similar to: treatment effect at specific time point within mixedeffects model

All, The code below is for a pseudo dataset of repeated measures on patients where there is also a treatment factor called "drug". Time is treated as categorical. What code is necessary to test for a treatment effect at a single time point, e.g., time = 3? Does the answer matter if the design is a crossover design, i.e, each patient received drug and placebo? Finally, what would

All, I'm using lmer for some repeated measures data and have specified the contrasts for a time factor such that say time 3 is the base. This works fine. However, when I next use the subset argument to remove the last two time values, the output indicates that the specified contrast is not maintained (see below). I can solve this by creating a new dataframe for the subset of interest

Dear list members, I am new to R and to the R-help list. I am trying to perform a mixed-model analysis using the lmer() function. I have a problem with the output anova table when using the anova() function on the lmer output object: I only get the numerator d.f., the sum of squares and the mean squares, but not the denominator d.f., F statistics and P values. Below is a sample output, following

All, Is there an efficient way to apply say "mean" or "median" to a dataframe according to say all combinations of two variables in the dataframe? Below is a simple example and the outline of a "manual" solution that will work but is not very efficient (could also generalize this to a function). Searched the archives and docs but didn't see anything close to

Dear R help list, I have done a lot of searching but have not been able to find an answer to my problem. I apologize in advance if this has been asked before. I am applying a mixed model to my data using lmer. I will use sample data to illustrate my question: >library(lme4) >library(arm) >data("HR", package = "SASmixed") > str(HR) 'data.frame': 120 obs.

All, For data consisting of serial measurements on subjects, one may use the aggregate function to say compute the peak response for each subject for each design condition. Is there a way to alter this or another one-liner to also retain the time at which the peak occurred and thus avoid writing a doing this via a loop? I suppose one could attempt to employ the split function but that's

All, When specifying colors to xyplot w/ a groups argument, using auto.key no longer maintains the colors properly. I've searched the docs and help but haven't found exactly what I need ... I saw a few examples in the archives involving par.settings but that doesn't seem to do it. I also saw some people using key instead of auto.key, but that didn't seem consistent. Is there a

All, I can't seem to get auto.key to work properly in an xyplot that is employing panel.text. Specifically, I often change the default grouping colors then use auto.key accordingly, but for some reason the same functionality isn't working for this different type of plot. Any help much appreciated. Cheers, David library("lattice") dat = data.frame( Y = c(rnorm(18,1),

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 extract the level-1 variance from a model fit via lmer()? In the code below the level-2 variance component may be obtained via subscripting, but what about the level-1 variance, viz., the 3.215072 term? (actually this term squared) Didn't see anything in the archives on this. Cheers, David > fm <- lmer( dv ~ time.num*drug + (1 | Patient.new), data=dat.new )

All, I'm re-running some analysis that has been augmented with additional data. When I use the exact same code for the augmented data, the behavior of the aggregate function is very strange, viz., one of the resulting variables is now coded as a factor while it was coded as numeric for the original data. Unfortunately, I cannot provide a reproducible code example since it only seems to occur

All, I'm trying to plot an all.effects() object, as shown in the help for all.effects and also Crawley's R book (p.178, 2007). The data has a repeated measures structure, but I'm using all.effects for the simple lm() fit here. Below is a reproducible example that yields the error message. fm.ex = lm(dv ~ time.num*drug*X, data = dat.new) fm.effects = all.effects(fm.ex, xlevels =

Dear R-users, In a randomized placebo-controlled within-subject design, subjects recieved a psycho-active drug and placebo. Subjects filled out a questionnaire containing 15 scales on four different time points after drug administration. In order to detect drug effects on each time point, I compared scale values between placebo and drug for all time conditions and scales, which sums up to

Dear all, The incredibly useful Hmisc package provides a method to generate summary tables that can be typeset in latex. The Alzola and Harrell book "An introduction to S and the Hmisc and Design libraries" provides an example that generates mean and quartiles for continuous variables, and numbers and percentages for count variables: summary() with method = 'reverse'. I

Dear All, I faced the following problem. With the same data.frame the results are different under Linux and Windows. Could you help on this topic? Thanks in advance, Ildiko Linux: > d = read.csv("CRP.csv") > d$drugCode = as.numeric(d$drug) > cor(d, use="pairwise.complete.obs") PATIENT BL.CRP X24HR.CRP X48HR.CRP drug drugCode PATIENT NA

All, I've been using aggregate() to compute means and standard deviations at time/treatment combinations for a longitudinal dataset, using na.rm = TRUE for missing data. This was working fine before, but now when I re-run some old code it isn't. I've backtracked my steps and can't seem to find out why it was working before but not now. In any event, below is a reproducible

All, How does one specify a model in lmer such that say the random effect for the intercept has a different variance per treatment group? Thus, in the model equation, we'd have say b_ij represent the random effect for patient j in treatment group i, with variance depending on i, i.e, var(b_ij) = tau_i. Didn't see this in the docs or Pinherio & Bates (section 5.2 is specific for

Hello, I am using Hmisc summary.formula, latex and Sweave to produce tables for publication. Is it possible to change the formats for binary and continuous variables? I would prefer to show 35 (10%) and 1.5 (1.2-1.8) rather than 10% (35) and 1.2 / 1.5 / 1.8. Here is a simple example: sex <- factor(sample(c("m","f"), 500, rep=TRUE)) age <- rnorm(500, 50, 5) treatment

Dear R-listers, Does anybody know what is the correct source of "rats" dataset in survival package? The help gives the following information: Rat data from survival5 Description: 48 rats were injected with a carcinogen, and then randomized to either drug or placebo. The number of tumors ranges from 0 to 13; all rats were censored at 6 months after randomization.

> library(MASS) > attach(bacteria) > table(y) y n y 43 177 > y<-1*(y=="y") > table(y,trt) trt y placebo drug drug+ 0 12 18 13 1 84 44 49 > library(lme4) > model1<-lmer(y~trt+(week|ID),family=binomial,method="PQL") Error in match.arg(method, c("Laplace", "AGQ")) : 'arg' should be one of