I am using TFAutoModelForSequenceClassification for regression. Training and evaluation looked correct. However, the predict method returned logits. How do I transform the logits to regression values that I care about?
The code I used to train the model
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I am using regsubsets method for linear regression and came across step() method for selecting columns for logistic regression methods.
I am not sure whether we can use regsubsets or steps for Poisson regression. It will be helpful if there is a method to find the best subsets for Poisson regression in R programming.
From here it looks like
step() (base R: works on glm objects -> includes Poisson regression models)
bestglm package
glmulti package
Possibly others.
Be prepared for the GLM (Poisson etc.) case to be much slower than the analogous problem for Gaussian responses (OLS/lm).
I need to use Cox's partial likelihood method to establish a Cox's proportional hazards regression model with the significant predictors of my model.
I am wondering if the coxph() function in R does this automatically or if there is a special function which can?
I found the following function in the link below, but I cannot seem to find a package that contains it:
https://www.rdocumentation.org/packages/survcomp/versions/1.22.0/topics/logpl
I am currently running some linear models and lmer (with replicate as a random effect) for continuous data and a glm and glmer (again, replicate as a random effect) for count data.
I was wondering if a lm, lmer, glm and glmer all need the data to be normally distributed and if not, do I need an alternative test?
Also, I have run a glm and looked at the pairwise differences and when reporting it other than P<0.001 I don't know what else I should report! As my glm output doesn't really give me that much. Thanks!
I am working on a dataset that has random effects (so I need a mixed-effects model). The response variable is a count (non-negative, integer) which is also zero-inflated (51% zeros). The model that I have arrived at is a zero-inflated generalized linear mixed-effects model (ZIGLMM). Several packages that I have attempted to use to fit such a model include glmmTMB and glmmADMB in R.
My question is: is it possible to account for spatial autocorrelation using such a model and if so, how can it be done? I am unsure if this has been done before since both packages are relatively new..
I'm trying to optimize a multivariate linear regression model lmMod=lm(depend_var~var1+var2+var3+var4....,data=df) and I'm presently working on the premises of the model: the constant variance of residuals and the absence of auto-correlation. For this I'm using:
Breusch-Pagan test for homo/heteroscedasticity: lmtest::bptest(lmMod)
Durbin Watson test for auto-correlation: durbinWatsonTest(lmMod)
I found examples which are testing either one independent variable at a time:
example for Breush-Pagan test – one independent variable:
https://datascienceplus.com/how-to-detect-heteroscedasticity-and-rectify-it/
example for Durbin Watson test - one independent variable:
http://math.furman.edu/~dcs/courses/math47/R/library/lmtest/html/dwtest.html
or the whole model with several independent variables at a time:
example for Durbin Watson test – multiple independent variable:
https://www.rdocumentation.org/packages/car/versions/2.1-6/topics/durbinWatsonTest
Here are the questions:
Can durbinWatsonTest() and bptest() be fed with a whole multivariate model
If answer to 1 is yes, how is it then possible to determine which variable is causing heteroscedasticity or auto-correlation in the model in order to fix it as each of those tests give only one p-value for the entire multivariate model?
If answer to 1 is no, the test should be then performed with one dependent variable at a time. But in the case of homoscedasticity, it can only be tested AFTER a particular regression has been modelled. Hence a pattern of homo/heteroscedasticity in an univariate regression model lmMod_1=lm(depend_var~var1, data=df) will be different from the pattern of a multivariate regression model lmMod_2=lm(depend_var~var1+var2+var3+var4....,data=df)
Thank very much in advance for your help!
I would like to try to give a first help
The answer to the first question: Yes, you can use the Breusch-Pagan test and the Durbin Watson test for mutlivariate models. (However, I have always used the dwtest() instead of the durbinWatsonTest()).
Also note that the dwtest() checks only the first-order autocorrelation. Unfortunately, I do not know how to find out which variable is causing heteroscedasticity or auto-correlation. However, if you encounter these problems, then one possible solution is that you use a robust estimation method, e.g. after NeweyWest (using: coeftest (regression model, vcov = NeweyWest)) at autocorrelation or with coeftest(regression model, vcov = vcovHC) at heteroscedasticity, both from the AER package.