similar to: lme, groupedData, random intercept and slope

Hello Please see the example below > class(testX) [1] "matrix" > class(testX[1,]) [1] "numeric" Why not matrix? What am I missing here? Is there a way to keep the same class? The reason for the question is that I want to implement a k-step ahead prediction for my own routines and R wrecks does not seem to like [1,] as shown below. >

Dear R users this may be a simple question - but i would appreciate any thoughts does anyone know how you would get one lower and one upper confidence interval for a set of data that consists of proportions. i.e. taking a usual confidence interval for normal data would result in the lower confidence interval being negative - which is not possible given the data (which is constrained between

Hi, I am looking at the change in N concentration in plant roots over 4 time points and I have fit a spline to the data using ns and lme: fit10 <- lme( N~ns(day, 3), data = rcn10G) I may want to adjust the model a little bit, but for now, let's assume it's good. I get output for the fixed effects: Fixed: N ~ ns(day, 3) (Intercept) ns(day, 3)1 ns(day, 3)2 ns(day, 3)3

Hello R-Users, I have recently run into several problems using vgam() in the VGAM package. I am hoping someone might have some solutions... Briefly, I have been trying to fit GAM models for zero-altered negative binomial models. 1. When fitting smoothed parameters (e.g. s(X, df=2)) changing the degrees-of-freedom has no effect on the level of smoothing (e.g. number of knots for the

Hi, I am looking at the change in N concentration in plant roots over 4 time points and I have fit a spline to the data using ns and lme: fit10 <- lme( N~ns(day, 3), data = rcn10G) I may want to adjust the model a little bit, but for now, let's assume it's good. I get output for the fixed effects: Fixed: N ~ ns(day, 3) (Intercept) ns(day, 3)1 ns(day, 3)2 ns(day, 3)3 1.15676524

I'm trying to set up a mixed model to solve using lme. It will have 3 fixed effects, two random effects and two interaction terms. I've been reading Pinheiro's and Bates's book on the nmle library, but find the part about display functions to be unclear. When creating a groupedData object from a data.frame, you need to enter a function of the form: response ~primary|grouping

Hi, Does anyone know how to make the color of the lines all black when plotting groupedData with an outer factor: For example, library(nlme) plot(Dialyzer, outer=~QB, key=F) This generated colored curves in R.2.4.1. How to make all the curves black ? (or how to alter the color (type) of lines for the nlme groupedData plotting function in general?) Thanks Qiong

Dear all, I tried to create a groupedData object, where the grouping factor is not ordered. Here ist the code: library(nlme) test<-groupedData(conc~Time|Subject,order.groups=F,data=as.data.frame(Theoph)) > getGroups(test) Levels: 6 < 7 < 8 < 11 < 3 < 2 < 4 < 9 < 12 < 10 < 1 < 5 I still get an ordered factor. As always thanks for your help peter

help.search("groupedData") says that it's part of the lme4 package, but it appears not to be there (details below). Is this because lme4 is new and (perhaps) still under development? > update.packages() trying URL `http://cran.r-project.org/bin/windows/contrib/1.9/PACKAGES' Content type `text/plain; charset=iso-8859-1' length 19113 bytes opened URL downloaded 18Kb >

I am trying to create a plot similar to Figure 3.2 in Bates and Pinheiro. I have repeated measurements on about 80 subjects from 2 treatment groups. I would like to have the panels for the two treatment groups in separate groups and within those groups have the panels ordered on maximum value (as is the default). I am ok with getting plots similar to Figs 3.1 and 3.2, but can't see how to

Dear R users, I have the following code: ---------------------------------------- require(nlme) myfunc <- function(data, n, m, maxIter = 3){ working <- groupedData(formula = y~x|id, data=data) val <- NULL r <- 0 while(r < maxIter){ new.data <- data.frame(x=rnorm(n),y=rnorm(n),id=rep(1:n,each=m)) working <- update(working, data = new.data) # val <- some

Dear All, I would be grateful if you can tell how can I suppress the legend (automatically created) when I plot a groupedData. suppose that I have data farme df_0 which contains the following columns: ID represents the clusters, t represents time of observation, Y: the variable of interest and X a binary covariate. Then: df_1 <- groupedData(Y ~ t | ID) plot(df_1,

Dear Friends, Is there a workaround to this problem or must the object plotted by plot(augPred(object, ...)) be a grouped object? score <- c(108, 103, 96, 84, 118, 110, 129, 90, 84, 96, 105, 113, 96, 117, 107, 85, 125, 107, 128, 84, 104, 100, 114, 117, 110, 127, 106, 92, 125, 96, 123, 101, 100, 103, 105, 132, 122, 133, 107, 99, 116, 91, 128, 113, 88, 105, 112, 130) mcc

Hello all, I am plotting a groupedData object and the key at the top of the graph runs off the page. I don't want to set key=F because it is useful (or would be if I could see it). Is it possible to explicitly cause the key to wrap? I have used this function before with no trouble but now I have just 5 groups with rather long descriptions (which I can't meaningfully shorten).

I am trying to fit a formula to my data, but I just can't find the right way to do it. My experiment consists of manipulating FRUITS and VEGETATION to two levels each(intact or removed) on 12 experimental plots. This leaves me with 4 treatment combinations Fruit intact Vegetation removed Fruit int. Veget int. Fruit rem. Veget rem. Fruit rem. Veget. intac those treatements are distributed

Hi, I'm trying to plot a grouped data object for modelling maximum branch size by distance from stem apex: >MAXBRD.group <- groupedData(MAXBRD ~ Dtop | Type/Site/Tree, inner=~Status, data=MAXBRD.data). The following code produces a plot of MAXBRD ~ Dtop for each site type: >plot(MAXBRD.group, collapse=1, display=1, aspect=1.2, pch=20, inner=~1, main="MAXBRD with

Hi all. I have what I'm guessing is a fairly easy question. I want to plot groupedData objects, but there are a large number of subjects in my data. When I use the simple command plot(MyData) where MyData is a groupedData object, there are simply too many individual plots to see anything. I would like, say, 15 per page and for the plots to cover multiple pages. All of the individuals have a

Hi,   I have an original data frame with 8 columns of variables, which are stored in 'data1' frame.   data1 <- read.csv("E:\\PHD GLASGOW UNIVERSITY\\Data\\R\\Colin\\Cailness21.csv") attach(data1) names(data1)   [1] "Date"       "d"          "m"          "y"          "Time"      [6] "Depth"      "Temp"      

Hi everyone. I am trying to add a formula to my data using the groupedData function. My experiment consists of randomized block design using fruits, vegetation and time as factors. The idea is to see if fruits, vegetation and time explain the abundance of mice. I am using tree density as a covariate. So I tried to fit the following structure to my data. >

Dear Group: I have been trying to write an R function within which the function groupedData() would be used. The following is a sample program: ####################### sid<-rep(1:6,times=2) time<-c(1:12) trt<-rep(letters[1:3],times=4) tem<-data.frame(sid,time,trt) test1.fun<- function(dat=dat) { groupedData(time ~ trt|sid, data=dat) } test1.fun(dat=tem)