for the GBM package in R. In the GBM package, I think it is... | How to get coefficients of gradient boosting models? | Like Zach mentioned earlier, "coefficients" don't really apply for a GBM. I'm not sure how you're implementing it, but in a package like CARET (for R) you can look at variable importance during model | How to get coefficients of gradient boosting models?
Like Zach mentioned earlier, "coefficients" don't really apply for a GBM. I'm not sure how you're implementing it, but in a package like CARET (for R) you can look at variable importance during model building. You can also see something similar in the vignette for th... | How to get coefficients of gradient boosting models?
Like Zach mentioned earlier, "coefficients" don't really apply for a GBM. I'm not sure how you're implementing it, but in a package like CARET (for R) you can look at variable importance during model |
41,148 | How to get coefficients of gradient boosting models? | Use PMML to transfer the model to other platforms, assuming your other platform is PMML-compatible. | How to get coefficients of gradient boosting models? | Use PMML to transfer the model to other platforms, assuming your other platform is PMML-compatible. | How to get coefficients of gradient boosting models?
Use PMML to transfer the model to other platforms, assuming your other platform is PMML-compatible. | How to get coefficients of gradient boosting models?
Use PMML to transfer the model to other platforms, assuming your other platform is PMML-compatible. |
41,149 | How to get coefficients of gradient boosting models? | For python, you could use import _pickle as cPickle to save the model to a pickle file and restore the model from the pickle file.
The codes for store the model:
with open("gbmFit.pkl", "wb") as pickle_file:
cPickle.dump(model, pickle_file)
For restore the model, one could use following code:
with open('gbmFit.pk... | How to get coefficients of gradient boosting models? | For python, you could use import _pickle as cPickle to save the model to a pickle file and restore the model from the pickle file.
The codes for store the model:
with open("gbmFit.pkl", "wb") as pick | How to get coefficients of gradient boosting models?
For python, you could use import _pickle as cPickle to save the model to a pickle file and restore the model from the pickle file.
The codes for store the model:
with open("gbmFit.pkl", "wb") as pickle_file:
cPickle.dump(model, pickle_file)
For restore the mode... | How to get coefficients of gradient boosting models?
For python, you could use import _pickle as cPickle to save the model to a pickle file and restore the model from the pickle file.
The codes for store the model:
with open("gbmFit.pkl", "wb") as pick |
41,150 | Weight Decay in Neural Neural Networks Weight Update and Convergence | It is not surprising that weight decay will hurt performance of your neural network at some point. Let the prediction loss of your net be $\mathcal{L}$ and the weight decay loss $\mathcal{R}$. Given a coefficient $\lambda$ that establishes a tradeoff between the two, one optimises
$$
\mathcal{L} + \lambda \mathcal{R}.
... | Weight Decay in Neural Neural Networks Weight Update and Convergence | It is not surprising that weight decay will hurt performance of your neural network at some point. Let the prediction loss of your net be $\mathcal{L}$ and the weight decay loss $\mathcal{R}$. Given a | Weight Decay in Neural Neural Networks Weight Update and Convergence
It is not surprising that weight decay will hurt performance of your neural network at some point. Let the prediction loss of your net be $\mathcal{L}$ and the weight decay loss $\mathcal{R}$. Given a coefficient $\lambda$ that establishes a tradeoff ... | Weight Decay in Neural Neural Networks Weight Update and Convergence
It is not surprising that weight decay will hurt performance of your neural network at some point. Let the prediction loss of your net be $\mathcal{L}$ and the weight decay loss $\mathcal{R}$. Given a |
41,151 | Avoiding numerical error propagation | Let $\mathbf \varepsilon = (\varepsilon_1, \varepsilon_2, \ldots, \varepsilon_n)$, $\mathbf y = (y_1,y_2,\ldots, y_n)$, and $\mathbf{\mu} = (\mu, \mu, \ldots, \mu)$. The relationship among these vectors given in the question is
$$\mathbf y - \mathbf\mu = \mathbf{X}\mathbf \varepsilon$$
The matrix $\mathbf{X} = \mathb... | Avoiding numerical error propagation | Let $\mathbf \varepsilon = (\varepsilon_1, \varepsilon_2, \ldots, \varepsilon_n)$, $\mathbf y = (y_1,y_2,\ldots, y_n)$, and $\mathbf{\mu} = (\mu, \mu, \ldots, \mu)$. The relationship among these vect | Avoiding numerical error propagation
Let $\mathbf \varepsilon = (\varepsilon_1, \varepsilon_2, \ldots, \varepsilon_n)$, $\mathbf y = (y_1,y_2,\ldots, y_n)$, and $\mathbf{\mu} = (\mu, \mu, \ldots, \mu)$. The relationship among these vectors given in the question is
$$\mathbf y - \mathbf\mu = \mathbf{X}\mathbf \varepsi... | Avoiding numerical error propagation
Let $\mathbf \varepsilon = (\varepsilon_1, \varepsilon_2, \ldots, \varepsilon_n)$, $\mathbf y = (y_1,y_2,\ldots, y_n)$, and $\mathbf{\mu} = (\mu, \mu, \ldots, \mu)$. The relationship among these vect |
41,152 | Avoiding numerical error propagation | Your linear recurrence relation for $\epsilon_t$ is unstable. If you know $\epsilon_1$ with some absolute error $\delta$, then the subsequent values for $\epsilon_t$ will have absolute error on the order of $\theta^t \delta$.
Note that the error doesn't have to come from the estimation error for the parameters, it coul... | Avoiding numerical error propagation | Your linear recurrence relation for $\epsilon_t$ is unstable. If you know $\epsilon_1$ with some absolute error $\delta$, then the subsequent values for $\epsilon_t$ will have absolute error on the or | Avoiding numerical error propagation
Your linear recurrence relation for $\epsilon_t$ is unstable. If you know $\epsilon_1$ with some absolute error $\delta$, then the subsequent values for $\epsilon_t$ will have absolute error on the order of $\theta^t \delta$.
