similar to: The Secret to Supercharge your MLM!

Hi, I was wondering if there is anything more efficient than split to do the kind of conversion in the subject. If I create a data frame as in system.time({fd = data.frame(x=1:2000, y = rnorm(2000), id = paste("x", 1:2000, sep =""))}) user system elapsed 0.004 0.000 0.004 and then I try to split it > system.time(split(fd, 1:nrow(fd))) user system elapsed

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)) >

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'm trying to extend some work in the car and heplots packages that requires getting a table of multivariate means for one (or later, more) terms in an mlm object. I can do this for concrete examples, using aggregate(), but can't figure out how to generalize it. I want to return a result that has the factor-level combinations as rownames, and the means as the body of the table

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

Hi, Am newish to stats and R, so I certainly appreciate any help. Basically I have 50 inidividuals whom I have 6 photos each of their optic nerve head. I want to check that the orientation of the nerve head is consistent, ie the 6 replicates show minimal or preferably no rotation differences. I'll draw an arbitrary line between some blood vessels (same reference in each set of replicates) and

I would like to perform multiple comparisons or post-hoc testing on the independent variable in an Anova.mlm object generated by the Anova function of the car package. I have defined a multivariate linear model and subsequently performed a repeated measures ANOVA as per the instructions in section #3 of the following comprehensive tutorial on the subject from the Gribble lab at UWO:

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