similar to: do multiple anova tests

Dear all, When analyzing data from a climate change experiment using linear mixed-effects models, I recently came across a situation where: - the summary(model) showed a significant difference between the levels of a two-level factor, - while the anova(model) showed no significance for that factor (see below). My question now is: Is the anova.lme() approach correct for that model? And why does

Hi! I'm running an anova on a lm and I get the message Error: invalid dimnames given for data frame Any idea where it comes from? Many thanks. Here is the fit and the attempted anova: > fit Call: lm(formula = as.formula(paste(chemical, "a~", cmd, sep = "")), singular.ok = TRUE) Coefficients: (Intercept) Odom Displace Weight

An unwanted side effect of the new restrictions on abrreviated names. The anova.coxph command, in a slavish copy of anova.lm etc, returns a data frame with column labels of loglik Chisq Df Pr(>|Chi|) If one tries to extract the final column of the table errors result since it is not a standard R variable name. > afit <- anova(lm(conc ~ uptake, CO2)) > afit$P [1]

Hi, For a n-level factor, I'd like to specify the first contrast and have the remaining n-2 constructed automatically so that the set is orthogonal. I then test the contrasts with summary.lm(anova-object). In S-Plus, the following works: >y.anova <- aov( y ~ C(CO2,c(1,0,-1)) ) >summary.lm(y.anova) In R, it fails with the following error: >levels(CO2) [1]

Hi everyone I'm using Tukey HSD as post-hoc test following a lme analysis. I'm measuring hemicelluloses in different species treated with three different CO2 concentrations (l=low, m=medium, h=high). The whole experiment is a split-plot design and the Tukey-function from the package multcomp is suitable for lme-analysis with random factors. The analysis works fine but I get a non

Hi I was wondering if there is a permutation test available in R for linear models with continuous dependent covariates. I want to do a test like the one shown here. bmi<-rnorm(100,25) x<-c(rep(0,75),rep(1,25)) y<-rnorm(100)+bmi^(1/2)+rnorm(100,2)*x+bmi*x H0<-lm(y~1+x+bmi) H1<-lm(y~1+x+bmi+x*bmi) anova(H0,H1) summary(lm(y~1+x+bmi)) But I want to use permutation testing to

Dear R-helpers, In the case of two categorical factors, say a and b, once I have fixed the constrasts, the model matrix is set according to these contrasts with "lm", and the t-tests for the significance of the parameters provided by "summary" indeed concern the comparison of the model with each submodel obtained by removing the corresponding column of the model matrix.

Hi, is there a specific/appropriate function/package to perform post hoc tests when running a two way repeated measures anova? I'm looking for something that will be equivalent to the 'TukeyHSD()' for between subjects anova (with 'aov()'). For one way repeated measures anova, the 'pairwise.t.test()' function seems to work correctly but the results are questionable for

Dear all, I have some questions regarding the validity an implementation of statistical tests based on linear models and loess. I've searched the R-help arhives and found several informative threads that related to my questions, but there are still a few issues I'm not clear about. I'd be grateful for guidance. Background and data set: I wish to compare the growth and metabolism

Hi, I was trying to figure out how to do post-hoc tests for Two Way ANOVAs and found the following 2 approaches: a. Do pairwise t-tests (bonferroni corrected) if one finds significance with the ANOVA. Link- http://rtutorialseries.blogspot.com/2011/01/r-tutorial-series-two-way-anova-with.html b. Do TukeyHSD on an aov model Link-

Hello!!! I'm trying to do multiple ANOVA tests with R (testing the affect off different factors on the same response). As a result I get many ANOVA tables, and I want to extract a list of the "Pr(>F)" from all the tables. Maybe someone have an idea how to do this? Thanks Imri -- View this message in context:

I apologize in advance if this is posted all ready I have not been able to find any information about it. I have this data frame and I want to sort smoking by retlevel. Age Gender BMI Calories Fat Fiber Alc retlevel Smoking 1 64 Female 18.87834 1828.0 63.4 14.7 0.0 Normal Non-Smoker 2 25 Female 20.64102 1517.4 59.1 5.9 0.0 Normal Smoker 3

Full_Name: Ben Fitzpatrick Version: 2.3.0 OS: Windows XP and Mac OSX Submission from: (NULL) (160.36.15.155) When you fit a linear model (lm) and use the summary command, the assessment of the statistical significance of explanatory variables is very different than if you use the anova command. The F statistics calculated by anova are much larger than what you get out of SAS, for example. I

Dear List, are there post-hoc tests like Scheffe, LSD, etc. available after ANOVA test is performed with significant F-statistic? I have tried help.search("Scheffe"), but "No documentation found" (and I have most of packages installed). Probably there are such tests in R, and I am just searching badly... My second question is: Which test/method I should use for ANOVA-like

Hi all I am running a 2-way repeated measure anova with 1 between-subjects factor (Group=treatment, control), and 1 within-subject factor (Time of measurement: time1, time2). I extract the results of the anova with: summary(aov(effect ~ Group*Time + Error=Subj/Time, data=mydata)) Now, this must be clearly a dumb question, but how can I quickly extract in R all the post-hoc t-tests for the

Hi All, I have performed a 3-way ANOVA analysis for my experimental data using aov function. My simple R funtion for this is: 3aof <- function(x){ m <- data.frame(R,S,T, x); anova(aov(x ~ R+S+T+R*S+R*T+S*T+R*S*T, m) ) } Now, I am getting P values for all the main and interactions effects. If I want to perform postdoc test on one of my main effects, say T, what method I should use (i have

# uname -a FreeBSD ck.com 7.0-STABLE FreeBSD 7.0-STABLE #1: Tue Jul 15 11:38:41 EDT 2008 ck@friendlybearonskates.com:/usr/obj/usr/src/sys/CK i386 # Make buildkernel kernconf=CK fails here : linking kernel.debug sbp.o(.text+0xd9d): In function `sbp_free_ocb': /usr/src/sys/dev/firewire/sbp.c:2905: undefined reference to `xpt_release_devq' sbp.o(.text+0x123e): In function

Dear useRs, I've just encountered a serious problem involving the F-test being carried out in aov() and anova(). In the provided example, aov() is not making the correct F-test for an hypothesis involving the expected mean square (EMS) of a factor divided by the EMS of another factor (i.e., instead of the error EMS). Here is the example: Expected Mean Square

Hello everyone, Like a lot of people, I have been looking for functions in R doing ANOVA (ok) and performing multicomparisons (like Student-Newman-Keuls, etc.). As I have been a little bit disappointed, I have bee looking through the net for such "open source" softwares. I found one in: http://www.statpages.org/miller/openstat/OS4.html I have begun to use it, and it seems good and

Hello everyone, I am trying to do within subjects repeated measures anova followed by the test of sphericity (sample dataset below). I am able to get either mixed model or linear model anova and TukeyHSD, but have no luck with Repeated-Measures Assuming Sphericity or Separate Sphericity Tests. I am trying to follow example from "car" package, but it seems that I am not getting something