Note that the error doesn't have to come from the estimat... | Avoiding numerical error propagation
Your linear recurrence relation for $\epsilon_t$ is unstable. If you know $\epsilon_1$ with some absolute error $\delta$, then the subsequent values for $\epsilon_t$ will have absolute error on the or |
41,153 | Calculate number of support vectors in SVM | Notice that since $y_{i} = \pm 1$, you can rewrite,
$$
\alpha_i = \frac{1}{y_i} \left[ \frac{1}{y_i} - \frac{1}{N} \sum_{n=1}^N \frac{1}{y_n}\right] = y_i \left[y_i - \frac{1}{N} \sum_{n=1}^N y_n\right] = 1 - y_{i}\frac{N^{+}-N^{-}}{N}
$$
where $N^{+}$ and $N^{-}$ are the number of samples in each of the classes. You c... | Calculate number of support vectors in SVM | Notice that since $y_{i} = \pm 1$, you can rewrite,
$$
\alpha_i = \frac{1}{y_i} \left[ \frac{1}{y_i} - \frac{1}{N} \sum_{n=1}^N \frac{1}{y_n}\right] = y_i \left[y_i - \frac{1}{N} \sum_{n=1}^N y_n\righ | Calculate number of support vectors in SVM
Notice that since $y_{i} = \pm 1$, you can rewrite,
$$
\alpha_i = \frac{1}{y_i} \left[ \frac{1}{y_i} - \frac{1}{N} \sum_{n=1}^N \frac{1}{y_n}\right] = y_i \left[y_i - \frac{1}{N} \sum_{n=1}^N y_n\right] = 1 - y_{i}\frac{N^{+}-N^{-}}{N}
$$
where $N^{+}$ and $N^{-}$ are the numb... | Calculate number of support vectors in SVM
Notice that since $y_{i} = \pm 1$, you can rewrite,
$$
\alpha_i = \frac{1}{y_i} \left[ \frac{1}{y_i} - \frac{1}{N} \sum_{n=1}^N \frac{1}{y_n}\right] = y_i \left[y_i - \frac{1}{N} \sum_{n=1}^N y_n\righ |
41,154 | Calculate number of support vectors in SVM | You are correct that for such a kernel, for all non-data points, $z$, you will get $w^T\theta(z) = \sum_i \alpha_i k(x_i, z) = 0$. For intuition, note that your kernel is equivalent to the RBF kernel with width converging to 0. Thus, you have no generalization capability at all; the decision function is based on 0-radi... | Calculate number of support vectors in SVM | You are correct that for such a kernel, for all non-data points, $z$, you will get $w^T\theta(z) = \sum_i \alpha_i k(x_i, z) = 0$. For intuition, note that your kernel is equivalent to the RBF kernel | Calculate number of support vectors in SVM
You are correct that for such a kernel, for all non-data points, $z$, you will get $w^T\theta(z) = \sum_i \alpha_i k(x_i, z) = 0$. For intuition, note that your kernel is equivalent to the RBF kernel with width converging to 0. Thus, you have no generalization capability at al... | Calculate number of support vectors in SVM
You are correct that for such a kernel, for all non-data points, $z$, you will get $w^T\theta(z) = \sum_i \alpha_i k(x_i, z) = 0$. For intuition, note that your kernel is equivalent to the RBF kernel |
41,155 | Combining $\chi^{2}$ tests | If the traps were all considered independent then this isn't a variation on a $\chi^2$ test, it just is one. But because they're used in pairs, and therefore not independent, then what you're looking for would be a variation on a McNemar's test. Unfortunately, any modification of that test for more than a 2x2 matrix w... | Combining $\chi^{2}$ tests | If the traps were all considered independent then this isn't a variation on a $\chi^2$ test, it just is one. But because they're used in pairs, and therefore not independent, then what you're looking | Combining $\chi^{2}$ tests
If the traps were all considered independent then this isn't a variation on a $\chi^2$ test, it just is one. But because they're used in pairs, and therefore not independent, then what you're looking for would be a variation on a McNemar's test. Unfortunately, any modification of that test f... | Combining $\chi^{2}$ tests
If the traps were all considered independent then this isn't a variation on a $\chi^2$ test, it just is one. But because they're used in pairs, and therefore not independent, then what you're looking |
41,156 | Combining $\chi^{2}$ tests | One uber-robust and uber-conservative approach would be non-parametric signed rank or matched pairs sign tests. In the first test, you assume identical distributions of two groups; in the second, you explicitly incorporate coupling of the two distributions.
. signrank trapped0 = trapped1
Wilcoxon signed-rank test
... | Combining $\chi^{2}$ tests | One uber-robust and uber-conservative approach would be non-parametric signed rank or matched pairs sign tests. In the first test, you assume identical distributions of two groups; in the second, you | Combining $\chi^{2}$ tests
One uber-robust and uber-conservative approach would be non-parametric signed rank or matched pairs sign tests. In the first test, you assume identical distributions of two groups; in the second, you explicitly incorporate coupling of the two distributions.
. signrank trapped0 = trapped1
Wil... | Combining $\chi^{2}$ tests
One uber-robust and uber-conservative approach would be non-parametric signed rank or matched pairs sign tests. In the first test, you assume identical distributions of two groups; in the second, you |
41,157 | How to test whether there is a significant difference in mean squared error between two datasets? | MSE Significance testing:
You can test for significantly different MSEs if your samples are large enough for the Central Limit Theorem to apply (say, larger than 30).
You want to test whether the means of your two samples are significantly different, where the samples in this case are the squared errors from your healt... | How to test whether there is a significant difference in mean squared error between two datasets? | MSE Significance testing:
You can test for significantly different MSEs if your samples are large enough for the Central Limit Theorem to apply (say, larger than 30).
You want to test whether the mean | How to test whether there is a significant difference in mean squared error between two datasets?
MSE Significance testing:
You can test for significantly different MSEs if your samples are large enough for the Central Limit Theorem to apply (say, larger than 30).
You want to test whether the means of your two samples ... | How to test whether there is a significant difference in mean squared error between two datasets?
MSE Significance testing:
You can test for significantly different MSEs if your samples are large enough for the Central Limit Theorem to apply (say, larger than 30).
You want to test whether the mean |
41,158 | How to test whether there is a significant difference in mean squared error between two datasets? | Sounds like an interesting set-up.
Just to re-state your plan, you're creating two populations -- healthy and Alzheimer's. You ten plan to use a 5-fold cross-validation method on only the healthy group. You then plan to fit a model to the training portion of the fold and finally compare the error when applied to the te... | How to test whether there is a significant difference in mean squared error between two datasets? | Sounds like an interesting set-up.
Just to re-state your plan, you're creating two populations -- healthy and Alzheimer's. You ten plan to use a 5-fold cross-validation method on only the healthy grou | How to test whether there is a significant difference in mean squared error between two datasets?
Sounds like an interesting set-up.
Just to re-state your plan, you're creating two populations -- healthy and Alzheimer's. You ten plan to use a 5-fold cross-validation method on only the healthy group. You then plan to fi... | How to test whether there is a significant difference in mean squared error between two datasets?
Sounds like an interesting set-up.
Just to re-state your plan, you're creating two populations -- healthy and Alzheimer's. You ten plan to use a 5-fold cross-validation method on only the healthy grou |
41,159 | Averaging binomial confidence intervals | Two points
First point: You could also perform a weighted average of the proportion of female instructors at the three schools (e.g. if A has ten times as many total instructors as B and C combined, one might argue that it should count more heavily). For example, $(n_{A}\times 0.23 + n_{B}\times 0.56 + n_{C} \times 0.8... | Averaging binomial confidence intervals | Two points
First point: You could also perform a weighted average of the proportion of female instructors at the three schools (e.g. if A has ten times as many total instructors as B and C combined, o | Averaging binomial confidence intervals
Two points
First point: You could also perform a weighted average of the proportion of female instructors at the three schools (e.g. if A has ten times as many total instructors as B and C combined, one might argue that it should count more heavily). For example, $(n_{A}\times 0.... | Averaging binomial confidence intervals
Two points
First point: You could also perform a weighted average of the proportion of female instructors at the three schools (e.g. if A has ten times as many total instructors as B and C combined, o |
41,160 | What is the difference between a "learner" and "classifier" in supervised learning? | I use to find this kind of difference mostly in the world of programmers interested in machine learning. This difference does not appear in the world of statisticians and researchers.
The idea is that a classifier is a program built by a learner. An illuminating intuition comes from one of the many definitions of mach... | What is the difference between a "learner" and "classifier" in supervised learning? | I use to find this kind of difference mostly in the world of programmers interested in machine learning. This difference does not appear in the world of statisticians and researchers.
The idea is tha | What is the difference between a "learner" and "classifier" in supervised learning?
I use to find this kind of difference mostly in the world of programmers interested in machine learning. This difference does not appear in the world of statisticians and researchers.
The idea is that a classifier is a program built by... | What is the difference between a "learner" and "classifier" in supervised learning?
I use to find this kind of difference mostly in the world of programmers interested in machine learning. This difference does not appear in the world of statisticians and researchers.
The idea is tha |
41,161 | What is the difference between a "learner" and "classifier" in supervised learning? | I know this is an old post but I just had the same question as to the OP. Thanks to/based on @rapio's description, this is how I would say the same thing:
Both a learner and a classifier refer to the same thing: a machine learning model. It's just that an ML model has aspects to it while in the training phase which are... | What is the difference between a "learner" and "classifier" in supervised learning? | I know this is an old post but I just had the same question as to the OP. Thanks to/based on @rapio's description, this is how I would say the same thing:
Both a learner and a classifier refer to the | What is the difference between a "learner" and "classifier" in supervised learning?
I know this is an old post but I just had the same question as to the OP. Thanks to/based on @rapio's description, this is how I would say the same thing:
Both a learner and a classifier refer to the same thing: a machine learning model... | What is the difference between a "learner" and "classifier" in supervised learning?
I know this is an old post but I just had the same question as to the OP. Thanks to/based on @rapio's description, this is how I would say the same thing:
Both a learner and a classifier refer to the |
41,162 | Probability mass function of the sample mean | If $Z \sim Bernoulli(\frac12)$, then $X=2Z-1$ has a Rademacher distribution where $X=-1$ or $1$ with equal probability. Let $X_1, ..., X_n$ denote indepedent Rademacher random variables.
Then, the pmf of:
$$ S = \sum_{i=1}^n X_i = (2 \sum_{i=1}^n Z_i) - n = 2 Y -n$$
where $Y \sim Binomial(n,\frac12)$ (since the sum ... | Probability mass function of the sample mean | If $Z \sim Bernoulli(\frac12)$, then $X=2Z-1$ has a Rademacher distribution where $X=-1$ or $1$ with equal probability. Let $X_1, ..., X_n$ denote indepedent Rademacher random variables.
Then, the pm | Probability mass function of the sample mean
If $Z \sim Bernoulli(\frac12)$, then $X=2Z-1$ has a Rademacher distribution where $X=-1$ or $1$ with equal probability. Let $X_1, ..., X_n$ denote indepedent Rademacher random variables.
Then, the pmf of:
$$ S = \sum_{i=1}^n X_i = (2 \sum_{i=1}^n Z_i) - n = 2 Y -n$$
where... | Probability mass function of the sample mean
If $Z \sim Bernoulli(\frac12)$, then $X=2Z-1$ has a Rademacher distribution where $X=-1$ or $1$ with equal probability. Let $X_1, ..., X_n$ denote indepedent Rademacher random variables.
Then, the pm |
41,163 | Is the Dunning–Kruger effect mostly caused by regression to the mean? | "Just an artefact" sounds like the effect could be entirely due to regression to the mean. That is a very strong statement. In fact, Kruger & Dunning already addressed this in section 4.1.3 in their original paper ("regression effect" here stands for "regression to the mean"):
Despite the inevitability of the regressi... | Is the Dunning–Kruger effect mostly caused by regression to the mean? | "Just an artefact" sounds like the effect could be entirely due to regression to the mean. That is a very strong statement. In fact, Kruger & Dunning already addressed this in section 4.1.3 in their o | Is the Dunning–Kruger effect mostly caused by regression to the mean?
"Just an artefact" sounds like the effect could be entirely due to regression to the mean. That is a very strong statement. In fact, Kruger & Dunning already addressed this in section 4.1.3 in their original paper ("regression effect" here stands for... | Is the Dunning–Kruger effect mostly caused by regression to the mean?
"Just an artefact" sounds like the effect could be entirely due to regression to the mean. That is a very strong statement. In fact, Kruger & Dunning already addressed this in section 4.1.3 in their o |
41,164 | Is the Dunning–Kruger effect mostly caused by regression to the mean? | Dunning-Kruger only showed that almost everyone, regardless of actual ability level (with the exception of those with very high ability), believes their own performance is above average. This isn't surprising at all, because people judge their own ability relative to the more typical person - because there are more ty... | Is the Dunning–Kruger effect mostly caused by regression to the mean? | Dunning-Kruger only showed that almost everyone, regardless of actual ability level (with the exception of those with very high ability), believes their own performance is above average. This isn't s | Is the Dunning–Kruger effect mostly caused by regression to the mean?
Dunning-Kruger only showed that almost everyone, regardless of actual ability level (with the exception of those with very high ability), believes their own performance is above average. This isn't surprising at all, because people judge their own a... | Is the Dunning–Kruger effect mostly caused by regression to the mean?
Dunning-Kruger only showed that almost everyone, regardless of actual ability level (with the exception of those with very high ability), believes their own performance is above average. This isn't s |
41,165 | Why does a kernel density estimate of a normally distributed vector have a non-smooth second derivative? | As whuber commented, what you're seeing is due to the approximation (via a fast fourier transform) that density uses.
If you calculate the kernel density estimate by brute force and compare it to the estimate that density gives, you'll see this sort of cyclic pattern in the differences.
The 2nd differences in the resul... | Why does a kernel density estimate of a normally distributed vector have a non-smooth second derivat | As whuber commented, what you're seeing is due to the approximation (via a fast fourier transform) that density uses.
If you calculate the kernel density estimate by brute force and compare it to the | Why does a kernel density estimate of a normally distributed vector have a non-smooth second derivative?
As whuber commented, what you're seeing is due to the approximation (via a fast fourier transform) that density uses.
If you calculate the kernel density estimate by brute force and compare it to the estimate that d... | Why does a kernel density estimate of a normally distributed vector have a non-smooth second derivat
As whuber commented, what you're seeing is due to the approximation (via a fast fourier transform) that density uses.
If you calculate the kernel density estimate by brute force and compare it to the |
41,166 | Why does a kernel density estimate of a normally distributed vector have a non-smooth second derivative? | In your codes in the gist you used a hard-entered bandwidth (bw=5) which is very different from a optimal one returned for example as:
library(ks)
h <- hpi(x)
which is actually about h=0.85.
It is interesting to execute your codes where bw is set to h. Then the final figure is as below. Here the differences are not so... | Why does a kernel density estimate of a normally distributed vector have a non-smooth second derivat | In your codes in the gist you used a hard-entered bandwidth (bw=5) which is very different from a optimal one returned for example as:
library(ks)
h <- hpi(x)
which is actually about h=0.85.
It is in | Why does a kernel density estimate of a normally distributed vector have a non-smooth second derivative?
