Hi list,
can I extract the cov.unscaled (the unscaled covariance matrix) from a
gls fit (package nlme), like with summary.lm? Background: In a fixed
effect meta analysis regression the standard errors of the coefficients
can be computed as sqrt(diag(cov.unscaled)) where cov.unscaled is
(X'WX). I
, Sven Garbade [EMAIL PROTECTED] wrote:
Dear list members,
I have problems to interpret the coefficients from a lm model
involving
the interaction of a numeric and factor variable compared to separate
lm
models for each level of the factor variable.
## data:
y1 - rnorm(20
Dear list members,
I have problems to interpret the coefficients from a lm model involving
the interaction of a numeric and factor variable compared to separate lm
models for each level of the factor variable.
## data:
y1 - rnorm(20) + 6.8
y2 - rnorm(20) + (1:20*1.7 + 1)
y3 - rnorm(20) +
Hi all,
how can I change the background color in lattice strips according to a
factor level, eg:
library(lattice)
x - rnorm(100)
y - sqrt(x)
f - gl(2, 50, c(A, B))
xyplot(y ~ x | f)
I like to change the background color of the strips according to the
levels in f and tried several things like
Hi,
I'm confused about how to specify random and fixed factors in an aov()
term. I tried to reproduce a textbook example: One fixed factor (Game, 4
levels) and one random factor (Store, 12 levels), response is Points.
The random factor Store is nested in Game. I tried
str(kh.df)
`data.frame':
Hi all,
is it posible to alter the length of the lines in the legend() function?
I think they are a little bit to short, so I changed the default value
of seg.len from 2 to 6. But maybe it would be nice to have an argument,
so users can change the default computed line length as they like.
Hi all,
suppose I've got a vector y with some data (from a repeated measure
design) observed given the conditions in f1 and f2. I've got a model
with two unknown fix constants a and b which tries to predict y with
respect to the values in f1 and f2. Here is an exsample
# data
y - c(runif(10,
