similar to: R-beta: more model.matrix

You may recall that I was recently constructing a function to bootstrap the coefficients in a linear regression model. In S-PLUS I was using the model.matrix function applied to the fitted model, then taking the QR decomposition of that. I discovered that it was in fact easier to accomplish the bootstrapping in R because the QR decomposition of the model matrix is stored with the fitted model.

When regressing by month, how do I get the coefficients out into a new data set? I'm looking for [ month, a, b, c ] from the Pastor-Stambaugh model I'm using which is: r[i+1] = a + b * r[i] + c * v[i] + e the model I'm using wants to create a new dataseries based on the coefficient in each month. I'm doing a simple linear regression on DataSet, and >

Hi All, Need help regarding the samba version samba-3.0.33-3.37.el5.x86_64 1) In the samba version (samba-3.0.33-3.37.el5.x86_64) that we are planning to use , if we put multiple password server names like the one shown below workgroup = EUROPE --------- we do have a domain named as EUROPE [ as per windows team ] password server = EUREDD-HUB10 EUREDD-HUB09 *

Full_Name: Ben Bolker Version: 2.2.1 OS: Windows XP and 2000 Submission from: (NULL) (128.227.60.124) The following code, using confint() to try to get confidence intervals on an nls object that has been fitted with algorithm="port" reliably crashes R 2.2.0 and 2.2.1 with the latest version of MASS on a Windows 2000 and a Windows XP machine here. I *think* earlier versions of MASS

Dear R contributors, I recently dowloaded the R-1.3.1 on my SGi Origin 200 run by Irix6.5. In order to instal it, I launched the command shown below: env MAKE=gmake ./configure The output was as follow:: R is now configured for mips-sgi-irix6.5 Source directory: . Installation directory: /usr/local C compiler: cc -OPT:IEEE_NaN_inf=ON -g C++ compiler:

Does anyone have a description of the S help format? -k =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- r-devel mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-devel-request@stat.math.ethz.ch

Hi R, This is a problem, which I have tried to present in a simple way: Let, x1=1:10 x2=2:11 y=2+3*x1 lm_obj=lm(y~x1+x2) lm_obj step(lm_obj) # Step function for the first time y=y^0.1 lm_obj step(lm_obj) #Step function after a transformation on Y, but 'lm_obj' is not modified. The two step function behave differently. The first one is before the

We have an R package which needs to include a JAR file. Is there a canonical directory for it?

Hi all, I believe this is a bug in the model.matrix function. I'd like a second opinion before filing a bug report. If I have a nested covariate B with multiple values for just one level of A, I can not get a non-singular design matrix out of model.matrix > df <- data.frame(A = factor(c("a", "a", "x", "x"), levels = c("x",

Dear R helpers, I would like to have abline, for a lm model for example, lying within data range. Do you know how to get it? Thank in advance Nguyen D Nguyen #CODE x<- rnorm(200, 35,5) y<- rnorm(200, 0.87,0.12) plot(y~x, xlim=c(0,50), pch=17, bty="l") abline(lm(y~x)) # I would like abline is between min(x) and max(x) [[alternative HTML version deleted]]

Hello, I have problem I have a, and b in regression then I can't plot x,y with (and) a, b lines can you help me ? thx [[alternative HTML version deleted]]

I have a simple 1 way anova coded like summary(ANOVA1way <- aov(Value ~ WellID, data = welldata)) How can I use the BY function to do this ANOVA for each group using another variable in the dataset?? I tried coding it like this, but it doesn't seem to work. summary(ANOVA1way <- by(welldata, Analyte, function(x) aov(Value ~ WellID, data = welldata))) In SAS I would code it like this:

Since you only have 3 predictors, each categorical with a small number of categories, you can use expand.grid to make a data.frame containing all possible combinations and give that the predict method for your model to get all possible predictions. Something like the following untested code. newdata <- expand.grid( Humidity = levels(Humidity), #(High, Medium,Low)

Hi there, I have a the following function which takes a name(fname), total number of rows (Total), number of times to make a sample(nSample), number of records/rows to be included in each sample(nPatient). then it generates required samples and calculates the mean and standard deviation of the columns for each sample and finally in calculates the grand mean and standard deviation. The problem

Dear R developers and users: I have the following data, x is the response vaiable, nsample(individual) nested within trt, and subsample nested within nsample, I want to fit trt as fixed effect, and block, nsample(trt) as random effects using lme, is the following coding correct? dat$vgrp <- getGroups(dat, form = ~ 1|trt/nsample, level = 2) ge.lme1 <- lme(fixed=x~trt, data=dat,

Does anyone know how to extract residuals in lmer? Here's the error I get: > crop.lme=lmer(response~variety*irrigation*pesticide+(1|rep)+(1|rep: pesticide)+(1|rep:pesticide:irrigation), crop.data) > qqnorm(crop.lme) Error in qqnorm.default(crop.lme) : y is empty or has only NAs > resid(crop.lme) NULL Thanks! --Jake

> From: rossini@biostat.washington.edu (A.J. Rossini) > Date: 23 Nov 1998 12:46:03 -0800 > > Thanks much. I'm hacking Emacs/ESS on MS Windows platforms starting > mid next month, thanks to some external prodding (and now, product > availability from both R, Splus). And I think XLispStat might be able > to talk (at least DDE), as well, which would be nice > > If

Documentation for S3 functions is apparently written in troff markup with macro tags like .BG .FN .TL .DN .CS ... Inspection of S3 documentation source files gives a pretty clear idea of what these mean, semantically (and Sd2Rd is a perl script which converts this markup into the Rd format, providing further semantic information along the way). My query is: Can anyone point to troff

Hi, I have a query on regression output generated by R. > result=lm( Y~X , data=trail) > summary(result) After running this 2 statements the following output is generated. Call: lm(formula = Y ~ X, data = trail) Residuals: Min 1Q Median 3Q Max -245.30 -90.77 -30.30 54.99 532.78 Coefficients: Estimate Std. Error t value Pr(>|t|)

this is the command i made for a normal distribution, but when i try to plot the histograms, i dont know why the bars don't stick on the line... nsamples<-1000 sampsize<-15 Samples<-matrix(rnorm(nsamples*sampsize,0,1),nrow=nsamples) a<-apply(Samples,1,var) NC14<-a*14 x<-0:40 plot(x,dchisq(x,14),type='h') hist(NC14,freq=F,add=T) -- View this message in context: