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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41,201 | Measuring the smoothness of time series | If you assume that the relationship is linear, you can use this trend estimator: $T_{n-1}=\frac{V(t_n)-V(t_{n-1})}{t_{n}-t_{n-1}}$ ,take the mean of these for each patient (which is the measure of linearity), calculate the sum of the residuals^2 (which gives an idea about how much you can trust this linearity. You can ... | Measuring the smoothness of time series | If you assume that the relationship is linear, you can use this trend estimator: $T_{n-1}=\frac{V(t_n)-V(t_{n-1})}{t_{n}-t_{n-1}}$ ,take the mean of these for each patient (which is the measure of lin | Measuring the smoothness of time series
If you assume that the relationship is linear, you can use this trend estimator: $T_{n-1}=\frac{V(t_n)-V(t_{n-1})}{t_{n}-t_{n-1}}$ ,take the mean of these for each patient (which is the measure of linearity), calculate the sum of the residuals^2 (which gives an idea about how muc... | Measuring the smoothness of time series
If you assume that the relationship is linear, you can use this trend estimator: $T_{n-1}=\frac{V(t_n)-V(t_{n-1})}{t_{n}-t_{n-1}}$ ,take the mean of these for each patient (which is the measure of lin |
41,202 | Correct way to compare two (very) different regression models? | Since the two models are not nested (that is, the independent variables of one model are not a subset of independent variables of the other model), one cannot use a maximum likelihood test. However, you can consider the AIC (Akaike Information Criterion) or one of its variants. If you have likelihoods of your models, l... | Correct way to compare two (very) different regression models? | Since the two models are not nested (that is, the independent variables of one model are not a subset of independent variables of the other model), one cannot use a maximum likelihood test. However, y | Correct way to compare two (very) different regression models?
Since the two models are not nested (that is, the independent variables of one model are not a subset of independent variables of the other model), one cannot use a maximum likelihood test. However, you can consider the AIC (Akaike Information Criterion) or... | Correct way to compare two (very) different regression models?
Since the two models are not nested (that is, the independent variables of one model are not a subset of independent variables of the other model), one cannot use a maximum likelihood test. However, y |
41,203 | Correct way to compare two (very) different regression models? | You might be interesting in taking a look at the Vuong's test and the subsequent literature. | Correct way to compare two (very) different regression models? | You might be interesting in taking a look at the Vuong's test and the subsequent literature. | Correct way to compare two (very) different regression models?
You might be interesting in taking a look at the Vuong's test and the subsequent literature. | Correct way to compare two (very) different regression models?
You might be interesting in taking a look at the Vuong's test and the subsequent literature. |
41,204 | Volume of the 95% confidence ellipsoid | Suppose $U$, $V$, and $W$ are independent and have a standard normal distribution. Then by definition of the chi-squared distribution (as a sum of squares of iid normal variables), the sum of squares $U^2 + V^2 + W^2$ has a chi-squared distribution with three degrees of freedom. We find (via tables or calculation) th... | Volume of the 95% confidence ellipsoid | Suppose $U$, $V$, and $W$ are independent and have a standard normal distribution. Then by definition of the chi-squared distribution (as a sum of squares of iid normal variables), the sum of squares | Volume of the 95% confidence ellipsoid
Suppose $U$, $V$, and $W$ are independent and have a standard normal distribution. Then by definition of the chi-squared distribution (as a sum of squares of iid normal variables), the sum of squares $U^2 + V^2 + W^2$ has a chi-squared distribution with three degrees of freedom. ... | Volume of the 95% confidence ellipsoid
Suppose $U$, $V$, and $W$ are independent and have a standard normal distribution. Then by definition of the chi-squared distribution (as a sum of squares of iid normal variables), the sum of squares |
41,205 | Interpretation of weights in non-linear least squares regression | The weights should equal the counts, because those will be inversely proportional to the variances of the errors. Specifically, the model for the data $(x_i, y_i, n_i)$ is
$$y_i \sim \lambda \Phi((\log(x_i) - \mu)/\sigma + \varepsilon_i$$
with $\mu, \sigma \gt 0,$ and $\lambda \gt 0$ the parameters and $\varepsilon_i$... | Interpretation of weights in non-linear least squares regression | The weights should equal the counts, because those will be inversely proportional to the variances of the errors. Specifically, the model for the data $(x_i, y_i, n_i)$ is
$$y_i \sim \lambda \Phi((\l | Interpretation of weights in non-linear least squares regression
The weights should equal the counts, because those will be inversely proportional to the variances of the errors. Specifically, the model for the data $(x_i, y_i, n_i)$ is
$$y_i \sim \lambda \Phi((\log(x_i) - \mu)/\sigma + \varepsilon_i$$
with $\mu, \sig... | Interpretation of weights in non-linear least squares regression
The weights should equal the counts, because those will be inversely proportional to the variances of the errors. Specifically, the model for the data $(x_i, y_i, n_i)$ is
$$y_i \sim \lambda \Phi((\l |
41,206 | Winning probability in a game with multiple players | If you assume that the weaker players won't gang up on the stronger player (a very strong assumption!), then a reasonable model would be the following. (I'm following the notation of the "theory" section of the Wikipedia article on ELO.)
let $R_A, R_B, R_C$ be the ratings of the three players.
let $Q_A = 10^{R_A/400}... | Winning probability in a game with multiple players | If you assume that the weaker players won't gang up on the stronger player (a very strong assumption!), then a reasonable model would be the following. (I'm following the notation of the "theory" sec | Winning probability in a game with multiple players
If you assume that the weaker players won't gang up on the stronger player (a very strong assumption!), then a reasonable model would be the following. (I'm following the notation of the "theory" section of the Wikipedia article on ELO.)
let $R_A, R_B, R_C$ be the r... | Winning probability in a game with multiple players
If you assume that the weaker players won't gang up on the stronger player (a very strong assumption!), then a reasonable model would be the following. (I'm following the notation of the "theory" sec |
41,207 | Winning probability in a game with multiple players | Would the weaker players not band together against the stronger players? That is what happens in the truel (three player pistol fight with one shot per person).
I observed that in monopoly and in three-player chess also. That said, if one assumes the winning or losing of A over B and C are independent, then with the ... | Winning probability in a game with multiple players | Would the weaker players not band together against the stronger players? That is what happens in the truel (three player pistol fight with one shot per person).
I observed that in monopoly and in thr | Winning probability in a game with multiple players
Would the weaker players not band together against the stronger players? That is what happens in the truel (three player pistol fight with one shot per person).
I observed that in monopoly and in three-player chess also. That said, if one assumes the winning or losi... | Winning probability in a game with multiple players
Would the weaker players not band together against the stronger players? That is what happens in the truel (three player pistol fight with one shot per person).
I observed that in monopoly and in thr |
41,208 | Inverting a hypothesis test: nitpicky detail | First of all, welcome to the site!
As Glen_b pointed out in a comment, for discrete distributions $P_\theta(\theta\in C(\mathbf{X}))$ won't be exactly equal to $1-\alpha$ for all $\theta$. You are however correct in that the classic defintion of a confidence interval is that $\inf_{\theta\in\Theta}P_\theta(\theta\in C(... | Inverting a hypothesis test: nitpicky detail | First of all, welcome to the site!
As Glen_b pointed out in a comment, for discrete distributions $P_\theta(\theta\in C(\mathbf{X}))$ won't be exactly equal to $1-\alpha$ for all $\theta$. You are how | Inverting a hypothesis test: nitpicky detail
First of all, welcome to the site!
As Glen_b pointed out in a comment, for discrete distributions $P_\theta(\theta\in C(\mathbf{X}))$ won't be exactly equal to $1-\alpha$ for all $\theta$. You are however correct in that the classic defintion of a confidence interval is that... | Inverting a hypothesis test: nitpicky detail
First of all, welcome to the site!
As Glen_b pointed out in a comment, for discrete distributions $P_\theta(\theta\in C(\mathbf{X}))$ won't be exactly equal to $1-\alpha$ for all $\theta$. You are how |
41,209 | MDS and PCA eigenvalues and eigenvectors | I find it helpful to consider the singular value decomposition for questions like this with the assumption that $X$ is a real matrix. Writing $X = UDV^T$, we can see that $XX^T = UD^2U^T$ and $X^TX = VD^2V^T$. As we can see, the eigenvalues of both $XX^T$ and $X^TX$ are contained in the diagonal matrix $D^2$ and are in... | MDS and PCA eigenvalues and eigenvectors | I find it helpful to consider the singular value decomposition for questions like this with the assumption that $X$ is a real matrix. Writing $X = UDV^T$, we can see that $XX^T = UD^2U^T$ and $X^TX = | MDS and PCA eigenvalues and eigenvectors
I find it helpful to consider the singular value decomposition for questions like this with the assumption that $X$ is a real matrix. Writing $X = UDV^T$, we can see that $XX^T = UD^2U^T$ and $X^TX = VD^2V^T$. As we can see, the eigenvalues of both $XX^T$ and $X^TX$ are containe... | MDS and PCA eigenvalues and eigenvectors
I find it helpful to consider the singular value decomposition for questions like this with the assumption that $X$ is a real matrix. Writing $X = UDV^T$, we can see that $XX^T = UD^2U^T$ and $X^TX = |
41,210 | Continuous Kolmogorov-Smirnov test? | Indeed. Check out Anderson-Darling, Cramér-von Mises tests, etc. http://en.wikipedia.org/wiki/Anderson%E2%80%93Darling_test is a start.
All these tests seem problematic to me for other reasons, not least because they don't tell you directly what is wrong. Increasingly with bigger datasets they reject hypotheses that a... | Continuous Kolmogorov-Smirnov test? | Indeed. Check out Anderson-Darling, Cramér-von Mises tests, etc. http://en.wikipedia.org/wiki/Anderson%E2%80%93Darling_test is a start.
All these tests seem problematic to me for other reasons, not l | Continuous Kolmogorov-Smirnov test?
Indeed. Check out Anderson-Darling, Cramér-von Mises tests, etc. http://en.wikipedia.org/wiki/Anderson%E2%80%93Darling_test is a start.
All these tests seem problematic to me for other reasons, not least because they don't tell you directly what is wrong. Increasingly with bigger da... | Continuous Kolmogorov-Smirnov test?
Indeed. Check out Anderson-Darling, Cramér-von Mises tests, etc. http://en.wikipedia.org/wiki/Anderson%E2%80%93Darling_test is a start.
All these tests seem problematic to me for other reasons, not l |
41,211 | Why do categorical predictor variables in regression need to be recoded as multiple predictors? | To elaborate on the answers of our colleagues above: say you map purple, blue, red to $x = 1, 2, 3$. Say $x$ represents the colour of a hat, and $y$ sales. Then if we have a model with an intercept, call it $a$ and the coefficient of $x$, call it $b$, then we'd be saying:
$y = a + b x$
We only get to choose one $b$ her... | Why do categorical predictor variables in regression need to be recoded as multiple predictors? | To elaborate on the answers of our colleagues above: say you map purple, blue, red to $x = 1, 2, 3$. Say $x$ represents the colour of a hat, and $y$ sales. Then if we have a model with an intercept, c | Why do categorical predictor variables in regression need to be recoded as multiple predictors?
To elaborate on the answers of our colleagues above: say you map purple, blue, red to $x = 1, 2, 3$. Say $x$ represents the colour of a hat, and $y$ sales. Then if we have a model with an intercept, call it $a$ and the coeff... | Why do categorical predictor variables in regression need to be recoded as multiple predictors?
To elaborate on the answers of our colleagues above: say you map purple, blue, red to $x = 1, 2, 3$. Say $x$ represents the colour of a hat, and $y$ sales. Then if we have a model with an intercept, c |
41,212 | Why do categorical predictor variables in regression need to be recoded as multiple predictors? | It depends what data are represented by colours.
You can use colour as a single variable with colours expressed by numbers in regression if colours represent a scale for some data. For example, purple means wind speed 10 m/s, blue means 20 m/s, and red colour means wind speed 30 m/s. So we can say something (wind in ex... | Why do categorical predictor variables in regression need to be recoded as multiple predictors? | It depends what data are represented by colours.
You can use colour as a single variable with colours expressed by numbers in regression if colours represent a scale for some data. For example, purple | Why do categorical predictor variables in regression need to be recoded as multiple predictors?
It depends what data are represented by colours.
You can use colour as a single variable with colours expressed by numbers in regression if colours represent a scale for some data. For example, purple means wind speed 10 m/s... | Why do categorical predictor variables in regression need to be recoded as multiple predictors?
It depends what data are represented by colours.
You can use colour as a single variable with colours expressed by numbers in regression if colours represent a scale for some data. For example, purple |
41,213 | Cluster analysis of ordinal variables (Likert scale) | You are trying to determine an appropriate distance measure, and clearly you are noticing how tricky this can be.
Ordinal data is not interval data. You should consider:
whether the distance between each category is the same (is the distance between 2 and 3 the same as the distance between 3 and 4?)
whether special ... | Cluster analysis of ordinal variables (Likert scale) | You are trying to determine an appropriate distance measure, and clearly you are noticing how tricky this can be.
Ordinal data is not interval data. You should consider:
whether the distance between | Cluster analysis of ordinal variables (Likert scale)
You are trying to determine an appropriate distance measure, and clearly you are noticing how tricky this can be.
Ordinal data is not interval data. You should consider:
whether the distance between each category is the same (is the distance between 2 and 3 the sam... | Cluster analysis of ordinal variables (Likert scale)
You are trying to determine an appropriate distance measure, and clearly you are noticing how tricky this can be.
Ordinal data is not interval data. You should consider:
whether the distance between |
41,214 | Cluster analysis of ordinal variables (Likert scale) | I am not sure about the amount of data you need to fit the a model, but you are asking for a common usage of Factor Analysis. Check this page of Quick-R that will help you to deal with some initials examples on how to map your questions to a latent space. You can use it to see the relations among similar questions that... | Cluster analysis of ordinal variables (Likert scale) | I am not sure about the amount of data you need to fit the a model, but you are asking for a common usage of Factor Analysis. Check this page of Quick-R that will help you to deal with some initials e | Cluster analysis of ordinal variables (Likert scale)
I am not sure about the amount of data you need to fit the a model, but you are asking for a common usage of Factor Analysis. Check this page of Quick-R that will help you to deal with some initials examples on how to map your questions to a latent space. You can use... | Cluster analysis of ordinal variables (Likert scale)
I am not sure about the amount of data you need to fit the a model, but you are asking for a common usage of Factor Analysis. Check this page of Quick-R that will help you to deal with some initials e |
41,215 | Cluster analysis of ordinal variables (Likert scale) | You need multiple correspondence analysis (MCA). Check out this article: https://datascienceplus.com/using-mca-and-variable-clustering-in-r-for-insights-in-customer-attrition/ | Cluster analysis of ordinal variables (Likert scale) | You need multiple correspondence analysis (MCA). Check out this article: https://datascienceplus.com/using-mca-and-variable-clustering-in-r-for-insights-in-customer-attrition/ | Cluster analysis of ordinal variables (Likert scale)
You need multiple correspondence analysis (MCA). Check out this article: https://datascienceplus.com/using-mca-and-variable-clustering-in-r-for-insights-in-customer-attrition/ | Cluster analysis of ordinal variables (Likert scale)
You need multiple correspondence analysis (MCA). Check out this article: https://datascienceplus.com/using-mca-and-variable-clustering-in-r-for-insights-in-customer-attrition/ |
41,216 | Intuition behind Dvoretzky Kiefer Wolfowitz inequality | Consider an i.i.d. sample $X_{1}, \dots, X_{n}$ from a distribution with CDF $F\left(x\right)$, and let
\begin{equation}
\widehat{F}_{n}\left(x\right) = \dfrac{1}{n}\sum_{i = 1}^{n}\mathbb{I}_{X_{i} \leq x}
\end{equation}
denote the usual empirical distribution function. The Dvoretzky-Kiefer-Wolfowitz inequality states... | Intuition behind Dvoretzky Kiefer Wolfowitz inequality | Consider an i.i.d. sample $X_{1}, \dots, X_{n}$ from a distribution with CDF $F\left(x\right)$, and let
\begin{equation}
\widehat{F}_{n}\left(x\right) = \dfrac{1}{n}\sum_{i = 1}^{n}\mathbb{I}_{X_{i} \ | Intuition behind Dvoretzky Kiefer Wolfowitz inequality
Consider an i.i.d. sample $X_{1}, \dots, X_{n}$ from a distribution with CDF $F\left(x\right)$, and let
\begin{equation}
\widehat{F}_{n}\left(x\right) = \dfrac{1}{n}\sum_{i = 1}^{n}\mathbb{I}_{X_{i} \leq x}
\end{equation}
denote the usual empirical distribution fun... | Intuition behind Dvoretzky Kiefer Wolfowitz inequality
Consider an i.i.d. sample $X_{1}, \dots, X_{n}$ from a distribution with CDF $F\left(x\right)$, and let
\begin{equation}
\widehat{F}_{n}\left(x\right) = \dfrac{1}{n}\sum_{i = 1}^{n}\mathbb{I}_{X_{i} \ |
41,217 | Intuition behind Dvoretzky Kiefer Wolfowitz inequality | This answer might help you to understand its origin. You will need to fill some details up, though.
Another reference of interest with a formal proof of an improved version of the inequallity is
Massart, P. (1990). The Tight Constant in the Dvoretzky-Kiefer-Wolfowitz Inequality. Annals of Probability 18: 1269-1283.
T... | Intuition behind Dvoretzky Kiefer Wolfowitz inequality | This answer might help you to understand its origin. You will need to fill some details up, though.
Another reference of interest with a formal proof of an improved version of the inequallity is
Mass | Intuition behind Dvoretzky Kiefer Wolfowitz inequality
This answer might help you to understand its origin. You will need to fill some details up, though.
Another reference of interest with a formal proof of an improved version of the inequallity is
Massart, P. (1990). The Tight Constant in the Dvoretzky-Kiefer-Wolfow... | Intuition behind Dvoretzky Kiefer Wolfowitz inequality
This answer might help you to understand its origin. You will need to fill some details up, though.
Another reference of interest with a formal proof of an improved version of the inequallity is
Mass |
41,218 | Is there a multivariate version of logistic regression? | You can do this with a multilevel model or a regression which takes clustering into account, or a structural equation model. You convert your data from wide to long, so each person has two rows in the dataset, and you have a variable that identifies a person.
You could also do it with multinomial logistic regression -... | Is there a multivariate version of logistic regression? | You can do this with a multilevel model or a regression which takes clustering into account, or a structural equation model. You convert your data from wide to long, so each person has two rows in th | Is there a multivariate version of logistic regression?
You can do this with a multilevel model or a regression which takes clustering into account, or a structural equation model. You convert your data from wide to long, so each person has two rows in the dataset, and you have a variable that identifies a person.
You... | Is there a multivariate version of logistic regression?
You can do this with a multilevel model or a regression which takes clustering into account, or a structural equation model. You convert your data from wide to long, so each person has two rows in th |
41,219 | Is there a multivariate version of logistic regression? | I don't know much about SEMs, but I've heard they're pretty complicated. An easy to understand alternative would be a multinomial logistic regression. In R, you can formulate this with the vglm() function from the VGAM package. Here's a quick gist:
In binomial logistic regression, your model looks like this:
$$\log\... | Is there a multivariate version of logistic regression? | I don't know much about SEMs, but I've heard they're pretty complicated. An easy to understand alternative would be a multinomial logistic regression. In R, you can formulate this with the vglm() fu | Is there a multivariate version of logistic regression?
I don't know much about SEMs, but I've heard they're pretty complicated. An easy to understand alternative would be a multinomial logistic regression. In R, you can formulate this with the vglm() function from the VGAM package. Here's a quick gist:
In binomial ... | Is there a multivariate version of logistic regression?
I don't know much about SEMs, but I've heard they're pretty complicated. An easy to understand alternative would be a multinomial logistic regression. In R, you can formulate this with the vglm() fu |
41,220 | Fitting t distribtution to financial data | The estimators correspond to $(\mu,\sigma,\nu)$, the parameters of a Student-$t$ distribution with location parameter $\mu\in{\mathbb R}$, scale parameter $\sigma>0$ and $\nu>0$ degrees of freedom. This density is simply given by
$$f(x;\mu,\sigma,\nu)=\dfrac{1}{\sigma}\dfrac{\Gamma \left(\frac{\nu+1}{2} \right)} {\sqrt... | Fitting t distribtution to financial data | The estimators correspond to $(\mu,\sigma,\nu)$, the parameters of a Student-$t$ distribution with location parameter $\mu\in{\mathbb R}$, scale parameter $\sigma>0$ and $\nu>0$ degrees of freedom. Th | Fitting t distribtution to financial data
The estimators correspond to $(\mu,\sigma,\nu)$, the parameters of a Student-$t$ distribution with location parameter $\mu\in{\mathbb R}$, scale parameter $\sigma>0$ and $\nu>0$ degrees of freedom. This density is simply given by
$$f(x;\mu,\sigma,\nu)=\dfrac{1}{\sigma}\dfrac{\G... | Fitting t distribtution to financial data
The estimators correspond to $(\mu,\sigma,\nu)$, the parameters of a Student-$t$ distribution with location parameter $\mu\in{\mathbb R}$, scale parameter $\sigma>0$ and $\nu>0$ degrees of freedom. Th |
41,221 | Which are differences between the hypergeometric distribution and chi-square distribution | Take care to note you're discussing two different statistics here.
Let's set up the sampling situation in detail first so we can be clear:
We have red balls and not-red balls (for simplicity I will call them all 'black', but they could be a mix of non-red colors - it's irrelevant to this set up since they all are simpl... | Which are differences between the hypergeometric distribution and chi-square distribution | Take care to note you're discussing two different statistics here.
Let's set up the sampling situation in detail first so we can be clear:
We have red balls and not-red balls (for simplicity I will ca | Which are differences between the hypergeometric distribution and chi-square distribution
Take care to note you're discussing two different statistics here.
Let's set up the sampling situation in detail first so we can be clear:
We have red balls and not-red balls (for simplicity I will call them all 'black', but they ... | Which are differences between the hypergeometric distribution and chi-square distribution
Take care to note you're discussing two different statistics here.
Let's set up the sampling situation in detail first so we can be clear:
We have red balls and not-red balls (for simplicity I will ca |
41,222 | Generalized Linear Model in SPSS with common values among predictors treated as subpopulations. Why? | Apparently you are using the NOMREG procedure. From the SPSS NOMREG help. Note that you can also use the newer GENLIN procedure to fit a logistic model. All three will give the same coefficients and standard errors but may differ in other outputs.
Binary logistic regression models can be fitted using either the Logi... | Generalized Linear Model in SPSS with common values among predictors treated as subpopulations. Why? | Apparently you are using the NOMREG procedure. From the SPSS NOMREG help. Note that you can also use the newer GENLIN procedure to fit a logistic model. All three will give the same coefficients an | Generalized Linear Model in SPSS with common values among predictors treated as subpopulations. Why?
Apparently you are using the NOMREG procedure. From the SPSS NOMREG help. Note that you can also use the newer GENLIN procedure to fit a logistic model. All three will give the same coefficients and standard errors b... | Generalized Linear Model in SPSS with common values among predictors treated as subpopulations. Why?
Apparently you are using the NOMREG procedure. From the SPSS NOMREG help. Note that you can also use the newer GENLIN procedure to fit a logistic model. All three will give the same coefficients an |
41,223 | Generalized Linear Model in SPSS with common values among predictors treated as subpopulations. Why? | I did some research after posting my question and basically figured it out. @JKP is right, basically. There is a detailed discussion of this in McCullagh and Nelder, Generalized Linear Models. The subpopulation model assumes that the number of categories remains constant as the population increases. Think of a continge... | Generalized Linear Model in SPSS with common values among predictors treated as subpopulations. Why? | I did some research after posting my question and basically figured it out. @JKP is right, basically. There is a detailed discussion of this in McCullagh and Nelder, Generalized Linear Models. The sub | Generalized Linear Model in SPSS with common values among predictors treated as subpopulations. Why?
I did some research after posting my question and basically figured it out. @JKP is right, basically. There is a detailed discussion of this in McCullagh and Nelder, Generalized Linear Models. The subpopulation model as... | Generalized Linear Model in SPSS with common values among predictors treated as subpopulations. Why?
I did some research after posting my question and basically figured it out. @JKP is right, basically. There is a detailed discussion of this in McCullagh and Nelder, Generalized Linear Models. The sub |
41,224 | Normal distributed random numbers in time - expected behaviour - askew distributions? | This is a random walk with drift.
Each step in the first scenario goes through two stages. Initially, there is an array of zeros. Because there appears to be no use of the geometry of the array, we may think of this simply as a (fixed-length) vector of values.
Stage 1: Add independent Gaussian values to each entry of... | Normal distributed random numbers in time - expected behaviour - askew distributions? | This is a random walk with drift.
Each step in the first scenario goes through two stages. Initially, there is an array of zeros. Because there appears to be no use of the geometry of the array, we | Normal distributed random numbers in time - expected behaviour - askew distributions?
This is a random walk with drift.
Each step in the first scenario goes through two stages. Initially, there is an array of zeros. Because there appears to be no use of the geometry of the array, we may think of this simply as a (fix... | Normal distributed random numbers in time - expected behaviour - askew distributions?
This is a random walk with drift.
