Hi,
Wondering if anyone could help me out with this error.Im trying to fill a
matrix with random numbers taken from an exponential distribution using a loop:
x.3<-matrix(rep(0,3000),nrow=1000,byrow=T)for(i in
1:1000){x[i,]<-rexp(3,rate=2/3)}
I get the error message:
Error in x[i, ] <- rexp(3, ra
From: andresa...@hotmail.com
To: r-help@r-project.org
Subject: Regression coefficient constraints
Date: Sun, 4 Sep 2011 15:50:09 +1000
Hi Guys,
Does anyone know how I could constrain my regression coefficients so that they
are positive and add up to one? Any help will be greatly appreci
Hi Guys,
Does anyone know how I could constrain my regression coefficients so that they
are positive and add up to one? Any help will be greatly appreciated.
Kind Regards,
Andre
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Hi,Just a quick one, does anyone know the command for accessing the standard
errors from a survreg object? I can access the coefficients by
model$coefficients, but I cant seem to find a command to access the errors. Any
help would be greatly appreciated.Regards,Andre
Hi,
I'm hoping someone can offer some advice:I have a matrix "x" of dimensions 160
by 1. I need to create a matrix "y", where the first 7 elements are equal
to x[1]^1/7, then the next 6 equal to x[2]^1/6, next seven x[3]^1/7 and so on
all the way to the 1040th element. I have implemente
Hi,
I'm quite new to R so this question will sound quite fundamental. I need to
create a vector of length 160. The first element should be (1+r)^159 and each
element thereafter should decrease by a factor of (1+r) until the 160th element
that should be 1. Is there a function similar to seq() bu
Hi,
Im new to R so this question is quite fundamental.
Im trying to compare some autocorrelations generated by the acf function to
some theoretical correlations. How can I have acces to just the
autocorrelations, for computation?
This is some of my code:
> acf.data<-c(acf(x))
> acf.dat
Hi guys,
Im new to R and am having a bit of trouble with what should be a simple loop.
It sprobably something very fundamental that im doing wrong.
for(i in c(1:520))
{
tmp1<- ((1-samp.pct[i])^2)*(log(1-theor.pct[i])-log(1-theor.pct[i+1]))
tmp2<- ((samp.pct[i])^2)*(log(theor.pct[i+1])-log(th
Hi,
I am attempting to graph a Kaplan Meier estimate for some claims using the
survfit function. However, I was wondering if it is possible to plot a cdf of
the kaplan meier rather than the survival function. Here is some of my code:
library(survival)
Surv(claimj,censorj==0)
survfit(Surv
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