jlfmssm schrieb:
Does anyone know which package include the computation of Cochran?s Q
statistic in R?
jlfmssm
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I might be wrong but the packages rmeta and meta report a Q-statistic.
For an example, see
library(meta)
meta1
metagen(meta1$TE, meta
Dear all,
I do not fully understand how ggplot2 handles NAs. See the following
example:
library(ggplot2)
x <- rnorm(150)
g <- as.factor(c(rep(c(0,1,NA),50)))
mydf <- data.frame(x,g)
m <- ggplot(aes(x = x, group = g, color = g), data = mydf)
m + geom_density()
How do I get rid of the NAs (i.
Steven Lubitz schrieb:
Hello, I'm using the rmeta package to perform a meta analysis using
summary statistics rather than raw data, and would like to analyze
the effects in three different subgroups of my data. Furthermore, I'd
like to plot this on one forest plot, with corresponding summary
weig
Dear all,
given the following data
## original data
id <- c(1,1,1,2,2,3)
author <- c("A","B","C","D","E","F")
tmp <- data.frame(id,author)
tmp
> tmp
id author
1 1 A
2 1 B
3 1 C
4 2 D
5 2 E
6 3 F
What is the best (most efficient/vectorized/avoiding loops)
Philipp Pagel schrieb:
* when then looking at str(weblog),
the "-" will stay in the levels, mentioned for the variable weblog$V8
-> BAD!
Is this snormal behaviour?
Yes, it is. The idea is that a factor has a given set of levels
independent of how often you find them in your data - inclu
John Poulsen schrieb:
Hello,
I am using glmer() from lmer(lme4) to run generalized linear mixed
models. However, I am having a problem extracting the standard errors
for the fixed effects.
I have used:
summary(model)$coef
fixed.effects(model)
coef(model)
to get out the parameter estimates
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