search for: colms

...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)) &gt...

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:...

Hello, I use the simplest of examples that 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,

Hi everybody, I'm having trouble getting an eval-parse combination to work with lme in a for loop. Consider the code below. The call to lme outside the loop works. The call to aov inside the loop works, but the call to to lme inside the loop does not. The latter fails with Error in model.frame.default(formula = ~meanCol + Group + c1 + c3 + Subject, : variable lengths differ (found for

...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(summ...

Hi everybody, I'm trying to construct contrasts for an ANOVA to determine if there is a significant trend in the means of my groups. In the following example, based on the type of 2x3 ANOVA I'm trying to perform, does the linear polynomial contrast generated by contr.poly allow me to test for a linear trend across groups? doi=data.frame( Group=c( rep(1, 5), rep(2, 5), rep(3, 5),

...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...

Greetings all, I've done some ANOVAs and have found significant effects across my groups, so now I have to do some post-hoc tests to ascertain which of the groups is driving the significant effect. Usually one would do something like a Newman-Keuls or Scheffe test to get at this but based on googling and browsing through the r-help archives R doesn't support these and they seem to be

Hi all, I wonder if you could help me with (what is surely) a simple R question? I've written a simple R script (attached) to perform multiple ANOVAs on the columns in a table loaded in from a a file (also attached). I loop through the list of columns for which I want to perform the ANOVAs, storing the column name in a variable. When I try to perform the ANOVA using the variable name as a

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.n...

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

Hi everybody, I'm having problems with the barchart command in the lattice package. I'm creating barcharts from matrices with with anything from 20 to 71 columns. When I leave the column names alone, that is they are set in the read.table command (and inherited by subsequent commands) the correct number barcharts is created by the "print(barchart(...))" command.

...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) >> >...

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):