similar to: contrast in lme

Trying to use contrast to look at differences within an lme lme.fnl.REML <- lme(Max ~ S + Tr + Yr + Tr:Yr, random = ~1 |TID, method = "REML") I have three levels of Tr I'm trying to contrast among different years (R, T97, T98), years = 1997-1999, so I'm interested in contrasts of the interaction term. > anova(lme.fnl.REML) numDF denDF F-value

Hi! I try to explain the efffect of (1) forest where i took samples's soils (* Lugar*: categorical variable with three levels), (2) nitrogen addition treatments (*Tra*: categorical variable with two levels) on total carbon concentration's soil samples (*C: *continue* *variable) during four months of sampling (*Time:* categorical and ordered variable with four levels). I fitted the

Hi All How can I fit a repeated measures analysis using gls? I want to start with a unstructured correlation structure, as if the the measures at the occations are not longitudinal (no AR) but plainly multivariate (corSymm). My data (ignore the prox_pup and gender, occ means occasion): > head(dta,12) teacher occ prox_self prox_pup gender 1 1 0 0.76 0.41 1 2

Dear R-Users I have a problem accessing some values in the output from the summary of an lme fit. I fit the model below: ggg <- lme (ST~ -1 + as.factor(endp):Z.sas + as.factor(endp), data=dat4a, random=~-1 + as.factor(endp) + as.factor(endp):Z.sas|as.factor(trials), correlation = corSymm(form=~1|as.factor(trials)/as.factor(id)), weights=varIdent(form=~1|endp)) hh

Dear R users, I fitted a repeated measure model without random effects by using lme. I will use the estimates from that model as an initial estimates to do multiple imputation for missing values of the response variable in the model. I am trying to extract the within group correlation matrix or covariance matrix. here is my code: f = lme(y ~x0+x1+trt+tim+x1:tim +tim:trt,random=~-1|subj,

Good day R-Users, I have a problem accessing some values in the output from the summary of an lme fit. The structure of my data is as shown below (I have attached a copy of the full data). id trials endp Z.sas ST 1 1 -1 -1 42.42884 1 1 1 -1 48.12007 2 1 -1 -1 43.42878 2 1 1 -1

Dear listers, I am trying to fit a model using nlsList() using alternately a SSfol() selfstart function or its developped equivalent formulae. This preliminary trial works well mydata<-groupedData(Conc~Tps|Organ,data=mydata) mymod1<-nls(Conc~SSfol(Dose,Tps,lKe,lKa,lCl),data=mydata) as well as a developped form: mymod2<-nls(Conc~Dose * exp(lKe+lKa-lCl) *

Greetings- I need to extract, programatically, the standard deviations of the intercept and residuals from an lme model. These are presented by print.lme as: ... (Intercept) Residual StdDev: 1.410635 0.7800512 ... (data taken from ?lme's examples section) I can get the residuals with x$sigma where x is the fitted lme object. I can't find the intercept, though. The closest

Works perfectly. Thank you. -Hugh Rand -----Original Message----- From: Spencer Graves [mailto:spencer.graves at pdf.com] Sent: Sunday, January 15, 2006 6:41 PM To: Rand, Hugh Cc: 'r-help at lists.R-project.org' Subject: Re: [R] trouble with extraction/interpretation of variance structure para meters from a model built using gnls and varConstPower How about this: >

Hi All, I'm generating 10 different data sets with 1 and 0 in a matrix form and writing the output in separate files. Now I need to stack all these data sets in one vector and I know that stack only operates on list or data frame however I got these data sets by converting list to a matrix so can't go backwards now. Is there a way i can still use Stack? Please see the program:

Hi All, I'm generating 10 different data sets with 1 and 0 in a matrix form and writing the output in separate files. Now I need to stack all these data sets in one vector and I know that stack only operates on list or data frame however I got these data sets by converting list to a matrix so can't go backwards now. Is there a way i can still use Stack? Please see the program:

Dear R-user I used lme to fit a linear mixed model inlcuding weights=varPower(). Additionally I wanted to use glht to calculate Tukey-Kramer multiple comparision. error: > glht(modelF, linfct=mcp(Species="Tukey")) Error in glht.matrix(model = list(modelStruct = list(reStruct = list(SubPlot = -0.305856275920955, : ?ncol(linfct)? is not equal to ?length(coef(model))? >

Hi all, I am running some linear mixed models for longitudinal data using the nlme package. Part of the code I've developed tests for differences among models using the anova function, but I can't simply pass the corresponding lme objects as parameters to the anova function because some of the models do not converge and others are retained for comparison only if certain criteria are met.

A couple of weeks ago there was a question regarding apparent convergence in nls when using the SSfol selfStart model for fitting a first-order pharmacokinetic model. I can't manage to find the original message either in my archive or in the list archives but the data were time conc dose 0.50 5.40 1 0.75 11.10 1 1.00 8.40 1 1.25 13.80 1 1.50 15.50 1

Dear R-users I am relatively new to R, i hope my many novice questions are welcome. I have problems accessing some objects (specifically the random effects, correlation structure and variance function) from an object of class gls and lme. I used the following models: yah <- gls (outcome~ -1 + as.factor(Trial):as.factor(endpoint)+

I have been using gnls with the weights argument (and varConstPower) to specify a variance structure for curve fits. In attempting to extract the parameters for the variance model I am seeing results I don't understand. When I simply display the model (or use "summary" on the model), I get what seem like reasonable values for both "power" and "const". When I

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I am trying to simulate a dataset using Parallel Latent CTT model and this is what i have done so far: (START) #Importing psych library for all the simulation related functions library(psych) # Settting the working directory path to C:/NCME path="C:/NCME" setwd(path) #Using the function to generate the data GenData <- congeneric.sim(N=500, loads =

Version 2.2-0 of the rms package is now available. This is a somewhat major update. One major change is not downward compatible: Instead of specifying predictor=. or predictor=NA to Predict, summary, nomogram, survplot, gendata, you just specify the name of the predictor. For example, to get predictions for the default range of x1 and for just 2 values of x2 you might specify Predict(fit,

Version 2.2-0 of the rms package is now available. This is a somewhat major update. One major change is not downward compatible: Instead of specifying predictor=. or predictor=NA to Predict, summary, nomogram, survplot, gendata, you just specify the name of the predictor. For example, to get predictions for the default range of x1 and for just 2 values of x2 you might specify Predict(fit,