similar to: Mixed model with multiple response variables?

Displaying 20 results from an estimated 10000 matches similar to: "Mixed model with multiple response variables?"

2003 Nov 27
2
lme v. aov?
I am trying to understand better an analysis mean RT in various conditions in a within subjects design with the overall mean RT / subject as one of the factors. LME seems to be the right way to do this. using something like m<- lme(rt~ a *b *subjectRT, random= ~1|subject) and then anova(m,type = "marginal"). My understanding is that lme is an easy interface for dummy coding
2010 Feb 23
1
how to assess the significance of regression between a set of response and predictor variables
Dear list, I have been using multivariate multiple regression (MMR) in the form lm(Y~X) where Y and X are matrices of response and predictor variables. I know that summary(mlm.object) would give the usual lm statistics for each response variable separately and that anova.mlm(mlm.object) will give the analysis of variance table of the mlm object. However, anova.mlm (also manova(mlm.object))
2018 May 26
0
TukeyHSD for multiple response
Hi Sergio Doing those tests 30 times is going to give you a huge Type I error rate, even if there was a function that did that. There is a reason why TukeyHSD doesn't make it easy. In general, if there are useful comparisons among the species, you are better off setting up and testing contrasts than doing all-pairwise Tukey tests. Also, the PCA scores are ordered in terms of variance
2011 Apr 07
1
transform() on selective names. Is it possible?
Hi all, I am whitening my data: # code begins N <- 300 M <- 2 x <- matrix(data=rnorm(N*M, 0, 3)-10, ncol=M, nrow=N) y <- matrix(c(1,-2,-2,1), ncol=M, nrow=M) z <- data.frame(x %*% y) colnames(z) <- c('x','y') par(mfrow=c(1,3)) plot(z, pch=5, col="blue") whiten <- function(x) { (x-mean(x))/sd(x) } zz <- transform(z, x=whiten(x), y=whiten(y))
2010 Dec 13
1
Multivariate binary response analysis
Greetings ~ I need some assistance determining an appropriate approach to analyzing multivariate binary response data, and how to do it in R. The setting: Data from an assay, wherein 6-hours-post-fertilization zebrafish embryos (n=20, say) are exposed in a vial to a chemical (replicated 3 times, say), and 5 days later observed for the presence/absence (1/0) of defects in several organ systems
2018 May 25
2
TukeyHSD for multiple response
Dear all, I'm testing the effect of species and sex in my sample by using the principal component scores of a PCA analysis. I have 30 PCs and I tried to see if there is any significant difference from males to females, given that there is a significant effect of phylogeny (factor with several species). I didi it like this: Y<-PCA$pc.scores[,1:30] fit <- manova(Y ~ sp*sex)
2006 Mar 30
2
Unbalanced Manova
Dear all, I need to do a Manova but I have an unbalanced design. I have morphological measurements similar to the iris dataset, but I don't have the same number of measurements for all species. Does anyone know a procedure to do Manova with this kind of input in R? Thank you very much, Naiara. -------------------------------------------- Naiara S. Pinto Ecology, Evolution and Behavior 1
2013 Oct 09
1
mixed model MANOVA? does it even exist?
