search for: transgender

Hi, I am a very occasional user of R, and will be grateful for some help in constructing a regression across groups. Here is an example: library(MASS) attach(cats) Sex[120:144]<-factor(TG) #Renaming some males to transgender, to create 3 groups, male, female and transgender out<-lm(Bwt~Sex/Hwt) #Gives me 3 separate linear regressions for groups M, F and TG What I now want to do is to examine subsets of this, where groups F and TG have the same slope (and/or intercept). I want to do this in such a way that...

...of clarity and minor amounts of less memory and parsing needed. But ifelse() is currently implemented somewhat too complexly for my taste. Just type "ifelse" at the prompt and you will see many lines of code that handle various scenarios. ? If you later want to add categories such as ?transgender? with a value of 61 or have other numbers for groups like ?Hispanic male?, you can amend the instructions as long as you put your conditions in an order so that they are tried until one of them matches, or it takes the default. Yes, in a sense the above is doable using a deeply nested ifelse() but...

I am making a little GUI for lme4, and I was wondering if there is a function that automatically detects on which level every variable exists. Furtheremore I got kind of confused about what a random effects model actually calculates. I have some experience with commercial software packages for multilevel analysis, like HLM6, and I was surprised that lme4 does not require the user to specify the

Yes, that will halve the number of multiplications. If you?re looking for such optimisations then you can also consider ifelse(G=='male', 65L, 58L). That will definitely use less time & memory if WC is integer, but the trade-offs are more complicated if WC is floating point. Regards, Jorgen Harmse. From: avi.e.gross at gmail.com <avi.e.gross at gmail.com> Date: Friday,