similar to: aov and non-categorical variables

I have a simple linear model (fitted with lm()) with 2 independant variables : one categorical and one integer. When I run summary.lm() on this model, I get a standard linear regression summary (in which one categorical variable has to be converted into many indicator variables) which looks like : Estimate Std. Error t value Pr(>|t|) (Intercept) -3595.3 2767.1 -1.299

Thanks for all the help on my previous questions. One more (hopefully last one) : I've been very surprised when I tried to fit a model (using aov()) for a sample of size 200 and 10 variables and their interactions. It turns out that even 2GB of RAM is not anough for aov() with this sample size, which does not seem so big for me. Am I doing something wrong or this is considered a normal

Help! I tried nearly everything I could and still was not able to get IE working with wine 0.9.19. Manual install using App DB, winetools, ies4linux, etc... everything fails, with different problems and error messages of course. It's really frustrating. Is there a way to install IE that actually works ?

I was wondering, whether there is a way to have statistical significance test for cluster agreement. I know that I can use classAgreement() function to get Rand index, which will give me some indication whether the clusters agree or not, but it would be interesting to have a formal test. Thanks.

It appears that while Linux has plenty of traffic shaping mechanism on transmit, there is nothing on receive side. While generally it does make sense since transmit is more CPU intensive operation, after all receive also consumes CPU cycles. It is clear that it''s best to drop the packet as soon as possible, i.e. on receive, if possible - by the driver itself. It may not be feasible in

Hi. I have to make some minor modifications to the text.tree function - I don't like the way it prints the split labels (they are too long in my case and overlap). I tried to make s simple modification to the text.tree function so that it will limit the number of significant digits in tree labels, but could not - the original function uses some undocumented "treeco" function, which

I''m trying to configure 4 queues with strict priorities based on DSCP. I tried to following commands, but it seems that the filters I defined have no effect tc qdisc add dev eth0 root handle 1: prio bands 4 tc qdisc add dev eth0 parent 1:0 handle 10: pfifo limit 100 tc qdisc add dev eth0 parent 1:1 handle 20: pfifo limit 100 tc qdisc add dev eth0 parent 1:2 handle 30: pfifo limit 100 tc

> From: Sirotkin, Alexander [mailto:demiurg at ti.com] > > P.S. Anybody who finds my postings insulting, please write a > mail filter > for my address. Although I can not understand why it may insult > anybody. > > P.P.S. Must be related to cultural differences, I guess... It's not insulting to me, personally, but quite possibly to the author of that

I'm going to implement AdaBoost algorithm in R. Just wanted to ensure that there is no implementation of any boosting algorithm in R... don't want to reinvent the wheel... -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

Hi. I was trying to import a very large data table to R using RODBC. By large meaning a table of 600 real numbers and 10000 rows. It failed saying that it reached a memory limit. This is extremely weird because S-Plus can import the same table, and it does this quite fast. Are there any limitations on objects size in current implementation of R ?

Hi R help, Hi R help, Which is the easiest (most elegant) way to force "aov" to treat numerical variables as categorical ? Sincerely, Andrea Bernasconi DG PROBLEM EXAMPLE I consider the latin squares example described at page 157 of the book: Statistics for Experimenters: Design, Innovation, and Discovery by George E. P. Box, J. Stuart Hunter, William G. Hunter. This example use

I have some data in the Linux version of S-Plus, which I can not use anymore. The program is just broken and won't run. I'm trying to find a way to import that data to either Windows version of S-Plus (which I have running on my other machine) or R (Linux or Windows, it doesn't matter). Unfortunately, nothing seems to work. Windows S-Plus seem to ignore files from Linux .Data

Dear R Users, I did not get any reply on my question so I am re-asking. This time I am giving sample data: 1 60.3162 -13.5993 -0.4353 46.0938 0.1877 -0.194E-07 2 60.3713 -13.5992 -0.4423 46.1241 0.2057 -0.231E-06 3 60.3430 -13.5981 -1.6163 44.9048 0.2237 -0.270E-06 4 60.3227 -13.5970 -2.6258 43.8785 0.2213

How about: x <- data.frame(matrix(rnorm(1550),c(50,31))) model <- step(lm(x[,1] ~ as.matrix(x[,2:31]))) --Matt -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch]On Behalf Of rongguiwong Sent: Monday, September 20, 2004 20:52 PM To: r-help at stat.math.ethz.ch Subject: [R] how to take this experiment with R? This message uses

Are there any suitable routines that perform factor analysis of categorical or mixed categorical/continuous data in R? Stuart Dr Stuart Leask MA MB BChir (Cantab) MRCPsych Clinical Lecturer in Psychiatry, University of Nottingham Dept Psychiatry Duncan Macmillan House, Porchester Road, Nottingham. NG3 6AA. UK tel. 00 44 115 969 1300 xtn 40784 fax 0115 955 5352

<ripley at stats.ox.ac.uk> wrote: > Hint 2: in the absence of balance, ...., and lme can do that Would it be possible to make aov like wrappers to the various special lm variants allowing for a uniform syntax for anova? aov(resp~f1*f2+Error(S/(f1*f2))) ## uses lm aov.lme(resp~f1*f2+Error(S/(f1*f2))) ## uses lme aov.rlm(resp~f1*f2+Error(S/(f1*f2))) ## uses rlm ... I'd do it, but I

I am looking to fit one or more latent categorical variables to data that is a mixture of categorical and continuous variables. Factor analysis would work for continuous data, latent class analysis for categorical data. I understand that in a package such as MPlus I could perform a single analysis of both data types. Are there similar routines available in R? Stuart -----Original Message-----

Hello, I wonder when I do coxph in R: coxph( Surv(start, stop, event) ~ x, data=test) If x is a categorical varible (1,2,3,4,5), should I creat four dummy varibles for it? if yes, how can I get the overall p value on x other than for each dummy variable? Thanks Lisa Wang Princess Margaret Hospital Phone 416 946 4501

Dear All, Can the x matrix in the glmnet() function of glmnet package be a data.frame with numeric columns and factor columns? I am asking this because I have a model with both numeric and categorical predictors, which I would like to study with glmnet. I have already tried to use a data.frame, but with no success -- as far as I know, the matrix object can only have data of a single type. Is

Hello All: I know the function loglin() and loglm() from librarary(MASS) performs analysis on nominal categorical data. Are there any libraries, functions or examples available for analysis of ordinal categorical data? I have in mind procedures that can replicate results in Alan Agresti (1984) "Analysis of Ordinal Categorical Data." Thanks, ANDREW