search for: colm

...cts/ a regression- with particular regard to my variable lengths being different and the models refusing to compute any further. The codes I have been using are as follows: vc<-read.table("P:\\R\\Testvcomp10.txt",header=T) >> attach(vc) > > family<-factor(family) > colms<-(vc)[,4:13] ## this to assign the 10 columns containing marker > data to a new variable, as column names are themselves not in any > recognisable sequence > > vcdf<-data.frame(family,peg.no,ec.length,syll.length,colms) > library(lme4) >> for (c in levels(family)) &g...

Hi, I am using nsd as my authority-only dns servers and find it fast, stable, reliable - all of the design goals you set out for, great. I have a zone that is intended only to be visible to people on my network. In bind I would enforce this with an 'allow-query' configuration directive. I checked the latest 'nsd-for-bind-users' document, and 'allow-query' is not

...al looped lmer() as my principal approach. For that, the code that I was using, with some fresh modifications, is as follows: > vc<-read.table("E:\\R\\Text Files\\Testvcomp10.txt", header=T, sep="", dec=".", na.strings=NA, strip.white=T) > attach(vc) > colms<-(vc)[4:13] > family<-factor(family) > for (c in 1:levels(family)) + { + print("Family") + print(c) + { + for (i in 1:length(colms)) + { + print(anova(fit<-lmer(peg.no~1 + (1|c/i), na.action=na.exclude, vcdf))) + + }}} ..and its giving me the following copious errors:...

...at somebody can think of in order to generate a matrix of random numbers from within C, calling appropriate R functions. The concrete example is below: --- file pico.c #include <stdio.h> #include <stdlib.h> #include <math.h> #include <R.h> #include <Rmath.h> #define COLM( i, j, m ) ( m*j + i) void pico ( double *y, int n, int m ) { int i, j; GetRNGstate(); for ( i=0; i<n; i++ ) { for ( j=0; j<m; j++ ) { y[ COLM( i,j,m ) ] = rnorm( 0, 1 ); } } PutRNGstate(); } ----...

...##########################################################\n") ## this doe not work means.lmeWithCovariate=lme(eval(parse(text=meanCol)) ~ Group + c1 + c1 + c3, data=df, random = ~ 1 | Subject) print(summary(means.lmeWithCovariate)) print(anova(means.lmeWithCovariate)) } Regards, -- Colm G. Connolly, Ph. D. Dept of Psychiatry University of California, San Diego 9500 Gilman Dr. #0738 La Jolla, CA 92093-0738 Tel: +1-858-246-0684

...ce components for a phenotype 'peg.no', using the variable 'family' and a marker column 'P1L55' (which is categorical and has 2 levels- 0 and 1), as random effects. There are no fixed effects. The code I used is as follows: vc<-read.table(...) vcdf<-data.frame(vc) colms<-(vc)[4:13] ##these are the markers lapply(colms,factor) try(fit<-lmer(peg.no~1 + (1|family/P1L55), na.action=na.include, data=vcdf)) I thought that putting the data into a dataframe would help, along with the na.exclude command, because there is a lot of missing data in patches which I...

...without losing some of it somewhere.. Any help will be much much appreciated.. Aditi ------------------------------------------------------------------ Code for shorter sample file: model<-read.table("...) modeldf<-data.frame(model) modeldf[2:13<-lapply(modeldf[2:13],factor) colms<-(modeldf)[4:13] ## 10 markers only in this file se<-c(1:1000) for(f in colms) { print("Marker") { for( i in 1:1000) { print("perm no.") print(se[i]) { peg.no.prm<-sample(peg.no, length(peg.no)) try(fit5<-lmer(data=modeldf, peg.no.prm~1 + (1|family/f))) print(sum...

...ntrol", "Short", "Long"), ordered=T) ## construct contrasts contrasts(doi$Group)=contr.poly(levels(doi$Group)) model1=aov(Mean_1 ~ Group*eventType, data=doi) model1.summary=summary(model1) print(model1.summary) print(summary.lm(model1)) Thanks in advance, Regards, -- Dr Colm G. Connolly Institute of Neuroscience The Lloyd Building University of Dublin Trinity College, Dublin 2, ?ire Fax: +353-1-671-3183 Please note that electronic mail to, from or within the Trinity College Dublin, may be the subject of a request under the Freedom of Information Act.

...by each PxLyy, and by 'family' as well. I have managed to run the lmer model for the first marker column (P1L55), and have been able to block it according to 'family' as follows: vc<-read.table("P:\\R\\Testvcomp10.txt",header=T) attach(vc) family<-factor(family) colms<-(vc)[4:13] ## these are the marker columns lapply(colms,factor) library(lme4) for (f in levels(family)) { print("Family") print(f) { t<- vc [ family == f, ] try(lmer<-fit(peg.no~1 + (1|P1L55) + (1|family), data=t, na.action=na.exclude)) print(summary(fit)) }} However I need...

