Computing normal conf.intervals
Jonas Malmros wrote:
Hi everybody, I wonder if there is a built-in function similar to Matlab's "normfit" which computes 95% CI based on the normality assumption. So, I have a vector of values and I want to calculate 95% normal CI. Of course, I could write my own function, no problem, but I still wonder if built-in functionality exists. (I wish quantile() had this functionality included). Anyone knows?
First, be more clear about what the intention is. Prediction intervals, or confidence intervals for the mean? If the former, do you want the crude version (plus/minus 1.96s) or the version that takes the estimation variance into account
x <- rnorm(10) qnorm(c(.025,.975), mean=mean(x), sd=sd(x))
[1] -1.763791 1.465144
predict(lm(x~1), newdata=data.frame(1), interval="p")
fit lwr upr [1,] -0.1493235 -2.103664 1.805017
confint(lm(x~1))
2.5 % 97.5 % (Intercept) -0.7385793 0.4399324
Also, I wonder if there is a function similar to Matlab's "flipud". Obviously there is package "matlab" which has this function, but I wonder if I can turn a matrix upside-down without loading matlab package.
M[nrow(M):1,] or (safer if nrow==0) M[rev(seq_len(nrow(M))),]
Thanks for your help in advance! Best, JM
O__ ---- Peter Dalgaard ?ster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk) FAX: (+45) 35327907