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 |
|---|---|---|---|---|---|---|
53,401 | Fiducial distribution and sequential monte-carlo algorithm | The whole goal was to generate standard normal conditional on the $C$ (using all of the data). That proved to be a rather difficult problem and therefore we have chosen to do this sequentially. You have correctly understood the meaning of $C_t$ and $Q_t$.
By the way, $C_t$ is formally defined in the middle of the page... | Fiducial distribution and sequential monte-carlo algorithm | The whole goal was to generate standard normal conditional on the $C$ (using all of the data). That proved to be a rather difficult problem and therefore we have chosen to do this sequentially. You ha | Fiducial distribution and sequential monte-carlo algorithm
The whole goal was to generate standard normal conditional on the $C$ (using all of the data). That proved to be a rather difficult problem and therefore we have chosen to do this sequentially. You have correctly understood the meaning of $C_t$ and $Q_t$.
By t... | Fiducial distribution and sequential monte-carlo algorithm
The whole goal was to generate standard normal conditional on the $C$ (using all of the data). That proved to be a rather difficult problem and therefore we have chosen to do this sequentially. You ha |
53,402 | Fiducial distribution and sequential monte-carlo algorithm | Thank you for your questions. I just want to elaborate on a couple points you made above:
I just want to clarify your description of the interval, or "fat", data: each observation is considered an interval rather than an exact value, but the intervals are fixed and defined based on the data collection/data storing p... | Fiducial distribution and sequential monte-carlo algorithm | Thank you for your questions. I just want to elaborate on a couple points you made above:
I just want to clarify your description of the interval, or "fat", data: each observation is considered an | Fiducial distribution and sequential monte-carlo algorithm
Thank you for your questions. I just want to elaborate on a couple points you made above:
I just want to clarify your description of the interval, or "fat", data: each observation is considered an interval rather than an exact value, but the intervals are fi... | Fiducial distribution and sequential monte-carlo algorithm
Thank you for your questions. I just want to elaborate on a couple points you made above:
I just want to clarify your description of the interval, or "fat", data: each observation is considered an |
53,403 | Inner loop overfitting in nested cross-validation | Overfitting in model selection problems for classification is usually due to including too many parameters. Cross-validation or bootstrap error rate estimation should avoid this problem because it avoids the optimism of an estimate like resubstitution which tests the classifier on the same data used in the fit. If you... | Inner loop overfitting in nested cross-validation | Overfitting in model selection problems for classification is usually due to including too many parameters. Cross-validation or bootstrap error rate estimation should avoid this problem because it av | Inner loop overfitting in nested cross-validation
Overfitting in model selection problems for classification is usually due to including too many parameters. Cross-validation or bootstrap error rate estimation should avoid this problem because it avoids the optimism of an estimate like resubstitution which tests the c... | Inner loop overfitting in nested cross-validation
Overfitting in model selection problems for classification is usually due to including too many parameters. Cross-validation or bootstrap error rate estimation should avoid this problem because it av |
53,404 | Inner loop overfitting in nested cross-validation | I know it's an old thread, but anyway...
When I've designed algorithms to perform feature selection within a nested cross-validation, I've found that you can decrease overfitting to the inner segments by performing recursive elimination and making sure that you'll resample the inner segments for each iteration of the r... | Inner loop overfitting in nested cross-validation | I know it's an old thread, but anyway...
When I've designed algorithms to perform feature selection within a nested cross-validation, I've found that you can decrease overfitting to the inner segments | Inner loop overfitting in nested cross-validation
I know it's an old thread, but anyway...
When I've designed algorithms to perform feature selection within a nested cross-validation, I've found that you can decrease overfitting to the inner segments by performing recursive elimination and making sure that you'll resam... | Inner loop overfitting in nested cross-validation
I know it's an old thread, but anyway...
When I've designed algorithms to perform feature selection within a nested cross-validation, I've found that you can decrease overfitting to the inner segments |
53,405 | Inner loop overfitting in nested cross-validation | To answer my own question (I realize this material is probably actually stated elsewhere on this site):
I have found that when using complex classifier models (such as an SVM with an RBF kernel or an regularized discriminant classifier) there can be extremely large variance between folds in cross validation, if a large... | Inner loop overfitting in nested cross-validation | To answer my own question (I realize this material is probably actually stated elsewhere on this site):
I have found that when using complex classifier models (such as an SVM with an RBF kernel or an | Inner loop overfitting in nested cross-validation
To answer my own question (I realize this material is probably actually stated elsewhere on this site):
I have found that when using complex classifier models (such as an SVM with an RBF kernel or an regularized discriminant classifier) there can be extremely large vari... | Inner loop overfitting in nested cross-validation
To answer my own question (I realize this material is probably actually stated elsewhere on this site):
I have found that when using complex classifier models (such as an SVM with an RBF kernel or an |
53,406 | Inner loop overfitting in nested cross-validation | This is an old question, but I want to leave an answer in case anyone stumbles on this thread and happens to have made the same novice mistake as me.
I consistently found my errors on the outer CV loop were higher than the inner loops, even with very careful cross-validation in both loops (stratified, repeated in the i... | Inner loop overfitting in nested cross-validation | This is an old question, but I want to leave an answer in case anyone stumbles on this thread and happens to have made the same novice mistake as me.
I consistently found my errors on the outer CV loo | Inner loop overfitting in nested cross-validation
This is an old question, but I want to leave an answer in case anyone stumbles on this thread and happens to have made the same novice mistake as me.
I consistently found my errors on the outer CV loop were higher than the inner loops, even with very careful cross-valid... | Inner loop overfitting in nested cross-validation
This is an old question, but I want to leave an answer in case anyone stumbles on this thread and happens to have made the same novice mistake as me.
I consistently found my errors on the outer CV loo |
53,407 | Multilevel covariance structure and more | I'm not sure I can provide the kind of answer I'd like to, but I will try to throw out some pieces of information regarding your questions.
First, both @Seth and @gui11aume (+1 to each) have noted that lme() defaults to no within group correlations. The question is why, and whether that's likely to be a problem. I ... | Multilevel covariance structure and more | I'm not sure I can provide the kind of answer I'd like to, but I will try to throw out some pieces of information regarding your questions.
First, both @Seth and @gui11aume (+1 to each) have noted t | Multilevel covariance structure and more
I'm not sure I can provide the kind of answer I'd like to, but I will try to throw out some pieces of information regarding your questions.
First, both @Seth and @gui11aume (+1 to each) have noted that lme() defaults to no within group correlations. The question is why, and w... | Multilevel covariance structure and more
I'm not sure I can provide the kind of answer I'd like to, but I will try to throw out some pieces of information regarding your questions.
First, both @Seth and @gui11aume (+1 to each) have noted t |
53,408 | Multilevel covariance structure and more | You first point has been addressed by @Seth: the default is no within group correlation. Regarding your second point, I think it depends on the effect size of TIME. If the effect is minor compared to your other variables, like CONDITION and SUBJECT, the misfit should not be a problem. But if TIME is a major effect you ... | Multilevel covariance structure and more | You first point has been addressed by @Seth: the default is no within group correlation. Regarding your second point, I think it depends on the effect size of TIME. If the effect is minor compared to | Multilevel covariance structure and more
You first point has been addressed by @Seth: the default is no within group correlation. Regarding your second point, I think it depends on the effect size of TIME. If the effect is minor compared to your other variables, like CONDITION and SUBJECT, the misfit should not be a pr... | Multilevel covariance structure and more
You first point has been addressed by @Seth: the default is no within group correlation. Regarding your second point, I think it depends on the effect size of TIME. If the effect is minor compared to |
53,409 | Bayesian and frequentist optimization and intervals | The maximum aposteriori (MAP) approach isn't really fully Bayesian, ideally inference should involve marginalising over the whole posterior. Optimisation is the root of all evil in statistics; it is difficult to over-fit if you don't optimise! ;o) So the practical difference between Bayesian and frequentist runs rath... | Bayesian and frequentist optimization and intervals | The maximum aposteriori (MAP) approach isn't really fully Bayesian, ideally inference should involve marginalising over the whole posterior. Optimisation is the root of all evil in statistics; it is | Bayesian and frequentist optimization and intervals
The maximum aposteriori (MAP) approach isn't really fully Bayesian, ideally inference should involve marginalising over the whole posterior. Optimisation is the root of all evil in statistics; it is difficult to over-fit if you don't optimise! ;o) So the practical d... | Bayesian and frequentist optimization and intervals
The maximum aposteriori (MAP) approach isn't really fully Bayesian, ideally inference should involve marginalising over the whole posterior. Optimisation is the root of all evil in statistics; it is |
53,410 | Bayesian and frequentist optimization and intervals | I would agree that roughly speaking you are right. Priors noninformative or not will lead to different solutions. The solutions will converge when the data dominates the prior. Also Jeffreys needed improper priors in some cases to match Bayesian results with frequentist results. The real difference and the controve... | Bayesian and frequentist optimization and intervals | I would agree that roughly speaking you are right. Priors noninformative or not will lead to different solutions. The solutions will converge when the data dominates the prior. Also Jeffreys needed | Bayesian and frequentist optimization and intervals
I would agree that roughly speaking you are right. Priors noninformative or not will lead to different solutions. The solutions will converge when the data dominates the prior. Also Jeffreys needed improper priors in some cases to match Bayesian results with freque... | Bayesian and frequentist optimization and intervals
I would agree that roughly speaking you are right. Priors noninformative or not will lead to different solutions. The solutions will converge when the data dominates the prior. Also Jeffreys needed |
53,411 | Categorical fixed effect w/ 3 levels in LMER | In general, in a model which includes the intercept term (as yours does), the effect of a categorical predictor with k levels can be represented with k-1 codes. So, as expected, the effect of your 3-level predictor is represented with 2 codes (plus the intercept term). In this case, they appear to be "dummy codes," whe... | Categorical fixed effect w/ 3 levels in LMER | In general, in a model which includes the intercept term (as yours does), the effect of a categorical predictor with k levels can be represented with k-1 codes. So, as expected, the effect of your 3-l | Categorical fixed effect w/ 3 levels in LMER
In general, in a model which includes the intercept term (as yours does), the effect of a categorical predictor with k levels can be represented with k-1 codes. So, as expected, the effect of your 3-level predictor is represented with 2 codes (plus the intercept term). In th... | Categorical fixed effect w/ 3 levels in LMER
In general, in a model which includes the intercept term (as yours does), the effect of a categorical predictor with k levels can be represented with k-1 codes. So, as expected, the effect of your 3-l |
53,412 | Why is the power for this test so low? | Your effect size is extremely small: the true value is only $.07$ away from the null hypothesis, while the population standard deviation is $1.2$ and the sample size is $100$, making your standard error $.12$, so your standard error is almost twice as large as the distance from the null hypothesis, combining for a very... | Why is the power for this test so low? | Your effect size is extremely small: the true value is only $.07$ away from the null hypothesis, while the population standard deviation is $1.2$ and the sample size is $100$, making your standard err | Why is the power for this test so low?
Your effect size is extremely small: the true value is only $.07$ away from the null hypothesis, while the population standard deviation is $1.2$ and the sample size is $100$, making your standard error $.12$, so your standard error is almost twice as large as the distance from th... | Why is the power for this test so low?
Your effect size is extremely small: the true value is only $.07$ away from the null hypothesis, while the population standard deviation is $1.2$ and the sample size is $100$, making your standard err |
53,413 | Mixture model fixed effects | This type of model has been entertained by education researchers for years under the name of growth mixture model (do different students exhibit different rate of learning?), although they work with it as a random effects model. I don't think you'd be able to come up with a proper fixed effect estimation for this model... | Mixture model fixed effects | This type of model has been entertained by education researchers for years under the name of growth mixture model (do different students exhibit different rate of learning?), although they work with i | Mixture model fixed effects
This type of model has been entertained by education researchers for years under the name of growth mixture model (do different students exhibit different rate of learning?), although they work with it as a random effects model. I don't think you'd be able to come up with a proper fixed effe... | Mixture model fixed effects
This type of model has been entertained by education researchers for years under the name of growth mixture model (do different students exhibit different rate of learning?), although they work with i |
53,414 | Mixture model fixed effects | You are trying to fit a "mixture model", though I would suggest not having a separate set of $\pi_{ij}$ parameters for every person, but rather model these probabilities as a function (i.e. logistic) of some covariates. You could even start with a constant $\pi_j$. The typical approach for fitting mixture models is the... | Mixture model fixed effects | You are trying to fit a "mixture model", though I would suggest not having a separate set of $\pi_{ij}$ parameters for every person, but rather model these probabilities as a function (i.e. logistic) | Mixture model fixed effects
You are trying to fit a "mixture model", though I would suggest not having a separate set of $\pi_{ij}$ parameters for every person, but rather model these probabilities as a function (i.e. logistic) of some covariates. You could even start with a constant $\pi_j$. The typical approach for f... | Mixture model fixed effects
You are trying to fit a "mixture model", though I would suggest not having a separate set of $\pi_{ij}$ parameters for every person, but rather model these probabilities as a function (i.e. logistic) |
53,415 | Mixture model fixed effects | I suspect you actually mean $y_{it}=μ_i+∑_j I_{ij}θ_{jt}+ϵ_{it}$ where $I_{ij}$ is an indicator for individual $i$ belonging to group $j$ (with probability $\pi_{ij}$. I also wonder if you really want to allow for different individuals to have different group assignment probabilities? I suspect $\pi_j$ will suffice?
