glm

Please consider the following basic reproducible example: library(h2o) h2o.init() data("iris") iris.hex = as.h2o(iris, "iris.hex") mod = h2o.glm(y = "Sepal.Length", x = setdiff(colnames(iris), "Sepal.Length"), training_frame = iris.hex, nfolds = 2, seed = 100, lambda_search = T, early_stopping = F, family = "gamma", nlambdas = 100) When I run the above, I expect that h2o will iterate over 100 different values of lambda. However, running length(mod@allparameters$lambda) will show that only 79 values of lambda were actually tested. These 79 values are the first 79 values in the sequence:

When I train just using glm , everything works, and I don't even come close to exhausting memory. But when I run train(..., method='glm') , I run out of memory. Is this because train is storing a lot of data for each iteration of the cross-validation (or whatever the trControl procedure is)? I'm looking at trainControl and I can't find how to prevent this...any hints? I only care about the performance summary and maybe the predicted responses. (I know it's not related to storing data from each iteration of the parameter-tuning grid search because there's no grid for glm's, I believe.) The

I have a regression model with binary outcome. I fitted the model with glmnet and got the selected variables and their coefficients. Since glmnet doesn't calculate variable importance, I would like to feed the exact output (selected variables and their coefficients) to glm to get the information (Standard errors, etc). I searched r documents, it seems I can use "method" option in glm to specify user defined function. But I failed to do so, could someone help me with this? "It is a very natural question to ask for standard errors of regression coefficients or other estimated quantities. In

I am trying to fit two nested models and then test those against each other using anova function. The commands used are: probit <- glm(grad ~ afqt1 + fhgc + mhgc + hisp + black + male, data=dt, family=binomial(link = "probit")) nprobit <- update(probit, . ~ . - afqt1) anova(nprobit, probit, test="Rao") However, the variable afqt1 apparently contains NA s and because the update call does not take the same subset of data, anova() returns error Error in anova.glmlist(c(list(object), dotargs), dispersion = dispersion, : models were not all fitted to the same size of dataset Is there a simple way

Suppose we have a toy data frame: x <- data.frame(x1 = gl(3, 2, labels = letters[1:3]), x2 = gl(3, 2, labels = LETTERS[1:3])) I would like to construct a model matrix # x1b x1c x2B x2C # 1 0 0 0 0 # 2 0 0 0 0 # 3 1 0 1 0 # 4 1 0 1 0 # 5 0 1 0 1 # 6 0 1 0 1 by: model.matrix(~ x1 + x2 - 1, data = x, contrasts.arg = list(x1 = contr.treatment(letters[1:3]), x2 = contr.treatment(LETTERS[1:3]))) but actually I get: # x1a x1b x1c x2B x2C # 1 1 0 0 0 0 # 2 1 0 0 0 0 # 3 0 1 0 1 0 # 4 0 1 0 1 0 # 5 0 0 1 0 1 # 6 0 0 1 0 1 # attr(,"assign") # [1] 1 1 1 2 2 # attr(,"contrasts") # attr(,"contrasts")$x1 #