Each step in the first scenario goes through two stages. Initially, there is an array of zeros. Because there appears to be no use of the geometry of the array, we |
41,225 | Tools to detect jumps in a linear time series | A "Jump" in a time series is a permanent change in the equation's implied intercept. Consider a series 1,1,1,1,4,4,4,4,4 where the basic model is y(t)=1 + 3*x(t) and x is the level shift/step shift series 0,0,0,0,1,1,1,1,1 . Thus suggests an intercept change at period 5 from a "1" to a "4" . If a second series is 1,2,3... | Tools to detect jumps in a linear time series | A "Jump" in a time series is a permanent change in the equation's implied intercept. Consider a series 1,1,1,1,4,4,4,4,4 where the basic model is y(t)=1 + 3*x(t) and x is the level shift/step shift se | Tools to detect jumps in a linear time series
A "Jump" in a time series is a permanent change in the equation's implied intercept. Consider a series 1,1,1,1,4,4,4,4,4 where the basic model is y(t)=1 + 3*x(t) and x is the level shift/step shift series 0,0,0,0,1,1,1,1,1 . Thus suggests an intercept change at period 5 fro... | Tools to detect jumps in a linear time series
A "Jump" in a time series is a permanent change in the equation's implied intercept. Consider a series 1,1,1,1,4,4,4,4,4 where the basic model is y(t)=1 + 3*x(t) and x is the level shift/step shift se |
41,226 | Tools to detect jumps in a linear time series | You can use mcp for this kind of models. Time series often exhibit autocorrelation, so let's just to an AR(1) model with three linear segments. First, let's specify the model:
model = list(
y ~ 1 + x + ar(1), # Linear segment. Initiate AR(1)
~ 1 + x, # linear
~ 1 + x
)
Now fit it:
library(mcp)
fit = mcp(model,... | Tools to detect jumps in a linear time series | You can use mcp for this kind of models. Time series often exhibit autocorrelation, so let's just to an AR(1) model with three linear segments. First, let's specify the model:
model = list(
y ~ 1 + | Tools to detect jumps in a linear time series
You can use mcp for this kind of models. Time series often exhibit autocorrelation, so let's just to an AR(1) model with three linear segments. First, let's specify the model:
model = list(
y ~ 1 + x + ar(1), # Linear segment. Initiate AR(1)
~ 1 + x, # linear
~ 1 + ... | Tools to detect jumps in a linear time series
You can use mcp for this kind of models. Time series often exhibit autocorrelation, so let's just to an AR(1) model with three linear segments. First, let's specify the model:
model = list(
y ~ 1 + |
41,227 | Tools to detect jumps in a linear time series | Some things to toy with:
A 1 dimensional fused LASSO. This will fit piece wise constant lines
to the data, with jumps when the mean has changed. More info here: https://cran.r-project.org/web/packages/genlasso/vignettes/article.pdf
a BFAST model (breaks for additive season and trend) seems appropriate for this task. M... | Tools to detect jumps in a linear time series | Some things to toy with:
A 1 dimensional fused LASSO. This will fit piece wise constant lines
to the data, with jumps when the mean has changed. More info here: https://cran.r-project.org/web/package | Tools to detect jumps in a linear time series
Some things to toy with:
A 1 dimensional fused LASSO. This will fit piece wise constant lines
to the data, with jumps when the mean has changed. More info here: https://cran.r-project.org/web/packages/genlasso/vignettes/article.pdf
a BFAST model (breaks for additive season... | Tools to detect jumps in a linear time series
Some things to toy with:
A 1 dimensional fused LASSO. This will fit piece wise constant lines
to the data, with jumps when the mean has changed. More info here: https://cran.r-project.org/web/package |
41,228 | Find the limiting distribution of Sum over Sum of Squares | Notice that $\frac{X_1 + X_2 + \ldots+ X_n}{\sqrt n}$ is a a standard gaussian variable. Then apply Slutsky's theorem. | Find the limiting distribution of Sum over Sum of Squares | Notice that $\frac{X_1 + X_2 + \ldots+ X_n}{\sqrt n}$ is a a standard gaussian variable. Then apply Slutsky's theorem. | Find the limiting distribution of Sum over Sum of Squares
Notice that $\frac{X_1 + X_2 + \ldots+ X_n}{\sqrt n}$ is a a standard gaussian variable. Then apply Slutsky's theorem. | Find the limiting distribution of Sum over Sum of Squares
Notice that $\frac{X_1 + X_2 + \ldots+ X_n}{\sqrt n}$ is a a standard gaussian variable. Then apply Slutsky's theorem. |
41,229 | Find the limiting distribution of Sum over Sum of Squares | The numerator is normally distributed with mean $0$ and variance $n$, and the denominator is distributed $\chi^2$ with $n$ degrees of freedom. Start there. | Find the limiting distribution of Sum over Sum of Squares | The numerator is normally distributed with mean $0$ and variance $n$, and the denominator is distributed $\chi^2$ with $n$ degrees of freedom. Start there. | Find the limiting distribution of Sum over Sum of Squares
The numerator is normally distributed with mean $0$ and variance $n$, and the denominator is distributed $\chi^2$ with $n$ degrees of freedom. Start there. | Find the limiting distribution of Sum over Sum of Squares
The numerator is normally distributed with mean $0$ and variance $n$, and the denominator is distributed $\chi^2$ with $n$ degrees of freedom. Start there. |
41,230 | Find the limiting distribution of Sum over Sum of Squares | We can find the result with law of large numbers and central limit theorem, writing
$$W_n=\color{blue}{\frac n{\sum_{j=1}^nX_j^2}}\cdot\color{red}{\frac 1{\sqrt n}\sum_{j=1}^nX_j}.$$
The blue term converges to $E(X_1^2)^{-1}$ in probability by the law of large numbers. | Find the limiting distribution of Sum over Sum of Squares | We can find the result with law of large numbers and central limit theorem, writing
$$W_n=\color{blue}{\frac n{\sum_{j=1}^nX_j^2}}\cdot\color{red}{\frac 1{\sqrt n}\sum_{j=1}^nX_j}.$$
The blue term co | Find the limiting distribution of Sum over Sum of Squares
We can find the result with law of large numbers and central limit theorem, writing
$$W_n=\color{blue}{\frac n{\sum_{j=1}^nX_j^2}}\cdot\color{red}{\frac 1{\sqrt n}\sum_{j=1}^nX_j}.$$
The blue term converges to $E(X_1^2)^{-1}$ in probability by the law of large ... | Find the limiting distribution of Sum over Sum of Squares
We can find the result with law of large numbers and central limit theorem, writing
$$W_n=\color{blue}{\frac n{\sum_{j=1}^nX_j^2}}\cdot\color{red}{\frac 1{\sqrt n}\sum_{j=1}^nX_j}.$$
The blue term co |
41,231 | What is the difference between 'Laplace approximation' and 'Modified harmonic mean'? | I assume, you are refering to the estimation of marginal likelihood. Laplace approximation is used to approximate a marginal likelihood based on normal distribution. Resulting estimate is as follows:
$$f(y)\approx (2\pi)^{d}|\widetilde{\Sigma}|^{1/2}f(y|\widetilde{\theta})f(\widetilde{\theta})$$
Where $\widetilde{\thet... | What is the difference between 'Laplace approximation' and 'Modified harmonic mean'? | I assume, you are refering to the estimation of marginal likelihood. Laplace approximation is used to approximate a marginal likelihood based on normal distribution. Resulting estimate is as follows:
| What is the difference between 'Laplace approximation' and 'Modified harmonic mean'?
I assume, you are refering to the estimation of marginal likelihood. Laplace approximation is used to approximate a marginal likelihood based on normal distribution. Resulting estimate is as follows:
$$f(y)\approx (2\pi)^{d}|\widetilde... | What is the difference between 'Laplace approximation' and 'Modified harmonic mean'?
I assume, you are refering to the estimation of marginal likelihood. Laplace approximation is used to approximate a marginal likelihood based on normal distribution. Resulting estimate is as follows:
|
41,232 | Generalized Least Squares: Estimation of Variance-Covariance matrix | From what you've written, it appears that you know $\mathbf V$ already. Usually, if you write your covariance matrix as $\sigma^2 \mathbf V$, it means that you know the structure given by $\mathbf V$ (e.g., exchangeable for cluster correlations, or something like that), and you only need to estimate the scale $\sigma^2... | Generalized Least Squares: Estimation of Variance-Covariance matrix | From what you've written, it appears that you know $\mathbf V$ already. Usually, if you write your covariance matrix as $\sigma^2 \mathbf V$, it means that you know the structure given by $\mathbf V$ | Generalized Least Squares: Estimation of Variance-Covariance matrix
From what you've written, it appears that you know $\mathbf V$ already. Usually, if you write your covariance matrix as $\sigma^2 \mathbf V$, it means that you know the structure given by $\mathbf V$ (e.g., exchangeable for cluster correlations, or som... | Generalized Least Squares: Estimation of Variance-Covariance matrix
From what you've written, it appears that you know $\mathbf V$ already. Usually, if you write your covariance matrix as $\sigma^2 \mathbf V$, it means that you know the structure given by $\mathbf V$ |
41,233 | Nonparametric equivalent of ANCOVA for continuous dependent variables | Turning my comment to an answer, the sm package offers non-parametric ANCOVA as sm.ancova. Here is a toy example:
data(anorexia, package="MASS")
anorexia$Treat <- relevel(anorexia$Treat , ref="Cont")
# visual check for the parallel group assumption
xyplot(Postwt ~ Prewt, data=anorexia, groups=Treat, aspect="iso", type... | Nonparametric equivalent of ANCOVA for continuous dependent variables | Turning my comment to an answer, the sm package offers non-parametric ANCOVA as sm.ancova. Here is a toy example:
data(anorexia, package="MASS")
anorexia$Treat <- relevel(anorexia$Treat , ref="Cont") | Nonparametric equivalent of ANCOVA for continuous dependent variables
Turning my comment to an answer, the sm package offers non-parametric ANCOVA as sm.ancova. Here is a toy example:
data(anorexia, package="MASS")
anorexia$Treat <- relevel(anorexia$Treat , ref="Cont")
# visual check for the parallel group assumption
... | Nonparametric equivalent of ANCOVA for continuous dependent variables
Turning my comment to an answer, the sm package offers non-parametric ANCOVA as sm.ancova. Here is a toy example:
data(anorexia, package="MASS")
anorexia$Treat <- relevel(anorexia$Treat , ref="Cont") |
41,234 | Nonparametric equivalent of ANCOVA for continuous dependent variables | If relation between both regression models are linear, the function sen.adichie from NSM3 package (https://cran.r-project.org/web/packages/NSM3/) test the parallelism of several regression lines.
And a Kruskal-Wallis test of aligned observations (ao_ij = y_ij - b x_ij) can to test the elevations (intercepts) of paralle... | Nonparametric equivalent of ANCOVA for continuous dependent variables | If relation between both regression models are linear, the function sen.adichie from NSM3 package (https://cran.r-project.org/web/packages/NSM3/) test the parallelism of several regression lines.
And | Nonparametric equivalent of ANCOVA for continuous dependent variables
If relation between both regression models are linear, the function sen.adichie from NSM3 package (https://cran.r-project.org/web/packages/NSM3/) test the parallelism of several regression lines.
And a Kruskal-Wallis test of aligned observations (ao_... | Nonparametric equivalent of ANCOVA for continuous dependent variables
If relation between both regression models are linear, the function sen.adichie from NSM3 package (https://cran.r-project.org/web/packages/NSM3/) test the parallelism of several regression lines.
And |
41,235 | Generalizing Add-one/Laplacian Smoothing | The "uniform" in uniform prior doesn't just mean that hits and misses are equally likely. It means that you assume that you have a probability measure on the rates $[0,1]$ and this measure is the uniform measure. For example, it means the chance the true rate is between $0.9$ and $1.0$ is $0.1$. There are other measure... | Generalizing Add-one/Laplacian Smoothing | The "uniform" in uniform prior doesn't just mean that hits and misses are equally likely. It means that you assume that you have a probability measure on the rates $[0,1]$ and this measure is the unif | Generalizing Add-one/Laplacian Smoothing
The "uniform" in uniform prior doesn't just mean that hits and misses are equally likely. It means that you assume that you have a probability measure on the rates $[0,1]$ and this measure is the uniform measure. For example, it means the chance the true rate is between $0.9$ an... | Generalizing Add-one/Laplacian Smoothing
The "uniform" in uniform prior doesn't just mean that hits and misses are equally likely. It means that you assume that you have a probability measure on the rates $[0,1]$ and this measure is the unif |
41,236 | Does it make sense to apply a chi-squared test on a contingency table when the whole population has been surveyed? | If the whole population of interest has been surveyed, then there's really no statistical analysis to do - any differences you see are differences in the population by definition. | Does it make sense to apply a chi-squared test on a contingency table when the whole population has | If the whole population of interest has been surveyed, then there's really no statistical analysis to do - any differences you see are differences in the population by definition. | Does it make sense to apply a chi-squared test on a contingency table when the whole population has been surveyed?
