similar to: help with aggregate(): tables of means for terms in an mlm

I'm trying to write a function to find the means over factors of the responses in a mlm (something I would do easily in SAS with PROC SUMMARY). The not-working stub of a function to do what I want is below, and my problem is that I don't know how to call aggregate (or some other function) in the context of terms in a linear model extracted from a lm/mlm object. means.mlm <-

[Env: R 2.8.1, Win XP] For a package I'm working on, I need two small helper functions to manipulate the data used in an lm or mlm object, given the *name* of a term, which will always be a character string representing a factor ("A") or an interaction of two or more factors ("A:B", "A:B:C", ...). I'd prefer not to have to require() additional packages if

[Env: Tested under Win Xp, R 2.9.2 and R 2.10.1; sessionInfo() at end] I've run into a problem with the heplot3d() function in my heplots package which causes the R GUI to crash ('R for Windows GUI encountered a problem and needs to close...'). I think the problem comes from an rgl call, but, I can't get a traceback or other information because my R session crashes. I've

I'm working on some functions for generalized canonical discriminant analysis in conjunction with the heplots package. I've written a candisc.mlm function that takes an mlm object and computes a candisc object containing canonical scores, coeficients, etc. But I'm stumped on how to construct a mlm for the canonical scores, in a function using the *same* right-hand-side of the model

predict.mlm fails with the error: Object "X" not found. CAUSE: looking at the predict.mlm function, we have: x <- model.matrix(object, newdata) two lines later, we have: pred <- X[, piv, drop=FALSE] %*% object$coefficients[piv,] SOLUTION(?): change the case of one of the x's, since it seems like what is meant in the second line is lowercase x... platform:

[hope this doesn't get posted twice, my first mail bounced] Hi, again, this is about the call bug in anova.mlm. I originally reported it as PR#7898 and I suggested a fix at PR#7904. (None of these message were forwarded to R-Devel, instead I received a bounce telling me that my provider's SPF settings are incorrect. They are, though, so there seems to be a problem with the

>>>>> steven pav >>>>> on Thu, 19 Jul 2018 21:51:07 -0700 writes: > It seems that confint.default returns an empty data.frame > for objects of class mlm. For example: > It seems that confint.default returns an empty data.frame for objects of > class mlm. Not quite: Note that 'mlm' objects are also 'lm' objects, and so it is

Hello, I've just fitted a model with multi-responses, and I get an object of class "lm" "mlm". My problem is that as soon as I invoke the predict method for a dataframe "newdata", the methods runs and give me back prediction at the fitting points but not for newdata. Does someone has an explanation for this behavior, and some ideas to make predict.mlm work

Hello, I am using the function "lm" to fit several responses at the same time (100 responses). At the end of the fit, I get an object of class "mlm". I would like to know if there is a way to access to each of the 100 underlying models separately (is it a list, ... ?). Which syntax should I use to see and use the 15th model (for instance) just like it is possible for classical

On Sat, 12 Aug 2006, takahashi kohske wrote: > Dear list members: > > I'd like to one-way repeated measured anova by using mlm. > I'm using R-2.3.1 and my code is: > > dat<-matrix( c(9,7,8,8,12,11,8,13, 6,5,6,3,6,7,10,9, > 10,13,8,13,12,14,14,16, 9,11,13,14,16,12,15,14), > ncol=4, dimname=list(s=1:8, c=1:4)) >

Discover "The Secret to Supercharge your MLM!" A must read... Hi there, The secret is out! Here's the mail we've all been waiting for! Read carefully and take immediate action on it! I've discovered an amazing formula that will give your MLM an enormous enrollers explosion. You'll benefit hugely if you use it with YOUR primary MLM! -THE ULTIMATE RECRUITMENT

I had a look at some influence measures, and it seems to me that currently several methods handle multiple lm (mlm) objects wrongly in R. In some cases there are separate "mlm" methods, but usually "mlm" objects are handled by the same methods as univariate "lm" methods, and in some cases this fails. There are two general patterns of problems in influence measures:

I believe there is a coding error in the first part of predict.mlm in the splines package, but perhaps someone could explain the logic to me if I'm wrong. I don't see how the second if (missing(newdata)) could ever be true. (I would show the code but I'm using email on a different machine than R today.) Paul Gilbert

Full_Name: Yves Rosseel Version: 2.2.1 OS: i686-pc-linux-gnu Submission from: (NULL) (157.193.116.152) Dear developers, For the single-model case, the anova.mlm() function does not seem to handle multi-parameter predictors (eg factors) correctly. A toy example illustrates the problem: Y <- cbind(rnorm(100),rnorm(100),rnorm(100)) A <- factor(rep(c(1,2,3,4), each=25)) fit <- lm(Y ~ A)

Full_Name: Yves Rosseel Version: 2.2.1 OS: Submission from: (NULL) (157.193.116.152) The code below sketches a possible implementation of a function 'mlh.mlm' which I think would be a good complement to the 'anova.mlm' function in the stats package. It tests a single linear hypothesis of the form H_0: LBT'= 0 where B is the matrix of regression coefficients; L is a matrix

Dear R-devel'ers Below is an outline for a set of routines to improve support for multivariate linear models and "classical" repeated measurements analysis. Nothing has been coded yet, so everything is subject to change as loose ideas get confronted by the harsh realities of programming. Comments are welcome. They might even influence the implementation... -pd General

Hi, I have encountered this error when attempting a One-way Repeated-measure ANOVA with my data. I have read the "Anova in car: SSPE apparently deficient rank" thread by I'm not sure the within-subject interaction has more degrees of freedom than subjects in my case. I have prepared the following testing script: rm(list = ls())

I'm looking for something in R to fit a multivariate linear model robustly, using an M-estimator or any of the myriad of other robust methods for linear models implemented in robustbase or methods based on MCD or MVE covariance estimation (package rrcov). E.g., one can fit an mlm for the iris data as: iris.mod <- lm(cbind(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width) ~ Species,

When I fit a multivariate linear model, and the formula is defined outside the call to lm(), the method summary.mlm() fails. This works well: > y <- matrix(rnorm(20),nrow=10) > x <- matrix(rnorm(10)) > mod1 <- lm(y~x) > summary(mod1) ... But this does not: > f <- y~x > mod2 <- lm(f) > summary(mod2) Error en object$call$formula[[2L]] <- object$terms[[2L]]

Hi, yet another anova.mlm problem - it doesn't seem to end. This time, I have a setup with a few within-subject factors and a between-subject factor (SGROUP). Consider the most simple case with only one within-factor (apo): > mlmfit0 <- lm(data.n ~ 0 + SGROUP) > mlmfit1 <- lm(data.n ~ 1 + SGROUP) > anova(mlmfit1,mlmfit0,test="Spherical",M=~hemi,X=~1) Analysis of