similar to: [OFF] Nested or not nested, this is the question.

Hi all, I have problems in a dataframe variables types. Look: from a loop function: for(...){ ... dados.fin <- rbind(dados.fin, c(L=j, A=j^2, Nsp=nsps, N=length(amosfin$SP), AmT="am",NAm=nam, AMST=amst)) dados.fin <- rbind(dados.fin, c(L=j, A=j^2,

Hi, this is not a new doubt, but is a doubt that I cant find a good response. Look this output: > m.lme <- lme(Yvar~Xvar,random=~1|Plot1/Plot2/Plot3) > anova(m.lme) numDF denDF F-value p-value (Intercept) 1 860 210.2457 <.0001 Xvar 1 2 1.2352 0.3821 > summary(m.lme) Linear mixed-effects model fit by REML Data: NULL AIC BIC

Hello: Let me rephrase my question to attract interest in the problem I'm having. When I appply anova() to two equations estimated using glmmPQL, I get a complaint, > anova(fm1, fm2) Error in anova.lme(fm1, fm2) : Objects must inherit from classes "gls", "gnls" "lm","lmList", "lme","nlme","nlsList", or "nls"

Hi I am working on a Nested one-way ANOVA. I don't know how to implement R code to test the significance of the random factor My R code so far can only test the fixed factor : anova(lme(PCB~Area,random=~1|Sites, data = PCBdata)) numDF denDF F-value p-value (Intercept) 1 12 1841.7845 <.0001 Area 1 4 4.9846 0.0894 Here is my data and my hand

Dear R users I have run an regression and want to extract the p value of the F statistics, but I can find a way to do that. x<-summary(lm(log(RV2)~log(IV.m),data=b)) Call: lm(formula = log(RV2) ~ log(IV.m), data = b[[11]]) Residuals: Min 1Q Median 3Q Max -0.26511 -0.09718 -0.01326 0.11095 0.29777 Coefficients: Estimate Std. Error t value Pr(>|t|)

Hello, I ran two lme analyses and got expected results. However, I saw something suspicious regarding p-level for fixed effect. Models are the same, only experimental designs differ and, of course, subjects. I am aware that I could done nesting Subjects within Experiments, but it is expected to have much slower RT (reaction time) in the second experiment, since the task is more complex, so it

Hi, I'm having some problems regarding the packages lme4 and nlme, more specifically in the denominator degrees of freedom. I used data Orthodont for the two packages. The commands used are below. require(nlme) data(Orthodont) fm1<-lme(distance~age+ Sex, data=Orthodont,random=~1|Subject, method="REML") anova(fm1) numDF DenDF F-value p-value (Intercept) 1

Dear all, Using the data set and code below, I am interested in modelling how egg temperature (egg.temp) is related to energy expenditure (kjday) and clutch size (treat) in incubating birds using the lme-function. I wish to generate the F-distribution for my model, and have tried to do so using the anova()-function. However, in the resulting anova-table, the parameter kjday has gone from being

Hi, I have this: lme(y~x1+x2,random=~1|x1/x2) How to make this random effect using lmer? I try this: lmer(y~x1+x2+(1|x1/x2) But it dont work. Any idea? Thanks Ronaldo -- System halted! -- |> // | \\ [***********************************] | ( ?? ?? ) [Ronaldo Reis J??nior ] |> V [UFV/DBA-Entomologia ] | / \ [36570-000 Vi??osa -

Dear R-help list, I would like to perform multiple comparisons for lme. Can you report to me if my way to is correct or not? Please, note that I am not nor a statistician nor a mathematician, so, some understandings are sometimes quite hard for me. According to the previous helps on the topic in R-help list May 2003 (please, see Torsten Hothorn advices) and books such as Venables &

Dear All, I am writing to ask a clarification on what R, and in particular lme, is doing. I have an experiment where fly wing area was measured in 4 selection lines, measured at 18 and 25 degrees. I am using a lme model because I have three replicated per line (coded 1:12 so I need not use getGroups to creat an orederd factor). The lines are called: "18"; "25";

Hello: Will someone PLEASE help me with this problem. This is the third time I've posted it. When I appply anova() to two equations estimated using glmmPQL, I get a complaint, > anova(fm1, fm2) Error in anova.lme(fm1, fm2) : Objects must inherit from classes "gls", "gnls" "lm","lmList", "lme","nlme","nlsList", or

I am analyzing data from an experiment with two factors: Carbon (+/-) and O3 (+/-), with 4 replicates of each treatment, and 4 harvests over a year. The treatments are assigned in a block design to individual Rings. I have approaches this as a repeated measures design. Fixed factors are Carbon, O3 and Harvest, with Ring assigned as a random variable. I have performed repeated measures analysis

tl;dr anova.lme() claims to provide sums of squares, but it doesn't. And some names are misspelled in ?lme. I can submit all this stuff as a bug report if that's preferred. ?anova.lme says: When only one fitted model object is present, a data frame with the sums of squares, numerator degrees of freedom, denominator degrees of freedom, F-values, and P-values The output of fm1

Dear listers, Here we have a strange result we can hardly cope with. We want to compare a null mixed model with a mixed model with one independent variable. > lmmedt1<-lme(mediane~1, random=~1|site, na.action=na.omit, data=bdd2) > lmmedt9<-lme(mediane~log(0.0001+transat), random=~1|site, na.action=na.omit, data=bdd2) Using the Akaike Criterion and selMod of the package pgirmess

Hi all I tried to reproduce an example with lme and used the Orthodont dataset. library(nlme) fm2a.1 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1 | Subject) anova(fm2a.1) > numDF denDF F-value p-value > (Intercept) 1 80 4123.156 <.0001 > age 1 80 114.838 <.0001 > Sex 1 25 9.292 0.0054 or alternatively

Dear all, I am starting to use the lme package (and plan to teach a course based on it next semester...). To understand what lme is doing precisely, I used balanced datasets described in Pinheiro and Bates and tried to compare the lme outputs to that of aov. Here is what I obtained: > data(Machines) > summary(aov(score~Machine+Error(Worker/Machine),data=Machines)) Error: Worker

Hello, I am trying to run a mixed effects nested ANOVA but none of my codes are giving me any meaningful results and I am not sure what I am doing wrong. I am a new user on R and would appreciate some help. The experimental design is that I have some frogs that have been exposed to three acoustic Treatments and I am measuring neural activity (egr), in 12 brain regions. Some frogs also called

Dear R-community! I still have the problem reproducing the following example using lme. id<-factor(rep(rep(1:5,rep(3,5)),3)) factA <- factor(rep(c("a1","a2","a3"),rep(15,3))) factB <- factor(rep(c("B1","B2","B3"),15)) Y<-numeric(length=45) Y[ 1: 9]<-c(56,52,48,57,54,46,55,51,51) Y[10:18]<-c(58,51,50,54,53,46,54,50,49)

Hi, I am having trouble using the lme function to perform a nested ANOVA with a random nested factor. My design is as follows: Location (n=6) (Random) Site nested within each Location (n=12) (2 Sites nested within each Location) (Random) Dependent variable: sp (species abundance) By using the aov function I can generate a nested ANOVA, however this assumes that my nested