I would like to run a cross-validation function like cv.glm on a logistic regression model built with speedglm on a large (millions of rows) data set. Does any such function exist?
I am finding that cv.glm (from boot package) and the train function from caret do not recognize speedglm models.
Thanks in advance.
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I'm looking to add VIF to logistic regression in R, using the export_summs function.
This is the current code:
export_summs(model1,model2, error_format = "({p.value})", exp = TRUE)
Any ideas?
Thanks.
You can use library car package and find vif(model1) or vif(model2) to find multicollinearity
This is how you can get VIF of a logistic regression model using jtools package in r. Your_glm_model is your logistic model that is returned from glm function.
summ(`Your_glm_model`, vifs = TRUE)
library(forecast)
data(Nile,package="datasets")
train=Nile[1:50]##I want to use this to train model
test<-Nile[51:length(Nile)]
m1<-auto.arima(Nile)
My question is that now I got a arima model, and how can I use this model combined with the old data(train) to forecast the value in test in dynamics. What I want is like in an OLS regression, I got a model, then I can use other data to test this model. Finally I can draw a picture.
I would like to perform a model-based clustering using a mixture of ordinal logistic regressions (for outcome, not as concomitant model)
Does some one know if it implemented in R? For example, can I manage to use ordinal regression instead of multinomial in flexmix package?
Thanks a lot!
One can perform glm model fit with logistic link function for response from binomial family and then can apply step function to extract the best subset of explanatory features in terms of information criterion such as AIC or BIC.
Have anyone performed such procedure for an output model from cv.glmnetfunction or glmnet from glmnet package? Can you suggest how one can reproduce the same methodology used on regular glm model and cv.glmnet model?
This below doesn't look like it would work:
modelAIC <-step( object= model$glmnet.fit, direction = "backward")
I would like to compare two models using f-test fitting my data. For each model I performed Monte-Carlo simulation that provided statistical estimation for each model parameter and rms fit error. I would like to use f-test in R to determine which model is preferable.
Best to use the anova function.
anova(modle1, model2)
This preforms a model f test.