I ... | Mixture model fixed effects | I suspect you actually mean $y_{it}=μ_i+∑_j I_{ij}θ_{jt}+ϵ_{it}$ where $I_{ij}$ is an indicator for individual $i$ belonging to group $j$ (with probability $\pi_{ij}$. I also wonder if you really want | Mixture model fixed effects
I suspect you actually mean $y_{it}=μ_i+∑_j I_{ij}θ_{jt}+ϵ_{it}$ where $I_{ij}$ is an indicator for individual $i$ belonging to group $j$ (with probability $\pi_{ij}$. I also wonder if you really want to allow for different individuals to have different group assignment probabilities? I susp... | Mixture model fixed effects
I suspect you actually mean $y_{it}=μ_i+∑_j I_{ij}θ_{jt}+ϵ_{it}$ where $I_{ij}$ is an indicator for individual $i$ belonging to group $j$ (with probability $\pi_{ij}$. I also wonder if you really want |
53,416 | What is the distribution of the product of a Bernoulli & a normal random variable? | $-X$ has the same distribution as $X$ since its density is symmetric about the origin, and $Z$ is likewise symmetric, therefore the result is ... yet another normal random variable.
It's instructive to ponder how $Y$ is impacted by changes in the parameter $p=\mathrm P(Z=1)$ of the Bernoulli random variable $Z$. Here i... | What is the distribution of the product of a Bernoulli & a normal random variable? | $-X$ has the same distribution as $X$ since its density is symmetric about the origin, and $Z$ is likewise symmetric, therefore the result is ... yet another normal random variable.
It's instructive t | What is the distribution of the product of a Bernoulli & a normal random variable?
$-X$ has the same distribution as $X$ since its density is symmetric about the origin, and $Z$ is likewise symmetric, therefore the result is ... yet another normal random variable.
It's instructive to ponder how $Y$ is impacted by chang... | What is the distribution of the product of a Bernoulli & a normal random variable?
$-X$ has the same distribution as $X$ since its density is symmetric about the origin, and $Z$ is likewise symmetric, therefore the result is ... yet another normal random variable.
It's instructive t |
53,417 | What is the distribution of the product of a Bernoulli & a normal random variable? | May I suggest you start from first principles? You seek the distribution of $Y$, so you should be asking yourself about the chance that $Y \le t$ for some arbitrary real value $t$. To handle the discreteness of $Z$, consider enumerating its possible values:
$$\Pr[Y \le t] = \Pr[Z\ |X| \le t] = \Pr[|X| \le t \text{ an... | What is the distribution of the product of a Bernoulli & a normal random variable? | May I suggest you start from first principles? You seek the distribution of $Y$, so you should be asking yourself about the chance that $Y \le t$ for some arbitrary real value $t$. To handle the dis | What is the distribution of the product of a Bernoulli & a normal random variable?
May I suggest you start from first principles? You seek the distribution of $Y$, so you should be asking yourself about the chance that $Y \le t$ for some arbitrary real value $t$. To handle the discreteness of $Z$, consider enumeratin... | What is the distribution of the product of a Bernoulli & a normal random variable?
May I suggest you start from first principles? You seek the distribution of $Y$, so you should be asking yourself about the chance that $Y \le t$ for some arbitrary real value $t$. To handle the dis |
53,418 | What is the distribution of the product of a Bernoulli & a normal random variable? | Here, I have done following calculation:
P(Y <= y)
=P(Z*mod(X) <= y)
=0.5P(mod(X) <= y) + 0.5P(-mod(X) <= y)
=0.5*[ P(-y <= X <= y) + P(mod(X) >= -y) ]
=0.5*[ 2Phi(y) - 1 + 1 - P(mod(X) < -y) ]
=0.5*[ 2Phi(y) - P(y <= X <= -y) ]
=Phi(y) because, 2nd component is probability of impossible event
Therefore Y have st... | What is the distribution of the product of a Bernoulli & a normal random variable? | Here, I have done following calculation:
P(Y <= y)
=P(Z*mod(X) <= y)
=0.5P(mod(X) <= y) + 0.5P(-mod(X) <= y)
=0.5*[ P(-y <= X <= y) + P(mod(X) >= -y) ]
=0.5*[ 2Phi(y) - 1 + 1 - P(mod(X) < -y) ]
=0.5*[ | What is the distribution of the product of a Bernoulli & a normal random variable?
Here, I have done following calculation:
P(Y <= y)
=P(Z*mod(X) <= y)
=0.5P(mod(X) <= y) + 0.5P(-mod(X) <= y)
=0.5*[ P(-y <= X <= y) + P(mod(X) >= -y) ]
=0.5*[ 2Phi(y) - 1 + 1 - P(mod(X) < -y) ]
=0.5*[ 2Phi(y) - P(y <= X <= -y) ]
=Phi(y) ... | What is the distribution of the product of a Bernoulli & a normal random variable?
Here, I have done following calculation:
P(Y <= y)
=P(Z*mod(X) <= y)
=0.5P(mod(X) <= y) + 0.5P(-mod(X) <= y)
=0.5*[ P(-y <= X <= y) + P(mod(X) >= -y) ]
=0.5*[ 2Phi(y) - 1 + 1 - P(mod(X) < -y) ]
=0.5*[ |
53,419 | How to get coefficients from princomp object in R? | If you read the documentation on the princomp function, via ?princomp you'll see that there is a section titled "Value" that describes the components of the object returned.
The piece you are looking for is probably the loadings.
Additionally, if you type stats:::predict.princomp at the console you can see for yourself... | How to get coefficients from princomp object in R? | If you read the documentation on the princomp function, via ?princomp you'll see that there is a section titled "Value" that describes the components of the object returned.
The piece you are looking | How to get coefficients from princomp object in R?
If you read the documentation on the princomp function, via ?princomp you'll see that there is a section titled "Value" that describes the components of the object returned.
The piece you are looking for is probably the loadings.
Additionally, if you type stats:::predi... | How to get coefficients from princomp object in R?
If you read the documentation on the princomp function, via ?princomp you'll see that there is a section titled "Value" that describes the components of the object returned.
The piece you are looking |
53,420 | How to get coefficients from princomp object in R? | The particular calculation done by predict in this case is scale(newdata, object$center, object$scale) %*% object$loadings, where object is the object returned by princomp. There's no way to reduce this to a single matrix multiplication, as in the general case newdata must be rescaled to match the original data. | How to get coefficients from princomp object in R? | The particular calculation done by predict in this case is scale(newdata, object$center, object$scale) %*% object$loadings, where object is the object returned by princomp. There's no way to reduce th | How to get coefficients from princomp object in R?
The particular calculation done by predict in this case is scale(newdata, object$center, object$scale) %*% object$loadings, where object is the object returned by princomp. There's no way to reduce this to a single matrix multiplication, as in the general case newdata ... | How to get coefficients from princomp object in R?
The particular calculation done by predict in this case is scale(newdata, object$center, object$scale) %*% object$loadings, where object is the object returned by princomp. There's no way to reduce th |
53,421 | How to get p value and confidence intervals for nls functions? | 1.
- You could try (this is an approximation)
library(nls2)
summary(as.lm(model))
You can obtain a p-value for all parameters used in your model using
summary(model)
You can get p values for a model by comparing it to another ("nested") model using
anova(model1, model2)
where model 2 is a simplified version... | How to get p value and confidence intervals for nls functions? | 1.
- You could try (this is an approximation)
library(nls2)
summary(as.lm(model))
You can obtain a p-value for all parameters used in your model using
summary(model)
You can get p values for | How to get p value and confidence intervals for nls functions?
1.
- You could try (this is an approximation)
library(nls2)
summary(as.lm(model))
You can obtain a p-value for all parameters used in your model using
summary(model)
You can get p values for a model by comparing it to another ("nested") model usin... | How to get p value and confidence intervals for nls functions?
1.
- You could try (this is an approximation)
library(nls2)
summary(as.lm(model))
You can obtain a p-value for all parameters used in your model using
summary(model)
You can get p values for |
53,422 | How to get p value and confidence intervals for nls functions? | Regarding confidence intervals, other answers here seem to have issues with the use of functions (as.lm.nls, as.proto.list) that for some reason are not defined for some users (like me). After some surfing, I found an answer that works for me, requiring only the MASS package. At the urging of @etov, I am posting the ... | How to get p value and confidence intervals for nls functions? | Regarding confidence intervals, other answers here seem to have issues with the use of functions (as.lm.nls, as.proto.list) that for some reason are not defined for some users (like me). After some s | How to get p value and confidence intervals for nls functions?
Regarding confidence intervals, other answers here seem to have issues with the use of functions (as.lm.nls, as.proto.list) that for some reason are not defined for some users (like me). After some surfing, I found an answer that works for me, requiring on... | How to get p value and confidence intervals for nls functions?
Regarding confidence intervals, other answers here seem to have issues with the use of functions (as.lm.nls, as.proto.list) that for some reason are not defined for some users (like me). After some s |
53,423 | How to get p value and confidence intervals for nls functions? | A note regarding confidence intervals (2 above), and the answer by @Etienne Low-Décarie:
Even after attaching nls2, the as.lm functions is sometimes unavailable. Based on this (now stale) reference (originally authored by delichon), here's the function's source:
as.lm.nls <- function(object, ...) {
if (!inherits(ob... | How to get p value and confidence intervals for nls functions? | A note regarding confidence intervals (2 above), and the answer by @Etienne Low-Décarie:
Even after attaching nls2, the as.lm functions is sometimes unavailable. Based on this (now stale) reference (o | How to get p value and confidence intervals for nls functions?
A note regarding confidence intervals (2 above), and the answer by @Etienne Low-Décarie:
Even after attaching nls2, the as.lm functions is sometimes unavailable. Based on this (now stale) reference (originally authored by delichon), here's the function's so... | How to get p value and confidence intervals for nls functions?
A note regarding confidence intervals (2 above), and the answer by @Etienne Low-Décarie:
Even after attaching nls2, the as.lm functions is sometimes unavailable. Based on this (now stale) reference (o |
53,424 | How to get p value and confidence intervals for nls functions? | I was also banging my head on this one and eventually found predictNLS() function in the propagate package.
For example:
library(propagate)
Y <- c(282, 314, 581, 846, 1320, 2014, 2798, 4593, 6065, 7818, 9826)
temp <- data.frame(y = Y, x = seq(1:11))
mod <- nls(y ~ exp(a + b * x), data = temp, start = list(a = 0, ... | How to get p value and confidence intervals for nls functions? | I was also banging my head on this one and eventually found predictNLS() function in the propagate package.
For example:
library(propagate)
Y <- c(282, 314, 581, 846, 1320, 2014, 2798, 4593, 6065 | How to get p value and confidence intervals for nls functions?
I was also banging my head on this one and eventually found predictNLS() function in the propagate package.
For example:
library(propagate)
Y <- c(282, 314, 581, 846, 1320, 2014, 2798, 4593, 6065, 7818, 9826)
temp <- data.frame(y = Y, x = seq(1:11))
mo... | How to get p value and confidence intervals for nls functions?
I was also banging my head on this one and eventually found predictNLS() function in the propagate package.
For example:
library(propagate)
Y <- c(282, 314, 581, 846, 1320, 2014, 2798, 4593, 6065 |
53,425 | A Monte Carlo confidence interval method for the maximum of a uniform distribution? | Yes, this is a classic example of when the bootstrap is inconsistent, but subsampling yields valid inference. See Swanepoel (1986), Politis and Romano (1994) or Canty et. al. (2006). | A Monte Carlo confidence interval method for the maximum of a uniform distribution? | Yes, this is a classic example of when the bootstrap is inconsistent, but subsampling yields valid inference. See Swanepoel (1986), Politis and Romano (1994) or Canty et. al. (2006). | A Monte Carlo confidence interval method for the maximum of a uniform distribution?
Yes, this is a classic example of when the bootstrap is inconsistent, but subsampling yields valid inference. See Swanepoel (1986), Politis and Romano (1994) or Canty et. al. (2006). | A Monte Carlo confidence interval method for the maximum of a uniform distribution?
