R help - Nov 2013 - colours legend, for loop,density plot

Hi , thanks in advance I have the follow code:

       normal<-sort(rnorm(1000))	cauchy<-sort(rcauchy(1000))
t3<-sort(rt(1000,3))	t10<-sort(rt(1000, 10))
	col<-c("green","blue","orange","purple")
v<-list(normal,cauchy,t3,t10)	names(v)<-c("Normal",
"Cauchy", "T-stud 3 df", "T-stud 10 df")
	par(mfrow=c(1,2))		plot(density(normal),col=col[[1]],main="Funciones de
densidad")	for ( i in 2:4)	{	lines(density(v[[i]]),col=col[[i]],lty=i+2)	}
	legend(x=-4,y=0.3,names(v),col=col,cex=0.6)

The problem is that in the legend doesn't appear colours so I can not
identify which curve is each one, please could you tell me what do I neet to
change in order to solve it??
Thanks a lot, Tere 		 	   		  
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On 11/12/2013 09:52 AM, M? Teresa Martinez Soriano
wrote:
> normal<-sort(rnorm(1000))	cauchy<-sort(rcauchy(1000))
t3<-sort(rt(1000,3))	t10<-sort(rt(1000, 10))
> 
col<-c("green","blue","orange","purple")
v<-list(normal,cauchy,t3,t10)	names(v)<-c("Normal",
"Cauchy", "T-stud 3 df", "T-stud 10 df")
> 	par(mfrow=c(1,2))		plot(density(normal),col=col[[1]],main="Funciones
de densidad")	for ( i in 2:4)	{	lines(density(v[[i]]),col=col[[i]],lty=i+2)
}
> 	legend(x=-4,y=0.3,names(v),col=col,cex=0.6)
>
Hi Tere,
Try this:

legend(x=-2,y=0.04,names(v),col=col,cex=0.6,lty=c(1,4:6))

Jim

Hi to
everyone, I have a big data set where rows are observations and columns are
variables. It contains a lot of missing values. I have used multiple imputation
with library mice and I get an “exact” prediction of each missing value. Now, I
would like to know the error I can commit or the confidence interval.

How can I
get this?

This is
part of my code

library(mice)

mod1<-mice(dat,
method=c("","",rep("pmm",6)))

                ro<-round(cor(dat,
use = "pair"), 3)

 

                predictor<-quickpred(dat)#
esta matriz predictora se construye según las correlaciones

                

               
mod1<-mice(dat,method=c("","",rep("pmm",6)),
pred=predictor)

                imputados<-complete(mod1,'long')

                x.imp=split(imputados,
imputados$.imp)

                acumula=x.imp[[1]][,-c(1,2)]

                                               for(j
in 2:length(x.imp))

                                                               {
acumula=acumula+x.imp[[j]][,-c(1,2)]}

                med.imp=acumula/5

 

 

Thanks in
advance 		 	   		   		 	   		  
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