similar to: A question about nlme

I am trying to implement an ANOVA on a pair of quantile regression models in R. The anova.rq() function performs a basic check to see whether the models are nested, but I think this check is failing in my case. I think my models are nested despite the anova.rqlist() function saying otherwise. Here is an example where the GLM ANOVA regards the models as nested, but the quantile regression ANOVA

Hi, I am fitting a weighted least square regression and trying to compute SSE,SST and SSReg but I am not getting SST = SSReg + SSE and I dont know what I am coding wrong. Can you help please? xnam <-colnames(X) # colnames Design Matrix fmla1 <- as.formula(paste("Y ~",paste(xnam, collapse=

Hello: I am generating a random number with rnorm(1). The generated number has 8 decimals. I don't want so many decimals. How to control the number of decimals in R? Thanks! Zhongmiao Wang

Hello, I am trying to "paste" together a formula to use in the mob function of party. This means the formula will be of the form y ~ x1+ ...+xM | z1+..zN. I am doing some preliminary fits of y ~ x1+ ...+xM, then want to add the conditional part of the equation using update(). Here's the test code: var1 <- 1:78 x1 <- paste("x", var1, sep="") f1 <-

Hello: If the question is how likely an event will occur at a give time point, can we use logistic regression with time t as a predictor variable? For example, if the data is ID Gender Tenure Churn 1 M 17 0 2 M 3 1 3 M 6 0 4 F 10 1 5 F 9 0 6 F

Dear R Users. This seems like a simple task, but I'm stuck. I have a list with 3 elements: (2 vectors and 1 matrix). I wish to extract each of these data elements using index subscripts and multiply them with a vector multiplier. What I have: > betas [1] 0.01 0.01 0.01 > LData[1] $int [1] 1 1 1 1 1 1 1 1 1 1 $date [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,]

Dear R-users, This is a loop which is part of a bigger script. I managed to isolate the error in this loop and simplified it to the bare minimum and made it self-contained. a<-c(2,3,4,5,5,5,6,6,6,7) for(n in 1:10) { print(paste("n: ",n)) z1<-a[n] #make a list container ldata<-list() t=1 while(z1==a[n]) { #add dataframes to list ldata[[t]]<-paste("hello") n=n+1

Rafael Espíndola <rafael.espindola at gmail.com> writes: > Now that aliases can have any expressions, can't you use something like > > @data = private global [2 x i32] [i32 42, i32 43] > @symbol = alias getelementptr ([2 x i32]* @data, i32 0, i32 1) > > This produces > > .Ldata: > .long 42 # 0x2a > .long 43

I am currently puzzled by a seach path behavior. I have a library of a dozen routines getlabs(), getssn(), getecg(), ... that interface to local repositories and pull back patient information. All have a the first 6 arguments in common, and immediately call a second routine to do initial processing of these 6. The functions "joe" and "fred" below capture the relevant

I have been using lme from nlme to do a 3-way anova with all the effects treated as random. I was wondering if someone could direct me to an example of how to do this using lmer from lme4. I have 3 main effects, tim, trt, ctr, and all the interaction effects tim*trt*ctr. The response variable is ge. Here is my lme code: dat <-

Hi, I have a data set on germination and plant growth with the following variables: dataset=fm mass (response) sub (fixed effect) moist (fixed effect) pop (fixed effect) mum (random effect nested within population) iheight (covariate) plot (random effect- whole plot factor for split-plot design). I want to see if moist or sub interacts with mum for any of the pops, but I am getting an error

I am doing an analysis and would like to use lme() and the multcomp package to do multiple comparisons. My design is a within subjects design with three crossed fixed factors (every participant sees every combination of three fixed factors A,B,C). Of course, I can use aov() to analyze this with an error term (leaving out the obvious bits): y ~ A*B*C+Error(Subject/(A*B*C)) I'd also like

Try this data(Assay) as1 <- lme(logDens~sample*dilut, data=Assay, random=pdBlocked(list( pdIdent(~1), pdIdent(~sample-1), pdIdent(~dilut-1)))) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) I'm

Dear R-help group, How can I define a lme with 3 factors(a,b,c), where c is nested in b, and a is crossed with b/c? I think that: lme(response ~ ..., data = Data, random = pdBlocked(list(pdIdent(~ a - 1), pdIdent(~ b - 1)))) is one part of the answer and: lme(response~..., data=Data, random=~1|b/c) is the other part of the answer but how can I combine them?? Could anybody please help

I'm trying to do some smoothing with lme and am having some difficulty bringing commands over from S-PLUS to R. I have the following setup (modified from Ngo and Wand, 2004): set.seed(1) x <- runif(200) y <- sin(3*pi*x) + rnorm(200)*.4 ## library(splines) z <- ns(x, 4) The following runs without error on S-PLUS f <- lme(y ~ 1, random = pdIdent(~ -1 + z)) But in R I get

Dear all, I am trying to fit a model with crossed random effects using lme. In this experiment, I have been measuring oxygen consumption (mlmin) in bird nestlings, originating from three different treatments (treat), in a respirometer with 7 different channels (ch). I have also measured body mass (mass) for these birds. id nest treat year mlmin mass ch hack 1EP51711 17

Hi there, I am reading the 2004 paper "Smoothing with mixed model software" in Journal of Statistical Software, by Ngo and Wand. I tried to run their first example in Section 2.1 using R but I had some problems. Here is the code: library(nlme) fossil <- read.table("fossil.dat",header=T) x <- fossil$age y <- 100000*fossil$strontium.ratio knots <-

Hi, all, I have a question about random effects model. I am dealing with a three-factor experiment dataset. The response variable y is modeled against three factors: Samples, Operators, and Runs. The experimental design is as follow: 4 samples were randomly chosen from a large pool of test samples. Each of the 4 samples was analyzed by 4 operators, randomly selected from a group of

---------- Forwarded message ---------- From: Freddy Gamma <freddy.gamma@gmail.com> Date: 2011/1/21 Subject: TRADUCING lmer() syntax into lme() To: r-sig-mixed-models@r-project.org Dear Rsociety, I'd like to kingly ask to anyone is willing to answer me how to implement a NON NESTED random effects structure in lme() In particular I've tried the following translation from lmer to

hello! this is a question, how can i specify the random part in the GLMM-call (of the lme4 library) for compound matrices just in the the same way as they defined in the lme-Call (of the nlme library). For example i would just need random=list(my.Subject=pdBlocked(list(pdIdent(~... , ...),pdIdent(~... , ...)))) this specification , if i also attach library(nlme) , is not