Yes, this is a classic example of when the bootstrap is inconsistent, but subsampling yields valid inference. See Swanepoel (1986), Politis and Romano (1994) or Canty et. al. (2006). |
53,426 | A Monte Carlo confidence interval method for the maximum of a uniform distribution? | Why not just use the definition of confidence interval?
You seek an upper confidence limit for $\theta$, say with $1-\alpha$ coverage. Because this is tantamount to estimating a scale and $X_n$ is your test statistic, you should seek a UCL of the form $cX_n$ for some universal constant $c$. All this means (by definit... | A Monte Carlo confidence interval method for the maximum of a uniform distribution? | Why not just use the definition of confidence interval?
You seek an upper confidence limit for $\theta$, say with $1-\alpha$ coverage. Because this is tantamount to estimating a scale and $X_n$ is yo | A Monte Carlo confidence interval method for the maximum of a uniform distribution?
Why not just use the definition of confidence interval?
You seek an upper confidence limit for $\theta$, say with $1-\alpha$ coverage. Because this is tantamount to estimating a scale and $X_n$ is your test statistic, you should seek a... | A Monte Carlo confidence interval method for the maximum of a uniform distribution?
Why not just use the definition of confidence interval?
You seek an upper confidence limit for $\theta$, say with $1-\alpha$ coverage. Because this is tantamount to estimating a scale and $X_n$ is yo |
53,427 | When parsing text for n-grams - should punctuation be included? | Here's a hint: Google doesn't include punctuation in n-grams. | When parsing text for n-grams - should punctuation be included? | Here's a hint: Google doesn't include punctuation in n-grams. | When parsing text for n-grams - should punctuation be included?
Here's a hint: Google doesn't include punctuation in n-grams. | When parsing text for n-grams - should punctuation be included?
Here's a hint: Google doesn't include punctuation in n-grams. |
53,428 | When parsing text for n-grams - should punctuation be included? | Google ignores punctuation, but there are non-alphanumeric characters that are not ignored.
For example, search these word/phrases:
tech spec
tech-spec
tech,spec
tech_spec
The search results vary, showing Google does consider some characters significant.
Also, are you doing this in non-English languages?
If so, then ... | When parsing text for n-grams - should punctuation be included? | Google ignores punctuation, but there are non-alphanumeric characters that are not ignored.
For example, search these word/phrases:
tech spec
tech-spec
tech,spec
tech_spec
The search results vary, s | When parsing text for n-grams - should punctuation be included?
Google ignores punctuation, but there are non-alphanumeric characters that are not ignored.
For example, search these word/phrases:
tech spec
tech-spec
tech,spec
tech_spec
The search results vary, showing Google does consider some characters significant.... | When parsing text for n-grams - should punctuation be included?
Google ignores punctuation, but there are non-alphanumeric characters that are not ignored.
For example, search these word/phrases:
tech spec
tech-spec
tech,spec
tech_spec
The search results vary, s |
53,429 | Feature naming conventions | With a lot of variables, at some point you are going to want to figure out what's what, and this will be easier if they are meaningful when put in alphabetical order. You are less likely to group them by whether they are logged or not than whether they are in the same "family". So, I'd rearrange your example as this:
... | Feature naming conventions | With a lot of variables, at some point you are going to want to figure out what's what, and this will be easier if they are meaningful when put in alphabetical order. You are less likely to group them | Feature naming conventions
With a lot of variables, at some point you are going to want to figure out what's what, and this will be easier if they are meaningful when put in alphabetical order. You are less likely to group them by whether they are logged or not than whether they are in the same "family". So, I'd rearr... | Feature naming conventions
With a lot of variables, at some point you are going to want to figure out what's what, and this will be easier if they are meaningful when put in alphabetical order. You are less likely to group them |
53,430 | Feature naming conventions | You're making me think about a kind of Hungarian notation for feature names. Cool.
I like feature names that sort alphabetically on some meaningful domain category. In your example, I'd have all income-related features start with income_.
I also like feature name suffixes that make it obvious what kind of values the fe... | Feature naming conventions | You're making me think about a kind of Hungarian notation for feature names. Cool.
I like feature names that sort alphabetically on some meaningful domain category. In your example, I'd have all incom | Feature naming conventions
You're making me think about a kind of Hungarian notation for feature names. Cool.
I like feature names that sort alphabetically on some meaningful domain category. In your example, I'd have all income-related features start with income_.
I also like feature name suffixes that make it obvious... | Feature naming conventions
You're making me think about a kind of Hungarian notation for feature names. Cool.
I like feature names that sort alphabetically on some meaningful domain category. In your example, I'd have all incom |
53,431 | Non-parametric test for unequal samples with subsequent post-hoc analysis? | Yes and Yes. Kruskal-Wallis analysis does not require equal sample size. In latest SPSS versions (from 18, if I remember correctly) there is a new nonparametrics procedure that performs pairwise comparisons with sig. adjustment, as well as step-down post-hoc method. Alternative would be to use very nice macro by Marta ... | Non-parametric test for unequal samples with subsequent post-hoc analysis? | Yes and Yes. Kruskal-Wallis analysis does not require equal sample size. In latest SPSS versions (from 18, if I remember correctly) there is a new nonparametrics procedure that performs pairwise compa | Non-parametric test for unequal samples with subsequent post-hoc analysis?
Yes and Yes. Kruskal-Wallis analysis does not require equal sample size. In latest SPSS versions (from 18, if I remember correctly) there is a new nonparametrics procedure that performs pairwise comparisons with sig. adjustment, as well as step-... | Non-parametric test for unequal samples with subsequent post-hoc analysis?
Yes and Yes. Kruskal-Wallis analysis does not require equal sample size. In latest SPSS versions (from 18, if I remember correctly) there is a new nonparametrics procedure that performs pairwise compa |
53,432 | Non-parametric test for unequal samples with subsequent post-hoc analysis? | According to the formula for the Kruskal-Wallis test statistic, each group can have a different number of observations, so "yes".
Whether this is the best test or not I'm not sure - if you're still in doubt you'd need to post more details. But perhaps this is all you needed to know. Good luck! | Non-parametric test for unequal samples with subsequent post-hoc analysis? | According to the formula for the Kruskal-Wallis test statistic, each group can have a different number of observations, so "yes".
Whether this is the best test or not I'm not sure - if you're still in | Non-parametric test for unequal samples with subsequent post-hoc analysis?
According to the formula for the Kruskal-Wallis test statistic, each group can have a different number of observations, so "yes".
Whether this is the best test or not I'm not sure - if you're still in doubt you'd need to post more details. But ... | Non-parametric test for unequal samples with subsequent post-hoc analysis?
According to the formula for the Kruskal-Wallis test statistic, each group can have a different number of observations, so "yes".
Whether this is the best test or not I'm not sure - if you're still in |
53,433 | Non-parametric test for unequal samples with subsequent post-hoc analysis? | In case of heavy unbalanced groups the Kruskal-Wallis-test may be far off and you should not use it. There is a recent paper published on arXiv by Brunner et al. 2018 "Ranks and Pseudo-Ranks - Paradoxical Results of Rank Tests" in which the authors show that under certain conditions Kruskal-Wallis-test for more than tw... | Non-parametric test for unequal samples with subsequent post-hoc analysis? | In case of heavy unbalanced groups the Kruskal-Wallis-test may be far off and you should not use it. There is a recent paper published on arXiv by Brunner et al. 2018 "Ranks and Pseudo-Ranks - Paradox | Non-parametric test for unequal samples with subsequent post-hoc analysis?
In case of heavy unbalanced groups the Kruskal-Wallis-test may be far off and you should not use it. There is a recent paper published on arXiv by Brunner et al. 2018 "Ranks and Pseudo-Ranks - Paradoxical Results of Rank Tests" in which the auth... | Non-parametric test for unequal samples with subsequent post-hoc analysis?
In case of heavy unbalanced groups the Kruskal-Wallis-test may be far off and you should not use it. There is a recent paper published on arXiv by Brunner et al. 2018 "Ranks and Pseudo-Ranks - Paradox |
53,434 | Non-parametric test for unequal samples with subsequent post-hoc analysis? | 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.
Yes, Mann-Whitney tests are the normal post-hoc test t... | Non-parametric test for unequal samples with subsequent post-hoc analysis? | 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.
| Non-parametric test for unequal samples with subsequent post-hoc analysis?
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.
... | Non-parametric test for unequal samples with subsequent post-hoc analysis?
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.
|
53,435 | Difference between ANCOVA and Hierarchical Regression | There really isn't a difference. In matrix algebra form, regression, ANOVA and ANCOVA are all written as
$Y = X\beta + \epsilon$
They arose in different fields and the output is typically formatted differently, but the meaning is the same. However, in the usual usage of the words, regression incorporates the other two,... | Difference between ANCOVA and Hierarchical Regression | There really isn't a difference. In matrix algebra form, regression, ANOVA and ANCOVA are all written as
$Y = X\beta + \epsilon$
They arose in different fields and the output is typically formatted di | Difference between ANCOVA and Hierarchical Regression
There really isn't a difference. In matrix algebra form, regression, ANOVA and ANCOVA are all written as
$Y = X\beta + \epsilon$
They arose in different fields and the output is typically formatted differently, but the meaning is the same. However, in the usual usag... | Difference between ANCOVA and Hierarchical Regression
There really isn't a difference. In matrix algebra form, regression, ANOVA and ANCOVA are all written as
$Y = X\beta + \epsilon$
They arose in different fields and the output is typically formatted di |
53,436 | The reciprocal of $t$-distributed random variable | One can show that if $X$ has density $f(t)$, then $Y = 1/X$ has density $g(t) = {1\over t^2} f\left( {1\over t} \right)$ (for $t\ne0$).
The density of $T$ with $k$ degrees of freedom is
$$\frac{1}{\sqrt{k\pi}}\frac{\Gamma(\frac{k+1}{2})}{\Gamma(\frac{k}{2})}\frac{1}{(1+\frac{t^2}{k})^{\frac{k+1}{2}}}$$
so the density ... | The reciprocal of $t$-distributed random variable | One can show that if $X$ has density $f(t)$, then $Y = 1/X$ has density $g(t) = {1\over t^2} f\left( {1\over t} \right)$ (for $t\ne0$).
The density of $T$ with $k$ degrees of freedom is
$$\frac{1}{\s | The reciprocal of $t$-distributed random variable
One can show that if $X$ has density $f(t)$, then $Y = 1/X$ has density $g(t) = {1\over t^2} f\left( {1\over t} \right)$ (for $t\ne0$).
The density of $T$ with $k$ degrees of freedom is
$$\frac{1}{\sqrt{k\pi}}\frac{\Gamma(\frac{k+1}{2})}{\Gamma(\frac{k}{2})}\frac{1}{(1... | The reciprocal of $t$-distributed random variable
One can show that if $X$ has density $f(t)$, then $Y = 1/X$ has density $g(t) = {1\over t^2} f\left( {1\over t} \right)$ (for $t\ne0$).
The density of $T$ with $k$ degrees of freedom is
$$\frac{1}{\s |
53,437 | Sampling variables and calculating likelihood in WinBUGS/OpenBUGS | 1) The probabilistic dnorm, dunif etc. functions are just describing the probability distribution which the variable on the left hand side (lhs) is assumed to have. If the variable is a parameter, then it's a prior distribution. If the variable is data, then it's p(data | parameters).
