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Dear All, I have two hidden classes for my data set that I would like to learn them based on the Mixture of Binomial regression model. I was wondering if there is any package that I can use for that...

Hi All, When modeling with glm and family = binomial (link = logit) and response values of 0 and 1, I get the predicted probabilities of assigning to my class one, then I would like to compare it with my vector y...

Hi All, I have a data frame as follow: user_id time age location gender ..... and I learn a logistic regression to learn the weights (glm with family= (link = logit))), my response value is either zero or one. I would...

Hello All, I am trying to use FlexMix package for my two linear regression models: http://finzi.psych.upenn.edu/R/library/flexmix/doc/regression-examples.pdf y1 = w1 * x1 + w2 *x2 + w3 * x3 +beta1 y2 = w4 * x4 + w4 *x4...

Hi All, I am trying to fit my data with glm model, my data is a matrix of size n*100. So, I have n rows and 100 columns and my vector y is of size n which contains the...

Hi All, I have a fitted model called glm.fit which I used glm and data dat is my data frame pred= predict(glm.fit, data = dat, type="response") to predict how it predicts on my whole data but obviously...

Hello All, I have a data frame consisting of 4 columns (id1, id2, y, pred) where pred is the predicted value based on the glm function and my data frame is called "all". "data" is another data frame that has...

Hi All, I have a set of features of size p and I would like to separate my feature space into two sets so that p = p1 + p2, p1 is a set of features and p2 is another set of...

Hello All, I have a question about glm in R. I would like to fit a model with glm function, I have a vector y (size n) which is my response variable and I have matrix X which is by...

Thanks a lot Joshua. That clearly solved my problem. I actually tried number 3 and it works perfectly fine. I used the prediction function as follows: pred= predict(glm.fit,data = dat, type="response") (glm.fit is my fitted model...