Do you want a qqnorm() instead of a qqplot() ? [Reproducibility also
involves posting the code you used that lead to the error / warning]
This seems to work for me:
# mydata <-
source("http://r.789695.n4.nabble.com/file/n4623493/mydata.txt";)[[1]]
# Have to drop visible attribute
lmmodel <- lm(
Find the data attached,
http://r.789695.n4.nabble.com/file/n4623493/mydata.txt mydata.txt
The model would be /lmmodel <- lm(log(vdep) ~ v1 + sqrt(v2) + v3 +v5 + v6 +
v7 + v8 + v9 + v10, data = mydata)/
Thanks again,
u...@host.com
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http://r.789695.n4.nabble.
Reproducible example? https://github.com/hadley/devtools/wiki/Reproducibility
Michael
On Thu, May 10, 2012 at 6:31 AM, agent dunham wrote:
> Sorry, but I need the same and i don't understand your help.
>
> So, after fitting my lm model, how can i identify my data? I was trying the
> following, b
Sorry, but I need the same and i don't understand your help.
So, after fitting my lm model, how can i identify my data? I was trying the
following, but it doesn't work.
/identify(qnorm(c(0.25,
0.75)),quantile(rstandard(mymodel)[!is.na(rstandard(mymodel))], c(0.25,
0.75)),row.names(mydata))
warni
Try this
qqInteractive <- function(..., IDENTIFY = TRUE){
qqplot(...) -> X
if(IDENTIFY) return(identify(X))
invisisble(X)
}
The trick is that identify wants coordinates of the point in the
scatter plot which are not the inputs to qqplot() but rather a
transformation thereof.
Micha
Dear Community,
I want to identify outliers in my data. I don't know how to use identify
command in the plots obtained.
I've gone through help files and use mahalanobis example for my purpose:
NormalMultivarianteComparefunc <- function(x) {
Sx <- cov(x)
D2 <- mahalanobis(
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