similar to: Functions similar to step() and all.effects() that work for lme() objects?

Hi, This is really strange. Can anyone help explain what's going on here (on 3 and 7)? > targets <- seq(from=.1, to=.9, by=.1) > targets[1]==.1 [1] TRUE > targets[2]==.2 [1] TRUE > targets[3]==.3 [1] FALSE > targets[4]==.4 [1] TRUE > targets[5]==.5 [1] TRUE > targets[6]==.6 [1] TRUE > targets[7]==.7 [1] FALSE > targets[8]==.8 [1] TRUE >

Hi, I'm trying to randomize a sequence of trials for an experimental design. The trials consist of values for each of two factors. As is there are 30 combinations of the two factors, and I want them to be ordered randomly but with the requirement that for one of the factors, the value can never be the same as the previous value. I'm currently randomizing my dataframe by using:

Hi, I want to add error bars to a plot generated with xyplot. I've tried both errbar() and plotCI(), but in both cases the points are not in the same place. It's as if the two functions are using a different frame of reference for the plotting area. for example: means <- c(92.5, 92.25, 90.9, 91.0, 94.15, 90.05) #means time <- c(1,1,2,2,3,3) #occasion variable group <-

Hi, There is an object, ".Last.value" to which the result of the most recent evaluation is assigned. This is similar to "ans" in Matlab. In Matlab "ans" can be very useful and time-saving, but typing the larger R version is somewhat clunky and takes away from the usefulness. Is it possible to reassign '.Last.value' to something simpler, like

Hi, I often use plotmeans() from the gplots package to quickly visualize a pattern of change. I would like to be able to plot separate lines for different groups, but the function gives an error when a grouping variable is included in the formula argument. For instance, > require(gplots) > x <- data.frame(Score=rnorm(100), Time=rep(1:10, 10),

Hi, I'm having some problems with the aggregate() function in the {stats} package, and the documentation doesn't address them. 1) Why would the first line work, but the second not? According to the help file, it accepts a "data=" argument. > with(tsrc, aggregate(x=DistRatio, by=list(Condition), FUN=mean)) Group.1 x 1 Congruent 1.741789 2 Mismatch 1.771425

Hello, I am confused about the order of nesting in mixed models using functions like aov(), lme(), lmer(). I have the following data: n subjects in either condition A or B each subject tested at each of 3 numerical values ("distance" = 40,50,60), repeated 4 times for each of the 3 numerical values ("trial" = 1,2,3,4) Variable summary: Condition: 2 level factor Distance:

Hi, I've looked all over for a solution to this, but haven't had much look in specifying what I want to do with appropriate search terms. Thus I'm turning to R-help. In the process of trying to write a simple function to rename individual column names in a data frame, I ran into the following problem: When I rename the columns within my function, I can't seem to get it to

Hi, I've done my best to search for a solution to this, but had no luck. How can I create a lattice plot (I'm using xyplot() ) that does not have a border on the top and right side, but keeps the bottom/left axes? So far all I've found is this, which inserted into the xyplot call removes all 4 borders: / par.settings = list(axis.line = list(col = 0))/ xyplot( Sepal.Length ~

Hi, Can someone please offer a procedure for going from CIs produced by intervals.lme() to fixed-effects response CIs. Here's a simple example: library(mlmRev) library(nlme) hsb.lme <- lme(mAch ~ minrty * sector, random = ~ 1 | cses, Hsb82) (intervals(hsb.lme)) (hsb.new <- data.frame minrty = rep(c('No', 'Yes'), 2), sector = rep(c('Public',

Dear R-helpers, When I try > anova(txtE2.lme, txtE2.lme1) Model df AIC BIC logLik Test L.Ratio p-value txtE2.lme 1 10 8590 8638 -4285 txtE2.lme1 2 7 8591 8624 -4288 1 vs 2 6.79 0.0789 > latex(anova(txtE2.lme, txtE2.lme1)) Error: object "n.group" not found I don't even see n.group as one of the arguments of latex() I checked to see >

> sessionInfo() R version 2.15.0 (2012-03-30) Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit) locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] grid datasets splines utils stats [6] graphics grDevices methods base other attached packages: [1] effects_2.1-0 colorspace_1.1-1 [3] nnet_7.3-1

Dear R-helpers, I don't see a difference between the following two plots of effect objects, which I understand should be different. What am I missing? require(doBy) require(effects) data(budworm) m1 <- glm(ndead/20 ~ sex + log(dose), data=budworm, weight=ntotal, family=binomial) m1.eff <- all.effects(m1) plot(m1.eff, rescale.axis = FALSE, selection = 2, main = 'rescale =

Dear r-helpers, plot(all.effects()) uses trellis for its output, and often produces several effect plots in a layout. Can anyone tell me how to set the arguments to plot.effect which default to row=1, col=1, nrow=1, ncol=1, more=FALSE so that I can plot multiple effect plots on one layout? I was hoping that a call like last <- rep(c(TRUE, FALSE), c(3, 1)) lay <-matrix(c(1:4,), 2, 2,

Dear Friends, I have been following the discussions of mixed-effects models with some confusion, and I realize that much of this is work in progress, and that much of the discussion is beyond my knowledge of statistics. My question is simple, though: Is there a set of commands that will produce an output equivalent to the exceedingly useful predict(bl.lm, newdata = bl.new, type =

In the course of exploring response prediction, I stumbled upon a small discrepancy between the CIs produced by predict.lm() and all.effects() require(mlmRev) require(effects) hsb.lm <- lm(mAch ~ minrty * sector, Hsb82) hsb.new <- data.frame( minrty = rep(c('No', 'Yes'), 2), sector = rep(c('Public', 'Catholic'), each = 2)) hsb.eff <-

Dear R-helpers, How do I transform v <- c('insd-otsd', 'sppr-unsp') into c('insd--otsd', 'sppr--unsp') ? _____________________________ Professor Michael Kubovy University of Virginia Department of Psychology USPS: P.O.Box 400400 Charlottesville, VA 22904-4400 Parcels: Room 102 Gilmer Hall McCormick Road Charlottesville, VA 22903

Hi, I would like to use the effect() function (actually a slightly modified version of it) on the output of the lmer() function in the lme4 package. But the effects package requires the nlme pacvkage, which is incompatible with lme4. Workaround? ______________________________________________ Professor Michael Kubovy University of Virginia Department of Psychology for mail add: for FedEx or

How does one define PDFs as yet undefined in R, such as the ex- gaussian, the sum of two RVs, one exponential, one Gaussian? The PDF would then be the convolution of an exponential PDF, dexp(), and a normal, dnorm(). Kindly cc me in your reply to r-help. Thanks, _____________________________ Professor Michael Kubovy University of Virginia Department of Psychology USPS: P.O.Box 400400

Dear R-listers, Can someone suggest a method for generating a finite Bernoulli sequence that is likely to have a given point-biserial correlation with an existing Bernoulli sequence? _____________________________ Professor Michael Kubovy University of Virginia Department of Psychology USPS: P.O.Box 400400 Charlottesville, VA 22904-4400 Parcels: Room 102 Gilmer Hall