Hi, folks,
As seen in the following codes:
x1=rlnorm(10)
x2=rlnorm(10,mean=2)
y=rlnorm(10,mean=10)### Fake dataset
linmod=lm(log(y)~log(x1)+log(x2))
After the regression, I would like to know the mean of y. Since log(y) is
normal and y is lognormal, I need to know the mean and variance of
Hello,
If you just want the mean and variance of log(y) try:
mean(log(y))
var(log(y))
if there is missing data, you can add na.rm=TRUE to both of those. If
you want the mean and variance of the predicted ys
mean(predict(linmod))
var(predict(linmod))
see
?mean
?var
?predict.lm #the specific
On Mon, Jun 21, 2010 at 11:45 AM, Yi liuyi.fe...@gmail.com wrote:
Hi, Josh,
Thank you very much! It is what I want!
Because it is very obvious that the variance is not a constant in my linear
model. So I am thinking about robust standand error. Any code works for this
purpose in R?
I do not
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