If the whole population of interest has been surveyed, then there's really no statistical analysis to do - any differences you see are differences in the population by definition. | Does it make sense to apply a chi-squared test on a contingency table when the whole population has
If the whole population of interest has been surveyed, then there's really no statistical analysis to do - any differences you see are differences in the population by definition. |
41,237 | Calculating weight as 1/(stnd error) for weighted regression if stnd error = 0 | The problem you have is that you are using the estimated standard error in the denominator of the weight. The population standard deviation is not likely to be 0 in real situations. I do not think there is a standard way that applies this particular weighting scheme and some other weight when to estimated standard er... | Calculating weight as 1/(stnd error) for weighted regression if stnd error = 0 | The problem you have is that you are using the estimated standard error in the denominator of the weight. The population standard deviation is not likely to be 0 in real situations. I do not think t | Calculating weight as 1/(stnd error) for weighted regression if stnd error = 0
The problem you have is that you are using the estimated standard error in the denominator of the weight. The population standard deviation is not likely to be 0 in real situations. I do not think there is a standard way that applies this ... | Calculating weight as 1/(stnd error) for weighted regression if stnd error = 0
The problem you have is that you are using the estimated standard error in the denominator of the weight. The population standard deviation is not likely to be 0 in real situations. I do not think t |
41,238 | Calculating weight as 1/(stnd error) for weighted regression if stnd error = 0 | In other cases when I have tricky zeros that make my calculations hard, such as on the diagonal of a matrix I need to invert, I was taught something that was called regularisation: that is, adding a small value uniformly to the offending variable.
For instance, in my case where I had a covariance matrix that I need to ... | Calculating weight as 1/(stnd error) for weighted regression if stnd error = 0 | In other cases when I have tricky zeros that make my calculations hard, such as on the diagonal of a matrix I need to invert, I was taught something that was called regularisation: that is, adding a s | Calculating weight as 1/(stnd error) for weighted regression if stnd error = 0
In other cases when I have tricky zeros that make my calculations hard, such as on the diagonal of a matrix I need to invert, I was taught something that was called regularisation: that is, adding a small value uniformly to the offending var... | Calculating weight as 1/(stnd error) for weighted regression if stnd error = 0
In other cases when I have tricky zeros that make my calculations hard, such as on the diagonal of a matrix I need to invert, I was taught something that was called regularisation: that is, adding a s |
41,239 | Is principal component regression (PCR) using principal component scores for regression? | I think the wikipedia article is being a little sloppy in saying "uses principal component analysis when estimating regression coefficients". Better might be something like "uses principal component analysis to create explanatory variables before estimating regression coefficients." There's nothing objectionable in t... | Is principal component regression (PCR) using principal component scores for regression? | I think the wikipedia article is being a little sloppy in saying "uses principal component analysis when estimating regression coefficients". Better might be something like "uses principal component | Is principal component regression (PCR) using principal component scores for regression?
I think the wikipedia article is being a little sloppy in saying "uses principal component analysis when estimating regression coefficients". Better might be something like "uses principal component analysis to create explanatory ... | Is principal component regression (PCR) using principal component scores for regression?
I think the wikipedia article is being a little sloppy in saying "uses principal component analysis when estimating regression coefficients". Better might be something like "uses principal component |
41,240 | Is principal component regression (PCR) using principal component scores for regression? | The other answers use a different terminology than what the author may be familiar with. Below, I refer to the scores matrix and use principal components to refer to the unit variance eigenvectors.
If you consider the answer as applied to the general case of in-sample and out-of-sample regression, then knowing the prin... | Is principal component regression (PCR) using principal component scores for regression? | The other answers use a different terminology than what the author may be familiar with. Below, I refer to the scores matrix and use principal components to refer to the unit variance eigenvectors.
If | Is principal component regression (PCR) using principal component scores for regression?
The other answers use a different terminology than what the author may be familiar with. Below, I refer to the scores matrix and use principal components to refer to the unit variance eigenvectors.
If you consider the answer as app... | Is principal component regression (PCR) using principal component scores for regression?
The other answers use a different terminology than what the author may be familiar with. Below, I refer to the scores matrix and use principal components to refer to the unit variance eigenvectors.
If |
41,241 | Estimating a sparse inverse covariance matrix with known sparsity | Estimation of the covariance matrix with given restrictions on the inverse covariance matrix is of course a well studied problem. Restricting some entries to be 0 is an example of a linear restriction in the cone of positive semidefinite matrices.
If the distribution is multivariate normal, the inverse covariance matri... | Estimating a sparse inverse covariance matrix with known sparsity | Estimation of the covariance matrix with given restrictions on the inverse covariance matrix is of course a well studied problem. Restricting some entries to be 0 is an example of a linear restriction | Estimating a sparse inverse covariance matrix with known sparsity
Estimation of the covariance matrix with given restrictions on the inverse covariance matrix is of course a well studied problem. Restricting some entries to be 0 is an example of a linear restriction in the cone of positive semidefinite matrices.
If the... | Estimating a sparse inverse covariance matrix with known sparsity
Estimation of the covariance matrix with given restrictions on the inverse covariance matrix is of course a well studied problem. Restricting some entries to be 0 is an example of a linear restriction |
41,242 | Updating ARIMA models at frequent intervals | Suppose x contains the first series of observations and y contains the second series of observations, where there is a gap between them consisting of m time periods. Let z be the complete time series, given by
z <- ts(c(x,rep(NA,m),y), frequency=??, start=??)
I assume you will set the start and frequency to suitable v... | Updating ARIMA models at frequent intervals | Suppose x contains the first series of observations and y contains the second series of observations, where there is a gap between them consisting of m time periods. Let z be the complete time series, | Updating ARIMA models at frequent intervals
Suppose x contains the first series of observations and y contains the second series of observations, where there is a gap between them consisting of m time periods. Let z be the complete time series, given by
z <- ts(c(x,rep(NA,m),y), frequency=??, start=??)
I assume you wi... | Updating ARIMA models at frequent intervals
Suppose x contains the first series of observations and y contains the second series of observations, where there is a gap between them consisting of m time periods. Let z be the complete time series, |
41,243 | How to perform pooled cross-sectional time series analysis? | For each of the 86 companies , identify an appropriate ARMAX model which should incorporate the effects ( both contemporaneous and lag ) of the two user-suggested predictor variables and any necessary ARIMA structure. Incorporate any needed ( and empirically identifiable ) structure reflecting unspecified deterministic... | How to perform pooled cross-sectional time series analysis? | For each of the 86 companies , identify an appropriate ARMAX model which should incorporate the effects ( both contemporaneous and lag ) of the two user-suggested predictor variables and any necessary | How to perform pooled cross-sectional time series analysis?
For each of the 86 companies , identify an appropriate ARMAX model which should incorporate the effects ( both contemporaneous and lag ) of the two user-suggested predictor variables and any necessary ARIMA structure. Incorporate any needed ( and empirically... | How to perform pooled cross-sectional time series analysis?
For each of the 86 companies , identify an appropriate ARMAX model which should incorporate the effects ( both contemporaneous and lag ) of the two user-suggested predictor variables and any necessary |
41,244 | How to perform pooled cross-sectional time series analysis? | There are a few things that I would do differently.
First, because each stock has a different overall level, you should include a set of ticker fixed effects, which is a set of dummy variables for whether a particular observation belongs to a particular ticker.
Second, stock prices are (almost?) always assumed to have ... | How to perform pooled cross-sectional time series analysis? | There are a few things that I would do differently.
First, because each stock has a different overall level, you should include a set of ticker fixed effects, which is a set of dummy variables for whe | How to perform pooled cross-sectional time series analysis?
There are a few things that I would do differently.
First, because each stock has a different overall level, you should include a set of ticker fixed effects, which is a set of dummy variables for whether a particular observation belongs to a particular tick... | How to perform pooled cross-sectional time series analysis?
There are a few things that I would do differently.
First, because each stock has a different overall level, you should include a set of ticker fixed effects, which is a set of dummy variables for whe |
41,245 | How to perform pooled cross-sectional time series analysis? | In some ways the models seem to agree quite a bit. The standard error of the estimates and the Durbin-Watson statistic are very similar between the two models. Also the constant term and the regressors hbVolLog and wikiLog are significant in both models. The main difference seems to be that the first model includes t... | How to perform pooled cross-sectional time series analysis? | In some ways the models seem to agree quite a bit. The standard error of the estimates and the Durbin-Watson statistic are very similar between the two models. Also the constant term and the regresso | How to perform pooled cross-sectional time series analysis?
In some ways the models seem to agree quite a bit. The standard error of the estimates and the Durbin-Watson statistic are very similar between the two models. Also the constant term and the regressors hbVolLog and wikiLog are significant in both models. T... | How to perform pooled cross-sectional time series analysis?
In some ways the models seem to agree quite a bit. The standard error of the estimates and the Durbin-Watson statistic are very similar between the two models. Also the constant term and the regresso |
41,246 | How to perform pooled cross-sectional time series analysis? | There are several possibilities in this problem, and really the best discussion I can cite is The Theory and Practice of Econometrics, 2nd edition, George Judege, W.E. Griffiths, R. Carter Hill, Helmut Lutkepohl, and Tsoung-Chao Lee, Wiley Series in Probability and Mathematical Statistics 1985, Chapter 13, Inference in... | How to perform pooled cross-sectional time series analysis? | There are several possibilities in this problem, and really the best discussion I can cite is The Theory and Practice of Econometrics, 2nd edition, George Judege, W.E. Griffiths, R. Carter Hill, Helmu | How to perform pooled cross-sectional time series analysis?
There are several possibilities in this problem, and really the best discussion I can cite is The Theory and Practice of Econometrics, 2nd edition, George Judege, W.E. Griffiths, R. Carter Hill, Helmut Lutkepohl, and Tsoung-Chao Lee, Wiley Series in Probabil... | How to perform pooled cross-sectional time series analysis?
There are several possibilities in this problem, and really the best discussion I can cite is The Theory and Practice of Econometrics, 2nd edition, George Judege, W.E. Griffiths, R. Carter Hill, Helmu |
41,247 | Is the sample variance a useful measure for non-normal data? | Does it ever make sense? I don't know. It depends on what you are trying to do. But you say you aren't trying to do anything, so there's no good answer.
I can't think of a case where the variance of a bimodal distribution makes much sense. But you say (e.g.) bimodal. If the data are non-normal, but not all that non-nor... | Is the sample variance a useful measure for non-normal data? | Does it ever make sense? I don't know. It depends on what you are trying to do. But you say you aren't trying to do anything, so there's no good answer.
I can't think of a case where the variance of a | Is the sample variance a useful measure for non-normal data?
Does it ever make sense? I don't know. It depends on what you are trying to do. But you say you aren't trying to do anything, so there's no good answer.
I can't think of a case where the variance of a bimodal distribution makes much sense. But you say (e.g.) ... | Is the sample variance a useful measure for non-normal data?
Does it ever make sense? I don't know. It depends on what you are trying to do. But you say you aren't trying to do anything, so there's no good answer.
I can't think of a case where the variance of a |
41,248 | Is the sample variance a useful measure for non-normal data? | We frequently use scaling relationships between central moments to ascertain the most likely underlying generative process of biological data. I studied the dynamics of the HIV promoter and it produces very non-Gaussian distributions that are wide and highly skewed. The Var~Mean relationship was the first step in deter... | Is the sample variance a useful measure for non-normal data? | We frequently use scaling relationships between central moments to ascertain the most likely underlying generative process of biological data. I studied the dynamics of the HIV promoter and it produce | Is the sample variance a useful measure for non-normal data?
We frequently use scaling relationships between central moments to ascertain the most likely underlying generative process of biological data. I studied the dynamics of the HIV promoter and it produces very non-Gaussian distributions that are wide and highly ... | Is the sample variance a useful measure for non-normal data?
We frequently use scaling relationships between central moments to ascertain the most likely underlying generative process of biological data. I studied the dynamics of the HIV promoter and it produce |
41,249 | Normality of residuals in a regression model with a categorical IV | (Note that a regression model with only 1 explanatory variable that is categorical and has just 2 levels is equivalent to a t-test; there's nothing wrong with calling it a regression, but it would most commonly be discussed / referred to as a t-test.)
