similar to: Basic plot question: Figure 1.1 Pinheiro & Bates

Dear R users, I would like to exactly reproduce a figure like the 1.5 or 1.9 or 4.13 from the book Mixed effects models in S and S-Plus. Not for the sake of it, but because I have my own data I would like to plot in that fashion (no colors) If I write plot(ergoStool) I can get a good informative plot with colors, but I would like to have a B&W one instead. I've played a little with

Dear Mixed Effect Friends, Somehow, R(1021, Windows) seem to run differently from S Plus: The soybean example from Pinheiro/Bates on page 290 fails in R. (Soybean1 is Soybean with the NA and "critical" case removed. Same procedure with full Soybean). > fm1Soy.lis<-nlsList(weight~SSlogis(Time,Asym,xmid,scal),data=Soybean1) > fm1Soy.nlme<-nlme(fm1Soy.lis) Error: Singularity

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. >

Hi all, I'm trying to figure out the example about mixed models in the Pinheiro and Bates book (Mixed-Effects Models in S and S-Plus, 2000, pp. 135-137). One thing I don't understand is: When I run the command fm1Orth.lm <- lm( distance ~ age, Orthodont ) followed by fm2Orth.lm <- update( fm1Orth.lm, formula = distance ~ Sex*age ) and then do summary(fm2Orth.lm)

All, I've coded a function and it works manually if I copy it line by line into R. However, when I try to "load" (copy and paste) the entire function into R, I get the following error after the listed line of code: + N.j.list = lapply(rej.hyp, length) Error: symbol print-name too long Does anyone you know what this error means? Strangely, when I copy the same line verbatim

All, I'm trying to create an augPred plot in the nlme library, similar to the plot on p.43 of Pinheiro & Bates (Mixed Effects Models in S and S-Plus) for their Pixel data. My data structure is the same as the example but I still get the error msg below. > comp.adj.UKV <- groupedData(adj.UKV ~ Time | Patient_no/Lisinopril, data = comp.adj.UKV.frm, order.groups = F) >

How does one implement a likelihood-ratio test, to test whether the variances of the random effects differ between two groups of subjects? Suppose your data consist of repeated measures on subjects belonging to two groups, say boys and girls, and you are fitting a linear mixed-effects model for the response as a function of time. The within-subject errors (residuals) have the same variance in

All, I'm trying to make a basic plot: data points superimposed upon the a line connecting the points w/ a different color. Example below doesn't work as the first xyplot call doesn't remain. Suggestions? David Hour = c(NA,1,2,3,4) y = c(2,2,3,2,1.5) xyplot(y ~ Hour, xlab = list("Hour", font=2, cex=2), ylab= list("U_x * V", font=2, cex=2), type = 'l',

All, I have data across 5 time points that I am graphing via xyplot, along with error bars. For one of the variables I have missing data for two of the time points. The code below is okay but I can't seem to get the lines to connect across the missing time points. Does anyone now how to rectify this? Cheers, David Afshartous library(lattice) ## the data junk = data.frame( Visit =

All, How does one replace plot symbols with say subject IDs when using xyplot? Or superimpose them next to plot symbols? I searched the archives under various key words but haven't had much. Any suggestions or links much appreciated. Sample code below. David junk.frm = data.frame(ID = rep(1:16, each = 2), x, y, z = rep(c("D", "P"), 16)) y = c( 0.4, 0.6, -0.1,

Dear R-help, I am trying to create groupedData objects using the nlme library. I'm missing something basic, I know: Here is the first example in ch.1 of Pinheiro & Bates (2000): library(nlme) x2=Rail$travel;x1=Rail$Rail;eg1=data.frame(x1,x2);eg1gd=Rail print(eg1gd) x11();print(plot(eg1gd)) femodel=lm(x2~x1-1,data=eg1gd) print(femodel$coefficients) Result: x12 x15 x11

Hi all-- I am in the process of helping colleagues write up a ms in which we fit zero-inflated Poisson models. I would prefer plotting the rate ratios and 95% CI (as I've found Gelman and others convincing about plotting tables...), but our journals usually like the numbers themselves. Thus, I'm looking at a recent JAMA article in which both numbers and dotplot of RR and 95% CI are

Hi, I want to be able to create a vector of z-scores from a vector of continuous data, conditional on a group membership vector. Say you have 20 numbers distributed normally with a mean of 50 and an sd of 10: x <- rnorm(20, 50, 10) Then you have a vector that delineates 2 groups within x: group <- sort(rep(c("A", "B"), 10)) test.data <- data.frame(cbind(x,

All, I'm having problems w/ a simple attempt to subset an xyplot. The first plot below is a plot of y versus x for certain values of a third categorical variable z. Now I'd like to further restrict this to certain values of variable y. Neither of the two attempts below work. Any suggestions much appreciated. (note: I don't want to merely use ylim since I have a loess plot and I

sent this to the list yesterday but didn't see it listed in the daily summary ... apologies if you receive it twice ... ________________________________ From: Afshartous, David Sent: Tuesday, June 27, 2006 10:02 AM To: 'r-help@stat.math.ethz.ch' Subject: sapply question All: I'm trying to use sapply to break up data within another function. (tapply doens't seem to work

Hello, I am interested in using nlme to model repeated measurements, but I don't seem to get good CIs. With the code below I tried to generate data sets according to the model given by equations (1.4) and (1.5) on pages 7 and 8 of Pinheiro and Bates 2000 (having chosen values for beta, sigma.b and sigma similar to those estimated in the text). For each data set I used lme() to fit a model,

Hi R-users: I've got a problem with lme. In Rail data,I try to model the next one lme(travel ~ Rail, data = Rail, random = ~ Rail | Rail) I want travel = Rail(i) + Rail(j) + epsilon(i,j) say, an effect fixed for every Rail and other for Rail(j) random, R says fewer observations or false convergence. Why can't I model an effect fixed and other random? Thanks in advance. My e-mail is

All, When I take a subset of a factor the reduced factor still maintains all the original levels of the factor when say forming the key in a plot. The data is correct, but the variable still "remembers" the original levels. See below for reproducible code. Does anyone know how to fix this? cheers, dave fact = as.factor(c(rep("A", 3),rep("B", 3), rep("C",

All, The code below produces a color 3D graph. I'd like to make it black and white shading. I tried setting col.regions to FALSE but this just made it completely white. I want the graph to look exactly as is, except black (or grey) and white shading. Is this possible? Cheers, David p.list = c(.01, .1, .25, .5, .75, .9) cov.list = c(.2, .1, .05) ### leave out .01 X = rep(p.list, 3)

All, I have a simple lattice plot where I have set log = TRUE for the y scale. When I attempt to change the tick locations via the "at" argument within scales, the supplied numeric vector is not followed. Any suggestions much appreciated for the example below: y = c(10^1.5, 10^2, 10^3, 10^2) t = c(1,2,3,4) xyplot(y ~ t, type = "b", scales = list(y = list(log = TRUE))) ##