2) The distribution is not wha... | Sampling variables and calculating likelihood in WinBUGS/OpenBUGS | 1) The probabilistic dnorm, dunif etc. functions are just describing the probability distribution which the variable on the left hand side (lhs) is assumed to have. If the variable is a parameter, th | Sampling variables and calculating likelihood in WinBUGS/OpenBUGS
1) The probabilistic dnorm, dunif etc. functions are just describing the probability distribution which the variable on the left hand side (lhs) is assumed to have. If the variable is a parameter, then it's a prior distribution. If the variable is data... | Sampling variables and calculating likelihood in WinBUGS/OpenBUGS
1) The probabilistic dnorm, dunif etc. functions are just describing the probability distribution which the variable on the left hand side (lhs) is assumed to have. If the variable is a parameter, th |
53,438 | Sampling variables and calculating likelihood in WinBUGS/OpenBUGS | There are two categories of programming languages (which WinBUGS definitely is):
procedural (or imperative) languages - they specify how the thing should be done - they specify the algorithm, the sequence of steps
declarative languages - they specify what should be done - they just declare the principle
The WinBUGS m... | Sampling variables and calculating likelihood in WinBUGS/OpenBUGS | There are two categories of programming languages (which WinBUGS definitely is):
procedural (or imperative) languages - they specify how the thing should be done - they specify the algorithm, the seq | Sampling variables and calculating likelihood in WinBUGS/OpenBUGS
There are two categories of programming languages (which WinBUGS definitely is):
procedural (or imperative) languages - they specify how the thing should be done - they specify the algorithm, the sequence of steps
declarative languages - they specify wh... | Sampling variables and calculating likelihood in WinBUGS/OpenBUGS
There are two categories of programming languages (which WinBUGS definitely is):
procedural (or imperative) languages - they specify how the thing should be done - they specify the algorithm, the seq |
53,439 | Predicted by residual plot in R | A plot of residuals versus predicted response is essentially used to spot possible heteroskedasticity (non-constant variance across the range of the predicted values), as well as influential observations (possible outliers). Usually, we expect such plot to exhibit no particular pattern (a funnel-like plot would indica... | Predicted by residual plot in R | A plot of residuals versus predicted response is essentially used to spot possible heteroskedasticity (non-constant variance across the range of the predicted values), as well as influential observat | Predicted by residual plot in R
A plot of residuals versus predicted response is essentially used to spot possible heteroskedasticity (non-constant variance across the range of the predicted values), as well as influential observations (possible outliers). Usually, we expect such plot to exhibit no particular pattern ... | Predicted by residual plot in R
A plot of residuals versus predicted response is essentially used to spot possible heteroskedasticity (non-constant variance across the range of the predicted values), as well as influential observat |
53,440 | Predicted by residual plot in R | After you fit an lm object, you can plot it.
e.g.:
model <- lm(y~x,data=data.frame(y=rnorm(25),x=rnorm(25)))
plot(model)
?plot.lm
edit: example 2, which you should have posted yourself:
rm(list = ls(all = TRUE)) #CLEAR WORKSPACE
library(foreign)
Data <- read.dta('http://dl.dropbox.com/u/22681355/child.iq.dta')
model <... | Predicted by residual plot in R | After you fit an lm object, you can plot it.
e.g.:
model <- lm(y~x,data=data.frame(y=rnorm(25),x=rnorm(25)))
plot(model)
?plot.lm
edit: example 2, which you should have posted yourself:
rm(list = ls( | Predicted by residual plot in R
After you fit an lm object, you can plot it.
e.g.:
model <- lm(y~x,data=data.frame(y=rnorm(25),x=rnorm(25)))
plot(model)
?plot.lm
edit: example 2, which you should have posted yourself:
rm(list = ls(all = TRUE)) #CLEAR WORKSPACE
library(foreign)
Data <- read.dta('http://dl.dropbox.com/u... | Predicted by residual plot in R
After you fit an lm object, you can plot it.
e.g.:
model <- lm(y~x,data=data.frame(y=rnorm(25),x=rnorm(25)))
plot(model)
?plot.lm
edit: example 2, which you should have posted yourself:
rm(list = ls( |
53,441 | Autocorrelation and heteroskedasticity in panel data | A standard way of correcting for this is by using heteroskedasticity and autocorrelation consistent (HAC) standard errors. They are also known after their developers as Newey-West standard errors. They can be applied in Stata using the newey command. The Stata help file for this command is here:
http://www.stata.com/he... | Autocorrelation and heteroskedasticity in panel data | A standard way of correcting for this is by using heteroskedasticity and autocorrelation consistent (HAC) standard errors. They are also known after their developers as Newey-West standard errors. The | Autocorrelation and heteroskedasticity in panel data
A standard way of correcting for this is by using heteroskedasticity and autocorrelation consistent (HAC) standard errors. They are also known after their developers as Newey-West standard errors. They can be applied in Stata using the newey command. The Stata help f... | Autocorrelation and heteroskedasticity in panel data
A standard way of correcting for this is by using heteroskedasticity and autocorrelation consistent (HAC) standard errors. They are also known after their developers as Newey-West standard errors. The |
53,442 | Autocorrelation and heteroskedasticity in panel data | The answer depends on what do you define as heteroskedasticity. For panel data model:
$$y_{it}=x_{it}\beta+u_{it}$$
the heteroskedasticity can be defined in various ways:
$$Eu_{it}^2=\sigma^2_{it}$$
or
$$Eu_{it}^2=\sigma^2_{i}$$
or
$$Eu_{it}^2=\sigma^2_{t}.$$
I am not familiar with Stata, but quick check on the Intern... | Autocorrelation and heteroskedasticity in panel data | The answer depends on what do you define as heteroskedasticity. For panel data model:
$$y_{it}=x_{it}\beta+u_{it}$$
the heteroskedasticity can be defined in various ways:
$$Eu_{it}^2=\sigma^2_{it}$$
o | Autocorrelation and heteroskedasticity in panel data
The answer depends on what do you define as heteroskedasticity. For panel data model:
$$y_{it}=x_{it}\beta+u_{it}$$
the heteroskedasticity can be defined in various ways:
$$Eu_{it}^2=\sigma^2_{it}$$
or
$$Eu_{it}^2=\sigma^2_{i}$$
or
$$Eu_{it}^2=\sigma^2_{t}.$$
I am n... | Autocorrelation and heteroskedasticity in panel data
The answer depends on what do you define as heteroskedasticity. For panel data model:
$$y_{it}=x_{it}\beta+u_{it}$$
the heteroskedasticity can be defined in various ways:
$$Eu_{it}^2=\sigma^2_{it}$$
o |
53,443 | Autocorrelation and heteroskedasticity in panel data | In STATA you can deal with both problems by using the options (for fixed effects)
, fe vce(cluster code)
hope this helps | Autocorrelation and heteroskedasticity in panel data | In STATA you can deal with both problems by using the options (for fixed effects)
, fe vce(cluster code)
hope this helps | Autocorrelation and heteroskedasticity in panel data
In STATA you can deal with both problems by using the options (for fixed effects)
, fe vce(cluster code)
hope this helps | Autocorrelation and heteroskedasticity in panel data
In STATA you can deal with both problems by using the options (for fixed effects)
, fe vce(cluster code)
hope this helps |
53,444 | How to check if the volatility is stationary? | A simple method, useful both for exploration and hypothesis testing, takes the Durbin Watson statistic and the semivariogram as its point of departure: denoting the sequence of residuals by $e_t$ (with $t$ the "index" of the plot), compute
$$\gamma(t) = \frac{1}{2}(e_{t+1}-e_t)^2.$$
The data (as presented in the questi... | How to check if the volatility is stationary? | A simple method, useful both for exploration and hypothesis testing, takes the Durbin Watson statistic and the semivariogram as its point of departure: denoting the sequence of residuals by $e_t$ (wit | How to check if the volatility is stationary?
A simple method, useful both for exploration and hypothesis testing, takes the Durbin Watson statistic and the semivariogram as its point of departure: denoting the sequence of residuals by $e_t$ (with $t$ the "index" of the plot), compute
$$\gamma(t) = \frac{1}{2}(e_{t+1}-... | How to check if the volatility is stationary?
A simple method, useful both for exploration and hypothesis testing, takes the Durbin Watson statistic and the semivariogram as its point of departure: denoting the sequence of residuals by $e_t$ (wit |
53,445 | How to check if the volatility is stationary? | volatility (visually non-constant variability) can arise from a number of sources. To name a few
A non-constant mean of the errors caused by unspecified deterministic variables yielding Pulses, Level Shifts, Seasonal Pulses and /or Local Time Trends ( i.e. an underspecified model )
Actual parameter dynamics over time ... | How to check if the volatility is stationary? | volatility (visually non-constant variability) can arise from a number of sources. To name a few
A non-constant mean of the errors caused by unspecified deterministic variables yielding Pulses, Level | How to check if the volatility is stationary?
volatility (visually non-constant variability) can arise from a number of sources. To name a few
A non-constant mean of the errors caused by unspecified deterministic variables yielding Pulses, Level Shifts, Seasonal Pulses and /or Local Time Trends ( i.e. an underspecifie... | How to check if the volatility is stationary?
volatility (visually non-constant variability) can arise from a number of sources. To name a few
A non-constant mean of the errors caused by unspecified deterministic variables yielding Pulses, Level |
53,446 | How to check if the volatility is stationary? | One approach to test many of the assumptions at once is using visual tests:
Buja, A., Cook, D. Hofmann, H., Lawrence, M. Lee, E.-K., Swayne,
D.F and Wickham, H. (2009) Statistical Inference for exploratory
data analysis and model diagnostics Phil. Trans. R. Soc. A
367, 4361-4383 doi: 10.1098/rsta.2009.0120
This... | How to check if the volatility is stationary? | One approach to test many of the assumptions at once is using visual tests:
Buja, A., Cook, D. Hofmann, H., Lawrence, M. Lee, E.-K., Swayne,
D.F and Wickham, H. (2009) Statistical Inference for exp | How to check if the volatility is stationary?
One approach to test many of the assumptions at once is using visual tests:
Buja, A., Cook, D. Hofmann, H., Lawrence, M. Lee, E.-K., Swayne,
D.F and Wickham, H. (2009) Statistical Inference for exploratory
data analysis and model diagnostics Phil. Trans. R. Soc. A
36... | How to check if the volatility is stationary?
One approach to test many of the assumptions at once is using visual tests:
Buja, A., Cook, D. Hofmann, H., Lawrence, M. Lee, E.-K., Swayne,
D.F and Wickham, H. (2009) Statistical Inference for exp |
53,447 | How to calculate number of participants required to compare mean scores on a questionnaire between two groups? | Defining the problem
I am guessing that you are measuring sportiness on some form of multi-item scale.
Thus, you will have a numeric measure of sportiness.
I am assuming that you will be running an independent groups t-test to test the difference between group means.
Your problem is then a form of a priori power analy... | How to calculate number of participants required to compare mean scores on a questionnaire between t | Defining the problem
I am guessing that you are measuring sportiness on some form of multi-item scale.
Thus, you will have a numeric measure of sportiness.
I am assuming that you will be running an i | How to calculate number of participants required to compare mean scores on a questionnaire between two groups?
Defining the problem
I am guessing that you are measuring sportiness on some form of multi-item scale.
Thus, you will have a numeric measure of sportiness.
I am assuming that you will be running an independen... | How to calculate number of participants required to compare mean scores on a questionnaire between t
Defining the problem
I am guessing that you are measuring sportiness on some form of multi-item scale.
Thus, you will have a numeric measure of sportiness.
I am assuming that you will be running an i |
53,448 | How to calculate number of participants required to compare mean scores on a questionnaire between two groups? | Here is a link to a website that do that for you, but it is in german... :-\ But mybe you can use it...
http://www.bauinfoconsult.de/Stichproben_Rechner.html
This site calculates the number of needed respondents according to the standard error and the expected power. | How to calculate number of participants required to compare mean scores on a questionnaire between t | Here is a link to a website that do that for you, but it is in german... :-\ But mybe you can use it...
http://www.bauinfoconsult.de/Stichproben_Rechner.html
This site calculates the number of needed | How to calculate number of participants required to compare mean scores on a questionnaire between two groups?
Here is a link to a website that do that for you, but it is in german... :-\ But mybe you can use it...
http://www.bauinfoconsult.de/Stichproben_Rechner.html
This site calculates the number of needed responden... | How to calculate number of participants required to compare mean scores on a questionnaire between t
Here is a link to a website that do that for you, but it is in german... :-\ But mybe you can use it...
http://www.bauinfoconsult.de/Stichproben_Rechner.html
This site calculates the number of needed |
53,449 | How to calculate number of participants required to compare mean scores on a questionnaire between two groups? | It is a pretty old question here but since I know of, and made use of a straightforward way of calculating this I ‘d like to share it depending on whether you wish to have a double sided or a single sided hypothesis. Lets help the ones who also wish to know the answer hence forth.
Anyway,for now to keep things simpler ... | How to calculate number of participants required to compare mean scores on a questionnaire between t | It is a pretty old question here but since I know of, and made use of a straightforward way of calculating this I ‘d like to share it depending on whether you wish to have a double sided or a single s | How to calculate number of participants required to compare mean scores on a questionnaire between two groups?
It is a pretty old question here but since I know of, and made use of a straightforward way of calculating this I ‘d like to share it depending on whether you wish to have a double sided or a single sided hypo... | How to calculate number of participants required to compare mean scores on a questionnaire between t
It is a pretty old question here but since I know of, and made use of a straightforward way of calculating this I ‘d like to share it depending on whether you wish to have a double sided or a single s |
53,450 | How to calculate number of participants required to compare mean scores on a questionnaire between two groups? | Here's an English version
Questionnaire or survey sample size How do you work out what sample
size to use for your survey? It is actually a complex calculation.
And consequently, in my experience, people use rules of thumb - like
10%. Such rules of thumb cannot hope to give an adequate estimate of
the needed... | How to calculate number of participants required to compare mean scores on a questionnaire between t | Here's an English version
Questionnaire or survey sample size How do you work out what sample
size to use for your survey? It is actually a complex calculation.