You check the distribution of all the residuals simultaneously. T... | Normality of residuals in a regression model with a categorical IV | (Note that a regression model with only 1 explanatory variable that is categorical and has just 2 levels is equivalent to a t-test; there's nothing wrong with calling it a regression, but it would mos | Normality of residuals in a regression model with a categorical IV
(Note that a regression model with only 1 explanatory variable that is categorical and has just 2 levels is equivalent to a t-test; there's nothing wrong with calling it a regression, but it would most commonly be discussed / referred to as a t-test.) ... | Normality of residuals in a regression model with a categorical IV
(Note that a regression model with only 1 explanatory variable that is categorical and has just 2 levels is equivalent to a t-test; there's nothing wrong with calling it a regression, but it would mos |
41,250 | Dealing with a categorical variable that can take multiple levels simultaneously | Two models come to mind: the revenue may have a contribution from the presence of each speaker or it may have a contribution from the speaker's presence, weighted by their participation in the event. In either case the coding would be similar: to each speaker corresponds a variable that is zero when the speaker is not... | Dealing with a categorical variable that can take multiple levels simultaneously | Two models come to mind: the revenue may have a contribution from the presence of each speaker or it may have a contribution from the speaker's presence, weighted by their participation in the event. | Dealing with a categorical variable that can take multiple levels simultaneously
Two models come to mind: the revenue may have a contribution from the presence of each speaker or it may have a contribution from the speaker's presence, weighted by their participation in the event. In either case the coding would be sim... | Dealing with a categorical variable that can take multiple levels simultaneously
Two models come to mind: the revenue may have a contribution from the presence of each speaker or it may have a contribution from the speaker's presence, weighted by their participation in the event. |
41,251 | Which weighting factor to use for text categorization | Have you thought about storing the term frequency and the document frequency separately for the trained set, then when you add a new document you can update the document frequency of the new trained set, i.e. including the new document, and calculate tf-idf. Or am I missing something? | Which weighting factor to use for text categorization | Have you thought about storing the term frequency and the document frequency separately for the trained set, then when you add a new document you can update the document frequency of the new trained | Which weighting factor to use for text categorization
Have you thought about storing the term frequency and the document frequency separately for the trained set, then when you add a new document you can update the document frequency of the new trained set, i.e. including the new document, and calculate tf-idf. Or am ... | Which weighting factor to use for text categorization
Have you thought about storing the term frequency and the document frequency separately for the trained set, then when you add a new document you can update the document frequency of the new trained |
41,252 | Which weighting factor to use for text categorization | The same as simmmons clarified, we can store a term frequency vector for all terms in the training vocabulary. Then we can easily survive in this incremental context, by assuming the new document is also in the training corpus, and then update this vector with newly added files, and finally re-calculate the idf (tf-idf... | Which weighting factor to use for text categorization | The same as simmmons clarified, we can store a term frequency vector for all terms in the training vocabulary. Then we can easily survive in this incremental context, by assuming the new document is a | Which weighting factor to use for text categorization
The same as simmmons clarified, we can store a term frequency vector for all terms in the training vocabulary. Then we can easily survive in this incremental context, by assuming the new document is also in the training corpus, and then update this vector with newly... | Which weighting factor to use for text categorization
The same as simmmons clarified, we can store a term frequency vector for all terms in the training vocabulary. Then we can easily survive in this incremental context, by assuming the new document is a |
41,253 | How to be sure about the distribution of a discrete and limited stochastic variable? | If you have a Bernoulli variable (0 or 1) with probability $p$, its variance is $p(1-p)$ which is always less than or equal to $1/4$. The mean of $n$ independent Bernoulli variables tends to a Gaussian of variance $p(1-p)/n$ which is always less than or equal to $1/4n$. You can use the fact that a Gaussian variable has... | How to be sure about the distribution of a discrete and limited stochastic variable? | If you have a Bernoulli variable (0 or 1) with probability $p$, its variance is $p(1-p)$ which is always less than or equal to $1/4$. The mean of $n$ independent Bernoulli variables tends to a Gaussia | How to be sure about the distribution of a discrete and limited stochastic variable?
If you have a Bernoulli variable (0 or 1) with probability $p$, its variance is $p(1-p)$ which is always less than or equal to $1/4$. The mean of $n$ independent Bernoulli variables tends to a Gaussian of variance $p(1-p)/n$ which is a... | How to be sure about the distribution of a discrete and limited stochastic variable?
If you have a Bernoulli variable (0 or 1) with probability $p$, its variance is $p(1-p)$ which is always less than or equal to $1/4$. The mean of $n$ independent Bernoulli variables tends to a Gaussia |
41,254 | How to be sure about the distribution of a discrete and limited stochastic variable? | The Bayesian way of doing this loses no information:
Your variable $X$ is categorically distributed with probability vector $\mathbf p$. The conjugate prior of the categorical distribution is the Dirichlet distribution, so let $\mathbf p$ be Dirichlet-distributed with shape parameter vector $\boldsymbol\phi$. With ev... | How to be sure about the distribution of a discrete and limited stochastic variable? | The Bayesian way of doing this loses no information:
Your variable $X$ is categorically distributed with probability vector $\mathbf p$. The conjugate prior of the categorical distribution is the Dir | How to be sure about the distribution of a discrete and limited stochastic variable?
The Bayesian way of doing this loses no information:
Your variable $X$ is categorically distributed with probability vector $\mathbf p$. The conjugate prior of the categorical distribution is the Dirichlet distribution, so let $\mathb... | How to be sure about the distribution of a discrete and limited stochastic variable?
The Bayesian way of doing this loses no information:
Your variable $X$ is categorically distributed with probability vector $\mathbf p$. The conjugate prior of the categorical distribution is the Dir |
41,255 | Bayesian mixed model regression with a between subjects factor | I eventually figured this one out with much help from Doing Bayesian Data Analysis (Kruschke) and Data Analysis Using Regression and Multilevel/Hierarchical Models (Gelman). This model gives varying intercepts and slopes and the correlation between them.
y = dependent variable
sLvl = participant id at each... | Bayesian mixed model regression with a between subjects factor | I eventually figured this one out with much help from Doing Bayesian Data Analysis (Kruschke) and Data Analysis Using Regression and Multilevel/Hierarchical Models (Gelman). This model gives varyi | Bayesian mixed model regression with a between subjects factor
I eventually figured this one out with much help from Doing Bayesian Data Analysis (Kruschke) and Data Analysis Using Regression and Multilevel/Hierarchical Models (Gelman). This model gives varying intercepts and slopes and the correlation between them... | Bayesian mixed model regression with a between subjects factor
I eventually figured this one out with much help from Doing Bayesian Data Analysis (Kruschke) and Data Analysis Using Regression and Multilevel/Hierarchical Models (Gelman). This model gives varyi |
41,256 | Disease forecasting - counts or rates? | I've been pondering this question myself a lot recently, especially in the context of hospital epidemiology. My thoughts below:
The Case for Counts:
The real core of the strength for counts is that they're a more meaningful number on a practical scale. Tell a nurse they're going to see 0.20 cases/patient-day more infec... | Disease forecasting - counts or rates? | I've been pondering this question myself a lot recently, especially in the context of hospital epidemiology. My thoughts below:
The Case for Counts:
The real core of the strength for counts is that th | Disease forecasting - counts or rates?
I've been pondering this question myself a lot recently, especially in the context of hospital epidemiology. My thoughts below:
The Case for Counts:
The real core of the strength for counts is that they're a more meaningful number on a practical scale. Tell a nurse they're going t... | Disease forecasting - counts or rates?
I've been pondering this question myself a lot recently, especially in the context of hospital epidemiology. My thoughts below:
The Case for Counts:
The real core of the strength for counts is that th |
41,257 | Sorting answers, given overvotes and undervotes | I recently learned about Bayesian rating. Bayesian rating computes a weighted average of the naive uncorrected rating for this answer and the average rating for all answers. The fewer the votes for this answer, the more we weight things towards the average rating. The intuition is that if we have no votes on this an... | Sorting answers, given overvotes and undervotes | I recently learned about Bayesian rating. Bayesian rating computes a weighted average of the naive uncorrected rating for this answer and the average rating for all answers. The fewer the votes for | Sorting answers, given overvotes and undervotes
I recently learned about Bayesian rating. Bayesian rating computes a weighted average of the naive uncorrected rating for this answer and the average rating for all answers. The fewer the votes for this answer, the more we weight things towards the average rating. The ... | Sorting answers, given overvotes and undervotes
I recently learned about Bayesian rating. Bayesian rating computes a weighted average of the naive uncorrected rating for this answer and the average rating for all answers. The fewer the votes for |
41,258 | Sorting answers, given overvotes and undervotes | I found an essay (How Not To Sort By Average Rating) which argues that you should compute a 95% confidence interval for the true fraction of people who would vote positively, and then sort ratings by the lower end of the confidence interval.
This is an interesting approach. It is hard for me to judge whether this is i... | Sorting answers, given overvotes and undervotes | I found an essay (How Not To Sort By Average Rating) which argues that you should compute a 95% confidence interval for the true fraction of people who would vote positively, and then sort ratings by | Sorting answers, given overvotes and undervotes
I found an essay (How Not To Sort By Average Rating) which argues that you should compute a 95% confidence interval for the true fraction of people who would vote positively, and then sort ratings by the lower end of the confidence interval.
This is an interesting approac... | Sorting answers, given overvotes and undervotes
I found an essay (How Not To Sort By Average Rating) which argues that you should compute a 95% confidence interval for the true fraction of people who would vote positively, and then sort ratings by |
41,259 | Equivalent mixed models yielding different results in SAS | By default, SAS uses a very simple method to compute the degrees of freedom; it looks to see if the fixed effect appears in a term in the random effects; if so, it uses that random effect as the degrees of freedom.
In this case, the model with random effects of TUBE and TUBE*POSITION do the right thing as this method c... | Equivalent mixed models yielding different results in SAS | By default, SAS uses a very simple method to compute the degrees of freedom; it looks to see if the fixed effect appears in a term in the random effects; if so, it uses that random effect as the degre | Equivalent mixed models yielding different results in SAS
By default, SAS uses a very simple method to compute the degrees of freedom; it looks to see if the fixed effect appears in a term in the random effects; if so, it uses that random effect as the degrees of freedom.
In this case, the model with random effects of ... | Equivalent mixed models yielding different results in SAS
By default, SAS uses a very simple method to compute the degrees of freedom; it looks to see if the fixed effect appears in a term in the random effects; if so, it uses that random effect as the degre |
41,260 | Geometric distribution without replacement | I am going to interpret the question as follows: Find the expected number of white balls drawn before a black one is seen among $j$ draws without replacement, where if no black ball is seen, then the observation takes the value $j$. If this is not what you intend, please advise and I will revise accordingly.
We can use... | Geometric distribution without replacement | I am going to interpret the question as follows: Find the expected number of white balls drawn before a black one is seen among $j$ draws without replacement, where if no black ball is seen, then the | Geometric distribution without replacement
I am going to interpret the question as follows: Find the expected number of white balls drawn before a black one is seen among $j$ draws without replacement, where if no black ball is seen, then the observation takes the value $j$. If this is not what you intend, please advis... | Geometric distribution without replacement
I am going to interpret the question as follows: Find the expected number of white balls drawn before a black one is seen among $j$ draws without replacement, where if no black ball is seen, then the |
41,261 | Proportional odds assumption in ordinal logistic regression in R with the packages VGAM and rms | I recommend partial residual plots using the rms package's lrm and residuals.lrm functions. You can also fit a series of binary models using different cutoffs for $Y$ and plot the log odds ratios vs. cutoff. | Proportional odds assumption in ordinal logistic regression in R with the packages VGAM and rms | I recommend partial residual plots using the rms package's lrm and residuals.lrm functions. You can also fit a series of binary models using different cutoffs for $Y$ and plot the log odds ratios vs. | Proportional odds assumption in ordinal logistic regression in R with the packages VGAM and rms
I recommend partial residual plots using the rms package's lrm and residuals.lrm functions. You can also fit a series of binary models using different cutoffs for $Y$ and plot the log odds ratios vs. cutoff. | Proportional odds assumption in ordinal logistic regression in R with the packages VGAM and rms
I recommend partial residual plots using the rms package's lrm and residuals.lrm functions. You can also fit a series of binary models using different cutoffs for $Y$ and plot the log odds ratios vs. |
41,262 | Proportional odds assumption in ordinal logistic regression in R with the packages VGAM and rms | Are there robust standard errors available in the package? or will the sandwich package calculate them for you?
The dirty secret about model checking is there is almost never good statistical power to detect meaningful violations of your model. You can still check them, but more as post-hoc sensitivity analysis.
The... | Proportional odds assumption in ordinal logistic regression in R with the packages VGAM and rms | Are there robust standard errors available in the package? or will the sandwich package calculate them for you?
The dirty secret about model checking is there is almost never good statistical power to | Proportional odds assumption in ordinal logistic regression in R with the packages VGAM and rms
Are there robust standard errors available in the package? or will the sandwich package calculate them for you?