In your codes in the gist you used a hard-entered bandwidth (bw=5) which is very different from a optimal one returned for example as:
library(ks)
h <- hpi(x)
which is actually about h=0.85.
It is interesting to ex... | Why does a kernel density estimate of a normally distributed vector have a non-smooth second derivat
In your codes in the gist you used a hard-entered bandwidth (bw=5) which is very different from a optimal one returned for example as:
library(ks)
h <- hpi(x)
which is actually about h=0.85.
It is in |
41,167 | Clear steps to calculate coherence between two time series | The following are the issues that I summarize succinctly in sequence, as each of them are an interesting problem in itself and then follow it up with my solutions:
i) How to unlag two time-series so as to lose the least data due to the lagging, and at the same time to maximize the cross-correlation or spectral coheren... | Clear steps to calculate coherence between two time series | The following are the issues that I summarize succinctly in sequence, as each of them are an interesting problem in itself and then follow it up with my solutions:
i) How to unlag two time-series so | Clear steps to calculate coherence between two time series
The following are the issues that I summarize succinctly in sequence, as each of them are an interesting problem in itself and then follow it up with my solutions:
i) How to unlag two time-series so as to lose the least data due to the lagging, and at the same... | Clear steps to calculate coherence between two time series
The following are the issues that I summarize succinctly in sequence, as each of them are an interesting problem in itself and then follow it up with my solutions:
i) How to unlag two time-series so |
41,168 | Clear steps to calculate coherence between two time series | unless your task is to actually implement coherence calculation, I wouldn't do it, because there's got to be a package for that. e.g. I see coh package, haven't used it myself. this function seems to do all that you need for a specific frequency f, so you can loop a call to it over the frequency range.
In matlab there'... | Clear steps to calculate coherence between two time series | unless your task is to actually implement coherence calculation, I wouldn't do it, because there's got to be a package for that. e.g. I see coh package, haven't used it myself. this function seems to | Clear steps to calculate coherence between two time series
unless your task is to actually implement coherence calculation, I wouldn't do it, because there's got to be a package for that. e.g. I see coh package, haven't used it myself. this function seems to do all that you need for a specific frequency f, so you can l... | Clear steps to calculate coherence between two time series
unless your task is to actually implement coherence calculation, I wouldn't do it, because there's got to be a package for that. e.g. I see coh package, haven't used it myself. this function seems to |
41,169 | Clear steps to calculate coherence between two time series | Use seewave::coh() to extract the coherence and lag values for the maximum coherence point:
coherence <- coh(ts1, ts2, f=100, plot=TRUE)
lags <- coherence[,1]
vals <- coherence[,2]
val.max <- vals[which.max(vals)]
lag.max <- lags[which.max(vals)]
The coh() function is designed to work with ts objects. Th... | Clear steps to calculate coherence between two time series | Use seewave::coh() to extract the coherence and lag values for the maximum coherence point:
coherence <- coh(ts1, ts2, f=100, plot=TRUE)
lags <- coherence[,1]
vals <- coherence[,2]
val.max | Clear steps to calculate coherence between two time series
Use seewave::coh() to extract the coherence and lag values for the maximum coherence point:
coherence <- coh(ts1, ts2, f=100, plot=TRUE)
lags <- coherence[,1]
vals <- coherence[,2]
val.max <- vals[which.max(vals)]
lag.max <- lags[which.max(vals)]
... | Clear steps to calculate coherence between two time series
Use seewave::coh() to extract the coherence and lag values for the maximum coherence point:
coherence <- coh(ts1, ts2, f=100, plot=TRUE)
lags <- coherence[,1]
vals <- coherence[,2]
val.max |
41,170 | Estimating the covariance matrix in linear discriminant analysis | You are right. The equation for the shared variance-covariance matrix comes from Pooled Variance
The shared covariance matrix $\Sigma$ is taken as a weighted average of individual covariance matrices, weigted by $n_i-1$
So $\Sigma = \frac{(n_1-1) * \Sigma_1 + (n_2-1) * \Sigma_2 + .... + (n_k-1) * \Sigma_k}{(n_1-1)+(n_2... | Estimating the covariance matrix in linear discriminant analysis | You are right. The equation for the shared variance-covariance matrix comes from Pooled Variance
The shared covariance matrix $\Sigma$ is taken as a weighted average of individual covariance matrices, | Estimating the covariance matrix in linear discriminant analysis
You are right. The equation for the shared variance-covariance matrix comes from Pooled Variance
The shared covariance matrix $\Sigma$ is taken as a weighted average of individual covariance matrices, weigted by $n_i-1$
So $\Sigma = \frac{(n_1-1) * \Sigma... | Estimating the covariance matrix in linear discriminant analysis
You are right. The equation for the shared variance-covariance matrix comes from Pooled Variance
The shared covariance matrix $\Sigma$ is taken as a weighted average of individual covariance matrices, |
41,171 | What is the interval that relates to the mean as the equal tailed interval relates to the median and the highest density interval relates to the mode? | I don't know if the interval centered on the mean has a special name, but I can think of more than one way that one could define an interval centered on the mean in the sense that the interval will converge to the mean as the width of the inverval goes to zero. The most simple would perhaps be the interval
$$C = (\mu-k... | What is the interval that relates to the mean as the equal tailed interval relates to the median and | I don't know if the interval centered on the mean has a special name, but I can think of more than one way that one could define an interval centered on the mean in the sense that the interval will co | What is the interval that relates to the mean as the equal tailed interval relates to the median and the highest density interval relates to the mode?
I don't know if the interval centered on the mean has a special name, but I can think of more than one way that one could define an interval centered on the mean in the ... | What is the interval that relates to the mean as the equal tailed interval relates to the median and
I don't know if the interval centered on the mean has a special name, but I can think of more than one way that one could define an interval centered on the mean in the sense that the interval will co |
41,172 | Standard deviation for values below 1 | If my standard deviation and variance are above 1, the standard deviation will be smaller than the variance. But if they are below 1, the standard deviation will be bigger than the variance.
Actually, this is not the case, because you're ignoring the units. Standard deviation of a percentage is measured in percent, wh... | Standard deviation for values below 1 | If my standard deviation and variance are above 1, the standard deviation will be smaller than the variance. But if they are below 1, the standard deviation will be bigger than the variance.
Actually | Standard deviation for values below 1
If my standard deviation and variance are above 1, the standard deviation will be smaller than the variance. But if they are below 1, the standard deviation will be bigger than the variance.
Actually, this is not the case, because you're ignoring the units. Standard deviation of a... | Standard deviation for values below 1
If my standard deviation and variance are above 1, the standard deviation will be smaller than the variance. But if they are below 1, the standard deviation will be bigger than the variance.
Actually |
41,173 | Standard deviation for values below 1 | Variance (which ST is derived from) measures differences from the mean. Given differences from the average d1 and d2, whether D > 0 or D < 0 this always applies: d1 <=> d2 === d1^2 <=> d2^2.
Examples:
d1 = 2
d2 = 3
2 < 3 and 4 < 9
d1 = 0.1
d2 = 0.5
0.1 < 0.5 and 0.01 < 0.25
d1 = 0.5
d2 = 2
0.5 < 2 and 0.25 < 4
I do... | Standard deviation for values below 1 | Variance (which ST is derived from) measures differences from the mean. Given differences from the average d1 and d2, whether D > 0 or D < 0 this always applies: d1 <=> d2 === d1^2 <=> d2^2.
