search for: fatores

Hi, I make this model using lme m.lme <- lme(Glycogen~Treatment,random=~1|rTrt/Liver) How to make this using lmer? I try > m.lmer <- lmer(Glycogen~Treatment+(1|rTrt/Liver)) Erro em lmer(Glycogen ~ Treatment + (1 | rTrt/Liver)) : entry 0 in matrix[0,0] has row 2147483647 and column 2147483647 Al??m disso: Mensagem de aviso: / not meaningful for factors in: Ops.factor(rTrt, Liver)

Hello, I have a small test case : a = b * c + b * d and clang is fatorizing b to get only one multiplication (-O3) which is normally good for most architectures. But in my case, that pattern takes just one instruction and I would like to recover it as is. Where can I find this particular optimization in clang ? Is there a way of setting multiplications as being cheaper than

Hi all, I'm new to R and have the following problem: I have a 2 factor design (a has 2 levels, b has 3 levels). I have an object kidney.aov which is an aov(y ~ a*b), and when I ask for model.tables(kidney.avo, se=T) I get the following message along with the table of effects: Design is unbalanced - use se.contrast() for se's but the design is NOT unbalanced... each fator level

Le 5 janv. 2012 à 15:59, Ivan Llopard a écrit : > Hello, > > I have a small test case : > > a = b * c + b * d > > and clang is fatorizing b to get only one multiplication (-O3) which is > normally good for most architectures. But in my case, that pattern takes > just one instruction and I would like to recover it as is. > Where can I find this particular

...n package) object graph. I was able to move the labels with the coding below, but it moves the entire vector... CAPobj<-CAPpotiFT for(i in c(2:8))CAPobj$CCA[[i]][,1]<-CAPpotiFTI$CCA[[i]][,1] CAPpotiFTI$CCA[[i]][,1][2]<-0.85 And here the graphic script ScoresCAPFT<-scores(CAPpotiFTI) FatoresRep<-read.xls('Fatores.xls',sheet=1,rowNames=T) plot.cca(CAPpotiFTI,type='none',display=c('bp','sites'),main='Total Fauna - Sites vs. Environment') points(ScoresCAPFT$sites[FatoresRep$BiofaciesBC=='1',],pch=21) points(ScoresCAPFT$sites[FatoresRep$Bi...

...=T) PotiBio<-read.xls('FatorialReplica.xls',sheet=8,rowNames=T) attach(PotiAbio) LogPotiBio<-log(PotiBio+1) dbRDA<-capscale(t(LogPotiBio)~"Environmental Variables",dist="bray",add=T) dbRDA #Preparing to generate and save the graphic SymbolsRep<-read.xls('Fatores.xls',sheet=2) tiff('dbRDAPontos.tif',width=1250,height=1250,res=150) plot.cca(dbRDA,type='none',display=c('bp','sites')) text.cca(dbRDA,dis='cn',col=323232,cex=0.7,lwd=2,lty='dotted') text.cca(dbRDA,dis='sites',col='black',cex=0.8,...

...=T) PotiBio<-read.xls('FatorialReplica.xls',sheet=8,rowNames=T) attach(PotiAbio) LogPotiBio<-log(PotiBio+1) dbRDA<-capscale(t(LogPotiBio)~"Environmental Variables",dist="bray",add=T) dbRDA #Preparing to generate and save the graphic SymbolsRep<-read.xls('Fatores.xls',sheet=2) tiff('dbRDAPontos.tif',width=1250,height=1250,res=150) plot.cca(dbRDA,type='none',display=c('bp','sites')) text.cca(dbRDA,dis='cn',col=323232,cex=0.7,lwd=2,lty='dotted') text.cca(dbRDA,dis='sites',col='black',cex=0.8,...

Hi all, I'm adjusting a nonlinear regression model for data that has a categorigal variable present. So, I can use nls() to do this considering the categorical variable, like this #------------------------------------------------------------ da <- expand.grid(tr=gl(2,1,la=c("tr")), x=1:12) da$y <- 10*da$x/(3+da$x)+rnorm(da$x,0,0.1) plot(y~x, da) n0 <-