search for: covtest

...trying to match the coefficients of R output to that of SAS output, I came across this problem. The dataset I am using is this one: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_mixed_sect034.htm If I run the following code: proc mixed data=rc method=ML covtest; class Batch; model Y = Month / s; random Int Month / type=cs sub=Batch s; run; The Fixed effect coefficients match with that of R. But the random effect does not. Here is the R code: rc <- read.table('rc.csv', sep = ',', header=T, na.strings=".") m1 <- lmer(formu...

....435 0.495 PlantID PollClassApis 1.12364 1.06002 PollClassBombAnth 0.42769 0.65398 -0.759 Residual 3.09669 1.75974 My question is: if n random effects are included in a model, how can I test the hypothesis that the variance of such effects is 0 ? Some sort of COVTEST option in Proc MIXED in SAS (sorry, SAS is still more familar to me than R). -- ---------------------------------------------- Alfonso M. Sanchez-Lafuente Departamento de Biologia Vegetal y Ecologia Facultad de Biologia Universidad de Sevilla Avd. Reina Mercedes 9 E-41012, Sevilla, Spain email:...

...codes to R, but some of the result are quite different from SAS. When I ran proc mixed, I have an option ddfm=bw followed by the model. How can I show this method in R?(I am thinking that this maybe the reason that I can't get the similar results) below is my SAS codes: proc mixed data=test covtest empirical; class pair grade team school; model score = trt pair grade school/ solution covb ddfm=bw ; random int / sub=team solution type=un; run; I have tried both lmer and hglm, but non of them works. Could anyone tell me how can I covert this SAS codes to R? Thanks -- View this message in...

...to test when in time there is difference between treatments. I do not know if there is something like this is R. Thank you very much! PD. For SAS users, what I am using in SAS to perform this analysis (with an Autoregressive covariance structure) the program would read like this: proc MIXED covtest; CLASS cow treat time; MODEL yield= treat time treat*time; REPEATED time/SUB=cow(treat) TYPE=ARH(1) R RCORR; RANDOM cow; LSMEANS treat time treat*time/SLICE = time; RUN; Thank you very much, Sincerely, Alex

...y=y1; date=*1*; output; y=y2; date=*2*; output; y=y3; date=*3*; output; datalines; 1 3 6725 6750 925 2 8 6950 5800 11275 3 4 4200 6100 6475 Procedure: *proc* *mixed* data=Nosema method=ml covtest; class colony tmt; model y=tmt date tmt*date / s; repeated / type=un subject=colony; random colony; lsmeans tmt/cl adjust=tukey; I am able to get something close by running aov on it, even closer by using Anova{car} and calling type...

...nalyse this desing considering population, pollinator group and their interaction as fixed effects, and treatment nested within plant, and its interaction with population and pollinator group, as random factors. In SAS terminology, the model looks like this: proc mixed data=flwfunc.visitflower covtest method=reml; class site pollclass treatm plantid; model time = site|pollclass flwinflor / chisq; random treatm site*treatm pollclass*treatm / subject=plantid; lsmeans site pollclass site*pollclass; run; I've been successfully trying lm, but I think is not suitable for random effects. Thus...

...rs, What is the equivalent of SAS " '/diff' and '/slice'" in R ? I am successful in fitting the same model "PROC MIXED" fits using lme() : ###R code: model <- lme( y ~ g + h + yr + yr:h, random = ~ 1 | b, data = yield) *** SAS code: proc mixed data=yield covtest cl method=reml; class b g h yr; model y = g h yr yr*h; random b; lsmeans g / diff; The codes above give equivalent outputs (analysis of a 5(g) by 2(h) factorial, RCBD(b)), except for the "lsmeans g / diff" line, which I cannot reproduce in R! The below gives one of the comparisons I am...

Does anyone know of R functions for doing composite quantile regression (Hou and Yuan Ann Stat 36:1108, 2008)? The paper's authors do not talk about software in their paper or on their web sites. Thanks Frank ----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/Composite-Quantile-Regression-tp4663463.html

Hello, I am very new to R (as my Subject probably indicates). I want to do something that should, I think, be very simple. I have five vectors in a list and I want to construct a covariance matrix out of them. Given a 5X5 matrix cvm1, and the list of vectors, cvm1_list, I thought the following would work (sorry cannot find code tags): for(i in 1:5){ for(j in 1:5){ cvm1[i,j] <-

Hi All I am trying to translate a proc mixed into a lme() syntax. It seems that I was able to do it for part of the model, but a few things are still different. It is a 2-level bivariate model (some call it a pseudo-3-level model). PROC MIXED DATA=psdata.bivar COVTEST METHOD = ml; CLASS cluster_ID individual_id variable_id ; MODEL y = Dp Dq / SOLUTION NOINT; RANDOM Dp Dq / SUBJECT = cluster_ID TYPE=UN G GCORR; REPEATED variable_id / SUBJECT = individual_ID(cluster_ID) TYPE=UN R RCORR; RUN; Here is my try: dta = sqlQuery(odbcConnect("sasodbc", believe...