The dirty secret about model checking is there is almost never good statistical power to detect meaningful viola... | Proportional odds assumption in ordinal logistic regression in R with the packages VGAM and rms
Are there robust standard errors available in the package? or will the sandwich package calculate them for you?
The dirty secret about model checking is there is almost never good statistical power to |
41,263 | Generating from Dirichlet distribution with the differences in a sequence of ordered uniform | If $Y_i$ are independent $\mathrm{Gamma}(\alpha_i,\beta)$, for $i=1,\dots,k$, then
$$ (X_1,\dots,X_k) = \left(\frac{Y_1}{\sum_{j=1}^k Y_j}, \dots, \frac{Y_k}{\sum_{j=1}^k Y_j} \right) \sim \mathrm{Dirichlet}(\alpha_1,\dots,\alpha_k) \, .$$
So, in R just do something like
rdirichlet <- function(a) {
y <- rgamma(leng... | Generating from Dirichlet distribution with the differences in a sequence of ordered uniform | If $Y_i$ are independent $\mathrm{Gamma}(\alpha_i,\beta)$, for $i=1,\dots,k$, then
$$ (X_1,\dots,X_k) = \left(\frac{Y_1}{\sum_{j=1}^k Y_j}, \dots, \frac{Y_k}{\sum_{j=1}^k Y_j} \right) \sim \mathrm{Dir | Generating from Dirichlet distribution with the differences in a sequence of ordered uniform
If $Y_i$ are independent $\mathrm{Gamma}(\alpha_i,\beta)$, for $i=1,\dots,k$, then
$$ (X_1,\dots,X_k) = \left(\frac{Y_1}{\sum_{j=1}^k Y_j}, \dots, \frac{Y_k}{\sum_{j=1}^k Y_j} \right) \sim \mathrm{Dirichlet}(\alpha_1,\dots,\alp... | Generating from Dirichlet distribution with the differences in a sequence of ordered uniform
If $Y_i$ are independent $\mathrm{Gamma}(\alpha_i,\beta)$, for $i=1,\dots,k$, then
$$ (X_1,\dots,X_k) = \left(\frac{Y_1}{\sum_{j=1}^k Y_j}, \dots, \frac{Y_k}{\sum_{j=1}^k Y_j} \right) \sim \mathrm{Dir |
41,264 | Generating from Dirichlet distribution with the differences in a sequence of ordered uniform | Let $U_{(1)},U_{(2)}, \ldots, U_{(n)}$ be the order statistics from $U(0,1)$ distribution. Let $W_0 = U_{(1)},W_i = U_{(i+1)}-U{(i)}$, $1 \leq i \leq n-1$.
Then $(W_0, W_1, \ldots, W_{n-1}) \sim \cal{D}(\alpha)$, a dirichlet distribution with parameter $\alpha_{n+1,1} = (1,1,\ldots,1)$. That is, $(W_0, W_1, \ldots, W_{... | Generating from Dirichlet distribution with the differences in a sequence of ordered uniform | Let $U_{(1)},U_{(2)}, \ldots, U_{(n)}$ be the order statistics from $U(0,1)$ distribution. Let $W_0 = U_{(1)},W_i = U_{(i+1)}-U{(i)}$, $1 \leq i \leq n-1$.
Then $(W_0, W_1, \ldots, W_{n-1}) \sim \cal{ | Generating from Dirichlet distribution with the differences in a sequence of ordered uniform
Let $U_{(1)},U_{(2)}, \ldots, U_{(n)}$ be the order statistics from $U(0,1)$ distribution. Let $W_0 = U_{(1)},W_i = U_{(i+1)}-U{(i)}$, $1 \leq i \leq n-1$.
Then $(W_0, W_1, \ldots, W_{n-1}) \sim \cal{D}(\alpha)$, a dirichlet di... | Generating from Dirichlet distribution with the differences in a sequence of ordered uniform
Let $U_{(1)},U_{(2)}, \ldots, U_{(n)}$ be the order statistics from $U(0,1)$ distribution. Let $W_0 = U_{(1)},W_i = U_{(i+1)}-U{(i)}$, $1 \leq i \leq n-1$.
Then $(W_0, W_1, \ldots, W_{n-1}) \sim \cal{ |
41,265 | R knn variable selection | knnFit1$results is actually a data.frame, so you can print all of the R-squared results with:
knnFit1$results$Rsquared
Or the R-squared result for just the best model:
knnFit1.sorted <- results[order(results$Rsquared),]
knnFit1.sorted[1,'Rsquared']
Does this answer your question? | R knn variable selection | knnFit1$results is actually a data.frame, so you can print all of the R-squared results with:
knnFit1$results$Rsquared
Or the R-squared result for just the best model:
knnFit1.sorted <- results[order | R knn variable selection
knnFit1$results is actually a data.frame, so you can print all of the R-squared results with:
knnFit1$results$Rsquared
Or the R-squared result for just the best model:
knnFit1.sorted <- results[order(results$Rsquared),]
knnFit1.sorted[1,'Rsquared']
Does this answer your question? | R knn variable selection
knnFit1$results is actually a data.frame, so you can print all of the R-squared results with:
knnFit1$results$Rsquared
Or the R-squared result for just the best model:
knnFit1.sorted <- results[order |
41,266 | How to find quantiles for multivariate data using R? | I would try here (mvnormtest, mvoutlier, mvtnorm):
http://cran.r-project.org/web/views/Multivariate.html | How to find quantiles for multivariate data using R? | I would try here (mvnormtest, mvoutlier, mvtnorm):
http://cran.r-project.org/web/views/Multivariate.html | How to find quantiles for multivariate data using R?
I would try here (mvnormtest, mvoutlier, mvtnorm):
http://cran.r-project.org/web/views/Multivariate.html | How to find quantiles for multivariate data using R?
I would try here (mvnormtest, mvoutlier, mvtnorm):
http://cran.r-project.org/web/views/Multivariate.html |
41,267 | How to find quantiles for multivariate data using R? | Why don't you try my package cepp. It can compute PC's quantiles for you!
http://cran.r-project.org/web/packages/cepp/index.html
You need the function $evaluator$ in this package. It takes two arguments which correspond to the number of rows and columns in the data. It returns a function which can be optimized to get t... | How to find quantiles for multivariate data using R? | Why don't you try my package cepp. It can compute PC's quantiles for you!
http://cran.r-project.org/web/packages/cepp/index.html
You need the function $evaluator$ in this package. It takes two argumen | How to find quantiles for multivariate data using R?
Why don't you try my package cepp. It can compute PC's quantiles for you!
http://cran.r-project.org/web/packages/cepp/index.html
You need the function $evaluator$ in this package. It takes two arguments which correspond to the number of rows and columns in the data. ... | How to find quantiles for multivariate data using R?
Why don't you try my package cepp. It can compute PC's quantiles for you!
http://cran.r-project.org/web/packages/cepp/index.html
You need the function $evaluator$ in this package. It takes two argumen |
41,268 | How to find quantiles for multivariate data using R? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
You need to compute the CDF of the joint distribution ... | How to find quantiles for multivariate data using R? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| How to find quantiles for multivariate data using R?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
Y... | How to find quantiles for multivariate data using R?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
41,269 | Combining the results of two surveys | To be able to estimate to a population from a survey, the sampling method needs to be understood. The sampling method is used to create the sampling weights, which are then used to basically multiply up the survey estimates to be population estimates. There are all sorts of ways of creating weights, but they need to be... | Combining the results of two surveys | To be able to estimate to a population from a survey, the sampling method needs to be understood. The sampling method is used to create the sampling weights, which are then used to basically multiply | Combining the results of two surveys
To be able to estimate to a population from a survey, the sampling method needs to be understood. The sampling method is used to create the sampling weights, which are then used to basically multiply up the survey estimates to be population estimates. There are all sorts of ways of ... | Combining the results of two surveys
To be able to estimate to a population from a survey, the sampling method needs to be understood. The sampling method is used to create the sampling weights, which are then used to basically multiply |
41,270 | Combining the results of two surveys | It looks like your survey 2 is a convenience sample. I don't know what it can be useful for. Without a clear sampling strategy, you cannot generalize to the population in any meaningful way. At best, you might be able to utilize Survey 2 to build a model of how variables are interrelated, and then try to improve your e... | Combining the results of two surveys | It looks like your survey 2 is a convenience sample. I don't know what it can be useful for. Without a clear sampling strategy, you cannot generalize to the population in any meaningful way. At best, | Combining the results of two surveys
It looks like your survey 2 is a convenience sample. I don't know what it can be useful for. Without a clear sampling strategy, you cannot generalize to the population in any meaningful way. At best, you might be able to utilize Survey 2 to build a model of how variables are interre... | Combining the results of two surveys
It looks like your survey 2 is a convenience sample. I don't know what it can be useful for. Without a clear sampling strategy, you cannot generalize to the population in any meaningful way. At best, |
41,271 | Spatial econometrics -- computing residuals | The residuals that each of them calculating are different. Here is why:
The model is as follows:
$y = \rho Wy + xb + e$ with $e \sim n(0,1)$
Now if we play arround with it we get:
$y = (I - \rho W)^{-1}(xb + e)$
Now what Prof. LeSage does is:
$y - (I-\hat{\rho} \cdot W)^{-1} \cdot x\hat{b} = (I-\rho W)^{-1}\cdot e$
S... | Spatial econometrics -- computing residuals | The residuals that each of them calculating are different. Here is why:
The model is as follows:
$y = \rho Wy + xb + e$ with $e \sim n(0,1)$
Now if we play arround with it we get:
$y = (I - \rho W)^{ | Spatial econometrics -- computing residuals
The residuals that each of them calculating are different. Here is why:
The model is as follows:
$y = \rho Wy + xb + e$ with $e \sim n(0,1)$
Now if we play arround with it we get:
$y = (I - \rho W)^{-1}(xb + e)$
Now what Prof. LeSage does is:
$y - (I-\hat{\rho} \cdot W)^{-1... | Spatial econometrics -- computing residuals
The residuals that each of them calculating are different. Here is why:
The model is as follows:
$y = \rho Wy + xb + e$ with $e \sim n(0,1)$
Now if we play arround with it we get:
$y = (I - \rho W)^{ |
41,272 | Plot a generalised mixed effects model with binomial errors | The issue is that the mer class in R, and the lmer etc commands in lme4 all produce mer objects, and these are not compatible with some "normal" R commands.
You can get the fits out by using fitted but amending your code to
ndf$fit <- fitted(glmerb, ndf, type="response")
gave me the error
Error in `$<-.data.frame`(`*t... | Plot a generalised mixed effects model with binomial errors | The issue is that the mer class in R, and the lmer etc commands in lme4 all produce mer objects, and these are not compatible with some "normal" R commands.
You can get the fits out by using fitted bu | Plot a generalised mixed effects model with binomial errors
The issue is that the mer class in R, and the lmer etc commands in lme4 all produce mer objects, and these are not compatible with some "normal" R commands.
You can get the fits out by using fitted but amending your code to
ndf$fit <- fitted(glmerb, ndf, type=... | Plot a generalised mixed effects model with binomial errors
The issue is that the mer class in R, and the lmer etc commands in lme4 all produce mer objects, and these are not compatible with some "normal" R commands.
You can get the fits out by using fitted bu |
41,273 | Plot a generalised mixed effects model with binomial errors | Take a look at the ez package, particularly at ezPredict.
ps. if you would like to use 'to_predict' parameter, you'll need the dev version, see the instructions here: https://github.com/mike-lawrence/ez | Plot a generalised mixed effects model with binomial errors | Take a look at the ez package, particularly at ezPredict.
ps. if you would like to use 'to_predict' parameter, you'll need the dev version, see the instructions here: https://github.com/mike-lawrence | Plot a generalised mixed effects model with binomial errors
Take a look at the ez package, particularly at ezPredict.
ps. if you would like to use 'to_predict' parameter, you'll need the dev version, see the instructions here: https://github.com/mike-lawrence/ez | Plot a generalised mixed effects model with binomial errors
Take a look at the ez package, particularly at ezPredict.
ps. if you would like to use 'to_predict' parameter, you'll need the dev version, see the instructions here: https://github.com/mike-lawrence |
41,274 | Plot a generalised mixed effects model with binomial errors | To plot curves of fixed effects, I typically use code like this:
model.coefs <- fixef(model)
curve( invlogit( cbind(1, x) %*% model.coefs ), add=TRUE )
Note that invlogit is in the arm package. | Plot a generalised mixed effects model with binomial errors | To plot curves of fixed effects, I typically use code like this:
model.coefs <- fixef(model)
curve( invlogit( cbind(1, x) %*% model.coefs ), add=TRUE )
Note that invlogit is in the arm package. | Plot a generalised mixed effects model with binomial errors
To plot curves of fixed effects, I typically use code like this:
model.coefs <- fixef(model)
curve( invlogit( cbind(1, x) %*% model.coefs ), add=TRUE )
Note that invlogit is in the arm package. | Plot a generalised mixed effects model with binomial errors
To plot curves of fixed effects, I typically use code like this:
model.coefs <- fixef(model)
curve( invlogit( cbind(1, x) %*% model.coefs ), add=TRUE )
Note that invlogit is in the arm package. |
41,275 | Why is the variance of my random effect negative? | When you use ANOVA or MINQUE method of variance components estimation the estimate for the variance of a random factor may indeed occasionally be negative. There may be several causes, and it is not easy to detect the actual one. See Hocking R. R. (1985) The analysis of linear models. For example outliers and small sam... | Why is the variance of my random effect negative? | When you use ANOVA or MINQUE method of variance components estimation the estimate for the variance of a random factor may indeed occasionally be negative. There may be several causes, and it is not e | Why is the variance of my random effect negative?
