similar to: Question about contrasts and interpreting glm output for factors

Hi all! I've got this error while running example(Glm) library("rms") > example(Glm) Glm> ## Dobson (1990) Page 93: Randomized Controlled Trial : Glm> counts <- c(18,17,15,20,10,20,25,13,12) Glm> outcome <- gl(3,1,9) Glm> treatment <- gl(3,3) Glm> f <- glm(counts ~ outcome + treatment, family=poisson()) Glm> f Call: glm(formula = counts ~

# Your mailer is set to "none" (default on Windows), # hence we cannot send the bug report directly from R. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs@r-project.org # ###################################################### The multcomp functions work on "lm" objects as anticipated. They do not work on

Dear list members, I want to use a GLM with an unbalanced factor and continuous variables. My factor F has 12 unbalanced levels:

Hello, helpeRs, I recently used a linear mixed effects model followed by ANOVA to assess the relationship between a categorical predictor variable with 2 levels (and random effects) and a numeric response variable. As I was concerned about the lack of a power analysis prior to data collection, it was suggested that I use an equivalence test to complement the conventional hypothesis test.

Hello! I am using relatively simple linear model. By applying fastbw() on ols() results from rms package I would like to get subtable "Approximate Estimates after Deleting Factors". However, it seems this is not possible. Am I right? I can only get coefficients for variables kept in the model (for example: x$coefficients), but not S.E., Wald's Z and P? Is there any easy way to

I'm trying to fit a nested mixed model using lmer and have some questions about the output and my model formulations. I have replicate measures on Lines which are strictly nested within Populations. (a) So if I want to fit a model where Line is a random effect and Populations are fixed and the random Line effect is constant across Populations, I have: measure_ijk = mu + P_i + L_ij +

Hello, I use a type III anova ("car" package) to analyse an unbalanced data design. I have two factors and I would have the effect of the interaction. I read that the result could be strongly influenced by the contrasts. I am really not an expert and I am not sure to understand indeed about what it is... Consequently, I failed to properly used the fit.contrast function (gregmisc

Dear R users, Firstly, I would like to congratulate the R core team in bringing out R 1.0.0 and all who have helped in developing it. I have been having problems with using se.contrasts and would be pleased if someone help. I have been doing a repeated measures ANOVA using aov using a split plot design for a single variable, color. The aov results were as follows: > summary(aov(CD2~cont +

Dear list, I made a logistic regression model (MyModel) using lrm and penalization by pentrace for data of 104 patients, which consists of 5 explanatory variables and one binary outcome (poor/good). Then, I found bootcov and robcov function in rms package for calculation of confidence range of coefficients and odds ratio by bootstrap covariance matrix and Huber-White sandwich method,

On 16/02/18 15:28, Bert Gunter wrote: > This is really a statistical issue. What do you think the Intercept term > represents? See ?contrasts. > > Cheers, > Bert > > > > Bert Gunter > > "The trouble with having an open mind is that people keep coming along and > sticking things into it." > -- Opus (aka Berkeley Breathed in his "Bloom

I am having trouble understanding results I'm getting back from coxph doing a recurrent event analysis. I've included the model below and the summary. In some cases, with minor variations, the Robust variance and Wald tests are significant, but the individual covariates may or may not be significant. My main question is: If Wald and robust tests both take into account the

Hi, I'm using the rms package to do regression analysis using the lrm function. Retrieving odds ratios is possible using summary.rms. However, I could not find any information on how exactly the odds ratios for continuous variables are calculated. It doesn't appear to be the odds ratio at 1 unit increase, because the output of summary.rms did not match the coefficient's value. E.g.

Hi, I have a problem in which I have test score data on students from a number of schools. In each school I have a measure of whether or not they received special programming. I am interested in the interaction between school and attendance to the programming, but in a very select set of comparisons. I'd like to cast the test as one in which students in each school who attend are

Dear R users, Hopefully someone can help me, Maybe I just misunderstand the function in the package? I am working with a logistic regression model. Until now I always worked with the basic glm function, where for the model was: ¡§ glm( disease ~ test.value + cnct , family=binomial(link=¡¦logit¡¦) ¡¨. This works fine when test .value and concentration (cnct) are continuous vairables. However,

Hi everyone, I'm doing a logistic regression with an ordinal variable. I'd like to set the contrasts on the ordinal variable. However, when I set the contrasts, they work for ordinary linear regression (lm), but not logistic regression (lrm): ddist = datadist(bin.time, exp.loc) options(datadist='ddist') contrasts(exp.loc) = contr.treatment(3, base = 3, contrasts = TRUE) lrm.loc =

I'm using the following model to do an analysis faicout <- glm(cbind(events,patnums-events) ~ as.factor(treat) + as.factor(numtrial), family = binomial ) Is this example there are 4 treatments . In the glm object I can find the contrasts of the main treats vs the first i.e. 2v1, 3v1 and 4v1 ... however I would like to get the complete set including 3v2, 4v2, and 4v3 ... along with the

glm(A~B+C+D+E+F,family = binomial(link = "logit"),data=tre,na.action=na.omit) Error in `contrasts<-`(`*tmp*`, value = "contr.treatment") : contrasts can be applied only to factors with 2 or more levels however, glm(A~B+C+D+E,family = binomial(link = "logit"),data=tre,na.action=na.omit) runs fine glm(A~B+C+D+F,family = binomial(link =

Hi I am a new user the R and I am very grateful for all your help but....... I have a problem and I can't resolve yet. I am trying to get the contrasts for a binomial 3-way GLM (T= 4 temperature, t= 2 time and c= 2 substrate levels, plus treatment control) in total they are 17 treatments. I have tried with the glht but this function only work for 1-way GLM, acacia<-cbind(g,N-g)

Hi all I am one recent user of R and have a few doubts I did a binomial GLM with 3 - factor and now I have to test contrasts to identify that treatments are different. I know that the contrasts are used in ANOVA, it is not incorrect to use them in GLM? there is a way to do contrasts between treatments for GLM as a Tukey for the ANOVA? Susana

7 Jan 09 Hello, I am using R version 2.7.0 in a Windows XP context. I am also using the glmm.admb package (created by Dave Fournier, Hans Skaug, and Anders Nielson) to run mixed-effects negative binomial models. To the best of my knowledge and ability, I have searched and studied the R-help, R-sig-mixed models, and ADMB NBMM for R (through Otter Research Ltd) list servs; R help