And consequently, in my experienc | How to calculate number of participants required to compare mean scores on a questionnaire between two groups?
Here's an English version
Questionnaire or survey sample size How do you work out what sample
size to use for your survey? It is actually a complex calculation.
And consequently, in my experience, people... | How to calculate number of participants required to compare mean scores on a questionnaire between t
Here's an English version
Questionnaire or survey sample size How do you work out what sample
size to use for your survey? It is actually a complex calculation.
And consequently, in my experienc |
53,451 | How to calculate number of participants required to compare mean scores on a questionnaire between two groups? | Lehr's rule, as quoted by Van Belle is
$$n = \frac{16}{\Delta^2},$$
where $\Delta$ is the posited effect size, which in your case would be $(\mu_{\mbox{fb}} - \mu_{\mbox{non fb}}) / \sigma$, where $\mu$ is the mean 'sportiness', and $\sigma$ is the (pooled) standard deviation of sportiness. You want to collect $n/2$ pa... | How to calculate number of participants required to compare mean scores on a questionnaire between t | Lehr's rule, as quoted by Van Belle is
$$n = \frac{16}{\Delta^2},$$
where $\Delta$ is the posited effect size, which in your case would be $(\mu_{\mbox{fb}} - \mu_{\mbox{non fb}}) / \sigma$, where $\m | How to calculate number of participants required to compare mean scores on a questionnaire between two groups?
Lehr's rule, as quoted by Van Belle is
$$n = \frac{16}{\Delta^2},$$
where $\Delta$ is the posited effect size, which in your case would be $(\mu_{\mbox{fb}} - \mu_{\mbox{non fb}}) / \sigma$, where $\mu$ is the... | How to calculate number of participants required to compare mean scores on a questionnaire between t
Lehr's rule, as quoted by Van Belle is
$$n = \frac{16}{\Delta^2},$$
where $\Delta$ is the posited effect size, which in your case would be $(\mu_{\mbox{fb}} - \mu_{\mbox{non fb}}) / \sigma$, where $\m |
53,452 | At what value of mean and variance should I throw data away? | Let me clear up some misconceptions first. The estimate of your population variance is not high because your sample is small. In fact, just the opposite is often the case, the variance tends to be low because small samples over represent the peak of the distribution. The variances of larger samples are more represen... | At what value of mean and variance should I throw data away? | Let me clear up some misconceptions first. The estimate of your population variance is not high because your sample is small. In fact, just the opposite is often the case, the variance tends to be l | At what value of mean and variance should I throw data away?
Let me clear up some misconceptions first. The estimate of your population variance is not high because your sample is small. In fact, just the opposite is often the case, the variance tends to be low because small samples over represent the peak of the dis... | At what value of mean and variance should I throw data away?
Let me clear up some misconceptions first. The estimate of your population variance is not high because your sample is small. In fact, just the opposite is often the case, the variance tends to be l |
53,453 | Repeated measures ANOVA in R and Bonferroni adjusted intervals | I agree with suncoolsu that it is difficult to tell exactly what you were looking for. And in general it's not recommended to do standard repeated measures ANOVAs anymore since there are generally better alternatives.
Nevertheless, perhaps you want to generate a simple stratified ANOVA. By stratified I mean that yo... | Repeated measures ANOVA in R and Bonferroni adjusted intervals | I agree with suncoolsu that it is difficult to tell exactly what you were looking for. And in general it's not recommended to do standard repeated measures ANOVAs anymore since there are generally be | Repeated measures ANOVA in R and Bonferroni adjusted intervals
I agree with suncoolsu that it is difficult to tell exactly what you were looking for. And in general it's not recommended to do standard repeated measures ANOVAs anymore since there are generally better alternatives.
Nevertheless, perhaps you want to ge... | Repeated measures ANOVA in R and Bonferroni adjusted intervals
I agree with suncoolsu that it is difficult to tell exactly what you were looking for. And in general it's not recommended to do standard repeated measures ANOVAs anymore since there are generally be |
53,454 | Repeated measures ANOVA in R and Bonferroni adjusted intervals | You should provide more details about your data. From the limited details provided by you, assuming you have a data frame df which has response, trt, time, and subject information, then these are many ways to fit a LME model in R using lme4 package. However, I will illustrate three methods that I think will be useful f... | Repeated measures ANOVA in R and Bonferroni adjusted intervals | You should provide more details about your data. From the limited details provided by you, assuming you have a data frame df which has response, trt, time, and subject information, then these are many | Repeated measures ANOVA in R and Bonferroni adjusted intervals
You should provide more details about your data. From the limited details provided by you, assuming you have a data frame df which has response, trt, time, and subject information, then these are many ways to fit a LME model in R using lme4 package. However... | Repeated measures ANOVA in R and Bonferroni adjusted intervals
You should provide more details about your data. From the limited details provided by you, assuming you have a data frame df which has response, trt, time, and subject information, then these are many |
53,455 | Off the shelf tool for multi-label classification | As @mbq suggests, a battery of binary classifiers is a good place to start. Ridge regression classifiers work pretty well on text classification problems (choose the ridge parameter via leave-one-out cross-validation. If training time is not an issue, also use the bootstrap as a further protection against over-fittin... | Off the shelf tool for multi-label classification | As @mbq suggests, a battery of binary classifiers is a good place to start. Ridge regression classifiers work pretty well on text classification problems (choose the ridge parameter via leave-one-out | Off the shelf tool for multi-label classification
As @mbq suggests, a battery of binary classifiers is a good place to start. Ridge regression classifiers work pretty well on text classification problems (choose the ridge parameter via leave-one-out cross-validation. If training time is not an issue, also use the boo... | Off the shelf tool for multi-label classification
As @mbq suggests, a battery of binary classifiers is a good place to start. Ridge regression classifiers work pretty well on text classification problems (choose the ridge parameter via leave-one-out |
53,456 | Off the shelf tool for multi-label classification | About multilabel classification, the baseline (but usually quite good) approach is just to make a battery of binary classifiers, each trained to recognize one class versus all other, use them all on each sample and combine their answers.
This is trivial to implement, so almost any tool will do.
However, you have other ... | Off the shelf tool for multi-label classification | About multilabel classification, the baseline (but usually quite good) approach is just to make a battery of binary classifiers, each trained to recognize one class versus all other, use them all on e | Off the shelf tool for multi-label classification
About multilabel classification, the baseline (but usually quite good) approach is just to make a battery of binary classifiers, each trained to recognize one class versus all other, use them all on each sample and combine their answers.
This is trivial to implement, so... | Off the shelf tool for multi-label classification
About multilabel classification, the baseline (but usually quite good) approach is just to make a battery of binary classifiers, each trained to recognize one class versus all other, use them all on e |
53,457 | Off the shelf tool for multi-label classification | Try using R. You can use the factor data type to store what you are calling each document's "label."
Before you use any predictive functions, you'll have to calculate some quantitative attributes about each document. This is going to depend on the nature of your problem, but check out the Natural Language Processing ... | Off the shelf tool for multi-label classification | Try using R. You can use the factor data type to store what you are calling each document's "label."
Before you use any predictive functions, you'll have to calculate some quantitative attributes abo | Off the shelf tool for multi-label classification
Try using R. You can use the factor data type to store what you are calling each document's "label."
Before you use any predictive functions, you'll have to calculate some quantitative attributes about each document. This is going to depend on the nature of your probl... | Off the shelf tool for multi-label classification
Try using R. You can use the factor data type to store what you are calling each document's "label."
Before you use any predictive functions, you'll have to calculate some quantitative attributes abo |
53,458 | Off the shelf tool for multi-label classification | One of my homework assignments this semester was exactly to do his (well, not exactly, most of it was already implemented and we had to do some improvements and experimentation).
You can read the details here.
Anyway, whatever method you choose to use, I suggest to take a look at Weka.
Most probably the classifier you... | Off the shelf tool for multi-label classification | One of my homework assignments this semester was exactly to do his (well, not exactly, most of it was already implemented and we had to do some improvements and experimentation).
You can read the deta | Off the shelf tool for multi-label classification
One of my homework assignments this semester was exactly to do his (well, not exactly, most of it was already implemented and we had to do some improvements and experimentation).
You can read the details here.
Anyway, whatever method you choose to use, I suggest to take... | Off the shelf tool for multi-label classification
One of my homework assignments this semester was exactly to do his (well, not exactly, most of it was already implemented and we had to do some improvements and experimentation).
You can read the deta |
53,459 | Off the shelf tool for multi-label classification | In general, you'll need to specify what make each category unique to be able to choose which classifier to use. In a silly example, if categories are based on the size of the document... you got the idea.
One good classifier that I used in this task was using the Normalized Compression Distance (NCD). It measures how c... | Off the shelf tool for multi-label classification | In general, you'll need to specify what make each category unique to be able to choose which classifier to use. In a silly example, if categories are based on the size of the document... you got the i | Off the shelf tool for multi-label classification
In general, you'll need to specify what make each category unique to be able to choose which classifier to use. In a silly example, if categories are based on the size of the document... you got the idea.
One good classifier that I used in this task was using the Normal... | Off the shelf tool for multi-label classification
In general, you'll need to specify what make each category unique to be able to choose which classifier to use. In a silly example, if categories are based on the size of the document... you got the i |
53,460 | Off the shelf tool for multi-label classification | I haven't used them yet, but Mulan and Meka are multi-label addons for Weka. Meka seems more off the shelf, and includes support for Mulan. | Off the shelf tool for multi-label classification | I haven't used them yet, but Mulan and Meka are multi-label addons for Weka. Meka seems more off the shelf, and includes support for Mulan. | Off the shelf tool for multi-label classification
I haven't used them yet, but Mulan and Meka are multi-label addons for Weka. Meka seems more off the shelf, and includes support for Mulan. | Off the shelf tool for multi-label classification
I haven't used them yet, but Mulan and Meka are multi-label addons for Weka. Meka seems more off the shelf, and includes support for Mulan. |
53,461 | Convex Hull in R | I think you want the convex hull of your data. Try this
library(grDevices) # load grDevices package
df <- data.frame(X = c(-62, -40, 9, 13, 26, 27, 27),
Y = c( 7, -14, 10, 9, -8, -16, 12)) # store X,Y together
con.hull.pos <- chull(df) # find positions of convex hull
con.hull <- rbind(df[co... | Convex Hull in R | I think you want the convex hull of your data. Try this
library(grDevices) # load grDevices package
df <- data.frame(X = c(-62, -40, 9, 13, 26, 27, 27),
Y = c( 7, -14, 10, 9 | Convex Hull in R
I think you want the convex hull of your data. Try this
library(grDevices) # load grDevices package
df <- data.frame(X = c(-62, -40, 9, 13, 26, 27, 27),
Y = c( 7, -14, 10, 9, -8, -16, 12)) # store X,Y together
con.hull.pos <- chull(df) # find positions of convex hull
con.hu... | Convex Hull in R
I think you want the convex hull of your data. Try this
library(grDevices) # load grDevices package
df <- data.frame(X = c(-62, -40, 9, 13, 26, 27, 27),
Y = c( 7, -14, 10, 9 |
53,462 | Convex Hull in R | I'm not 100% sure I'm following what you are trying to do with abline, but maybe this will move you in the right direction. You can use the function which.min() and which.max() to return the minimum or maximum values from a vector. You can combine that with the [ operator to index a second vector with that condition. F... | Convex Hull in R | I'm not 100% sure I'm following what you are trying to do with abline, but maybe this will move you in the right direction. You can use the function which.min() and which.max() to return the minimum o | Convex Hull in R
I'm not 100% sure I'm following what you are trying to do with abline, but maybe this will move you in the right direction. You can use the function which.min() and which.max() to return the minimum or maximum values from a vector. You can combine that with the [ operator to index a second vector with ... | Convex Hull in R
I'm not 100% sure I'm following what you are trying to do with abline, but maybe this will move you in the right direction. You can use the function which.min() and which.max() to return the minimum o |
53,463 | Interpreting p-values associated with correlation measurements | One explanation is that outliers, even mild ones can affect the results in a pearson correlation. If the outlier is a legitimate point (not a typo or other error) then it should increase the significance of the correlation (as you see), but will not change much in the other 2, so it is easy for the pearson correlation... | Interpreting p-values associated with correlation measurements | One explanation is that outliers, even mild ones can affect the results in a pearson correlation. If the outlier is a legitimate point (not a typo or other error) then it should increase the signific | Interpreting p-values associated with correlation measurements
One explanation is that outliers, even mild ones can affect the results in a pearson correlation. If the outlier is a legitimate point (not a typo or other error) then it should increase the significance of the correlation (as you see), but will not change... | Interpreting p-values associated with correlation measurements
One explanation is that outliers, even mild ones can affect the results in a pearson correlation. If the outlier is a legitimate point (not a typo or other error) then it should increase the signific |
53,464 | Interpreting p-values associated with correlation measurements | @Greg Snow is on the money about your first question.