When you use ANOVA or MINQUE method of variance components estimation the estimate for the variance of a random factor may indeed occasionally be negative. There may be several causes, and it is not easy to detect the actual one. See Hocking R. R. (1985) The analysis of... | Why is the variance of my random effect negative?
When you use ANOVA or MINQUE method of variance components estimation the estimate for the variance of a random factor may indeed occasionally be negative. There may be several causes, and it is not e |
41,276 | Why is the variance of my random effect negative? | When you delete nobound from the code of SAS the variance of group=0.
Also when you use the following code you can get the same results;
PROC VARCOMP METHOD=REML;
CLASSES group;
MODEL response= group/FIXED=0;
run;
I think these results confirm that the variance of group =0 despite some methods gave negative value such... | Why is the variance of my random effect negative? | When you delete nobound from the code of SAS the variance of group=0.
Also when you use the following code you can get the same results;
PROC VARCOMP METHOD=REML;
CLASSES group;
MODEL response= group | Why is the variance of my random effect negative?
When you delete nobound from the code of SAS the variance of group=0.
Also when you use the following code you can get the same results;
PROC VARCOMP METHOD=REML;
CLASSES group;
MODEL response= group/FIXED=0;
run;
I think these results confirm that the variance of grou... | Why is the variance of my random effect negative?
When you delete nobound from the code of SAS the variance of group=0.
Also when you use the following code you can get the same results;
PROC VARCOMP METHOD=REML;
CLASSES group;
MODEL response= group |
41,277 | Why is the variance of my random effect negative? | When you doubled your data and perform the analysis you will get also negative value but less. For each increasing in data size the negative variance decrease. The second factor is the differences among three means of groups was close. These two factors are responsible about the negative value. | Why is the variance of my random effect negative? | When you doubled your data and perform the analysis you will get also negative value but less. For each increasing in data size the negative variance decrease. The second factor is the differences amo | Why is the variance of my random effect negative?
When you doubled your data and perform the analysis you will get also negative value but less. For each increasing in data size the negative variance decrease. The second factor is the differences among three means of groups was close. These two factors are responsible ... | Why is the variance of my random effect negative?
When you doubled your data and perform the analysis you will get also negative value but less. For each increasing in data size the negative variance decrease. The second factor is the differences amo |
41,278 | Quasibinomial vs negative binomial and hurdles | While both "negative binomial" and "quasibinomial" have the word "binomial" in them, they are very different. The negative binomial is an actual parametric distribution, and it has an infinite range, so you should think of it as a generalization of the Poisson distribution, not the binomial distribution. If your data a... | Quasibinomial vs negative binomial and hurdles | While both "negative binomial" and "quasibinomial" have the word "binomial" in them, they are very different. The negative binomial is an actual parametric distribution, and it has an infinite range, | Quasibinomial vs negative binomial and hurdles
While both "negative binomial" and "quasibinomial" have the word "binomial" in them, they are very different. The negative binomial is an actual parametric distribution, and it has an infinite range, so you should think of it as a generalization of the Poisson distribution... | Quasibinomial vs negative binomial and hurdles
While both "negative binomial" and "quasibinomial" have the word "binomial" in them, they are very different. The negative binomial is an actual parametric distribution, and it has an infinite range, |
41,279 | Quantile regression in JAGS | I used a data augmentation procedure suggested here with the following model:
model{
for(i in 1:length(y)){
mu[i] <- alpha + beta*x[i]
w[i] ~ dexp(tau)
me[i] <- (1-2*p)/(p*(1-p))*w[i] + mu[i]
pe[i] <- (p*(1-p)*tau)/(2*w[i])
y[i] ~ dnorm(me[i],pe[i])
}
#priors for regression
alpha ~ dnorm(0,1E-6)
be... | Quantile regression in JAGS | I used a data augmentation procedure suggested here with the following model:
model{
for(i in 1:length(y)){
mu[i] <- alpha + beta*x[i]
w[i] ~ dexp(tau)
me[i] <- (1-2*p)/(p*(1-p))*w[i] + mu[i | Quantile regression in JAGS
I used a data augmentation procedure suggested here with the following model:
model{
for(i in 1:length(y)){
mu[i] <- alpha + beta*x[i]
w[i] ~ dexp(tau)
me[i] <- (1-2*p)/(p*(1-p))*w[i] + mu[i]
pe[i] <- (p*(1-p)*tau)/(2*w[i])
y[i] ~ dnorm(me[i],pe[i])
}
#priors for regressio... | Quantile regression in JAGS
I used a data augmentation procedure suggested here with the following model:
model{
for(i in 1:length(y)){
mu[i] <- alpha + beta*x[i]
w[i] ~ dexp(tau)
me[i] <- (1-2*p)/(p*(1-p))*w[i] + mu[i |
41,280 | Test to prove two means do not have a difference | Normally in a statistical hypothesis test, you are seeking to reject the null hypothesis in favour of the alternative hypothesis, so the precautionary principle suggests that the onus is on us to show that the null hypothesis is likely false, so we require $\alpha$, the false-positive rate (the probability of rejecting... | Test to prove two means do not have a difference | Normally in a statistical hypothesis test, you are seeking to reject the null hypothesis in favour of the alternative hypothesis, so the precautionary principle suggests that the onus is on us to show | Test to prove two means do not have a difference
Normally in a statistical hypothesis test, you are seeking to reject the null hypothesis in favour of the alternative hypothesis, so the precautionary principle suggests that the onus is on us to show that the null hypothesis is likely false, so we require $\alpha$, the ... | Test to prove two means do not have a difference
Normally in a statistical hypothesis test, you are seeking to reject the null hypothesis in favour of the alternative hypothesis, so the precautionary principle suggests that the onus is on us to show |
41,281 | Test to prove two means do not have a difference | You cannot prove that there is absolutely no change unless you have infinite data/information. However you can show that the amount of change is limited. The best approach is probably the Bland-Altman methods, see the links on this page for more detail. The traditional Bland-Altman method deals with 2 different meas... | Test to prove two means do not have a difference | You cannot prove that there is absolutely no change unless you have infinite data/information. However you can show that the amount of change is limited. The best approach is probably the Bland-Altm | Test to prove two means do not have a difference
You cannot prove that there is absolutely no change unless you have infinite data/information. However you can show that the amount of change is limited. The best approach is probably the Bland-Altman methods, see the links on this page for more detail. The traditiona... | Test to prove two means do not have a difference
You cannot prove that there is absolutely no change unless you have infinite data/information. However you can show that the amount of change is limited. The best approach is probably the Bland-Altm |
41,282 | Test to prove two means do not have a difference | When you said that you made a change, I assume that you meant that you made a change that may impact the outcome that you are measuring. In this case you should consider trying to measure a treatment effect, which can be recovered by difference-in-differences.
You are correct that you cannot simply compare differenc... | Test to prove two means do not have a difference | When you said that you made a change, I assume that you meant that you made a change that may impact the outcome that you are measuring. In this case you should consider trying to measure a treatment | Test to prove two means do not have a difference
When you said that you made a change, I assume that you meant that you made a change that may impact the outcome that you are measuring. In this case you should consider trying to measure a treatment effect, which can be recovered by difference-in-differences.
You are... | Test to prove two means do not have a difference
When you said that you made a change, I assume that you meant that you made a change that may impact the outcome that you are measuring. In this case you should consider trying to measure a treatment |
41,283 | Test to prove two means do not have a difference | To show that 2 groups are eqivalent you can run an equivalence test where you compare the confidence limits against a predefined equivalence boundary. This test is similar to a t-test but the hypothesis are reversed. Instead of the null hypothesis being no difference and the alternative hypothesis being that there is a... | Test to prove two means do not have a difference | To show that 2 groups are eqivalent you can run an equivalence test where you compare the confidence limits against a predefined equivalence boundary. This test is similar to a t-test but the hypothes | Test to prove two means do not have a difference
To show that 2 groups are eqivalent you can run an equivalence test where you compare the confidence limits against a predefined equivalence boundary. This test is similar to a t-test but the hypothesis are reversed. Instead of the null hypothesis being no difference and... | Test to prove two means do not have a difference
To show that 2 groups are eqivalent you can run an equivalence test where you compare the confidence limits against a predefined equivalence boundary. This test is similar to a t-test but the hypothes |
41,284 | Why efficiency matters? | By definition, an inefficient estimator will have larger risk for quadratic losses. Under some additional assumptions and simplifications, I suppose one might equate larger risk with inadmissibility, which would imply there is no need ever to use inefficient estimators, because there are uniformly superior estimators ... | Why efficiency matters? | By definition, an inefficient estimator will have larger risk for quadratic losses. Under some additional assumptions and simplifications, I suppose one might equate larger risk with inadmissibility, | Why efficiency matters?
By definition, an inefficient estimator will have larger risk for quadratic losses. Under some additional assumptions and simplifications, I suppose one might equate larger risk with inadmissibility, which would imply there is no need ever to use inefficient estimators, because there are unifor... | Why efficiency matters?
By definition, an inefficient estimator will have larger risk for quadratic losses. Under some additional assumptions and simplifications, I suppose one might equate larger risk with inadmissibility, |
41,285 | Fitting data based on an unobserved variable | Here is a solution that is close to least squares, using a Gaussian mixture model. We use a family of probability distributions with PDFs that capture both centers of the conditional distribution of $y|x$:
$$p(y,x; (a,b,\delta,\pi, \sigma)) =(1-\pi)\phi_\sigma(y-a x^b)+\pi\phi_\sigma(y - a x^b(1+\delta)).$$
In this no... | Fitting data based on an unobserved variable | Here is a solution that is close to least squares, using a Gaussian mixture model. We use a family of probability distributions with PDFs that capture both centers of the conditional distribution of | Fitting data based on an unobserved variable
Here is a solution that is close to least squares, using a Gaussian mixture model. We use a family of probability distributions with PDFs that capture both centers of the conditional distribution of $y|x$:
$$p(y,x; (a,b,\delta,\pi, \sigma)) =(1-\pi)\phi_\sigma(y-a x^b)+\pi\... | Fitting data based on an unobserved variable
Here is a solution that is close to least squares, using a Gaussian mixture model. We use a family of probability distributions with PDFs that capture both centers of the conditional distribution of |
41,286 | Fitting data based on an unobserved variable | This looks like a 'mixture of regressions' problem, where z is an unobserved random indicator variable denoting which curves each x-y observation belongs to. You would then, as F. Tusell suggests, use an EM algorithm to maximise the likelihood of all the regression parameters.
