search for: contrasting

Dear experts: Is it possible to create a new function based on stats:::model.matrix.default so that an alternative factor coding is used when the function is called instead of the default factor coding? Basically, I'd like to reproduce the results in 'mat' below, without having to explicitly specify my desired factor coding (identity matrices) in the 'contrasts.arg'. dd

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 names are the names of columns of data containing factors." This isn't entirely

Hello all, (Apologies in advance if my terminology is incorrect, I'm relatively new to R and statistics). I have data from a factorial design with two treatments (CRF-23), and I'm trying to compute treatment-contrast interactions through analysis of variance. I can't figure out how to do contrasts properly, despite reading the help for "C" and "contrasts"

>>>>> Ben Bolker >>>>> on Thu, 21 Feb 2019 08:18:51 -0500 writes: > 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

I have a model (run with glm) that has a factor, type. Type has two levels, "general" and "regional". I am trying to get estimates (and SEs) for the model with type="general" and type ="regional" using fit.contrast but I can't get the syntax of the coefficients to use in fit.contrast correct. I hope someone can show me how to use fit.contrast, or some

I've been playing around with contrasts in lm by specifying the contrasts argument. So, I want to specify a specific contrast to be tested Say: > y _ rnorm(100) > x _ cut(rnorm(100, mean=y, sd=0.25),c(-3,-1.5,0,1.5,3)) > reg _ lm(y ~ x, contrasts=list(x=c(1,0,0,-1))) > coef(reg)[2] x1 -1.814101 I was surprised to see that I get a different estimate for the

Forgive my resending this post. To data I have received only one response (thank you Bert Gunter), and I still do not have an answer to my question. Respectfully, John Windows XP R 2.12.1 contrast package. I am trying to understand how to create contrasts for a model that contatains an interaction. I can get contrasts to work for a model without interaction, but not after adding the

Dear R-users, I want to fit a linear model with Y as response variable and X a categorical variable (with 4 categories), with the aim of comparing the basal category of X (category=1) with category 4. Unfortunately, there is another categorical variable with 2 categories which interact with x and I have to include it, so my model is s "reg3: Y=x*x3". Using fit.contrast to make the

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

David, Thank you for responding to my post. Please consider the following output (typeregional is a factor having two levels, "regional" vs. "general"): Call: glm(formula = events ~ type, family = poisson(link = log), data = data, offset = log(SS)) Deviance Residuals: Min 1Q Median 3Q Max -43.606 -17.295 -4.651 4.204 38.421 Coefficients:

>>>>> Fox, John >>>>> on Fri, 22 Feb 2019 17:40:15 +0000 writes: > 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

> On Oct 22, 2017, at 6:04 AM, Sorkin, John <jsorkin at som.umaryland.edu> wrote: > > I have a model (run with glm) that has a factor, type. Type has two levels, "general" and "regional". I am trying to get estimates (and SEs) for the model with type="general" and type ="regional" using fit.contrast ?fit.contrast No documentation for

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

Hello, I've got what I'd expect to be a pretty simple issue: I fit an aov object using multiple error strata, and would like some significance tests for the contrasts I specified. In this contrived example, I model some test score as the interaction of a subject's gender and two emotion variables (angry, happy, neutral), measured at entry to the experiment (entry) and later

David, Again you have my thanks!. You are correct. What I want is not technically a contrast. What I want is the estimate for "regional" and its SE. I don't mind if I get these on the log scale; I can get the anti-log. Can you suggest how I can get the point estimate and its SE for "regional"? The predict function will give the point estimate, but not (to my knowledge)

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

> On Oct 22, 2017, at 3:56 PM, Sorkin, John <jsorkin at som.umaryland.edu> wrote: > > David, > Thank you for responding to my post. > > Please consider the following output (typeregional is a factor having two levels, "regional" vs. "general"): > Call: > glm(formula = events ~ type, family = poisson(link = log), data = data, > offset =

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The man page for se.contrast, when describing the contrast.obj parameter, states that "Multiple contrasts should be specified by a matrix as returned by contrasts." When doing an unbalanced single factor ANOVA, using a contrast.obj as returned by contrasts results in the following error from qr.qty when se.contrast is called: Error in qr.qty(object$qr, contrast) : qr and y must have

I've tried without success to calculate interaction and main effects contrasts using R. I've found the functions C(), contrasts(), se.contrasts() and fit.contrasts() in package gmodels. Given the url for a small dataset and the two-way anova model below, I'd like to reproduce the results from appended SAS code. Thanks. --Dale. ## the dataset (from Montgomery) twoway <-