In regard to your second, comparing the two tests is misleading since two hypotheses are different even though the scientific question is (ostensibly) the same. This is a case where it's really important to be explicit about what hypothesis test you're using.
To be ... | Interpreting p-values associated with correlation measurements | @Greg Snow is on the money about your first question.
In regard to your second, comparing the two tests is misleading since two hypotheses are different even though the scientific question is (ostensi | Interpreting p-values associated with correlation measurements
@Greg Snow is on the money about your first question.
In regard to your second, comparing the two tests is misleading since two hypotheses are different even though the scientific question is (ostensibly) the same. This is a case where it's really important... | Interpreting p-values associated with correlation measurements
@Greg Snow is on the money about your first question.
In regard to your second, comparing the two tests is misleading since two hypotheses are different even though the scientific question is (ostensi |
53,465 | Rescaling for desired standard deviation | The SD is directly proportional to the data. Therefore, to change it from 10 to 15 = 1.5 * 10, multiply all scores by 1.5. The other way is to multiply all scores by -1.5, because negating all values does not change the SD. Of course you can also add an arbitrary constant to all the scores, too, without changing the... | Rescaling for desired standard deviation | The SD is directly proportional to the data. Therefore, to change it from 10 to 15 = 1.5 * 10, multiply all scores by 1.5. The other way is to multiply all scores by -1.5, because negating all value | Rescaling for desired standard deviation
The SD is directly proportional to the data. Therefore, to change it from 10 to 15 = 1.5 * 10, multiply all scores by 1.5. The other way is to multiply all scores by -1.5, because negating all values does not change the SD. Of course you can also add an arbitrary constant to ... | Rescaling for desired standard deviation
The SD is directly proportional to the data. Therefore, to change it from 10 to 15 = 1.5 * 10, multiply all scores by 1.5. The other way is to multiply all scores by -1.5, because negating all value |
53,466 | Rescaling for desired standard deviation | If you have a random variable (or observed data) $X$ with mean $\mu_x$ and standard deviation $\sigma_x$, and then apply any linear transformation $$Y=a+bX$$ then you will find the mean of $Y$ is $$\mu_y = a + b \mu_x$$ and the standard deviation of $Y$ is $$\sigma_y = |b|\; \sigma_x.$$
So for example, as whuber says, ... | Rescaling for desired standard deviation | If you have a random variable (or observed data) $X$ with mean $\mu_x$ and standard deviation $\sigma_x$, and then apply any linear transformation $$Y=a+bX$$ then you will find the mean of $Y$ is $$\m | Rescaling for desired standard deviation
If you have a random variable (or observed data) $X$ with mean $\mu_x$ and standard deviation $\sigma_x$, and then apply any linear transformation $$Y=a+bX$$ then you will find the mean of $Y$ is $$\mu_y = a + b \mu_x$$ and the standard deviation of $Y$ is $$\sigma_y = |b|\; \si... | Rescaling for desired standard deviation
If you have a random variable (or observed data) $X$ with mean $\mu_x$ and standard deviation $\sigma_x$, and then apply any linear transformation $$Y=a+bX$$ then you will find the mean of $Y$ is $$\m |
53,467 | Why does adding statistically significant interaction reduce true positives? | "True positives", like proportion classified correctly, requires arbitrary and information losing categorization of the predicted values. These are improper scoring rules. An improper scoring rule is a criterion that when optimized leads to a bogus model. Also, watch out when using P-values in any way to guide model... | Why does adding statistically significant interaction reduce true positives? | "True positives", like proportion classified correctly, requires arbitrary and information losing categorization of the predicted values. These are improper scoring rules. An improper scoring rule i | Why does adding statistically significant interaction reduce true positives?
"True positives", like proportion classified correctly, requires arbitrary and information losing categorization of the predicted values. These are improper scoring rules. An improper scoring rule is a criterion that when optimized leads to ... | Why does adding statistically significant interaction reduce true positives?
"True positives", like proportion classified correctly, requires arbitrary and information losing categorization of the predicted values. These are improper scoring rules. An improper scoring rule i |
53,468 | Why does adding statistically significant interaction reduce true positives? | No it should not. e.g. for logistic regression, which appears to be the case here, it may be that the greater p-value (I'm assuming you mean the right kind of p-value here, like from a likelihood ratio test) comes from increasing the odds for (correctly predicted) observations that are already relatively extreme (e.g. ... | Why does adding statistically significant interaction reduce true positives? | No it should not. e.g. for logistic regression, which appears to be the case here, it may be that the greater p-value (I'm assuming you mean the right kind of p-value here, like from a likelihood rati | Why does adding statistically significant interaction reduce true positives?
No it should not. e.g. for logistic regression, which appears to be the case here, it may be that the greater p-value (I'm assuming you mean the right kind of p-value here, like from a likelihood ratio test) comes from increasing the odds for ... | Why does adding statistically significant interaction reduce true positives?
No it should not. e.g. for logistic regression, which appears to be the case here, it may be that the greater p-value (I'm assuming you mean the right kind of p-value here, like from a likelihood rati |
53,469 | Using and/or operators in R | See ?"&": the single version does elementwise comparisons (for when you are doing logical operations on two vectors of the same length, e.g. if in your example x<-c(1.5,3.5). The other one works just like C++'s or java's &&: it only looks at the first element of each vector (this is typically an unexpected downside), b... | Using and/or operators in R | See ?"&": the single version does elementwise comparisons (for when you are doing logical operations on two vectors of the same length, e.g. if in your example x<-c(1.5,3.5). The other one works just | Using and/or operators in R
See ?"&": the single version does elementwise comparisons (for when you are doing logical operations on two vectors of the same length, e.g. if in your example x<-c(1.5,3.5). The other one works just like C++'s or java's &&: it only looks at the first element of each vector (this is typicall... | Using and/or operators in R
See ?"&": the single version does elementwise comparisons (for when you are doing logical operations on two vectors of the same length, e.g. if in your example x<-c(1.5,3.5). The other one works just |
53,470 | Probability that two values drawn at random from a normal distribution are separated by at most T | The distribution of the difference between two single independent samples from normal distributions has a mean which is the difference between the means of the original distributions and a variance which is the sum of the variances of the original distributions.
So in your case, if the normal distribution of $X$ has ... | Probability that two values drawn at random from a normal distribution are separated by at most T | The distribution of the difference between two single independent samples from normal distributions has a mean which is the difference between the means of the original distributions and a variance wh | Probability that two values drawn at random from a normal distribution are separated by at most T
The distribution of the difference between two single independent samples from normal distributions has a mean which is the difference between the means of the original distributions and a variance which is the sum of the ... | Probability that two values drawn at random from a normal distribution are separated by at most T
The distribution of the difference between two single independent samples from normal distributions has a mean which is the difference between the means of the original distributions and a variance wh |
53,471 | 2x2 chi-square test vs. binomial proportion statistic | Suppose you have
Case 1:
A=200, B=100
C=100, D=200
versus
Case 2:
A=200, B=0
C=200, D=200
The B=0 in case 2 means that case 2 provides much stronger evidence than case 1 of a relationship between X and Outcome; but in your test, both cases would be scored the same.
The Chi-Square test, informally speaking, not only... | 2x2 chi-square test vs. binomial proportion statistic | Suppose you have
Case 1:
A=200, B=100
C=100, D=200
versus
Case 2:
A=200, B=0
C=200, D=200
The B=0 in case 2 means that case 2 provides much stronger evidence than case 1 of a relationship between | 2x2 chi-square test vs. binomial proportion statistic
Suppose you have
Case 1:
A=200, B=100
C=100, D=200
versus
Case 2:
A=200, B=0
C=200, D=200
The B=0 in case 2 means that case 2 provides much stronger evidence than case 1 of a relationship between X and Outcome; but in your test, both cases would be scored the sa... | 2x2 chi-square test vs. binomial proportion statistic
Suppose you have
Case 1:
A=200, B=100
C=100, D=200
versus
Case 2:
A=200, B=0
C=200, D=200
The B=0 in case 2 means that case 2 provides much stronger evidence than case 1 of a relationship between |
53,472 | 2x2 chi-square test vs. binomial proportion statistic | Shouldn't you favour Fisher's exact test on a 2x2 contingency table? The advantages of the Chi-squared test would be preserved, with the additional advantages of an exact test. Based on a few handbook readings, I believe that Fisher's exact test is recommended chiefly with low cell counts, but it is also often also rec... | 2x2 chi-square test vs. binomial proportion statistic | Shouldn't you favour Fisher's exact test on a 2x2 contingency table? The advantages of the Chi-squared test would be preserved, with the additional advantages of an exact test. Based on a few handbook | 2x2 chi-square test vs. binomial proportion statistic
Shouldn't you favour Fisher's exact test on a 2x2 contingency table? The advantages of the Chi-squared test would be preserved, with the additional advantages of an exact test. Based on a few handbook readings, I believe that Fisher's exact test is recommended chief... | 2x2 chi-square test vs. binomial proportion statistic
Shouldn't you favour Fisher's exact test on a 2x2 contingency table? The advantages of the Chi-squared test would be preserved, with the additional advantages of an exact test. Based on a few handbook |
53,473 | 2x2 chi-square test vs. binomial proportion statistic | Fisher's exact was once only recommended for low cell counts because back in "the dark times" it was computationally infeasible to use it for large counts. Indeed, with some approximations doing small counts properly demands a correction be applied.
No matter, the hypergeometric tests involved are good and powerful. ... | 2x2 chi-square test vs. binomial proportion statistic | Fisher's exact was once only recommended for low cell counts because back in "the dark times" it was computationally infeasible to use it for large counts. Indeed, with some approximations doing smal | 2x2 chi-square test vs. binomial proportion statistic
Fisher's exact was once only recommended for low cell counts because back in "the dark times" it was computationally infeasible to use it for large counts. Indeed, with some approximations doing small counts properly demands a correction be applied.
No matter, the... | 2x2 chi-square test vs. binomial proportion statistic
Fisher's exact was once only recommended for low cell counts because back in "the dark times" it was computationally infeasible to use it for large counts. Indeed, with some approximations doing smal |
53,474 | 2x2 chi-square test vs. binomial proportion statistic | KL divergence is another good test statistic to use. It has very similar properties to the chi-square for large expected cell counts, but retains these "good" properties even when expected cell counts are small.
The KL divergence is given by:
$$\sum_{i}O_{i}\log\left(\frac{O_{i}}{E_{i}}\right)$$
And this statistic nev... | 2x2 chi-square test vs. binomial proportion statistic | KL divergence is another good test statistic to use. It has very similar properties to the chi-square for large expected cell counts, but retains these "good" properties even when expected cell count | 2x2 chi-square test vs. binomial proportion statistic
KL divergence is another good test statistic to use. It has very similar properties to the chi-square for large expected cell counts, but retains these "good" properties even when expected cell counts are small.
The KL divergence is given by:
$$\sum_{i}O_{i}\log\le... | 2x2 chi-square test vs. binomial proportion statistic
KL divergence is another good test statistic to use. It has very similar properties to the chi-square for large expected cell counts, but retains these "good" properties even when expected cell count |
53,475 | Spam filtering using naive Bayesian classifiers with the e1071/klaR package on R | The NaiveBayes() function in the klaR package obeys the classical formula R interface whereby you express your outcome as a function of its predictors, e.g. spam ~ x1+x2+x3. If your data are stored in a data.frame, you can input all predictors in the rhs of the formula using dot notation: spam ~ ., data=df means "spam ... | Spam filtering using naive Bayesian classifiers with the e1071/klaR package on R | The NaiveBayes() function in the klaR package obeys the classical formula R interface whereby you express your outcome as a function of its predictors, e.g. spam ~ x1+x2+x3. If your data are stored in | Spam filtering using naive Bayesian classifiers with the e1071/klaR package on R
The NaiveBayes() function in the klaR package obeys the classical formula R interface whereby you express your outcome as a function of its predictors, e.g. spam ~ x1+x2+x3. If your data are stored in a data.frame, you can input all predic... | Spam filtering using naive Bayesian classifiers with the e1071/klaR package on R
The NaiveBayes() function in the klaR package obeys the classical formula R interface whereby you express your outcome as a function of its predictors, e.g. spam ~ x1+x2+x3. If your data are stored in |
53,476 | Spam filtering using naive Bayesian classifiers with the e1071/klaR package on R | There is a new book out called:
Machine Learning for Email: Spam Filtering and Priority Inbox
http://www.amazon.com/Machine-Learning-Email-Filtering-Priority/dp/1449314309/ref=sr_1_1?s=books&ie=UTF8&qid=1323836340&sr=1-1
I browsed the contents and the authors are explaining Bayesian spam filtering.