Here, EM would work by alternating the co... | Fitting data based on an unobserved variable | This looks like a 'mixture of regressions' problem, where z is an unobserved random indicator variable denoting which curves each x-y observation belongs to. You would then, as F. Tusell suggests, us | Fitting data based on an unobserved variable
This looks like a 'mixture of regressions' problem, where z is an unobserved random indicator variable denoting which curves each x-y observation belongs to. You would then, as F. Tusell suggests, use an EM algorithm to maximise the likelihood of all the regression paramete... | Fitting data based on an unobserved variable
This looks like a 'mixture of regressions' problem, where z is an unobserved random indicator variable denoting which curves each x-y observation belongs to. You would then, as F. Tusell suggests, us |
41,287 | Fitting data based on an unobserved variable | I'm not sure that you conceptually can fit a parametrized curve directly on unobserved data. Maybe a two-step approach would help. but I admit, I have never seen or done such a thing: First fit the curve only for 2 variables $x$ and $y$. If the bands of points you mentioned do not cross then the fitted curve should lie... | Fitting data based on an unobserved variable | I'm not sure that you conceptually can fit a parametrized curve directly on unobserved data. Maybe a two-step approach would help. but I admit, I have never seen or done such a thing: First fit the cu | Fitting data based on an unobserved variable
I'm not sure that you conceptually can fit a parametrized curve directly on unobserved data. Maybe a two-step approach would help. but I admit, I have never seen or done such a thing: First fit the curve only for 2 variables $x$ and $y$. If the bands of points you mentioned ... | Fitting data based on an unobserved variable
I'm not sure that you conceptually can fit a parametrized curve directly on unobserved data. Maybe a two-step approach would help. but I admit, I have never seen or done such a thing: First fit the cu |
41,288 | Fitting data based on an unobserved variable | If you take logs, you would have
$$\log(y) = \log(a) + b\log(x) + \log(1+cz) + \epsilon.$$
Consider $\log(1+cz) + \epsilon$ as a random perturbation: call it $\eta$. If you regress $\log(y)$ on a column of "ones" and $\log(x)$, you get estimates of $\log(a)$, $b$ and the residuals $\hat\eta = \log(y) - \widehat\log(a)... | Fitting data based on an unobserved variable | If you take logs, you would have
$$\log(y) = \log(a) + b\log(x) + \log(1+cz) + \epsilon.$$
Consider $\log(1+cz) + \epsilon$ as a random perturbation: call it $\eta$. If you regress $\log(y)$ on a colu | Fitting data based on an unobserved variable
If you take logs, you would have
$$\log(y) = \log(a) + b\log(x) + \log(1+cz) + \epsilon.$$
Consider $\log(1+cz) + \epsilon$ as a random perturbation: call it $\eta$. If you regress $\log(y)$ on a column of "ones" and $\log(x)$, you get estimates of $\log(a)$, $b$ and the re... | Fitting data based on an unobserved variable
If you take logs, you would have
$$\log(y) = \log(a) + b\log(x) + \log(1+cz) + \epsilon.$$
Consider $\log(1+cz) + \epsilon$ as a random perturbation: call it $\eta$. If you regress $\log(y)$ on a colu |
41,289 | Density of a quadratic transformation of a normal random variable | The moments of such transformation can probably be found in Sec. 2.2.3 of Kollo and von Rosen (2005). Transformations of this kind have been used in some multivariate simulations. I understand there's a book on polynomials of multivariate distributions, but I have not seen it, and don't know if you'd be able to find th... | Density of a quadratic transformation of a normal random variable | The moments of such transformation can probably be found in Sec. 2.2.3 of Kollo and von Rosen (2005). Transformations of this kind have been used in some multivariate simulations. I understand there's | Density of a quadratic transformation of a normal random variable
The moments of such transformation can probably be found in Sec. 2.2.3 of Kollo and von Rosen (2005). Transformations of this kind have been used in some multivariate simulations. I understand there's a book on polynomials of multivariate distributions, ... | Density of a quadratic transformation of a normal random variable
The moments of such transformation can probably be found in Sec. 2.2.3 of Kollo and von Rosen (2005). Transformations of this kind have been used in some multivariate simulations. I understand there's |
41,290 | Density of a quadratic transformation of a normal random variable | A related question was answered here. For that question the focus was on a particular quadratic form (the squared Euclidean norm), but this must also be one of the first things to consider.
A recommended reference is the book "Quadratic forms in random variables" by Mathai and Provost (1992, Marcel Dekker, Inc.), but ... | Density of a quadratic transformation of a normal random variable | A related question was answered here. For that question the focus was on a particular quadratic form (the squared Euclidean norm), but this must also be one of the first things to consider.
A recomme | Density of a quadratic transformation of a normal random variable
A related question was answered here. For that question the focus was on a particular quadratic form (the squared Euclidean norm), but this must also be one of the first things to consider.
A recommended reference is the book "Quadratic forms in random ... | Density of a quadratic transformation of a normal random variable
A related question was answered here. For that question the focus was on a particular quadratic form (the squared Euclidean norm), but this must also be one of the first things to consider.
A recomme |
41,291 | Does PCA have any advantages or usages in the frequency domain? | The advantage of using PCA in the frequency domain is to choose a set of weights by exploiting the cross-correlations between the signals at particular cycles.
For example, (depending on the field of application) the behaviour of the variables under study can be different in the short, medium and long run. Using PCA in... | Does PCA have any advantages or usages in the frequency domain? | The advantage of using PCA in the frequency domain is to choose a set of weights by exploiting the cross-correlations between the signals at particular cycles.
For example, (depending on the field of | Does PCA have any advantages or usages in the frequency domain?
The advantage of using PCA in the frequency domain is to choose a set of weights by exploiting the cross-correlations between the signals at particular cycles.
For example, (depending on the field of application) the behaviour of the variables under study... | Does PCA have any advantages or usages in the frequency domain?
The advantage of using PCA in the frequency domain is to choose a set of weights by exploiting the cross-correlations between the signals at particular cycles.
For example, (depending on the field of |
41,292 | Does PCA have any advantages or usages in the frequency domain? | There was a paper at the recent ICML by Li and Prakash. It is about a complex linear dynamical system, which turns out to be a model of which Fourier transform and PCA are special cases. Have a look at Time Series Cluster: complex is simpler. | Does PCA have any advantages or usages in the frequency domain? | There was a paper at the recent ICML by Li and Prakash. It is about a complex linear dynamical system, which turns out to be a model of which Fourier transform and PCA are special cases. Have a look a | Does PCA have any advantages or usages in the frequency domain?
There was a paper at the recent ICML by Li and Prakash. It is about a complex linear dynamical system, which turns out to be a model of which Fourier transform and PCA are special cases. Have a look at Time Series Cluster: complex is simpler. | Does PCA have any advantages or usages in the frequency domain?
There was a paper at the recent ICML by Li and Prakash. It is about a complex linear dynamical system, which turns out to be a model of which Fourier transform and PCA are special cases. Have a look a |
41,293 | Does PCA have any advantages or usages in the frequency domain? | If you accept spectroscopy as frequency-domain: PCA is used a lot there. E.g. a pubmed search on principal component analysis and spectroscopy yields more than 2500 results. On the other hand, spectroscopists rarely look at the time domain (Fourier-transform spectroscopy does use a spatial domain as "intermediate" but ... | Does PCA have any advantages or usages in the frequency domain? | If you accept spectroscopy as frequency-domain: PCA is used a lot there. E.g. a pubmed search on principal component analysis and spectroscopy yields more than 2500 results. On the other hand, spectro | Does PCA have any advantages or usages in the frequency domain?
If you accept spectroscopy as frequency-domain: PCA is used a lot there. E.g. a pubmed search on principal component analysis and spectroscopy yields more than 2500 results. On the other hand, spectroscopists rarely look at the time domain (Fourier-transf... | Does PCA have any advantages or usages in the frequency domain?
If you accept spectroscopy as frequency-domain: PCA is used a lot there. E.g. a pubmed search on principal component analysis and spectroscopy yields more than 2500 results. On the other hand, spectro |
41,294 | Does PCA have any advantages or usages in the frequency domain? | Principal component analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables called principal components. This transformation is defined in such a way that the first principal compon... | Does PCA have any advantages or usages in the frequency domain? | Principal component analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrel | Does PCA have any advantages or usages in the frequency domain?
Principal component analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables called principal components. This transf... | Does PCA have any advantages or usages in the frequency domain?
Principal component analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrel |
41,295 | Does PCA have any advantages or usages in the frequency domain? | PCA, like wave transformations, is an orthogonal transformation. It tries to rotate data to maximize certain metric (i.e. the variance). Interestingly, if the data is sufficiently random (isotropic), no rotation will help to enlarge variance. In this case, PCA will basically turn to a wave transformation. | Does PCA have any advantages or usages in the frequency domain? | PCA, like wave transformations, is an orthogonal transformation. It tries to rotate data to maximize certain metric (i.e. the variance). Interestingly, if the data is sufficiently random (isotropic), | Does PCA have any advantages or usages in the frequency domain?
PCA, like wave transformations, is an orthogonal transformation. It tries to rotate data to maximize certain metric (i.e. the variance). Interestingly, if the data is sufficiently random (isotropic), no rotation will help to enlarge variance. In this cas... | Does PCA have any advantages or usages in the frequency domain?
PCA, like wave transformations, is an orthogonal transformation. It tries to rotate data to maximize certain metric (i.e. the variance). Interestingly, if the data is sufficiently random (isotropic), |
41,296 | When the population has a known fixed size, are tables for the t statistic wrong? | When you have a finite population and the sample size is more than 5-10% of the population then you should use the Finite Population Correction, that is you multiply your standard error times $\sqrt{\frac{N-n}{N-1}}$, see this link.
You should still use the t-distribution, but the standard error will be smaller to acco... | When the population has a known fixed size, are tables for the t statistic wrong? | When you have a finite population and the sample size is more than 5-10% of the population then you should use the Finite Population Correction, that is you multiply your standard error times $\sqrt{\ | When the population has a known fixed size, are tables for the t statistic wrong?
When you have a finite population and the sample size is more than 5-10% of the population then you should use the Finite Population Correction, that is you multiply your standard error times $\sqrt{\frac{N-n}{N-1}}$, see this link.
You s... | When the population has a known fixed size, are tables for the t statistic wrong?
When you have a finite population and the sample size is more than 5-10% of the population then you should use the Finite Population Correction, that is you multiply your standard error times $\sqrt{\ |
41,297 | When the population has a known fixed size, are tables for the t statistic wrong? | The difference between Z-statistics and t-statistics is that Z statistic is asymptoticaly normal, when t-statistic has exact Student's distribution (with appropriate degrees of freedom) if we assume that the errors are normal. In small sample sizes the difference can be quite substantial (actually this is why Student "... | When the population has a known fixed size, are tables for the t statistic wrong? | The difference between Z-statistics and t-statistics is that Z statistic is asymptoticaly normal, when t-statistic has exact Student's distribution (with appropriate degrees of freedom) if we assume t | When the population has a known fixed size, are tables for the t statistic wrong?
The difference between Z-statistics and t-statistics is that Z statistic is asymptoticaly normal, when t-statistic has exact Student's distribution (with appropriate degrees of freedom) if we assume that the errors are normal. In small sa... | When the population has a known fixed size, are tables for the t statistic wrong?
The difference between Z-statistics and t-statistics is that Z statistic is asymptoticaly normal, when t-statistic has exact Student's distribution (with appropriate degrees of freedom) if we assume t |
41,298 | Infinite p-value when checking normality of distribution | You have a very large data set (looks like over a million cases). With N this large, even the tiniest variation from normality will be highly significant. The key here is the QQ plot. It shows, as Nick pointed out, that your data are truncated. Why is this the case?
(added) I tried this:
library(nortest)
x <- rnorm(1... | Infinite p-value when checking normality of distribution | You have a very large data set (looks like over a million cases). With N this large, even the tiniest variation from normality will be highly significant. The key here is the QQ plot. It shows, as Ni | Infinite p-value when checking normality of distribution
You have a very large data set (looks like over a million cases). With N this large, even the tiniest variation from normality will be highly significant. The key here is the QQ plot. It shows, as Nick pointed out, that your data are truncated. Why is this the c... | Infinite p-value when checking normality of distribution
You have a very large data set (looks like over a million cases). With N this large, even the tiniest variation from normality will be highly significant. The key here is the QQ plot. It shows, as Ni |
41,299 | Do you include a covariate because of baseline group difference or if correlated with DV or both? | You might want to read the following article
Pocock, S.J. and Assmann, S.E. and Enos, L.E. and Kasten, L.E. (2002).
Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems. FREE PDF
Check out the discussion of answers to this question on best practice... | Do you include a covariate because of baseline group difference or if correlated with DV or both? | You might want to read the following article
Pocock, S.J. and Assmann, S.E. and Enos, L.E. and Kasten, L.E. (2002).
Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial | Do you include a covariate because of baseline group difference or if correlated with DV or both?
You might want to read the following article
Pocock, S.J. and Assmann, S.E. and Enos, L.E. and Kasten, L.E. (2002).
Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current pra... | Do you include a covariate because of baseline group difference or if correlated with DV or both?
You might want to read the following article
Pocock, S.J. and Assmann, S.E. and Enos, L.E. and Kasten, L.E. (2002).
Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial |
41,300 | Do you include a covariate because of baseline group difference or if correlated with DV or both? | If your goal is to show group differences on an outcome (Y), there's no benefit to including a covariate unless it's relevant to (correlated with) Y. But you have to carefully think things through. There are times when controlling for a covariate serves to "equalize," making for a fairer comparison (in addition to in... | Do you include a covariate because of baseline group difference or if correlated with DV or both? | If your goal is to show group differences on an outcome (Y), there's no benefit to including a covariate unless it's relevant to (correlated with) Y. But you have to carefully think things through. | Do you include a covariate because of baseline group difference or if correlated with DV or both?
If your goal is to show group differences on an outcome (Y), there's no benefit to including a covariate unless it's relevant to (correlated with) Y. But you have to carefully think things through. There are times when c... | Do you include a covariate because of baseline group difference or if correlated with DV or both?
If your goal is to show group differences on an outcome (Y), there's no benefit to including a covariate unless it's relevant to (correlated with) Y. But you have to carefully think things through. |
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