Hi, Sorry to bother you again. I would like to estimate the effect of several categorical factors (two between subjects and one within subjects) on two continuous dependent variables that probably covary, with subjects as a random effect. *I want to control for the covariance between those two DVs when estimating the effects of the categorical predictors** on those two DVs*. The thing is, i
2007 Feb 22
1
MANOVA usage
Hello, I had a couple questions about manova modeling in R. I have calculated a manova model, and generated a summary.manova output using both the Wilks test and Pillai test. The output is essentially the same, except that the Wilks lambda = 1 - Pillai. Is this normal? (The output from both is appended below.) My other question is about the use of MANOVA. If I have one variable which has a
2004 May 24
2
Manova and specifying the model
Hi, I would like to conduct a MANOVA. I know that there 's the manova() funciton and the summary.manova() function to get the appropriate summary of test statistics. I just don't manage to specify my model in the manova() call. How to specify a model with multiple responses and one explanatory factor? If I type:
2007 Apr 05
1
MANOVA with repeated measurements
Hello, I have a question regarding performing manova. I have an experiment where I want to measure 10 output variables for 3 different measurement methods. Since each of the methods requires some user interaction, I would also like to include repeated measures for each of the output variables to include intraobserver variability in the design. How can I perform such a repeated-measures
2006 Nov 09
2
Repeated Measures MANOVA in R
Can R do a repeated measures MANOVA and tell what dimensionality the statistical variance occupies? I have been using MATLAB and SPSS to do my statistics. MATLAB can do ANOVAs and MANOVAs. When it performs a MANOVA, it returns a parameter d that estimates the dimensionality in which the means lie. It also returns a vector of p-values, where each p_n tests the null hypothesis that the mean
2004 Feb 15
1
manova() with a data frame
I'm trying to learn to use manova(), and don't understand why none of the following work: > data(iris) > fit <- manova(~ Species, data=iris) Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : incompatible dimensions > fit <- manova(iris[,1:4] ~ Species, data=iris) Error in model.frame(formula, rownames, variables, varnames, extras,
2009 Feb 16
2
Whitening Time Series
Hi R users, I am doing cross correlation analysis on 2 time series (call them y-series and x-series) where I need the use the model developed on the x-series to prewhiten the yseries.. Can someone point me to a function/filter in R that would allow me to do that? Thanks in advance for any help! -- View this message in context:
2003 Nov 22
3
summary.manova and rank deficiency
Hi all, I have received the following error from summary.manova: Error in summary.manova(manova.test, test = "Pillai") : residuals have rank 36 < 64 The data is simulated data for 64 variables. The design is a 2*2 factorial with 10 replicates per treatment. Looking at the code for summary.manova, the error involves a problem with qr(). Does anyone have a suggestion as to how to
2008 Jul 15
2
extracting elements from print object of Manova()
Hi there, Does anyone know how to extract elements from the table returned by Manova()? Using the univariate equivalent, Anova(), it's easy: a.an<-Anova(lm(y~x1*x2)) a.an$F This will return a vector of the F-values in order of the terms of the model. However, a similar application using Manova(): m.an<-Manova(lm(Y~x1~x2)) m.an$F Returns NULL. So does any attempt at calling the
2012 Aug 25
2
Standard deviation from MANOVA??
Hi, I have problem getting the standard deviation from the manova output. I have used the manova function: myfit <- manova(cbind(y1, y2) ~ x1 + x2 + x3, data=mydata) . I tried to get the predicted values and their standard deviation by using: predict(myfit, type="response", se.fit=TRUE) But the problem is that I don't get the standard deviation values, I only
2003 Jun 10
1
Bootstraping with MANOVA
Hi, Does anyone know what the error message mean? > Boot2.Pillai <- function(x, ind) { + x <- as.matrix(x[,2:ncol(x)]) + boot.x <- as.factor(x[ind, 1]) + boot.man <- manova(x ~ boot.x) + summary(manova(boot.man))[[4]][[3]] + } > > man.res <- manova(as.matrix(pl.nosite) ~ + as.factor(plankton.new[,1]))$residuals > boot2.plank <-
2012 Mar 19
1
car/MANOVA question
Dear colleagues, I had a question wrt the car package. How do I evaluate whether a simpler multivariate regression model is adequate? For instance, I do the following: ami <- read.table(file = "http://www.public.iastate.edu/~maitra/stat501/datasets/amitriptyline.dat", col.names=c("TCAD", "drug", "gender", "antidepressant","PR",
2008 Aug 13
1
summary.manova rank deficiency error + data
Dear R-users; Previously I posted a question about the problem of rank deficiency in summary.manova. As somebody suggested, I'm attaching a small part of the data set. #*************************************************** "test" <- structure(.Data = list(structure(.Data = c(rep(1,3),rep(2,18),rep(3,10)), levels = c("1", "2", "3"), class =