HTH | Spam filtering using naive Bayesian classifiers with the e1071/klaR package on R | There is a new book out called:
Machine Learning for Email: Spam Filtering and Priority Inbox
http://www.amazon.com/Machine-Learning-Email-Filtering-Priority/dp/1449314309/ref=sr_1_1?s=books&ie=UTF8&q | Spam filtering using naive Bayesian classifiers with the e1071/klaR package on R
There is a new book out called:
Machine Learning for Email: Spam Filtering and Priority Inbox
http://www.amazon.com/Machine-Learning-Email-Filtering-Priority/dp/1449314309/ref=sr_1_1?s=books&ie=UTF8&qid=1323836340&sr=1-1
I browsed the cont... | Spam filtering using naive Bayesian classifiers with the e1071/klaR package on R
There is a new book out called:
Machine Learning for Email: Spam Filtering and Priority Inbox
http://www.amazon.com/Machine-Learning-Email-Filtering-Priority/dp/1449314309/ref=sr_1_1?s=books&ie=UTF8&q |
53,477 | What does plotting residuals from one regression against the residuals from another regression give us? | This sounds like what I call an "added variable" plot. The idea behind these is to provide a visual way of whether adding a variable to a model (ht9 in your case) is likely to add anything to the model (soma on wt9 in your case).
It was explained to me like this. When you fit a linear regression, the order of the var... | What does plotting residuals from one regression against the residuals from another regression give | This sounds like what I call an "added variable" plot. The idea behind these is to provide a visual way of whether adding a variable to a model (ht9 in your case) is likely to add anything to the mod | What does plotting residuals from one regression against the residuals from another regression give us?
This sounds like what I call an "added variable" plot. The idea behind these is to provide a visual way of whether adding a variable to a model (ht9 in your case) is likely to add anything to the model (soma on wt9 ... | What does plotting residuals from one regression against the residuals from another regression give
This sounds like what I call an "added variable" plot. The idea behind these is to provide a visual way of whether adding a variable to a model (ht9 in your case) is likely to add anything to the mod |
53,478 | What does plotting residuals from one regression against the residuals from another regression give us? | Judging by the details and variable names, soma.WT9 and HT9.WT9, you are obtaining the residuals by first regressing, soma on WT9 and HT9 on WT9 (right?). If I understood you correctly, the scatter plot between soma.WT9 and HT9.WT9 will tell you -- if after removing the effects of WT9 (possibly linear effects in your c... | What does plotting residuals from one regression against the residuals from another regression give | Judging by the details and variable names, soma.WT9 and HT9.WT9, you are obtaining the residuals by first regressing, soma on WT9 and HT9 on WT9 (right?). If I understood you correctly, the scatter pl | What does plotting residuals from one regression against the residuals from another regression give us?
Judging by the details and variable names, soma.WT9 and HT9.WT9, you are obtaining the residuals by first regressing, soma on WT9 and HT9 on WT9 (right?). If I understood you correctly, the scatter plot between soma.... | What does plotting residuals from one regression against the residuals from another regression give
Judging by the details and variable names, soma.WT9 and HT9.WT9, you are obtaining the residuals by first regressing, soma on WT9 and HT9 on WT9 (right?). If I understood you correctly, the scatter pl |
53,479 | What is the right statistical test to use when attesting claims like "60% people switched over from group A to group B"? | I think what you're probably looking for are the so-called mover-stayer models. The basic model is a loglinear model that allows for an "inflated" diagonal and usually involves some form of symmetry on the off-diagonals.
A good place to start is A. Agresti, Categorical Data Analysis, 2nd ed., pp. 423--428. (That's the ... | What is the right statistical test to use when attesting claims like "60% people switched over from | I think what you're probably looking for are the so-called mover-stayer models. The basic model is a loglinear model that allows for an "inflated" diagonal and usually involves some form of symmetry o | What is the right statistical test to use when attesting claims like "60% people switched over from group A to group B"?
I think what you're probably looking for are the so-called mover-stayer models. The basic model is a loglinear model that allows for an "inflated" diagonal and usually involves some form of symmetry ... | What is the right statistical test to use when attesting claims like "60% people switched over from
I think what you're probably looking for are the so-called mover-stayer models. The basic model is a loglinear model that allows for an "inflated" diagonal and usually involves some form of symmetry o |
53,480 | What is the right statistical test to use when attesting claims like "60% people switched over from group A to group B"? | I would start with building the sums:
Stage 1 Stage 2
A B C D E | Sum
-----------------------------------
Group A 18 0 0 0 2 | 20
Group B 0 6 0 0 3 | 9
Group C 4 0 2 0 2 | 8
Group D 0 0 0 0 0 | 0
Group E 4 2 0 1 8 | ... | What is the right statistical test to use when attesting claims like "60% people switched over from | I would start with building the sums:
Stage 1 Stage 2
A B C D E | Sum
-----------------------------------
Group A 18 0 0 0 2 | 20
Group B 0 6 | What is the right statistical test to use when attesting claims like "60% people switched over from group A to group B"?
I would start with building the sums:
Stage 1 Stage 2
A B C D E | Sum
-----------------------------------
Group A 18 0 0 0 2 | 20
Group B 0 6 ... | What is the right statistical test to use when attesting claims like "60% people switched over from
I would start with building the sums:
Stage 1 Stage 2
A B C D E | Sum
-----------------------------------
Group A 18 0 0 0 2 | 20
Group B 0 6 |
53,481 | Visualizing the distribution of something within a very large body of data | While Whuber is correct in principle you still might be able to see something because your word is very infrequent and you only want plots of the one word. Something quite uncommon might only appear 30 times, probably not more than 500.
Let's say you convert your words into a single vector of words that's a million lo... | Visualizing the distribution of something within a very large body of data | While Whuber is correct in principle you still might be able to see something because your word is very infrequent and you only want plots of the one word. Something quite uncommon might only appear | Visualizing the distribution of something within a very large body of data
While Whuber is correct in principle you still might be able to see something because your word is very infrequent and you only want plots of the one word. Something quite uncommon might only appear 30 times, probably not more than 500.
Let's s... | Visualizing the distribution of something within a very large body of data
While Whuber is correct in principle you still might be able to see something because your word is very infrequent and you only want plots of the one word. Something quite uncommon might only appear |
53,482 | Visualizing the distribution of something within a very large body of data | With a 1200 dpi printer using the thinnest possible line (one pixel) for each word, your plot of a million words would still be almost 20 meters long!
Maybe a density plot would be more helpful. | Visualizing the distribution of something within a very large body of data | With a 1200 dpi printer using the thinnest possible line (one pixel) for each word, your plot of a million words would still be almost 20 meters long!
Maybe a density plot would be more helpful. | Visualizing the distribution of something within a very large body of data
With a 1200 dpi printer using the thinnest possible line (one pixel) for each word, your plot of a million words would still be almost 20 meters long!
Maybe a density plot would be more helpful. | Visualizing the distribution of something within a very large body of data
With a 1200 dpi printer using the thinnest possible line (one pixel) for each word, your plot of a million words would still be almost 20 meters long!
Maybe a density plot would be more helpful. |
53,483 | Visualizing the distribution of something within a very large body of data | Let's take a derivative (difference) here, so instead of working with location, you work directly with what you want: distance.
Say word FOO appears in the text 30 times. Calculate the distance (number of other words) between each consecutive occurrence of FOO, creating a vector of 29 distances. Then pick your plot: hi... | Visualizing the distribution of something within a very large body of data | Let's take a derivative (difference) here, so instead of working with location, you work directly with what you want: distance.
Say word FOO appears in the text 30 times. Calculate the distance (numbe | Visualizing the distribution of something within a very large body of data
Let's take a derivative (difference) here, so instead of working with location, you work directly with what you want: distance.
Say word FOO appears in the text 30 times. Calculate the distance (number of other words) between each consecutive oc... | Visualizing the distribution of something within a very large body of data
Let's take a derivative (difference) here, so instead of working with location, you work directly with what you want: distance.
Say word FOO appears in the text 30 times. Calculate the distance (numbe |
53,484 | Visualizing the distribution of something within a very large body of data | I don't know if this may be useful in your case, but in bioinformatics I often feel the need to visualize the distribution of gene counts in a give data set. This is definitely not as large as your data set, but I think the strategy can be followed for most of the large data sets.
A typical strategy would be to find a ... | Visualizing the distribution of something within a very large body of data | I don't know if this may be useful in your case, but in bioinformatics I often feel the need to visualize the distribution of gene counts in a give data set. This is definitely not as large as your da | Visualizing the distribution of something within a very large body of data
I don't know if this may be useful in your case, but in bioinformatics I often feel the need to visualize the distribution of gene counts in a give data set. This is definitely not as large as your data set, but I think the strategy can be follo... | Visualizing the distribution of something within a very large body of data
I don't know if this may be useful in your case, but in bioinformatics I often feel the need to visualize the distribution of gene counts in a give data set. This is definitely not as large as your da |
53,485 | Can constrained optimization techniques be applied to unconstrained problems? | As far as I'm concerned, constrained optimization is a less-than-optimal way of avoiding strong fluctuations in your parameters for the independents due to a bad model-specification. Pretty often a constraint is "needed" when the variance-covariance matrix is ill-structured, when there is a lot of (unaccounted) correla... | Can constrained optimization techniques be applied to unconstrained problems? | As far as I'm concerned, constrained optimization is a less-than-optimal way of avoiding strong fluctuations in your parameters for the independents due to a bad model-specification. Pretty often a co | Can constrained optimization techniques be applied to unconstrained problems?
As far as I'm concerned, constrained optimization is a less-than-optimal way of avoiding strong fluctuations in your parameters for the independents due to a bad model-specification. Pretty often a constraint is "needed" when the variance-cov... | Can constrained optimization techniques be applied to unconstrained problems?
As far as I'm concerned, constrained optimization is a less-than-optimal way of avoiding strong fluctuations in your parameters for the independents due to a bad model-specification. Pretty often a co |
53,486 | Can constrained optimization techniques be applied to unconstrained problems? | As far as I know, there is no reason to stop you from applying constrained optimization to an unconstrainted problem. However, this may not be a great idea in terms of computational complexity and convergence. For example, fitting a logistic regression model can done efficiently with the Newton-Raphson approach (or the... | Can constrained optimization techniques be applied to unconstrained problems? | As far as I know, there is no reason to stop you from applying constrained optimization to an unconstrainted problem. However, this may not be a great idea in terms of computational complexity and con | Can constrained optimization techniques be applied to unconstrained problems?
As far as I know, there is no reason to stop you from applying constrained optimization to an unconstrainted problem. However, this may not be a great idea in terms of computational complexity and convergence. For example, fitting a logistic ... | Can constrained optimization techniques be applied to unconstrained problems?
As far as I know, there is no reason to stop you from applying constrained optimization to an unconstrainted problem. However, this may not be a great idea in terms of computational complexity and con |
53,487 | Can constrained optimization techniques be applied to unconstrained problems? | The general sense in optimization is that if you have a convex function and no constraints, you want to use the "powerful stuff", gradient descent, Newton, etc. Without constraints interior point methods are not very good (competitive).
In particular for the problem you're studying (binary logistic regression) you sho... | Can constrained optimization techniques be applied to unconstrained problems? | The general sense in optimization is that if you have a convex function and no constraints, you want to use the "powerful stuff", gradient descent, Newton, etc. Without constraints interior point meth | Can constrained optimization techniques be applied to unconstrained problems?
The general sense in optimization is that if you have a convex function and no constraints, you want to use the "powerful stuff", gradient descent, Newton, etc. Without constraints interior point methods are not very good (competitive).
In p... | Can constrained optimization techniques be applied to unconstrained problems?
The general sense in optimization is that if you have a convex function and no constraints, you want to use the "powerful stuff", gradient descent, Newton, etc. Without constraints interior point meth |
53,488 | CI for a difference based on independent CIs | No, you can't compute a CI for the difference that way I'm afraid, for the same reason you can't use whether the CIs overlap to judge the statistical significance of the difference. See, for example,
"On Judging the Significance of Differences by Examining the Overlap Between Confidence Intervals"
Nathaniel Schenker, ... | CI for a difference based on independent CIs | No, you can't compute a CI for the difference that way I'm afraid, for the same reason you can't use whether the CIs overlap to judge the statistical significance of the difference. See, for example, | CI for a difference based on independent CIs
No, you can't compute a CI for the difference that way I'm afraid, for the same reason you can't use whether the CIs overlap to judge the statistical significance of the difference. See, for example,
"On Judging the Significance of Differences by Examining the Overlap Betwe... | CI for a difference based on independent CIs
No, you can't compute a CI for the difference that way I'm afraid, for the same reason you can't use whether the CIs overlap to judge the statistical significance of the difference. See, for example, |
53,489 | Options for 3D coordinate systems? | Along with the positions of the amino acids, protein folding can also be described by the angles in the peptide bonds such as used in a Ramachandran plot, and with global values such as the radius of gyration.
