similar to: Plot fixed effect with covariates failed

Dear R list members; I have a 10 x 423 data frame which consisting of response, time, subject, site, plot and covariates (continueous and categorical) measured at the plot level. When the data frame was converted into a groupedData object, a warning appeared > A <- groupedData(ht ~ time | Subject, data = tt, outer = ~ site * plot, + labels=list(y = "Height", x =

library(nlme) data(Phenobarb) na.include <- function(x)x phe1 <- nlme(conc~phenoModel(Subject, time, dose, lCl, lV), data = Phenobarb, fixed = lCl+lV~1, random= pdDiag(lCl+lV~1), start = c(-5,0), na.action = na.include, naPattern = ~!is.na(conc)) phe.ranef <- ranef(phe1,augFrame=TRUE) plot(phe.ranef, form=lCl~Wt+ApgarInd) [Error in max(length(x0),

Hi I am following the model building strategy that is outlined in the Pinheiro and Bates book wrt including covariates but am having a problem with the plot. Basically I am using 4 covariates (1 of them is continuous) and 3 of them are fine but the 4th one is being shown as a scatterplot despite the fact that it is a factor. I have explicitly declared this to be a factor (pcat<-as.factor(pcat))

Hi everybody, as you may be aware the function augPred.lme does not work as soon as the covariate is a factor. The problem lies in the line newprimary <- seq(from = minimum, to = maximum, length.out = length.out) which does not make sense for factors. I think augPred.lme can be useful for models with a factor covariate as well. Thus, I propose to change the code to: augPred.lme <-

Hi I have been asked by a colleague to perform a statistical analysis which uses random effects - but I am struggling to get this to work with nlme in R. Help would be very much appreciated! Essentially, the data consists of: 10 patients. Each patient has been given three different treatments (on three separate days). 15 measurements (continuous variable) have been taken from each patient

1. I'm attempting to test for Random Effects. I've grouped the data on subject (grid) but want to use lme to build the model without subject as a RE then add it and do anova between the 2 models. This is the result I get and it appears it's adding Random Effects. tmp.dat4 <- groupedData(Trials ~ 1 | grid, data = tmp.dat4) mod2a <- lme(Trials ~ factor(group_id) + reversal,

R-List, I'm trying to compute pairwise distances among pairs of observations, which each pair containing data from 2 groups. There are more than 100000 unique pairs. I have programmed a distance function that has three parameters, a vector of covariates from the ith observation in Group 1, a vector of covarites from the jth observation in Group 2, and a weighting matrix. I have used

Hi, there, I have 5 datasets. I would like to choose a basis spline with same knots in GAM function in order to obtain same basis function for 5 datasets. Moreover, the basis spline is used to for an interaction of two covarites. I used "cr" in one covariate, but it can only smooth w.r.t 1 covariate. Can anyone give me some suggestion about how to choose a proper smoothing spline

I would appreciate confirmation that the function vcov(model.nlme) gives the var-cov matrix of the fixed effects in an nlme model. Presumably the random-effects var-cov matrix is given by cov(ranef (model.nlme)? Rob Forsyth

Dear R-help; Following the installation of R 1.5.0 and library packages to my Win 2K machine, I tested the new R system: > library(nlme) Loading required package: nls > library(lattice) Loading required package: grid >data(Soybean) >plot(Soybean) A program error appears: "Rgui.exe has generated errors and will be closed by Windows. You will need to restart the program. An

I am fitting models to the responses to a questionnaire that has seven yes/no questions (Item). For each combination of Subject and Item, the variable Response is coded as 0 or 1. I want to include random effects for both Subject and Item. While I understand that the datasets are fairly small, and there are a lot of invariant subjects, I do not understand something that is happening here, and in

Dear John You are only allowed to have the primary covariate on the left hand side of the vertical bar. Other covariates go in inner or outer. Michael On 22/03/2018 12:59, Sorkin, John wrote: > Windows 10 64-bit, R-Studio, R version 3.4.3 > > > Several questions relating to groupedData: > > (1) I am trying to create a groupedData object that can be used to run an analysis

I have been struggling all day to import a particular function/method combination (ranef(), which extracts the random effects from a mixed model fit) from the nlme package into another package ... so far without success. The NAMESPACE for nlme contains the following lines: export(..., ranef, ...) S3method(ranef, lme) ranef is defined as a standard S3 generic, function (object, ...)

Windows 10 64-bit, R-Studio, R version 3.4.3 Several questions relating to groupedData: (1) I am trying to create a groupedData object that can be used to run an analysis that I have been able to urn using lmer. When I include the interaction terms in the groupedData opbject I get an error message stating that + is not meaningful for factors. How do I include factors in my model? See code and

Used for celt_pitch_xcorr on aarch64, and celt_fir and celt_iir on both armv7 and aarch64. --- celt/arm/arm_celt_map.c | 17 +++++++++++++ celt/arm/celt_neon_intr.c | 61 ++++++++++++++++++++++++++++++++++++++++++++++- celt/arm/pitch_arm.h | 31 +++++++++++++++++++++++- 3 files changed, 107 insertions(+), 2 deletions(-) diff --git a/celt/arm/arm_celt_map.c b/celt/arm/arm_celt_map.c index

The coxme package has a ranef() method, as does lme4. I'm having trouble getting them to play together, as shown below. (The particular model in the example isn't defensible, but uses a standard data set.) The problem is that most of the time only one of lme4 or coxme will be loaded, so each needs to define the basic ranef function as well as a method for it. But when loaded together

User of the coxme library (mixed effects Cox models) are instructed to use ranef(), fixed(), VarCorr(), etc to retrieve bits out of a fitted model; it purposely uses the same methods as nlme and/or lmer. The current behavior is to "depend" on nlme. If I defined the methods myself in coxme, then someone who had both nlme and coxme loaded will suffer from "last loaded wins",

Dear R-users Thanks to Jose Pinheiro, Douglas Bates and coworkes for providing R with the nlme package. Could someone help me, please, to specify a correct random formula for a mixed model, that specifies no random effect on a higher level? I have the following dataset of timeseries of respiration measurements (column resp) of biomass including a parameter initial biomass x0. Respiration

In the non linear mixed effects package a groupedData object can be created to facilitate modeling. The gD object includes a formula of the form 'response variable' ~ 'primary covariate' | 'grouping factor'. In experiments creating response surfaces there are 2 or more primary covariates. Is there any way to use the groupedData() function to include 2 primary

Dear list In HLM, one can specify a covariate at one of the "levels". For example, if the data structure are repeated observations nested within students nested within schools, school size might be a covariate that is used at level 3, but not at the other levels. In HLM this is rather easy to do. However, how can one specify a covariate in R for only one of the levels? I have a