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Dear all, I have a question on applying a function to the data according to factor levels. For example, for the data below, what is the best way to apply a function to "values" according to different levels of "samples" (1,2,3,4,5)? values ind sample 1 0.03325 1 1 2 0.03305 1 1 3 0.03185 1 1 4 0.03515 1 1 5 0.03375 1 1 6 0.01180

Dear all, I am trying to use R to fit mixed models. Take the following example, where ind is a random effect and sample is fixed. I wanted to fit Model 1: values = ind + sample Model 2: values =ind * sample Model 3: values=ind(sample) + sample Tried to use the below for mod1, but it did not work. Can anyone give some help on this. Thanks so much. mod1 <- lme(values ~ sample +

Hi, I have been using arima.sim from the stats package recently, and I'm wondering why I get different results when using what seem to be the same parameters. For example, I've given examples of three different ways to run arima.sim with what I believe are the same parameters. It's my understanding from the R documentation that rnorm is the default function for rand.gen if not

Dear all, I want to apply weights to my sample data set and I am struggling with the Efron Approximation with weights. I have got one sample data shown as below: customer week arrest fin age race weight 1 weight 2 weight 3 1 20 1 1 27 1 2 15 2 2 17 1 0 18 1 2 19 1 3 25 1 1 19 0 2 20 1 4 52 0 1 23 1 2 5 1 5 52 0 0 19 0 2 11 1 6 25 1 0 19 0 2 26 1 I applied four different weighted Efron