similar to: anova.mlm for single model (one-way repeated measured anova)

Would someone be so kind as to explain in English what the ANOVA code (anova.lm) is doing? I am having a hard time reconciling what the text books have as a brute force regression and the formula algorithm in 'R'. Specifically I see: p <- object$rank if (p > 0L) { p1 <- 1L:p comp <- object$effects[p1] asgn <-

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)

Hi, I wish to compute multivariate test statistics for a within-subjects repeated measures design with anova.mlm. This works great if I only have two factors, but I don't know how to compute interactions with more than two factors. I suspect, I have to create a new "grouping" factor and then test with this factor to get these interactions (as it is hinted in R News 2007/2), but

Hi, I'm encountering a very odd problem with calls to anova.mlm() from within a function. Consider the following code (data.n is a matrix of numeric values): mlmfit <- lm(data.n ~ 1) mlmfit0 <- lm(data.n ~ 0) print(mlmfit) anova(mlmfit,mlmfit0,test="Spherical") If I run it just like this from the console, it works just fine. If, however, I call it from within a function,

Dear R folk, First off I want to thank those of you who responded with comments for my R quick and dirty stats tutorial. They've been quite helpful, and I'm in the process of revising them. When it comes to repeated measures MANOVA, I'm in a bit of a bind, however. I'm beginning to see that all of the documentation is written for psychologists, who have a slightly

Dear R Masters, I'm an anesthesiology resident trying to make his way through basic statistics. Recently I have been confronted with longitudinal data in a treatment vs. control analysis. My dataframe is in the form of: subj | group | baseline | time | outcome (long) or subj | group | baseline | time1 |...| time6 | (wide) The measured variable is a continuous one. The null hypothesis in

I think there are two bugs in aov() that shows up when the right hand side of `formula' contains both `-1' and an Error() term, e.g., aov(y ~ a + b - 1 + Error(c), ...). Without `-1' or `Error()' there is no problem. I've included and example, and the source of aov() with suggested fixes below. The first bug (labeled BUG 1 below) creates an extra, empty stratum inside

I believe you are right, but can you please explain why anyone would want to fit this model? It differs only in the coding from aov(y ~ a + b + Error(c), data=test.df) and merely lumps together the top two strata. There is a much simpler fix: in the line if(intercept) nmstrata <- c("(Intercept)", nmstrata) remove the condition (and drop the empty stratum later if you

For some time I have been looking for a convenient way of performing post-hoc analysis to Repeated Measures ANOVA, that would be acceptable if sphericity is violated (i.e. leaving aside post-hoc to lme models). The best solution I found was John Fox's proposal to similar requests in R-help: http://tolstoy.newcastle.edu.au/R/e2/help/07/09/26518.html

predict.lm(..., type="terms") gives wrong standard errors. Below, I have provided what I believe are the necessary fixes. However, there are subtleties, and the code needs careful checking. Some of the looping is surely not necessary, but it is surely best to begin with the minimum necessary changes. My tests, including checks against S-PLUS, have extended to fitting spline curves. I

>From e980153 Tue Apr 25 14:42:27 2000 To: r-help@stat.math.ethz.ch Subject: Wrong SEs in predict.lm(..., type="terms") For what it is worth, I am using RW-1.0.0 under Windows 98. I submitted this earlier to r-help. There is one change below to my proposed corrected code: predict.lm(..., type="terms") gives wrong standard errors. Below, I have provided what I believe are

Dear R-list, the following code had been running well over the last months: exam <- matrix(rnorm(100,0,1), 10, 10) gg <- factor(c(rep("A", 5), rep("B", 5))) mlmfit <- lm(exam ~ 1); mlmfitG <- lm(exam ~ gg) result <- anova(mlmfitG, mlmfit, X=~0, M=~1) Until, all of a sudden the following error occured: Fehler in

Hi The final line of the example in ?setRefClass induces an error: > ## debugging all objects from class mEdit in method $undo() > mEdit$trace(undo, browser) Error in envRefInferField(x, what, getClass(class(x)), selfEnv) : 'undo' is not a valid field or method name for reference class "refGeneratorSlot" $trace tries to embed the trace in the generator object (instead

Hi With package:gmp, is this an expected behavior? > rep(1:3, rep(3, 3)) [1] 1 1 1 2 2 2 3 3 3 > rep(as.bigz(1:3), rep(3, 3)) Big Integer ('bigz') object of length 9: [1] 1 2 3 1 2 3 1 2 3 This code is used inside `outer`, so more worse > outer(1:3, 1:3, `*`) [,1] [,2] [,3] [1,] 1 2 3 [2,] 2 4 6 [3,] 3 6 9 > outer(as.bigz(1:3),

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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:

# devtools The aim of `devtools` is to make your life as a package developer easier by providing R functions that simplify many common tasks. Devtools is opinionated about how to do package development, and requires that you use `roxygen2` for documentation and `testthat` for testing. Future version will relax these opinions - patches are welcome! You can track (and contribute to) development of

# devtools The aim of `devtools` is to make your life as a package developer easier by providing R functions that simplify many common tasks. Devtools is opinionated about how to do package development, and requires that you use `roxygen2` for documentation and `testthat` for testing. Future version will relax these opinions - patches are welcome! You can track (and contribute to) development of

>>>>> 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