similar to: R-beta: a suggestion for constraints

Hello, I have a problem with creating an identity matrix for glmnet by using the contrasts function. I have a factor with 4 levels. When I create dummy variables I think there should be n-1 variables (in this case 3) - so that the contrasts would be against the baseline level. This is also what is written in the help file for 'contrasts'. The problem is that the function

Is there any logical reason why glm prints out the labels of factor levels after variable names when baseline contrasts (contr.treatment) are used but the codes for the levels when mean contrasts (contr.sum) are used? Jim -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

Several bugs (no solutions, yet). These might be well known. 1) If one does, e.g., mymod <- lm(y ~ x); formula(mymod) then one does not get back the formula (one gets, Error: invalid formula) 2) if x is of mode numeric, then the model formula mymod <- lm(y ~ x + x^2) is not processed as S would do it. The model is fit ignoring the x^2 term, however mymod$call includes the x^2 term.

Several bugs (no solutions, yet). These might be well known. 1) If one does, e.g., mymod <- lm(y ~ x); formula(mymod) then one does not get back the formula (one gets, Error: invalid formula) 2) if x is of mode numeric, then the model formula mymod <- lm(y ~ x + x^2) is not processed as S would do it. The model is fit ignoring the x^2 term, however mymod$call includes the x^2 term.

Hi R-Helpers: I?m trying to fit the same linear model to a bunch of variables in a data frame, so I was trying to adapt the codes John Fox, Spencer Graves and Peter Dalgaard proposed and discused yesterday on this e-mail list: for (y in df[, 3:5]) { mod = lm(y ~ Trt*Dose, data = x, contrasts = list(Trt = contr.sum, Dose = contr.sum)) Anova(mod, type = "III") } ## by John Fox or for

Hi all, I asked this before the holiday, didn't get any response. So would like to resend the message, hope to get any fresh attention. Since this is not purely lme technical question, so I also cc-ed R general mailing list, hope to get some suggestions from there as well. I asked some questions on how to analyze longitudinal study with only 2 time points (baseline and a follow-up)

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My previous question put in a simpler way: How would I pass a value of a variable to a function such as lm(Effect1~Trt*Dose, data = x, contrasts = list(Trt = contr.sum, Dose = contr.sum))? Here, 'Effect' is a column name in my data matrix, and I want "Effect1" to be replaced by "Effect2" and so on (my other column names in the data frame) for successive anova

Hi, I don't understand what the meaning of the following lines returned by model.matrix(). Can somebody help me understand it? What can they be used for? attr(,"assign") [1] 0 1 2 2 attr(,"contrasts") attr(,"contrasts")$A [1] "contr.treatment" attr(,"contrasts")$B [1] "contr.treatment" Regards, Peng > a=2 > b=3 > n=4

R1.2.0 with Linux RH5.2 I do not believe that the problems below are new to 1.2 but I only cover this sort of thing once a year in my course and some of that happened to be last Friday so too late to report for 1.2. I see that one or two things that I was going to report have been corrected. I like the fact that interactions now show : instead of . Here is some output with comments inserted. R

R1.2.0 with Linux RH5.2 I do not believe that the problems below are new to 1.2 but I only cover this sort of thing once a year in my course and some of that happened to be last Friday so too late to report for 1.2. I see that one or two things that I was going to report have been corrected. I like the fact that interactions now show : instead of . Here is some output with comments inserted. R

I currently have a program that automates 2-way ANOVA on a series of endpoints, but before the ANOVA is carried out I want the code to test the assumptions of normality and equal variance and report along with each anova result in the output file.  How can I do this? I have pasted below the code that I currently use.   library(car) numFiles = x #

Hello, I have a problem concerning estimation of GLMM. I used methods from 3 different packages (see program). I would expect similar results for glmm and glmmML. The result differ in the estimated standard errors, however. I compared the results to MASS, 4th ed., p. 297. The results from glmmML resemble the given result for 'Numerical integration', but glmm output differs. For the

Dear all, my aim is to estimate the efficacy over time of a treatment for headache prevention. Data consist of long sequences of repeated binary outcomes (1 if the subject has at least 1 episode of headache , 0 otherwise) on subjects randomized to placebo or treatment. I have fit a logistic regression model with Huber-White cluster sandwich covariance estimator. I have put in the model the

Hello, I have been searching for ways to obtain these for combinations of fixed factors and levels other than the 'baseline' group (contrasts coded all 0's) from a mixed-effects model in lme. I've modelled the continuous variable y as a function of a continuous covariate x, and fixed factors A, B, and C. The fixed factors have two levels each and I'd like to know whether

Dear R-devel list members, I'd like to suggest a more flexible procedure for generating contrast names. I apologise for a relatively long message -- I want my proposal to be clear. I've never liked the current approach. For example, the names generated by contr.treatment paste factor to level names with no separation between the two; contr.sum simply numbers contrasts (I recall an

Hello, I am trying to perform a two-factor ANOVA analysis using a blocking design with "Vol" as the response variable. My intent is to have "Rater" treated as the treatment variable and the "Pipe" treated as the blocking variable. I am reading and preparing my dataset using the following three lines of code: values <- read.table("filename",

This is my first post to this list so I suppose a quick intro is in order. I've been using SPLUS 2000 and R1.6.2 for just a couple of days, and love S already. I'm reading MASS and also John Fox's book - both have been very useful. My background in stat software was mainly SPSS (which I've never much liked - thanks heavens I've found S!), and Perl is my tool of choice for

Dear, I want to fit an inverse gaussion distribution to a data set. The predictor variables are gender, area and agecategory. For each of these variables I've defined a baseline e.g. #agecat: baseline is 3 data<-transform(data, agecat=C(factor(agecat,ordered=TRUE), contr.treatment(n=6,base=3))) The variable 'area' goes from A to F (6 areas: A,B,C,D,E,F) How can i