similar to: effects package for adjusted predictions

Dear R listers, I am learning the MLE utility optim() in R to program ordered logit models just as an exercise. See below I have three independent variables, x1, x2, and x3. Y is coded as ordinal from 1 to 4. Y is not yet a factor variable here. The ordered logit model satisfies the parallel regression assumption. The following codes can run through, but results were totally different from what I

Hi, I have a matrix that is indexed by groups and looks something like this: 1 1 2 1 2 1 . . . 2 1 1 2 1 2 2 NA 1 . . . 3 1 NA 3 2 NA etc. The first column is the group variable and I would like to apply categorical data imputation functions to the other two columns, doing so by groups. I have tried APPLY, BY and SPLIT, but have not had much luck getting it to work. I wonder if anyone has

Dear R-help listers, I am a new convert to R. I am trying to use a r package to conduct latent class analysis as a triangulation check of my cluster analysis using the cluster package in R. I have about 30 cases and 6 indicators, some of which are binary indicators and others are ratio-level variables (percentages). I looked around for information in flexmix, lca, and poLCA, and couldn't find

I'm trying to develop some graphic methods for glm objects, but they only apply for models where all predictors are discrete factors. How can I test for this in a function, given the glm model object? That is, I want something that will serve as an equivalent of is.discrete.glm() in the following context: myplot.glm <- function(model, ...) { if (!inherits(model,"glm"))

Dear R devel I've been wondering about this for a while. I am sorry to ask for your time, but can one of you help me understand this? This concerns duplicated labels, not levels, in the factor function. I think it is hard to understand that factor() fails, but levels() after does not > x <- 1:6 > xlevels <- 1:6 > xlabels <- c(1, NA, NA, 4, 4, 4) > y <- factor(x,

Dear list, I would like to expand a DF with all the missing levels of a variable. a <- c(2,2,3,4,5,6,7,8,9) a.cut <- cut(a, breaks=c(0,2,6,9,12), right=FALSE ) (x <- data.frame(a, a.cut)) # In 'x' the level "[0,2)" is "missing". AddMissingLevel <- function(xdf) { xfac <- factor( c("[0,2)", "[2,6)", "[6,9)",

Hi, Some try: > names(mi$xlevels) [1] "f" > all.vars(mi$formula) [1] "D" "x" "f" "Y" > names(mx$xlevels) [1] "f" > all.vars(mx$formula) [1] "D" "x" "f" When offset is indicated out of the formula, it does not work... Marc Le 07/03/2018 ? 06:20, Bendix Carstensen a ?crit?: > I would like

I'm working on predict.survreg and am confused about xlevels. The model.frame method has the argument, but none of the standard methods (model.frame.lm, model.frame.glm) appear to make use of it. The documentation for model.matrix states: xlev: to be used as argument of model.frame if data has no "terms" attribute. But the terms attribute has no xlevels information in it, so I

all.vars works fine, EXCEPT, it give a bit too much. I only want the regression variables, but in the following example I also get "k" the variable holding the chosen knots. Any machinery to find only "real" regression variables? cheers, Bendix library( splines ) y <- rnorm(100) x <- rnorm(100) k <- -1:1 ml <- lm( y ~ bs(x,knots=k) ) mg <- glm( y ~

# 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

library(effects) mod <- lm(Measurement ~ Age + Sex, data=d) e <-effect("Sex",mod) The effect is evaluated at the mean age. > e Sex effect Sex F M 43.33083 44.48531 > > e$model.matrix (Intercept) Age SexM 1 1 130.5859 0 23 1 130.5859 1 To evaluate the effect at Age=120 I tried: e

Dear R help listers, I am trying to replicate results in Gelman and Hill's book (Chapter 3 in regressions and multilevel models). Below I estimated two models (chp3.1 and chp3.3 in R codes) with the same data and dependent variable but different independent variables. I have been using Stata for quite a while, and I know I can use foreach to build a loop to condense the codes (especially if I

Hello, Everybody: This may not be a "bug", but for me it is an unexpected outcome. A factor variable's levels do not retain their ordering after the levels function is used. I supply an example in which a factor with values "BC" "AD" (in that order) is unintentionally re-alphabetized by the levels function. To me, this is very bad behavior. Would you agree? #

>>>>> Paul Johnson <pauljohn32 at gmail.com> >>>>> on Wed, 14 Jun 2017 19:00:11 -0500 writes: > Dear R devel > I've been wondering about this for a while. I am sorry to ask for your > time, but can one of you help me understand this? > This concerns duplicated labels, not levels, in the factor function. > I think it

Dear R-devel list members, I've discovered an inconsistency in how lm() and similar functions handle logical predictors as opposed to factor or character predictors. An "lm" object for a model that includes factor or character predictors includes the levels of a factor or unique values of a character predictor in the $xlevels component of the object, but not the FALSE/TRUE values

Dear Abby, > On Aug 30, 2019, at 8:20 PM, Abby Spurdle <spurdle.a at gmail.com> wrote: > >> I think that it would be better to handle factors, character predictors, and logical predictors consistently. > > "logical predictors" can be regarded as categorical or continuous (i.e. 0 or 1). > And the model matrix should be the same, either way. I think that

I would like to extract the names, modes [numeric/factor] and levels of variables needed in a data frame supplied as newdata= argument to predict.glm() Here is a small example illustrating my troubles; what I want from (both of) the glm objects is the vector c("x","f","Y") and an indication that f is a factor: library( splines ) dd <- data.frame( D =

Dear Bill, Thanks for pointing this difference out -- I was unaware of it. I think that the difference occurs in model.matrix.default(), which coerces character variables but not logical variables to factors. Later it treats both factors and logical variables as "factors" in that it applies contrasts to both, but unused factor levels are dropped while an unused logical level is not. I

Happy New Year I recognize this is a low priority issue, but... I'll fix it if you let me. There are some TABs where R style calls for 4 spaces. For example R-3.0.2/src/library/stats/R/dummy.coef.R. I never noticed this until today, when I was stranded on a deserted island with only the R source code and a Swiss Army knife (vi). Now I realize my ~.vimrc has tabstop set at 2, and it makes

2012/4/7 <r-help-es-request@r-project.org> > Envíe los mensajes para la lista R-help-es a > r-help-es@r-project.org > > Para subscribirse o anular su subscripción a través de la WEB > https://stat.ethz.ch/mailman/listinfo/r-help-es > > O por correo electrónico, enviando un mensaje con el texto "help" en > el asunto (subject) o en el cuerpo a: