similar to: contrasts in lm~-1+(numeric.variable)/(factor) (PR#2037)

Dear List, After running a compound symmetric model using gls, I realized that the default contrasts were not the ones that made the most sense given the biological relationships among the factor levels. When I either changed the factor levels to re-arrange the order they occur in the gls model (not shown below) OR specifically change the contrasts I get the exact same estimates for the

I am new to statistics, R, and this list, so apologies in advance for the errors etiquette I am certain to make (in spite of reading the posting guide, help on various commands, etc.). ?Any help is greatly appreciated. Here is my data: fatigue = c(3,2,2,3,2,3,4,3,2,4,5,3,3,2,4,5,4,5,5,6,4,6,9,8,4,3,5,5,6,6,6,7,9,10,12,9) n <- 3 train <- gl(3, 4*n, labels=c("6wks",

The documentation (in ver 1.1) for contrasts and contrasts<- does not list all the arguments for those functions. In addition to x, the factor whose contrasts are being extracted or set, contrasts() has the argument 'contrasts=TRUE', and contrasts<-() has the argument 'how.many'. It was this latter that had me flummoxed, because I wanted to reparametrize a model by

Dear all, I've been getting different results from the sparse and dense version of model.Matrix when used with sparse contrasts and interactions between factors. The same happens when using model.matrix and sparse.model.matrix. When calculating list.contrasts I get the same results for sparse and dense contrasts (except the type of the matrix is different of course). However, when I use these

An lme4 user pointed out <https://github.com/lme4/lme4/issues/491> that passing contrasts as a string or symbol to [g]lmer (which would work if we were using `contrasts<-` to set contrasts on a factor variable) is *silently ignored*. This goes back to model.matrix(), and seems bad (this is a very easy mistake to make, because of the multitude of ways to specify contrasts for factors in R

Hello everyone, I was trying to calculate linear contrasts with coxph via the contrasts function, but I'm not sure if it is correct. First, all the statistics change, if I state 2 contrasts instead of 3. Second, stipulating common linear contrasts does not even nearly produce an expected result (i.e. -1,1,0,0 should lead to nonsignificatn result, as group A and B are not differing very much)

On Thu, Feb 21, 2019 at 7:49 AM Fox, John <jfox at mcmaster.ca> wrote: > > Dear Ben, > > Perhaps I'm missing the point, but contrasts.arg is documented to be a list. From ?model.matrix: "contrasts.arg: A list, whose entries are values (numeric matrices or character strings naming functions) to be used as replacement values for the contrasts replacement function and whose

Hi, I need to build a function to generate one column for each level of a factor in the model matrix created on an arbitrary formula (instead of using the available contrasts options such as contr.treatment, contr.SAS, etc). My approach to this was first to use the built-in function for contr.treatment but changing the default value of the contrasts argument to FALSE (I named this function

Dear all, Today I figured out that there is a neat function called droplevels, which, well, drops unused levels in a data frame. I tried the function with some of my data sets and it turned out that not only the unused levels were dropped but also the contrasts I set via "C". I had a look into the code, and this behaviour arises from the fact that droplevels uses simply factor to drop

Dear Martin and Ben, I agree that a warning is a good idea (and perhaps that wasn't clear in my response to Ben's post). Also, it would be nice to correct the omission in the help file, which as far as I could see doesn't mention that a contrast-generating function (as opposed to its quoted name) can be an element of the contrasts.arg list. Best, John > -----Original

Hi all, I am trying to do a ordered probit regression using polr(), replicating a result from SAS. >polr(y ~ x, dat, method='probit') suppose the model is y ~ x, where y is a factor with 3 levels and x is a factor with 5 levels, To get coefficients, SAS by default use the last level as reference, R by default use the first level (correct me if I was wrong), The result I got is a

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 =

Hi, perhaps this is a stupid question, but i need some help about Helmert contrasts in the Cox model. I have a survival data frame with an unordered factor `group' with levels 0 ... 5. Calculating the Cox model with Helmert contrasts, i expected that the first coefficient would be the same as if i had used treatment contrasts, but this is not true. I this a error in reasoning, or is it

Hello all R-helpers, I've performed an experiment to test for differential effects of elevated temperatures on three different groups of corals. I'm currently performing a cox proportional hazards regression with censoring on the survivorship (days to mortality) of each individual in the experiment with two factors: Temperature Treatment (2 levels: ambient and elevated) and

I have the following data set > data A B X1 X2 Y 1 A1 B1 1.1 2.9 1.2 2 A1 B2 1.0 3.2 2.3 3 A2 B1 1.0 3.3 1.6 4 A2 B2 0.5 2.6 3.1 > sapply(data, class) A B X1 X2 Y "factor" "factor" "numeric" "numeric" "numeric" I'd like to set a specific type of contrasts to all the categorical factors

and this breaks multinom from the nnet library [yes, Kurt, I'm working on that and I'll send you patches..] Is this a bug or an (half-)intentional difference to S ? --------------------- In R (0.50-0.60) model.matrix(formula =, data =) Arguments: formula: A model formula or terms object data: A data frame created with `model.frame' In S-plus USAGE:

Hello, I'm trying to reproduce some SAS result wit R (after I got suspicious with the result in R). I struggle with the contrasts in a linear model. I've got three factors > d$dose <- as.factor(d$dose) # 5 levels > d$time <- as.factor(d$time) # 2 levels > d$batch <- as.factor(d$batch) # 3 levels the data frame d contains 82 rows. There are 2 to 4 replicates of

OK, my last question didn't get any replies so I am going to try and ask a different way. When I generate contrasts with contr.sum() for a 3 level categorical variable I get the 2 orthogonal contrasts: > contr.sum( c(1,2,3) ) [,1] [,2] 1 1 0 2 0 1 3 -1 -1 This provides the contrasts <1-3> and <2-3> as expected. But I also want it to create <1-2> (i.e.

I have been trying for hours now to perform an orthogonal contrast through an ANOVA in R. I have done a two-factor factorial experiment, each factor having three levels. I converted this dataset to a dataframe with one factor with nine treatments, as I couldn't work out what else to do. I have set up a matrix with the eight orthogonal contrasts that I wish to perform, but despite

I have a question regarding customising contrasts for linear models I read the section in Fox/Weisberg's CAR (2nd ed.) and was thinking--apparently erroneously--that the following two snippets would do the same: # approach 1: default treatment contrasts # generate data set.seed(1111) y <- c(rnorm(1000, -1), rnorm(1000, 0), rnorm(1000, 1)) x <- factor(rep(letters[1:3], each=1000)) #