Examples | Standard deviation for values below 1
Variance (which ST is derived from) measures differences from the mean. Given differences from the average d1 and d2, whether D > 0 or D < 0 this always applies: d1 <=> d2 === d1^2 <=> d2^2.
Examples:
d1 = 2
d2 = 3
2 < 3 and 4 < 9
d1 = 0.1
d2 = 0.5
0.1 < 0.5 and 0.01 < 0.25
d1 =... | Standard deviation for values below 1
Variance (which ST is derived from) measures differences from the mean. Given differences from the average d1 and d2, whether D > 0 or D < 0 this always applies: d1 <=> d2 === d1^2 <=> d2^2.
Examples |
41,174 | Coverage Proof of Confidence Intervals | Probably because you got the quantiles wrong (I suppose your $q(\cdot )$ are the quantiles of the standard normal), which should be $q_{1-\frac{\alpha}{2}}$. Basically what you want to show is that
$$Pr\left( E(Y) \in \left( \widehat{E(Y)} - q_{1-\frac{\alpha}{2}} \sqrt{\frac{\widehat{Var(Y)}}{n}}, \widehat{E(Y)} + q_{... | Coverage Proof of Confidence Intervals | Probably because you got the quantiles wrong (I suppose your $q(\cdot )$ are the quantiles of the standard normal), which should be $q_{1-\frac{\alpha}{2}}$. Basically what you want to show is that
$$ | Coverage Proof of Confidence Intervals
Probably because you got the quantiles wrong (I suppose your $q(\cdot )$ are the quantiles of the standard normal), which should be $q_{1-\frac{\alpha}{2}}$. Basically what you want to show is that
$$Pr\left( E(Y) \in \left( \widehat{E(Y)} - q_{1-\frac{\alpha}{2}} \sqrt{\frac{\wid... | Coverage Proof of Confidence Intervals
Probably because you got the quantiles wrong (I suppose your $q(\cdot )$ are the quantiles of the standard normal), which should be $q_{1-\frac{\alpha}{2}}$. Basically what you want to show is that
$$ |
41,175 | extremely left-skewed response variable - how do I model this dataset? | Hurdle models and zero-inflated models could both work on the inverted variable. If you wanted to keep it as is, you might have to do some programming.
In R the pscl package offers both hurdle and zeroinfl functions. There is a vignette here that also covers some other packages that do some of the same things.
This b... | extremely left-skewed response variable - how do I model this dataset? | Hurdle models and zero-inflated models could both work on the inverted variable. If you wanted to keep it as is, you might have to do some programming.
In R the pscl package offers both hurdle and ze | extremely left-skewed response variable - how do I model this dataset?
Hurdle models and zero-inflated models could both work on the inverted variable. If you wanted to keep it as is, you might have to do some programming.
In R the pscl package offers both hurdle and zeroinfl functions. There is a vignette here that a... | extremely left-skewed response variable - how do I model this dataset?
Hurdle models and zero-inflated models could both work on the inverted variable. If you wanted to keep it as is, you might have to do some programming.
In R the pscl package offers both hurdle and ze |
41,176 | extremely left-skewed response variable - how do I model this dataset? | @whuber's answer to a related question ("How to model this odd-shaped distribution (almost a reverse-J)") recommends censored regression and provides an example with the censReg package. Your case appears more strictly right censored, but you might have a tiny bit of true zero-inflation too (not worth worrying about ac... | extremely left-skewed response variable - how do I model this dataset? | @whuber's answer to a related question ("How to model this odd-shaped distribution (almost a reverse-J)") recommends censored regression and provides an example with the censReg package. Your case app | extremely left-skewed response variable - how do I model this dataset?
@whuber's answer to a related question ("How to model this odd-shaped distribution (almost a reverse-J)") recommends censored regression and provides an example with the censReg package. Your case appears more strictly right censored, but you might ... | extremely left-skewed response variable - how do I model this dataset?
@whuber's answer to a related question ("How to model this odd-shaped distribution (almost a reverse-J)") recommends censored regression and provides an example with the censReg package. Your case app |
41,177 | Regression Terminology | The "regression of W on Z" means you're predicting W from Z. Using your equations, this corresponds to the first one, namely $W=\alpha+\beta Z+\epsilon$ | Regression Terminology | The "regression of W on Z" means you're predicting W from Z. Using your equations, this corresponds to the first one, namely $W=\alpha+\beta Z+\epsilon$ | Regression Terminology
The "regression of W on Z" means you're predicting W from Z. Using your equations, this corresponds to the first one, namely $W=\alpha+\beta Z+\epsilon$ | Regression Terminology
The "regression of W on Z" means you're predicting W from Z. Using your equations, this corresponds to the first one, namely $W=\alpha+\beta Z+\epsilon$ |
41,178 | Overdispersed Poisson regression | The "resulting Poisson" is only the conditional distribution of the response given an (unobserved) realization of the normal error term: the unconditional response (or the response conditional only on the realized values of the predictors, if these are random variables) does not have a Poisson distribution.
Suppose the... | Overdispersed Poisson regression | The "resulting Poisson" is only the conditional distribution of the response given an (unobserved) realization of the normal error term: the unconditional response (or the response conditional only on | Overdispersed Poisson regression
The "resulting Poisson" is only the conditional distribution of the response given an (unobserved) realization of the normal error term: the unconditional response (or the response conditional only on the realized values of the predictors, if these are random variables) does not have a ... | Overdispersed Poisson regression
The "resulting Poisson" is only the conditional distribution of the response given an (unobserved) realization of the normal error term: the unconditional response (or the response conditional only on |
41,179 | CDF of a random vector | Let us take the simplest example of Bernoulli random variables with parameter $\frac12$. The value of the (joint) CDF $F_{X_1,X_2}(x,y)$ of $X_1$ and $X_2$
is the
total probability mass in the southwest quadrant with northeast corner $(x,y)$
If $X_1$ and $X_2$ are two independent Bernoulli random variables,
then we h... | CDF of a random vector | Let us take the simplest example of Bernoulli random variables with parameter $\frac12$. The value of the (joint) CDF $F_{X_1,X_2}(x,y)$ of $X_1$ and $X_2$
is the
total probability mass in the southw | CDF of a random vector
Let us take the simplest example of Bernoulli random variables with parameter $\frac12$. The value of the (joint) CDF $F_{X_1,X_2}(x,y)$ of $X_1$ and $X_2$
is the
total probability mass in the southwest quadrant with northeast corner $(x,y)$
If $X_1$ and $X_2$ are two independent Bernoulli rand... | CDF of a random vector
Let us take the simplest example of Bernoulli random variables with parameter $\frac12$. The value of the (joint) CDF $F_{X_1,X_2}(x,y)$ of $X_1$ and $X_2$
is the
total probability mass in the southw |
41,180 | CDF of a random vector | Random objects can have the same distribution and be almost surely different. Take a look:
Can two random variables have the same distribution, yet be almost surely different? | CDF of a random vector | Random objects can have the same distribution and be almost surely different. Take a look:
Can two random variables have the same distribution, yet be almost surely different? | CDF of a random vector
Random objects can have the same distribution and be almost surely different. Take a look:
Can two random variables have the same distribution, yet be almost surely different? | CDF of a random vector
Random objects can have the same distribution and be almost surely different. Take a look:
Can two random variables have the same distribution, yet be almost surely different? |
41,181 | CDF of a random vector | The key is the difference between joint and marginal distributions. When the book talks about the cdf of a random vector $X=(X_1,X_2)$ they mean the function $$F_X(x_1,x_2):=\mathbb{P}(X_1\leq x_1,X_2\leq x_2 ).$$ As you probably know, if $X_1$ and $X_2$ are independent, this equals $$F_{X_1}(x_1)F_{X_2}(x_2)=\mathbb{P... | CDF of a random vector | The key is the difference between joint and marginal distributions. When the book talks about the cdf of a random vector $X=(X_1,X_2)$ they mean the function $$F_X(x_1,x_2):=\mathbb{P}(X_1\leq x_1,X_2 | CDF of a random vector
The key is the difference between joint and marginal distributions. When the book talks about the cdf of a random vector $X=(X_1,X_2)$ they mean the function $$F_X(x_1,x_2):=\mathbb{P}(X_1\leq x_1,X_2\leq x_2 ).$$ As you probably know, if $X_1$ and $X_2$ are independent, this equals $$F_{X_1}(x_1... | CDF of a random vector
The key is the difference between joint and marginal distributions. When the book talks about the cdf of a random vector $X=(X_1,X_2)$ they mean the function $$F_X(x_1,x_2):=\mathbb{P}(X_1\leq x_1,X_2 |
41,182 | What type of regression to use with 1 to 10 scale dependent variable? | Here is R example using package MASS with polr function (ordered factorial response) which can use logistic link or be probit.
