similar to: repolr output

Hello. One of my teaching assistants was experimenting and encountered unexpected behaviour with the lrtest function in the rms package. It appears that when you have a pair of non-nested models that employ an RCS, the error checking for non-nested models appears not to work. Here is a reproducible example. > library(rms) Loading required package: Hmisc Loading required package: lattice

Hello r-help, I am using the excellent np package to conduct a nonparametric kernel regression and am having some trouble plotting the results. I have 2 covariates, x1 and x2, and a continuous outcome variable y. I am conducting a nonparametric regression of y on x1 and x2. The one somewhat unusual feature of these data is that, to be included in the dataset, x1 must be at least as large as x2.

Dear Help, After loading the pd.Citrus library and checking the DataFrame, I ran > the R code for: > > 1) 'oligo' > > > > {> library(pd.citrus) > Loading required package: RSQLite > Loading required package: DBI > > data(pmSequence) > > > show(pmSequence) > DataFrame with 341730 rows and 2 columns > fid sequence > <integer>

Hi all, I wonder if there is any way to calculate a moving average on an irregular time series, or use the rollapply function in zoo? I have a set of dates where I want to check if there has been an event 14 days prior to each time point in order to mark these timepoints for removal, and can't figure out a good way to do it. Many thanks in advance! Gustaf Example data:

For the following model, library(vcd) arth.polr <- polr(Improved ~ Sex + Treatment + Age, data=Arthritis) summary(arth.polr) where Improved is an ordered, 3-level response I'm looking for a *simple* way to test the validity of the proportional odds assumption, typically done via a score test for equal slopes/effects over the predictors. I do find a po.test= option in the repolr package

Hello dear R-help mailing list, I wish to perform a non-parametric repeated measures anova. If what I read online is true, this could be achieved using a mixed Ordinal Regression model (a.k.a: Proportional Odds Model). I found two packages that seems relevant, but couldn't find any vignette on the subject: http://cran.r-project.org/web/packages/repolr/

On Mon, 2015-08-17 at 14:13 +0200, Stephan Bosch wrote: > but until then you can avoid this by using the the exdata hg revision before tip > (57c8d3e6b562). Great, thanks for the quick reply, that fixed the compilation problem. I'm still getting a segfault though. This time the backtrace is: Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1". Core was

Andrew Beverley schreef op 17-8-2015 om 14:05: > On Thu, 2015-08-06 at 16:10 +0200, Stephan Bosch wrote: >> Since the extdata plugin is not part of the normal Debian packages (not >> afaik anyway), you likely compiled and installed the extdata plugin >> manually in the past while using Dovecot 2.1. Do that again, but use >> this repository:

Andrew Beverley schreef op 17-8-2015 om 14:35: > On Mon, 2015-08-17 at 14:13 +0200, Stephan Bosch wrote: >> but until then you can avoid this by using the the exdata hg revision before tip >> (57c8d3e6b562). > Great, thanks for the quick reply, that fixed the compilation problem. > > I'm still getting a segfault though. This time the backtrace is: Still looks like the

Hello Prof. Harrell and dear R-help mailing list, I wish to perform a non-parametric repeated measures anova. If what I read online is true, this could be achieved using a mixed Ordinal Regression model (a.k.a: Proportional Odds Model). I found two packages that seems relevant, but couldn't find any vignette on the subject: http://cran.r-project.org/web/packages/repolr/

Hello! Someone know what are the difference between R and S-PLUS in the density() function? For example, I would like to reply this simple S-PLUS code in R, but I don't understand which parameter I should modify to get the same results. S-PLUS CODE: density(1:1000, width = 4) R-CODE: density(1:1000, bw = 4, window = "g", n = 50, cut = 0.75) I obtain the same x values, but

Hello, I am working in a longitudinal study, with a categorical variable as a dependent variable (alcohol consumption: no use, use, abuse and dependence) with repeated measures (baseline, 1 year, 2 years). Besides I have another variable with two groups: control and experimental. I would like to analyze the evolution in each group, I have though in lme, but I am not sure if I can do an lme with

hey all, I'm just getting used to R and i'm having issues when it comes to reading my data in rows rather than columns. any good advice would be much appreciated ! here is the error: > data1 <- read.table(file.choose(),header=T) > x1 <- c(data1[1,1:5]) > shapiro.test(x1) Error in sort.int(x, na.last = na.last, decreasing = decreasing, ...) : 'x' must be atomic

On Thu, 2015-08-06 at 16:10 +0200, Stephan Bosch wrote: > Since the extdata plugin is not part of the normal Debian packages (not > afaik anyway), you likely compiled and installed the extdata plugin > manually in the past while using Dovecot 2.1. Do that again, but use > this repository: http://hg.rename-it.nl/pigeonhole-0.4-sieve-extdata/ I'm just trying to install this now,

Hi, I would like to fit Logit models for ordered data, such as those suggested by Greene (2003), p. 736. Does anyone suggests any package in R for that? By the way, my dependent variable is ordinal and my independent variables are ratio/intervalar. Thanks, Iuri. Greene, W. H. Econometric Analysis. Upper Saddle River, NJ: Prentice Hall, 2003

Hi All, I have a dataset with a column named "Condition", Sample Condition 1 c20 2 c20 3 c10 4 c10 5 c9 6 c9 7 c5 8 c5 9 c20 10 c10 Could you let me know the fastest way to change c20->"AA", c20->"BB", c9->"CC", c5->"DD"

Dear R help gurus, I have the following problem and I would be delighted if you could help me. >From a large (1500) cohort of patients we have been taking some measurements (ECG measurements, but its not important). The measurements are ordinal in 4 grades (Grade I-IV, grade IV being the most severe form). Every patients has been measured several times (usually once per year). The