glm

I'm trying to get to grips with performing regression analyses in R. Below is some random dummy data that I have generated in R, run a logistic glm in R. I have saved the data into a test file, read that into python with ipython (ipython notebook is awesome btw, only just started using it!), and then tried to run the same analyis with python. The results are very similar but they are different. I kind of would have expected them to be the same. Have I done something wrong, is there a parameter I am missing, or the difference due to some underlying calculation? Any help appreciated! EDIT: I don

I don't want to use the standard log link in glm for Poisson regression, since I have zeros. Consider the following code: foo = 0:10 bar = 2 * foo glm(bar ~ foo, family = poisson(link = "identity")) I get the error: Error: no valid set of coefficients has been found: please supply starting values I'm not certain what this means. Is the "identity" link function what I think it is (i.e. it doesn't transform the data at all)? What does this error mean and how can I resolve it? You can get an answer if you start somewhere other than the default (0,0) starting point. The start parameter is a vector

I normally generate logit model marginal effects using the mfx package and the logitmfx function. However, the current survey I am using has weights (which have a large effect on the proportion of the DV in the sample because of oversampling in some populations) and logitmfx doesn't appear to have any way to include weights. I have fitted the model with svyglm as follows: library(survey) survey.design <- svydesign(ids = combined.survey$id, weights = combined.survey$weight, data = combined.survey) vote.pred.1 <- svyglm(formula = turnout ~ gender + age.group + education + income, design = survey

I was trying to run a logistic regression on 320,000 rows of data (6 variables). Stepwise model selection on a sample of the data (10000) gives a rather complex model with 5 interaction terms: Y~X1+ X2*X3+ X2*X4+ X2*X5+ X3*X6+ X4*X5 . The glm() function could fit this model with 10000 rows of data, but not with the whole dataset (320,000). Using bigglm to read data chunk by chunk from a SQL server resulted in an error, and I couldn't make sense of the results from traceback() : fit <- bigglm(Y~X1+ X2*X3+ X2*X4+ X2*X5+ X3*X6+ X4*X5, data=sqlQuery(myconn,train_dat),family=binomial(link="logit"),

I am doing a regression using Generalized Linear Model.I am caught offguard using the crossVal function. My implementation so far; x = 'Some dataset, containing the input and the output' X = x(:,1:7); Y = x(:,8); cvpart = cvpartition(Y,'holdout',0.3); Xtrain = X(training(cvpart),:); Ytrain = Y(training(cvpart),:); Xtest = X(test(cvpart),:); Ytest = Y(test(cvpart),:); mdl = GeneralizedLinearModel.fit(Xtrain,Ytrain,'linear','distr','poisson'); Ypred = predict(mdl,Xtest); res = (Ypred - Ytest); RMSE_test = sqrt(mean(res.^2)); The code below is for calculating cross validation for mulitple