similar to: Error with Design.Function(fit)

Hi all, Do any packages implement density estimation in a modelling framework? I want to be able to do something like: dmodel <- density(~ a + b, data = mydata) predict(dmodel, newdata) This isn't how sm or KernSmooth or base density estimation works. Are there other packages that do density estimation? Or is there some reason that this is a bad idea. Hadley -- http://had.co.nz/

Dear R help, I'm having some trouble with model formulas for the ols function in the rms package. I want to have two variables represented as restricted cubic splines, and also include an interaction as a product of linear terms, but I get an error message. library(rms) d <- data.frame(x1 = rnorm(50), x2 = rnorm(50), y = rnorm(50)) ols(y ~ rcs(x1,3) + rcs(x2,3) + x1*x2, data=d) Error in

Hello, I have a general question about combining imputations as well as a question specific to the rms and Hmisc packages. The situation is multiple regression on a data set where multiple imputation has been used to give M imputed data sets. I know how to get the combined estimate of the covariance matrix of the estimated coefficients (average the M covariance matrices from the individual

Dear UseRs, I just learnt that the number of columns of a data frame is not always what I thought it to be, and I wonder where I should have learnt about this. Consider the following example: dat <- data.frame(X1=1:10, X2=LETTERS[1:10]) ncol(dat) ## evaluates to 2 (of course) dat$X1poly <- poly(dat$X1,3) dat ## five columns displayed ncol(dat) ##

Hi, I read data from a file. I'm trying to understand how to use Design.rcs by using simple test data first. I use 1000 integer values (1,...,1000) for x (the predictor) with some noise (x+.02*x) and I set the response variable y=x. Then, I try rcs and ols as follows: m = ( sqrt(y1) ~ ( rcs(x1,3) ) ); #I tried without sqrt also f = ols(m, data=data_train.df); print(f); [I plot original

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Hi, You may try: dat1 <- structure(list(SubID = 1:8, CSE1 = c(6L, 6L, 5L, 5L, 5L, 5L, 3L, 3L), CSE2 = c(5L, 4L, 5L, 4L, 6L, 4L, 6L, 6L), CSE3 = c(6L, 7L, 5L, 3L, 7L, 3L, 6L, 6L), CSE4 = c(2L, 2L, 5L, 4L, 5L, 6L, 3L, 3L), WSE1 = c(6L, 6L, 5L, 4L, 6L, 4L, 6L, 6L), WSE2 = c(2L, 6L, 5L, 4L, 4L, 3L, 5L, 5L), WSE3 = c(2L, 2L, 4L, 5L, 4L, 7L, 2L, 4L), WSE4 = c(4L, 3L, 5L, 2L, 1L, 3L, 1L, 7L)),

Hi, I am new at R and still trying to get the hang of things. I am running a while loop and wish to save the results of each iteration. The results are a vector x of length two and I wish to save the results of each iteration as two vectors, one for x[1] and the other for x[2]. Thanks! [[alternative HTML version deleted]]

Hello, I think I am beginning to understand what is involved in the so-called "Type-I, II, ..." ANOVAS (thanks to all the replies I got for yesterday's post). I have a question that will help me (and others?) understand it better (or remove a misunderstanding): I know that ANOVA is really a special case of regression where the predictor variable is categorical. I know that there

Hello Rhelpers, I need to run the following loop over a large number of data-sets, and was wondering if it could somehow be vectorized. It's more or less a cumulative sum, but slightly more complex. Here's the code, and an example dataset (called tab in my code) follows. Thanks in advance for any suggestions! res<-0 for (i in min(tab$Date):max(tab$Date)) { if

Dear List: Below is the validation output of a fitted ordinal logistic model using the bootstrap in the rms package. My interpretation is that most of the corrected indices indicate little overfitting, however the slope seems to indicate that the model is too optimistic. Given that most of the corrected indices seem reasonable, would it be appropriate to use this model on future data if the

I am writing this to help those that have experienced the problem of clients having problems with the Samba Shares and the error noted in the logs is the dreaded oplock issue. Perhaps this would be a good addition to the FAQ. Start of oplock analysis ---------------------------------- We spent a 48hr session trying to solve this, it turn out to be a hub, Started with the oplock condition and

A number of as.data.frame methods do names(x) <- NULL Replacing that with if(!is.null(names(x))) names(x) <- NULL appears to save making one copy of the data (based on tracemem and Rprofmem in a copy of R compiled with --enable-memory-profiling) and gives a modest but consistent boost in speed, e.g.: # old new # user system

https://rhn.redhat.com/errata/RHSA-2005-049.html refers Updated files :- updates/x86_64/RPMS/cups-1.1.17-13.3.24.x86_64.rpm updates/x86_64/RPMS/cups-devel-1.1.17-13.3.24.x86_64.rpm updates/x86_64/RPMS/cups-libs-1.1.17-13.3.24.x86_64.rpm updates/x86_64/RPMS/cups-libs-1.1.17-13.3.24.i386.rpm updates/x86_64/SRPMS/cups-1.1.17-13.3.24.src.rpm To update run 'yum update cups' Tru Huynh --

Hello everybody, I have a problem with the model.table of anova. I have data (datos2) with 4 cluster (V7), calculated with daisy and hclust. str(datos2) 'data.frame': 56 obs. of 7 variables: $ Estacion: Factor w/ 56 levels "Abradelo","AltoDoRodicio",..: 1 2 3 4 5 6 7 8 9 10 ... $ Invierno: num 36 53.9 37.1 63.6 12.5 ... $ X : int 643449 616292 562796

Thank you very much for the example. I think interactively I could get something. But my obstacle is to write an R script that processes my set of data automatically. My difficulty is to extract the information that appears on the screen, when R is operated interactively, from a scripts. Let me go over some steps to make sure I am doing things right. Assume my data have been read into the matrix

I was unsure of what suitable goodness-of-fit tests existed in R for logistic regression. After searching the R-help archive I found that using the Design models and resid, could be used to calculate this as follows: d <- datadist(mydataframe) options(datadist = 'd') fit <- lrm(response ~ predictor1 + predictor2..., data=mydataframe, x =T, y=T) resid(fit, 'gof'). I set up a

Hi, I have sort of a newbie question. I've seriously put a lot of effort into how to handle simple replicates in a repeated ANOVA design, but haven't had much luck. I really liked reading "Notes on the use of R for psychology experiments and questionnaires", by Jonathan Baron and Yuelin Li ( http://www.psych.upenn.edu/~baron/rpsych/rpsych.html ) but still didn't run across

The Wine development release 4.20 is now available. What's new in this release (see below for details): - New version of the Mono engine with an FNA update. - Code persistence in VBScript and JScript. - Vulkan spec updates. - Improved support for LLVM MinGW. - Various bug fixes. The source is available from the following locations:

Target: FreeBSD 7.0-RC1 on amd64. autoconf says: configure:2122: checking build system type configure:2140: result: x86_64-unknown-freebsd7.0 [...] configure:2721: gcc -v >&5 Using built-in specs. Target: amd64-undermydesk-freebsd Configured with: FreeBSD/amd64 system compiler Thread model: posix gcc version 4.2.1 20070719 [FreeBSD] [...] objdir != srcdir, for both llvm and gcc. Release