library(MASS);
recmodel=polr(recom~trust+solutions+proact+qs+es,data=surveydata,method=c("logistic"));
summary(recmodel);
I think it is important to give R notice that all variables are ... | What type of regression to use with 1 to 10 scale dependent variable? | Here is R example using package MASS with polr function (ordered factorial response) which can use logistic link or be probit.
library(MASS);
recmodel=polr(recom~trust+solutions+proact+qs+es,data=su | What type of regression to use with 1 to 10 scale dependent variable?
Here is R example using package MASS with polr function (ordered factorial response) which can use logistic link or be probit.
library(MASS);
recmodel=polr(recom~trust+solutions+proact+qs+es,data=surveydata,method=c("logistic"));
summary(recmodel... | What type of regression to use with 1 to 10 scale dependent variable?
Here is R example using package MASS with polr function (ordered factorial response) which can use logistic link or be probit.
library(MASS);
recmodel=polr(recom~trust+solutions+proact+qs+es,data=su |
41,183 | What type of regression to use with 1 to 10 scale dependent variable? | 1) Judging by the questions in the survey, the likelihood of multi-collinearity is high. You should definitely check for correlation and VIF between your predictor variables before thinking of running the regression.
2) Apart from regression and at the cost of sounding simplistic, I suggest you do some summary statist... | What type of regression to use with 1 to 10 scale dependent variable? | 1) Judging by the questions in the survey, the likelihood of multi-collinearity is high. You should definitely check for correlation and VIF between your predictor variables before thinking of running | What type of regression to use with 1 to 10 scale dependent variable?
1) Judging by the questions in the survey, the likelihood of multi-collinearity is high. You should definitely check for correlation and VIF between your predictor variables before thinking of running the regression.
2) Apart from regression and at ... | What type of regression to use with 1 to 10 scale dependent variable?
1) Judging by the questions in the survey, the likelihood of multi-collinearity is high. You should definitely check for correlation and VIF between your predictor variables before thinking of running |
41,184 | What type of regression to use with 1 to 10 scale dependent variable? | As pointed out in the comments, the dependent variable can be handled with ordinal logit. For the independent variables, the most flexible approach is to convert each variable to 10 dummy variables (assuming you drop the constant from the model). Interpretation will get a little bit messy, but you are not imposing any ... | What type of regression to use with 1 to 10 scale dependent variable? | As pointed out in the comments, the dependent variable can be handled with ordinal logit. For the independent variables, the most flexible approach is to convert each variable to 10 dummy variables (a | What type of regression to use with 1 to 10 scale dependent variable?
As pointed out in the comments, the dependent variable can be handled with ordinal logit. For the independent variables, the most flexible approach is to convert each variable to 10 dummy variables (assuming you drop the constant from the model). Int... | What type of regression to use with 1 to 10 scale dependent variable?
As pointed out in the comments, the dependent variable can be handled with ordinal logit. For the independent variables, the most flexible approach is to convert each variable to 10 dummy variables (a |
41,185 | What type of regression to use with 1 to 10 scale dependent variable? | You can also try the stereotype logistic regression model (a type of reduced-rank generalized linear regression model) that relaxes the constraint of ordering (without reverting to a multinomial logistic regression model, which with ten outcome levels will present a great deal of parameters that might hinder your abili... | What type of regression to use with 1 to 10 scale dependent variable? | You can also try the stereotype logistic regression model (a type of reduced-rank generalized linear regression model) that relaxes the constraint of ordering (without reverting to a multinomial logis | What type of regression to use with 1 to 10 scale dependent variable?
You can also try the stereotype logistic regression model (a type of reduced-rank generalized linear regression model) that relaxes the constraint of ordering (without reverting to a multinomial logistic regression model, which with ten outcome level... | What type of regression to use with 1 to 10 scale dependent variable?
You can also try the stereotype logistic regression model (a type of reduced-rank generalized linear regression model) that relaxes the constraint of ordering (without reverting to a multinomial logis |
41,186 | Example of "real life" use of Bayesian inference on $\mu$ from a normal distribution? | From my perspective the Normal distribution is useful as a prior on the the mean of a Normal distribution because:
The Normal is the conjugate prior to the mean of a Normal distribution. Using conjugate priors can really speed up computation in, for example, JAGS
It is pretty easy to make the Normal distribution both ... | Example of "real life" use of Bayesian inference on $\mu$ from a normal distribution? | From my perspective the Normal distribution is useful as a prior on the the mean of a Normal distribution because:
The Normal is the conjugate prior to the mean of a Normal distribution. Using conjug | Example of "real life" use of Bayesian inference on $\mu$ from a normal distribution?
From my perspective the Normal distribution is useful as a prior on the the mean of a Normal distribution because:
The Normal is the conjugate prior to the mean of a Normal distribution. Using conjugate priors can really speed up com... | Example of "real life" use of Bayesian inference on $\mu$ from a normal distribution?
From my perspective the Normal distribution is useful as a prior on the the mean of a Normal distribution because:
The Normal is the conjugate prior to the mean of a Normal distribution. Using conjug |
41,187 | Example of "real life" use of Bayesian inference on $\mu$ from a normal distribution? | Before delving directly into an example, I'd like to review some of the math for Normal-Normal Bayesian data models. Consider a random sample of n continuous values denoted by $y_1, ..., y_n$. Here the vector $y = (y_1, ..., y_n)^T$ represents the data gathered. The probability model for Normal data with known variance... | Example of "real life" use of Bayesian inference on $\mu$ from a normal distribution? | Before delving directly into an example, I'd like to review some of the math for Normal-Normal Bayesian data models. Consider a random sample of n continuous values denoted by $y_1, ..., y_n$. Here th | Example of "real life" use of Bayesian inference on $\mu$ from a normal distribution?
