similar to: lack of memory for logistic regression in R?

Hello - I apologize if this question is simple/obvious, but I couldn't find a satisfactory answer online, and I am not very accustomed to working with R (Matlab is my poison. :-)). Any help would be greatly appreciated. I have a model with a three-level factor and a continuous covariate. The call I use is: mymodel <- glm(Response ~ Factor_covariate + continuous_covariate - 1,

A time series text with a title that seems designed to hide its wide scope is: Forecasting with Exponential Smoothing The State Space Approach Hyndman, R.J., Koehler, A.B., Ord, J.K., Snyder, R.D. Springer 2009. This book is actually an excellent overview of time series theory, ARIMA as well as state space. It is of course, in part, a manual for the forecast and other packages in what has been

Dear List, creating factors in a given non-default orders is notoriously difficult to explain in a course. Students love the ifelse construct given below most, but I remember some comment from Martin M?chler (?) that ifelse should be banned from courses. Any better idea? Not necessarily short, easy to remember is important. Dieter data = c(1,7,10,50,70) levs =

I sent this to the list last week, and haven't seen it pop up. Either I deleted it when it did appear, or possibly it was destroyed as spam...? If it did appear and I somehow missed it, appologies. In a nutshell, can the function FUN supplied to by() deduce what level of factor by() was on when FUN was called? I've been digging through the functions, and can't see where the

Lattice experts: Can you think of a way to produce a levelplot as below and then add a histogram of the z variable to the top margin of the plot that would sit on top of the color key? x <- seq(pi/4, 5 * pi, length.out = 100) y <- seq(pi/4, 5 * pi, length.out = 100) r <- as.vector(sqrt(outer(x^2, y^2, "+"))) grid <- expand.grid(x=x, y=y) grid$z <- cos(r^2) *

I am new to using S4 methods and have run into this problem (on Windows XP using R 2.4.1): I am writing a package in which I use the graph package. I define my own classes of graphs as: setOldClass("graphsh") setOldClass("ugsh") setIs("ugsh", "graphsh") (I know that I "should have" used setClass instead - and I will eventually - but right now

Given the example: R> (levs <- quantile(volcano,c(0,0.1,0.5,0.9,0.99,1))) 0% 10% 50% 90% 99% 100% 94 100 124 170 189 195 R> levelplot(volcano,at=levs) How can I make the key categorical with the size of the divisions equally spaced in the key? E.g., five equal size rectangles with labels at levs c(100,124,170,189,195)? Apologies if this is obvious. -A R> version

lrm does some binning to make the calculations faster. The exact calculation is obtained by running f <- lrm(...) rcorr.cens(predict(f), DA), which results in: C Index Dxy S.D. n missing 0.96814404 0.93628809 0.03808336 32.00000000 0.00000000 uncensored Relevant Pairs Concordant Uncertain 32.00000000

I have 3 questions (below). Background: I am teaching an introductory statistics course in which we are covering (among other things) repeated measures anova. This time around teaching it, we are using R for all of our computations. We are starting by covering the univariate approach to repeated measures anova. Doing a basic repeated measures anova (univariate approach) using aov() seems

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

Dear R-devel list members, I'd like to suggest a more flexible procedure for generating contrast names. I apologise for a relatively long message -- I want my proposal to be clear. I've never liked the current approach. For example, the names generated by contr.treatment paste factor to level names with no separation between the two; contr.sum simply numbers contrasts (I recall an

I am trying once again. By just changing f <- match(xlevs[f], nlevs) to f <- match(xlevs, nlevs)[f] , function 'factor' in R devel could be made more consistent and back-compatible. Why not picking it? -------------------------------------------- On Sat, 25/11/17, Suharto Anggono Suharto Anggono <suharto_anggono at yahoo.com> wrote: Subject: Re: [Rd] Function

In R devel, function 'factor' has been changed, allowing and merging duplicated 'labels'. Issue 1: Handling of specified 'labels' without duplicates is slower than before. Example: x <- rep(1:26, 40000) system.time(factor(x, levels=1:26, labels=letters)) Function 'factor' is already rather slow because of conversion to character. Please don't add slowdown.

Hi, I am trying to construct a logistic regression model from my data (104 patients and 25 events). I build a full model consisting of five predictors with the use of penalization by rms package (lrm, pentrace etc) because of events per variable issue. Then, I tried to approximate the full model by step-down technique predicting L from all of the componet variables using ordinary least squares

Dear CSAG R users, I will be glad if someone can point out what I am doing wrong or not doing at all in this. I am trying to write out netcdf file in R. I have 26 time step but only the first time step is written. For example: >library(ncdf) >path <- '/home/work/' >forecast <- open.ncdf(paste(path,'cam.1980.2005.nc',sep="")) > fore <-

Dear CSAG R users, I will be glad if someone can point out what I am doing wrong or not doing at all in this. I am trying to write out netcdf file in R. I have 26 time step but only the first time step is written. For example: >library(ncdf) >path <- '/home/work/' >forecast <- open.ncdf(paste(path,'cam.1980.2005.nc',sep="")) > fore <-

Dear list, after fitting an lrm with the Design package (stored as "mymodel") I try running a summary, but I get the following error: dim(mydata) [1] 235 9 names(mydata) [1] "id" "VAR1" "VAR2" "VAR3" "VAR4" "VAR5" "VAR6" "VAR7" "VAR8" summary(mymodel) Error in `contrasts<-`(`*tmp*`,

Dear R-list members, I have been a user of SPSS for a few years and quite new to R. I read the documentation and tried samples but I have some problems to obtain results for a logistic regression under R. The following SPSS script LOGISTIC REGRESSION vir /METHOD = FSTEP(LR) d007 d008 d009 d010 d011 d012 d013 d014 d015 d016 d017 d018 d069 d072 d073 /SAVE = PRED COOK SRESID

Dear All, I am trying to reproduce the example that I found online here http://bit.ly/11VG4ha However, when I run my script (pasted at the end of the email), I notice that there is a factor 2 between the values for the coefficients for the categorical variable female calculated by my script and in the online example. Any idea about where this difference comes from? Besides, how can I

Hi, the following R lines work fine in R 2.4.0, but not in R 2.4.1 or any devel versions of R 2.5.0 (see below for details). library(drc) # to load the dataset 'PestSci' library(nlme) ## Setting starting values sv <- c(0.43355869, 2.49963220, 0.05861799, 1.73290589, 0.38153146, 0.24316978) ## No error m1 <- nlme(SLOPE ~ c + (d-c)/(1+exp(b*(log(DOSE)-log(e)))), fixed =