...arranted? In the absence of NK or Scheefe tests could someone recommend an appropriate test for me to conduct in this instance? From what I've read in Explaining Psychological Statistics, the REGWQ test seems to be the way to go. Can R perform this test? Thanks for your time, Regards, -- Dr Colm G. Connolly School of Psychology and Institute of Neuroscience The Lloyd Building University of Dublin Trinity College, Dublin 2, ?ire

...s follows: Error in the model.frame(formula, rownames, variables, varnames, extras, extranames, : invalid variable type The line causing the problem is the first line inside the for loop. The question is what have I done wrong? Is there a better way of doing the ANOVAs? Thanks in advance, Bye, Colm. -------------- next part -------------- roi.errs=read.table("../Group.results/allGroupsROI.errs", header=T, sep="\t"); roi.stops=read.table("../Group.results/allGroupsROI.stops", header=T, sep="\t"); # extract the names of the columns containing the means r...

I have a plot created with strat.plot() from package rioja. When the plot is created with scale.percent=FALSE, each x axes is labeled at 0 and its maximum. However, when scale.percent=TRUE, the x axes are not labeled. I need to use scale.percent=TRUE and I need labels for the x axes. I have been able to add labels to the x axes with mtext but it is very tedious to find the correct

I HAVE A VERY BIG DATA OF 67 COLMS AND 25000 ROWS AND WOULD LIKE TO TRANSPOSE IT THE R HELP WAS NOT ENOUGH INFORMATION BECOUSE I AM NOT A PROGRAMMER AND FIRST TIME R USER. SO CAN YOU GIVE SOME HINTS OF CODING, AA TT GG GG CC AA TT GG GG CC AA TT GG GG CC AA TT GG GG CC AA TT GG GG CC TO AA AA AA AA AA TT TT TT TT TT GG GG...

...le writing to a text file? Thanks a lot for any suggestions that might help, Aditi --------------------------------------------------------- Code for a test file with 10 markers: model<-read.table(".......) modeldf<-data.frame(model) modeldf[2:13]<-lapply(modeldf[2:13],factor) colms<-(modeldf)[4:13] ## ten marker columns se<-c(1:10000) peg.no<-(modeldf)[,14] library(lme4) for(f in colms) { print("Marker") { for( i in 1:10000) { print("perm no.") print(se[i]) { peg.no.prm<-sample(peg.no, length(peg.no)) try(fit5<-lmer(data=modeldf, peg....

Hi All, Went to Dags page and did not see an entry or Centos for yum configuration. Is Dag ok to use for Centos? If so, how would I enter the info in my dag repo file? Phil

...############################################## # pdf(paste(resultsDir, 'errorsByGroup.pdf', sep="/"), paper='a4') # fontsize=trellis.par.get("fontsize"); # fontsize$text=textFontSize; # trellis.par.set("fontsize", fontsize); roi.errs.ctrl.means=colMeans(roi.errs.ctrl.matrix) roi.errs.short.means=colMeans(roi.errs.short.matrix) roi.errs.long.means=colMeans(roi.errs.long.matrix) yylim=c(floor(min(roi.errs[, 4:length(roi.errs)])), ceiling(max (roi.errs[, 4:length(roi.errs)]))) errs.Means=rbind(roi.errs.ctrl.means, roi.errs.short.mea...

...t;> >> has 2 levels- 0 and 1), as random effects. There are no >> fixed effects. >> >> >> >> The code I used is as follows: >> >> >> >> vc<-read.table(...) >> >> >> >> vcdf<-data.frame(vc) >> >> colms<-(vc)[4:13] ##these are the markers >> >> lapply(colms,factor) >> > >> > Note that you did not change columns 4:13 of vcdf to factors: >> > you changed copies of them to factors. Do >> > vcdf[4:13] <- lapply(vcdf[4:13], factor) >> &gt...

On Tue 07-07-20 10:07:26, Pavel Machek wrote: > Hi! > > > > > > This patch adds logic to the kernel power code to zero out contents of > > > > > all MADV_WIPEONSUSPEND VMAs present in the system during its transition > > > > > to any suspend state equal or greater/deeper than Suspend-to-memory, > > > > > known as S3. > > >

On Tue 07-07-20 10:07:26, Pavel Machek wrote: > Hi! > > > > > > This patch adds logic to the kernel power code to zero out contents of > > > > > all MADV_WIPEONSUSPEND VMAs present in the system during its transition > > > > > to any suspend state equal or greater/deeper than Suspend-to-memory, > > > > > known as S3. > > >

I have a couple of hundred American Community Survey Summary Files files containing rectangular arrays of data, mainly though not exclusively numeric. Each file is referred to as a sequence (henceforth "seq"). From these files I am trying to extract particular subsets (tables) consisting of a sets of columns. These tables are defined by three numbers (now in columns in a data frame):