You could apply all those values together in a single model such as is being done with QSAR models to predict ... | Options for 3D coordinate systems? | Along with the positions of the amino acids, protein folding can also be described by the angles in the peptide bonds such as used in a Ramachandran plot, and with global values such as the radius of | Options for 3D coordinate systems?
Along with the positions of the amino acids, protein folding can also be described by the angles in the peptide bonds such as used in a Ramachandran plot, and with global values such as the radius of gyration.
You could apply all those values together in a single model such as is bein... | Options for 3D coordinate systems?
Along with the positions of the amino acids, protein folding can also be described by the angles in the peptide bonds such as used in a Ramachandran plot, and with global values such as the radius of |
53,490 | Options for 3D coordinate systems? | If we know something about the problem we're trying to model, then representing that knowledge to the model via a good set of features is incredibly valuable. This is often called "feature-engineering," and it can yield dramatic improvements in model quality. Sextus's answer provides an example, in the context of proti... | Options for 3D coordinate systems? | If we know something about the problem we're trying to model, then representing that knowledge to the model via a good set of features is incredibly valuable. This is often called "feature-engineering | Options for 3D coordinate systems?
If we know something about the problem we're trying to model, then representing that knowledge to the model via a good set of features is incredibly valuable. This is often called "feature-engineering," and it can yield dramatic improvements in model quality. Sextus's answer provides ... | Options for 3D coordinate systems?
If we know something about the problem we're trying to model, then representing that knowledge to the model via a good set of features is incredibly valuable. This is often called "feature-engineering |
53,491 | forecasting without data | Use the queue function in the utilities package
This type of problem can be dealt with as a queueing problem, which is a class of problem dealt with in statistical theory. For the case where you have the inputs for the use of the facilities by a set of users you can use a deterministic function to turn this into a set... | forecasting without data | Use the queue function in the utilities package
This type of problem can be dealt with as a queueing problem, which is a class of problem dealt with in statistical theory. For the case where you have | forecasting without data
Use the queue function in the utilities package
This type of problem can be dealt with as a queueing problem, which is a class of problem dealt with in statistical theory. For the case where you have the inputs for the use of the facilities by a set of users you can use a deterministic functio... | forecasting without data
Use the queue function in the utilities package
This type of problem can be dealt with as a queueing problem, which is a class of problem dealt with in statistical theory. For the case where you have |
53,492 | forecasting without data | First off, I would not put ARIMA at the top of my list. On the one hand, as you write, you need data to fit an ARIMA model. On the other hand, ARIMA allows for non-integer and negative values, which does not make a lot of sense in your situation, but this is usually not a major problem. On the third hand, I see little ... | forecasting without data | First off, I would not put ARIMA at the top of my list. On the one hand, as you write, you need data to fit an ARIMA model. On the other hand, ARIMA allows for non-integer and negative values, which d | forecasting without data
First off, I would not put ARIMA at the top of my list. On the one hand, as you write, you need data to fit an ARIMA model. On the other hand, ARIMA allows for non-integer and negative values, which does not make a lot of sense in your situation, but this is usually not a major problem. On the ... | forecasting without data
First off, I would not put ARIMA at the top of my list. On the one hand, as you write, you need data to fit an ARIMA model. On the other hand, ARIMA allows for non-integer and negative values, which d |
53,493 | Calculating the Brier or log score from the confusion matrix, or from accuracy, sensitivity, specificity, F1 score etc | Short answer
You can't.
Somewhat longer answer
The Brier score or log score are calculated from probabilistic classifications and corresponding outcomes. The confusion matrix, accuracy etc. are calculated from hard 0-1 classifications and the corresponding outcomes. If you have probabilistic classifications, you can tu... | Calculating the Brier or log score from the confusion matrix, or from accuracy, sensitivity, specifi | Short answer
You can't.
Somewhat longer answer
The Brier score or log score are calculated from probabilistic classifications and corresponding outcomes. The confusion matrix, accuracy etc. are calcul | Calculating the Brier or log score from the confusion matrix, or from accuracy, sensitivity, specificity, F1 score etc
Short answer
You can't.
Somewhat longer answer
The Brier score or log score are calculated from probabilistic classifications and corresponding outcomes. The confusion matrix, accuracy etc. are calcula... | Calculating the Brier or log score from the confusion matrix, or from accuracy, sensitivity, specifi
Short answer
You can't.
Somewhat longer answer
The Brier score or log score are calculated from probabilistic classifications and corresponding outcomes. The confusion matrix, accuracy etc. are calcul |
53,494 | Calculating the Brier or log score from the confusion matrix, or from accuracy, sensitivity, specificity, F1 score etc | A point raised in some related conversations that seem to have inspired the posting of this question is that a confusion matrix can contain probability values if you normalize by the sum of the entries.
$$
\begin{pmatrix}
4 & 2\\
1 & 3
\end{pmatrix}
\overset{\div 10}{\rightarrow}
\begin{pmatrix}
0.4 & 0.2\\
0.1 & 0.3
\... | Calculating the Brier or log score from the confusion matrix, or from accuracy, sensitivity, specifi | A point raised in some related conversations that seem to have inspired the posting of this question is that a confusion matrix can contain probability values if you normalize by the sum of the entrie | Calculating the Brier or log score from the confusion matrix, or from accuracy, sensitivity, specificity, F1 score etc
A point raised in some related conversations that seem to have inspired the posting of this question is that a confusion matrix can contain probability values if you normalize by the sum of the entries... | Calculating the Brier or log score from the confusion matrix, or from accuracy, sensitivity, specifi
A point raised in some related conversations that seem to have inspired the posting of this question is that a confusion matrix can contain probability values if you normalize by the sum of the entrie |
53,495 | Time to event = 0 in survival analysis? | Depending on the implementation, software for fitting a Cox regression or other continuous-time survival model might not even accept a survival time of 0. The idea with such models is that you start with 100% survival at time = 0 and proceed down toward lower survival fractions in continuous time.
In the situation you ... | Time to event = 0 in survival analysis? | Depending on the implementation, software for fitting a Cox regression or other continuous-time survival model might not even accept a survival time of 0. The idea with such models is that you start w | Time to event = 0 in survival analysis?
Depending on the implementation, software for fitting a Cox regression or other continuous-time survival model might not even accept a survival time of 0. The idea with such models is that you start with 100% survival at time = 0 and proceed down toward lower survival fractions i... | Time to event = 0 in survival analysis?
Depending on the implementation, software for fitting a Cox regression or other continuous-time survival model might not even accept a survival time of 0. The idea with such models is that you start w |
53,496 | Where does e come from in this problem about dice? | The reason $e =\lim\limits_{n \to \infty}\left(1+\frac1n\right)^{n}=\frac1{\lim\limits_{n \to \infty}\left(1-\frac1n\right)^{n}}$ appears is precisely because you are taking a particular limit, though I am not sure you took exactly the correct one.
The answer should head towards $0$ as $n$ increases, since player B has... | Where does e come from in this problem about dice? | The reason $e =\lim\limits_{n \to \infty}\left(1+\frac1n\right)^{n}=\frac1{\lim\limits_{n \to \infty}\left(1-\frac1n\right)^{n}}$ appears is precisely because you are taking a particular limit, though | Where does e come from in this problem about dice?
The reason $e =\lim\limits_{n \to \infty}\left(1+\frac1n\right)^{n}=\frac1{\lim\limits_{n \to \infty}\left(1-\frac1n\right)^{n}}$ appears is precisely because you are taking a particular limit, though I am not sure you took exactly the correct one.
The answer should he... | Where does e come from in this problem about dice?
The reason $e =\lim\limits_{n \to \infty}\left(1+\frac1n\right)^{n}=\frac1{\lim\limits_{n \to \infty}\left(1-\frac1n\right)^{n}}$ appears is precisely because you are taking a particular limit, though |
53,497 | Calculating functions of truncated and censored normal variables | The first result simulates the mean of a truncated normal distribution with truncation point 1, whose expected value is, from here and standard normality with $\mu=0$, $\sigma=1$,
$$
\mu_T=\frac{\phi(1)}{1-\Phi(1)}=\frac{\phi(1)}{\Phi(-1)},
$$
where the second equality uses symmetry of the standard normal around zero.
... | Calculating functions of truncated and censored normal variables | The first result simulates the mean of a truncated normal distribution with truncation point 1, whose expected value is, from here and standard normality with $\mu=0$, $\sigma=1$,
$$
\mu_T=\frac{\phi( | Calculating functions of truncated and censored normal variables
The first result simulates the mean of a truncated normal distribution with truncation point 1, whose expected value is, from here and standard normality with $\mu=0$, $\sigma=1$,
$$
\mu_T=\frac{\phi(1)}{1-\Phi(1)}=\frac{\phi(1)}{\Phi(-1)},
$$
where the s... | Calculating functions of truncated and censored normal variables
The first result simulates the mean of a truncated normal distribution with truncation point 1, whose expected value is, from here and standard normality with $\mu=0$, $\sigma=1$,
$$
\mu_T=\frac{\phi( |
53,498 | Can I apply a confusion matrix to classification tasks outside of ML? | Sure you can. Those metrics are older than machine learning. For example, the ROC curves calculated based on TPR and FPR were designed during World War II for judging the accuracy of radars. The metrics calculated based on the confusion matrix are also commonly used in medicine for judging the performance of diagnostic... | Can I apply a confusion matrix to classification tasks outside of ML? | Sure you can. Those metrics are older than machine learning. For example, the ROC curves calculated based on TPR and FPR were designed during World War II for judging the accuracy of radars. The metri | Can I apply a confusion matrix to classification tasks outside of ML?
Sure you can. Those metrics are older than machine learning. For example, the ROC curves calculated based on TPR and FPR were designed during World War II for judging the accuracy of radars. The metrics calculated based on the confusion matrix are al... | Can I apply a confusion matrix to classification tasks outside of ML?
Sure you can. Those metrics are older than machine learning. For example, the ROC curves calculated based on TPR and FPR were designed during World War II for judging the accuracy of radars. The metri |
53,499 | Can I apply a confusion matrix to classification tasks outside of ML? | YES AND NO
FIRST THE YES
In fact, there was (and still is, I suppose) an area of artificial intelligence called expert systems which were , more or less, decision trees designed by subject matter experts (doctors, scientists, etc). Given some data, the expert system would go down the decision tree and arrive at a predi... | Can I apply a confusion matrix to classification tasks outside of ML? | YES AND NO
FIRST THE YES
In fact, there was (and still is, I suppose) an area of artificial intelligence called expert systems which were , more or less, decision trees designed by subject matter expe | Can I apply a confusion matrix to classification tasks outside of ML?
YES AND NO
FIRST THE YES
In fact, there was (and still is, I suppose) an area of artificial intelligence called expert systems which were , more or less, decision trees designed by subject matter experts (doctors, scientists, etc). Given some data, t... | Can I apply a confusion matrix to classification tasks outside of ML?
YES AND NO
FIRST THE YES
In fact, there was (and still is, I suppose) an area of artificial intelligence called expert systems which were , more or less, decision trees designed by subject matter expe |
53,500 | Continuous and differentiable bell-shaped distribution on $[a, b]$ | Let's construct all possible solutions.
By "distribution" you appear to refer to a density function (PDF) $f.$ The properties you require are
Supported on $[a,b].$ That is, $f(x)=0$ for any $x\le a$ or $x \ge b.$
$f$ should be (continuously) differentiable on $(a,b)$ with derivative $f^\prime.$
"Bell-curved," whic... | Continuous and differentiable bell-shaped distribution on $[a, b]$ | Let's construct all possible solutions.
By "distribution" you appear to refer to a density function (PDF) $f.$ The properties you require are
Supported on $[a,b].$ That is, $f(x)=0$ for any $x\le a | Continuous and differentiable bell-shaped distribution on $[a, b]$
Let's construct all possible solutions.
By "distribution" you appear to refer to a density function (PDF) $f.$ The properties you require are
Supported on $[a,b].$ That is, $f(x)=0$ for any $x\le a$ or $x \ge b.$
$f$ should be (continuously) differe... | Continuous and differentiable bell-shaped distribution on $[a, b]$
Let's construct all possible solutions.
By "distribution" you appear to refer to a density function (PDF) $f.$ The properties you require are
Supported on $[a,b].$ That is, $f(x)=0$ for any $x\le a |
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