similar to: Possible bug in factor (PR#531)

I have a variable which is roughly age categories in decades. In the original data, it came in coded: > str(xxx) 'data.frame': 58271 obs. of 29 variables: $ issuecat : Factor w/ 5 levels "0 - 39","40 - 49",..: 1 1 1 1... snip I then defined issuecat as ordered: > xxx$issuecat<-as.ordered(xxx$issuecat) When I include issuecat in a glm model, the result

Dear R-help, I'm applying the surrogate Poisson glm, by following Venables & Ripley (7.3 pp238-42). >overall_cbind(expand.grid(treatment=c("Pema","control"),age=c("young","adult","old"),repair=c("excellent","good","poor")),Fr=c(8,0,7,1,2,0,2,7,1,4,7,1, 0,3,2,5,1,9))

Hi, Somebody knows how to make contrasts if i'm using the function adonis? Thanks.

Dear R-help members, Apologies - I am posting on behalf of a colleague, who is a little puzzled as STATA and R seem to be yielding different survival estimates for the same dataset when treating a variable as ordinal. Ordered() is used to represent an ordinal variable) I understand that R's coxph (by default) uses the Efron approximation, whereas STATA uses (by default) the Breslow. but we

On converting character variables to ordered factors, regression result has strange names. Is it possible to obtain same variable names with and without intercept? Thanks, Naresh mydf <- data.frame(date = seq.Date(as.Date("2024-01-01"), as.Date("2024-03-31"), by = 1)) mydf[, "wday"] <- weekdays(mydf$date, abbreviate = TRUE) mydf.work <- subset(mydf, !(wday

Hello; I am having a problems with the interpretation of models using ordered or unordered predictors. I am running models in lmer but I will try to give a simplified example data set using lm. Both in the example and in my real data set I use a predictor variable referring to 3 consecutive days of an experiment. It is a factor, and I thought it would be more correct to consider it ordered. Below

Merci beaucoup, Jean, for the bug report -- which I'm no "completeing" to R-bugs >>>>> "Jean" == Jean Coursol <coursol@cristal.math.u-psud.fr> >>>>> on Thu, 24 Jun 2004 15:22:37 +0200 (CEST) writes: Jean> I was exploring the polynom library with students: <and found a segmentation fault from parsing a long expression>

I'm working on a rather interesting consulting problem with a client. A number of physical variables are measured on a number of cricket bowlers in the performance of a delivery. An example variable might be a directional component of angular momentum for a particular joint measured at a large number (101) of equally spaced timepoints. Each bowler generates a (fairly smooth) curve for

Dear Mr. and Mrs. I'm very grateful for these software and this list. My question is: when a use linear multiple regression (lm()) for my data, abundance ichthyoplankton ~ salinity + temperature + month of the year(f1 is a factor: 1 for january, 2 for february, ..., 12 for december), the summary() of results is ... Coefficients: Estimate Std. Error t value Pr(>|t|)

Hi all, after an uprade to new kernel 2.4.21-15.0.4.EL on my server appears a lot of these messages: EXT3-fs error (device sd(8,17)): ext3_free_blocks: bit already cleared for block 19268224 EXT3-fs error (device sd(8,17)): ext3_free_blocks: bit already cleared for block 19268225 EXT3-fs error (device sd(8,17)): ext3_free_blocks: bit already cleared for block 19268226 I try to rebuild array but

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Greetings folks, I'm currently redoing my home server as one of the hard drives in my RAID-1 array went down and I'm going to replace the lot of it with a 3Ware Escalade 9500S RAID capable card and four Seagate 200GB SATA drives. These drives are supposedly the coolest and quietest SATA drives available (better than the Maxtors I had anyway)

Hi guys been browsing some Forums and came up with the following topics: -News & Forum ---News & Announcements ---Updates ---Forums Feedback ---(please suggest further topics) Documentation & How-To's (the posts in this area will be done by moderators. only certified how-to's and tutorials) ---Known Mirrors ---Instalation ---Configuration ---Multimedia ---Desktop Environments

For ordered factor the natural contrast coding would be to parametrize by the succsessive differences between levels, which does not assume equal spacing of factor levels as does the polynomial contrasts (implicitly at least). This requires the contr.cum, which could be: contr.cum <- function (n, contrasts = TRUE) { if (is.numeric(n) && length(n) == 1) levs <- 1:n

Hello! I tried to compile the latest 1.0-test (79) with mysql support, but there is the following error: ... /usr/local/mysql/lib/libmysqlclient.a(sha1.o)(.text+0x5c): In function `sha1_result': : multiple definition of `sha1_result' ../lib/liblib.a(sha1.o)(.text+0x830):/usr/local/src/dovecot/dovecot-1.0-test79/src/lib/sha1.c:233: first defined here ... So 'sha1_result' is

Dear all, I do not seem to grasp how contrasts are set for ordered factors. Perhaps someone can elighten me? When I work with ordered factors, I would often like to be able to reduce the used polynomial to a simpler one (where possible). Thus, I would like to explicetly code the polynomial but ideally, the intial model (thus, the full polynomial) would be identical to one with an ordered factor.

Hello I am trying to fit a REML to some soil mineral data which has been collected over the time period 1999 - 2004. I want to know if the 19 different treatments imposed, differ in terms of their soil mineral content. A tree model of the data has shown differences between the treatments can be attributed to the Magnesium, Potassium and organic matter content of the soil, with Magnesium being the

Consider the following analysis of a class experiment done as a Latin Square: > spinner <- gl(4,4,16,label=c("Murray","Angela","Shasha","Stephen")) > order <- gl(4,1,16) > treat <- scan() 1: 1 2 4 3 5: 4 3 1 2 9: 3 4 2 1 13: 2 1 3 4 17: Read 16 items > coin <-

Dear all, I'm working with Cox-regression, because data could be censored. But in this particular case not. Now I have a simple example: PRO and PRE are (0,1) coded. The response is not normal distributed. We are interested in a model which could describe interaction. But my results are depending strongly in the choose of the contrast option. It is clear that there is some dependence in

In S-Plus, I can obtain polynomial contrasts for an ordered factor with contr.poly(). The function also exists in R, however is limited to factors where the levels are equally spaced. In S-Plus, one can obtain the contrasts for a set of numeric values representing unequally spaced ordered factors. Has anyone implemented this in R? I see that the S-Plus function calls another function (poly.raw())