Before delving directly into an example, I'd like to review some of the math for Normal-Normal Bayesian data models. Consider a random sample of n continuous values denoted by $y_1, ..., y_n$. Here the vector $y = (y_1, ..., y_n)^T$ r... | Example of "real life" use of Bayesian inference on $\mu$ from a normal distribution?
Before delving directly into an example, I'd like to review some of the math for Normal-Normal Bayesian data models. Consider a random sample of n continuous values denoted by $y_1, ..., y_n$. Here th |
41,188 | Data Sets suitable for k-means | I would answer that the only really suitable data set would be 2. K-means pushes towards, kind of, spherical clusters of the same size. I say kind of because the divisions are more like voronoi cells. From here that in the first example you would end up with overlapped clusters. There are clearly three clusters, a big ... | Data Sets suitable for k-means | I would answer that the only really suitable data set would be 2. K-means pushes towards, kind of, spherical clusters of the same size. I say kind of because the divisions are more like voronoi cells. | Data Sets suitable for k-means
I would answer that the only really suitable data set would be 2. K-means pushes towards, kind of, spherical clusters of the same size. I say kind of because the divisions are more like voronoi cells. From here that in the first example you would end up with overlapped clusters. There are... | Data Sets suitable for k-means
I would answer that the only really suitable data set would be 2. K-means pushes towards, kind of, spherical clusters of the same size. I say kind of because the divisions are more like voronoi cells. |
41,189 | Data Sets suitable for k-means | In complement to JEquihua's great answer, I would like to add 2 points.
Case 3 is a nice example of a case where it would be useful to have a clustering algorithm that doesn't give only the cluster assignment but also some way to assess the degree of certitude that a point belongs to a cluster (e.g. membership degree i... | Data Sets suitable for k-means | In complement to JEquihua's great answer, I would like to add 2 points.
Case 3 is a nice example of a case where it would be useful to have a clustering algorithm that doesn't give only the cluster as | Data Sets suitable for k-means
In complement to JEquihua's great answer, I would like to add 2 points.
Case 3 is a nice example of a case where it would be useful to have a clustering algorithm that doesn't give only the cluster assignment but also some way to assess the degree of certitude that a point belongs to a cl... | Data Sets suitable for k-means
In complement to JEquihua's great answer, I would like to add 2 points.
Case 3 is a nice example of a case where it would be useful to have a clustering algorithm that doesn't give only the cluster as |
41,190 | Taylor series expansion of maximum likelihood estimator, Newton-Raphson, Fisher scoring and distribution of MLE by Delta method | I will denote $\hat \theta$ the maximum likelihood estimator, while $\theta^{\left(m+1\right)}$ and $\theta^{\left(m\right)}$ are any two vectors. $\theta_0$ will denote the true value of the parameter vector. I am suppressing the appearance of the data.
The (untruncated) 2nd-order Taylor expansion of the log-likelih... | Taylor series expansion of maximum likelihood estimator, Newton-Raphson, Fisher scoring and distribu | I will denote $\hat \theta$ the maximum likelihood estimator, while $\theta^{\left(m+1\right)}$ and $\theta^{\left(m\right)}$ are any two vectors. $\theta_0$ will denote the true value of the paramet | Taylor series expansion of maximum likelihood estimator, Newton-Raphson, Fisher scoring and distribution of MLE by Delta method
I will denote $\hat \theta$ the maximum likelihood estimator, while $\theta^{\left(m+1\right)}$ and $\theta^{\left(m\right)}$ are any two vectors. $\theta_0$ will denote the true value of the... | Taylor series expansion of maximum likelihood estimator, Newton-Raphson, Fisher scoring and distribu
I will denote $\hat \theta$ the maximum likelihood estimator, while $\theta^{\left(m+1\right)}$ and $\theta^{\left(m\right)}$ are any two vectors. $\theta_0$ will denote the true value of the paramet |
41,191 | Taylor series expansion of maximum likelihood estimator, Newton-Raphson, Fisher scoring and distribution of MLE by Delta method | Under certain regularity conditions, the maximum likelihood estimates follow asymptotically a normal distribution with mean the true parameter values and covariance matrix the inverse of the Fisher information matrix also evaluated at the true parameter values.
The Delta method is typically used to derive standard erro... | Taylor series expansion of maximum likelihood estimator, Newton-Raphson, Fisher scoring and distribu | Under certain regularity conditions, the maximum likelihood estimates follow asymptotically a normal distribution with mean the true parameter values and covariance matrix the inverse of the Fisher in | Taylor series expansion of maximum likelihood estimator, Newton-Raphson, Fisher scoring and distribution of MLE by Delta method
Under certain regularity conditions, the maximum likelihood estimates follow asymptotically a normal distribution with mean the true parameter values and covariance matrix the inverse of the F... | Taylor series expansion of maximum likelihood estimator, Newton-Raphson, Fisher scoring and distribu
Under certain regularity conditions, the maximum likelihood estimates follow asymptotically a normal distribution with mean the true parameter values and covariance matrix the inverse of the Fisher in |
41,192 | PCA and PLS: testing variables for significance | Inclusion/exclusion of variates (step 3):
I understand that you ask which of the original measurement channels to include into the modeling.
Is such a decision sensible for your data?
E.g. I work mainly with spectroscopic data, for which PLS is frequently and successfully used. Well measured spectra have a high correl... | PCA and PLS: testing variables for significance | Inclusion/exclusion of variates (step 3):
I understand that you ask which of the original measurement channels to include into the modeling.
Is such a decision sensible for your data?
E.g. I work mai | PCA and PLS: testing variables for significance
Inclusion/exclusion of variates (step 3):
I understand that you ask which of the original measurement channels to include into the modeling.
Is such a decision sensible for your data?
E.g. I work mainly with spectroscopic data, for which PLS is frequently and successfull... | PCA and PLS: testing variables for significance
Inclusion/exclusion of variates (step 3):
I understand that you ask which of the original measurement channels to include into the modeling.
Is such a decision sensible for your data?
E.g. I work mai |
41,193 | Inverse transformation sampling for mixture distribution of two normal distributions | The "two uniforms" are not absolutely necessary when generating from a mixture, but they make the simulation easy to understand. The mixture of normal distributions,
$$rf_a(x)+(1-r)f_b(x)$$
has a probability mass of $r$ associated with the first normal and $(1-r)$ with the second normal. This means that the distributio... | Inverse transformation sampling for mixture distribution of two normal distributions | The "two uniforms" are not absolutely necessary when generating from a mixture, but they make the simulation easy to understand. The mixture of normal distributions,
$$rf_a(x)+(1-r)f_b(x)$$
has a prob | Inverse transformation sampling for mixture distribution of two normal distributions
The "two uniforms" are not absolutely necessary when generating from a mixture, but they make the simulation easy to understand. The mixture of normal distributions,
$$rf_a(x)+(1-r)f_b(x)$$
has a probability mass of $r$ associated with... | Inverse transformation sampling for mixture distribution of two normal distributions
The "two uniforms" are not absolutely necessary when generating from a mixture, but they make the simulation easy to understand. The mixture of normal distributions,
$$rf_a(x)+(1-r)f_b(x)$$
has a prob |
41,194 | Inverse transformation sampling for mixture distribution of two normal distributions | Is this the problem from the STA511 class?:)
pnorm() won't give you the right result, because it's a CDF. What you are looking is an inverse of the CDF, so you have to use qnorm() to get it. | Inverse transformation sampling for mixture distribution of two normal distributions | Is this the problem from the STA511 class?:)
pnorm() won't give you the right result, because it's a CDF. What you are looking is an inverse of the CDF, so you have to use qnorm() to get it. | Inverse transformation sampling for mixture distribution of two normal distributions
Is this the problem from the STA511 class?:)
pnorm() won't give you the right result, because it's a CDF. What you are looking is an inverse of the CDF, so you have to use qnorm() to get it. | Inverse transformation sampling for mixture distribution of two normal distributions
Is this the problem from the STA511 class?:)
pnorm() won't give you the right result, because it's a CDF. What you are looking is an inverse of the CDF, so you have to use qnorm() to get it. |
41,195 | "Bayesglm", p-values and degrees of freedom? | I'm not sure how you got 5 degrees of freedom with seven independent variables for your glm-based model, but I'll assume that's just a typo somewhere or that I'm missing something minor.
Anyway, counting degrees of freedom with models that are constrained by a prior can be tricky, and there isn't necessarily a "correct... | "Bayesglm", p-values and degrees of freedom? | I'm not sure how you got 5 degrees of freedom with seven independent variables for your glm-based model, but I'll assume that's just a typo somewhere or that I'm missing something minor.
Anyway, count | "Bayesglm", p-values and degrees of freedom?
I'm not sure how you got 5 degrees of freedom with seven independent variables for your glm-based model, but I'll assume that's just a typo somewhere or that I'm missing something minor.
Anyway, counting degrees of freedom with models that are constrained by a prior can be t... | "Bayesglm", p-values and degrees of freedom?
I'm not sure how you got 5 degrees of freedom with seven independent variables for your glm-based model, but I'll assume that's just a typo somewhere or that I'm missing something minor.
Anyway, count |
41,196 | Measuring the smoothness of time series | I think that your problem is simply overfitting - you want to have a linearity measure that isn't inflated at very small N.
Here are two candidates:
Adjusted r-square.
In your case (a single regressor), that would be $R^2-\frac{1-R^2}{N-2}$
Cross-validated $R^2$: leave out one measurement at a time. Estimate the linea... | Measuring the smoothness of time series | I think that your problem is simply overfitting - you want to have a linearity measure that isn't inflated at very small N.
Here are two candidates:
Adjusted r-square.
In your case (a single regresso | Measuring the smoothness of time series
I think that your problem is simply overfitting - you want to have a linearity measure that isn't inflated at very small N.
Here are two candidates:
Adjusted r-square.
In your case (a single regressor), that would be $R^2-\frac{1-R^2}{N-2}$
Cross-validated $R^2$: leave out one m... | Measuring the smoothness of time series
I think that your problem is simply overfitting - you want to have a linearity measure that isn't inflated at very small N.
Here are two candidates:
Adjusted r-square.
In your case (a single regresso |
41,197 | Measuring the smoothness of time series | Although it would be better if you had described your problem mathematically, from what I read I understand that
a) You have an estimator function that estimates the evolution of brain volume over time, say, $\hat V_t= f($data$)$ and
b) You want to measure the distance of this estimator function from the simple linea... | Measuring the smoothness of time series | Although it would be better if you had described your problem mathematically, from what I read I understand that
a) You have an estimator function that estimates the evolution of brain volume over t | Measuring the smoothness of time series
Although it would be better if you had described your problem mathematically, from what I read I understand that
a) You have an estimator function that estimates the evolution of brain volume over time, say, $\hat V_t= f($data$)$ and
b) You want to measure the distance of this ... | Measuring the smoothness of time series
Although it would be better if you had described your problem mathematically, from what I read I understand that
a) You have an estimator function that estimates the evolution of brain volume over t |
41,198 | Measuring the smoothness of time series | Since you are computing $r^2$ for each patient $i$ and you want the following properties:
So patients with more data points will likely have a worse metric, if nothing is done.
Series with more measurements should be higher weighted
My concern is that those with 3 and those with 6 should be treated in a way that d... | Measuring the smoothness of time series | Since you are computing $r^2$ for each patient $i$ and you want the following properties:
So patients with more data points will likely have a worse metric, if nothing is done.
Series with more mea | Measuring the smoothness of time series
Since you are computing $r^2$ for each patient $i$ and you want the following properties:
So patients with more data points will likely have a worse metric, if nothing is done.
Series with more measurements should be higher weighted
My concern is that those with 3 and those ... | Measuring the smoothness of time series
Since you are computing $r^2$ for each patient $i$ and you want the following properties:
So patients with more data points will likely have a worse metric, if nothing is done.
Series with more mea |
41,199 | Measuring the smoothness of time series | I suggest two different approaches:
First of all, I have read that you suffered from sampling effects. One recommendation is using Wavelets/Fourier coefficients to obtain linearity. In many cases, there are solutions to avoid sampling effect with this tool.
Secondly, you should try kurtosis and skewness to see the var... | Measuring the smoothness of time series | I suggest two different approaches:
First of all, I have read that you suffered from sampling effects. One recommendation is using Wavelets/Fourier coefficients to obtain linearity. In many cases, th | Measuring the smoothness of time series
I suggest two different approaches:
First of all, I have read that you suffered from sampling effects. One recommendation is using Wavelets/Fourier coefficients to obtain linearity. In many cases, there are solutions to avoid sampling effect with this tool.
Secondly, you should ... | Measuring the smoothness of time series
I suggest two different approaches:
First of all, I have read that you suffered from sampling effects. One recommendation is using Wavelets/Fourier coefficients to obtain linearity. In many cases, th |
41,200 | Measuring the smoothness of time series | How about doing your linear regression, then take the residual by subtracting your regression line from the data, and look at its standard deviation (SD) or median absolute deviation (MAD)? (The SD part may be basically equivalent to the R^2, I'm not sure of the math.)
The problem is the "one to five or six" part. That... | Measuring the smoothness of time series | How about doing your linear regression, then take the residual by subtracting your regression line from the data, and look at its standard deviation (SD) or median absolute deviation (MAD)? (The SD pa | Measuring the smoothness of time series
How about doing your linear regression, then take the residual by subtracting your regression line from the data, and look at its standard deviation (SD) or median absolute deviation (MAD)? (The SD part may be basically equivalent to the R^2, I'm not sure of the math.)
The proble... | Measuring the smoothness of time series
How about doing your linear regression, then take the residual by subtracting your regression line from the data, and look at its standard deviation (SD) or median absolute deviation (MAD)? (The SD pa |
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