similar to: bootstrapped cox regression (rms package)

Hi, I am trying to convert a colleague from using SPSS to R, but am having trouble generating a result that is similar enough to a bootstrapped cox regression analysis that was run in SPSS. I tried unsuccessfully with bootcens, but have had some success with the bootcov function in the rms package, which at least generates confidence intervals similar to what is observed in SPSS. However, the

Hello, I am trying to plot two sets of boxplots together. These are estimates of two experiments and?seven?factors. The results of the two experiments I want to plot as boxplots stacked to each other. Therefore I plot first the results of the first experiment; and next with the add option the second set of boxplots. The boxplots are plotted at 'at = 1:7 - 0.15 for the first experiment and

Hi all, I had some trouble in?regrouping factor levels for a variable. After some experiments, I have figured out how I can recode to modify the factor levels. I would now like some help to understand why some methods work and others don't. Here's my code : rm(list=ls()) ###some trials in recoding factor levels char<-letters[1:10] fac<-factor(char) levels(fac) print(fac) ##first

Hello, Warning, I'm guessing only those who have used the Hmisc package's summary.formula function with LaTeX will be able to offer much help here. I am using the Hmisc package's summary.formula function to produce tables for a LaTeX report. The "latex" function in the same package supports longtables in LaTeX. Ideally, I would like for page breaks in the LaTeX output

Hi, I wish to compute multivariate test statistics for a within-subjects repeated measures design with anova.mlm. This works great if I only have two factors, but I don't know how to compute interactions with more than two factors. I suspect, I have to create a new "grouping" factor and then test with this factor to get these interactions (as it is hinted in R News 2007/2), but

Dear List, I am using function recode, from package car, within sapply, as follows: L3 <- LETTERS[1:3] (d <- data.frame(cbind(x = 1, y = 1:10), fac1 = sample(L3, 10, replace=TRUE), fac2 = sample(L3, 10, replace=TRUE), fac3 = sample(L3, 10, replace=TRUE))) str(d) d[, c("fac1", "fac2")] <- sapply(d[, c("fac1", "fac2")], recode, "c('A',

Dear R Users, I am trying to write a wrapper around summarize and xYplot from Hmisc and am having trouble understanding how to pass arguments from the function I am writing to the nested functions. There must be a way, but I have not been able to figure it out. An example is below. Any advice would be greatly appreciated. Thanks, Dan # some example data df=expand.grid(rep=1:4,

Hey, hopefully there is an easy way to solve my problem. All that i think off is lengthy and clumsy. Given a data.frame d with columns VALUE, FAC1, FAC2, FAC3. Let FAC1 be something like experiment number, so that there are exactly the same number of rows for each level of FAC1 in the data.frame. Now i would like to scale all values according to the center of its experiment. So i can apply s

Sorry for the stupid question, but is there the factorial function in R? I tried to find it using help.search('factorial') but got nothing appropriate. Many thanks, -Serge

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Hello, Using a fixed effects linear model (with lm), I can get exact p-values out of the AVOVA table, even if they are very small, eg. 1.0e-200. Using lme (linear mixed effects) from the nlme library, it appears that there is rounding of the p-values to zero, if the p-value is less than about 1.0e-16. Is there a way we can obtain the exact p-values from lme without rounding? used commands:

Dear all, I'm using the mgcv library by Simon Wood to fit gam models with interactions and I have been reading (and running) the "factor 'by' variable example" given on the gam.models help page (see below, output from the two first models b, and b1). The example explains that both b and b1 fits are similar: "note that the preceding fit (here b) is the same as

Stefan Roepcke <stefan.roepcke at metagen.de> writes: > Hey, > > hopefully there is an easy way to solve my problem. > All that i think off is lengthy and clumsy. > > Given a data.frame d with columns VALUE, FAC1, FAC2, FAC3. > Let FAC1 be something like experiment number, > so that there are exactly the same number of rows for each level of FAC1 > in the

Hello again, I was too quick before. What I was looking for was a function that constructs the design (or incidence) matrix (X in a linear model) from a factor. Uwe Ligges suggested using model.matrix and this does almost what I want, but it is first necessary to construct a data variable. It also asigns ones to all rows of the first column (because this is set to be the contrast, not really what

Let's say I have a within-subject experiment with 2 observables, obs1 and ob2 and 2 independent factors, fac1 and fac2. I can do summary( aov( obs1~fac1*fac2 + Error(Subject/(fac1*fac2)) ) ) summary( aov( obs2~fac1*fac2 + Error(Subject/(fac1*fac2)) ) ) to test the 2 observables separately. > summary( fit<-manova( cbind(obs1,obs2)~fac1*fac2 + Error(Subject/(fac1*fac2)) ) ) gives

hello list, i'd very much appreciate help with setting up the contrast for a 2-factorial crossed design. here is a toy example: library(multcomp) dat<-data.frame(fac1=gl(4,8,labels=LETTERS[1:4]), fac2=rep(c("I","II"),16),y=rnorm(32,1,1)) mod<-lm(y~fac1*fac2,data=dat) ## the contrasts i'm interressted in: c1<-rbind("fac2-effect in

Hello, unfortunately, I don't know a better subject. I would like to be very flexible in how to process my data. Assume the following dataset: par1 <- seq(0,1,length.out = 100) par2 <- seq(1,100) fac1 <- factor(rep(c("group1", "group2"), each = 50)) fac2 <- factor(rep(c("group3", "group4", "group5", "group6"), each =

Hello Again... I?m making a faceted plot of a response on two categorical variables using ggplot2 and having troubles with the coloring. Here is a sample that produces the desired plot: compareCats <- function(data, res, fac1, fac2, colors) { require(ggplot2) p <- ggplot(data, aes(fac1, res)) + facet_grid(. ~ fac2) jit <- position_jitter(width = 0.1) p <- p +

Hello I?m need fitt growth curve with data length-age. I want to evaluate which is the function that best predicts my data, to do so I compare the Akaikes of different models. I'm now need to evaluate if changing the initial values changes the parameters and which do not allow to estimate the model. To do this I use the function nls(); and I randomize the initial values (real positive number).

Dear R-users I seem to be stumped on something simple. I want to split a data frame by factor levels given in one or more columns e.g. given dat <- data.frame(x = runif(100), fac1 = rep(c("a", "b", "c", "d"), each = 25), fac2 = rep(c("A", "B"), 50)) I know I can split it by fac1, fac2 by: