Dear all,
I am using the book "Generalized Linera Models and Extension" by Hardin and
Hilbe (second edition, 2007) at the moment. The authors suggest that
instead of OLS models, "the log link is generally used for response data
that take only positive values on the continuous scale". Of course the
Just thought about it, you can have this of course "cheaper" without the
loops. Like this:
fun1 <- function(x) sum((x-mean(x))^2)/(length(x)+1)
fun2 <- function(x) sum((x-mean(x))^2)/(length(x))
x <- rexp(15)
fun1(x)
fun2(x)
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On Exercise 5, how about this (I'm rather new to R as well, so no guarantee
this is right and I'm sure there are more efficient ways to do this!):
fun1 <- function(x) {
y <- 0
for (i in 1:length(x))
{y[i] <- (x[i]-mean(x))^2}
sum(y)/(length(x)+1)
}
fun2 <- function(x) {
y <- 0
for
Dear all,
I am new to network analysis, but since I have good data I started to read
about it and learned how to use the ergm and related packages. I generally
get interesting results, but when I run a model including sociality and
selective mixing effects for different groups, the model runs (an
Dear all,
I am trying to impute data for a range of variables in my data set, of which
unfortunately most variables have missing values, and some have quite a few.
So I set up the predictor matrix to exclude certain variables (setting the
relevant elements to zero) and then I run the imputation. T
Thanks a lot all of you!
@ Berend, your code works fine, thanks.
@ Milan, you have a point there, makes sense to create 1 instead of 10
objects!
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Thanks for the answer, and sorry if I was not clear.
So I run the data imputation using mice with 10 chains and then I get a
mids-object. From that object I can then extract 10 data sets using the
complete(imp, n) command (with n=c(1:10)).
Now I can type this out 10 times:
set1 <- complete(imp, 1
Dear all,
I have a (probably very basic) question. I am imputing data with the mice
package, using 10 chains. I can then write out the 10 final values of the
chains simply by
name1 <- complete(imp, 1)
:
:
name10 <- complete(imp,10)
Not a big deal, I ju
Thanks a lot for your help!
I found that the relation="free" command was very helpful, but then I had a
little space between the axis and where the bars actually begun. I tried to
deal with it in a panel function but was unable to do so. However, I found a
way providing a list to xlim:
barchart(
Dear all,
I have a question about the lattice package, more specifically about the
control of the x-axis length in the different panels. I use the following
code to make the stacked barchart:
barchart(country ~ climatechangefocalpoint + meteorologyservice +
adaptationorvulnerability + cdmcarbonm
Dear all,
I have a problem with my stacked bar charts. I have one very long bar, hence
I would like to break the x-axis at a certain point so that the shorter bars
can be seen better.
Here is a cooked up example:
library(lattice)
group <- rep(1:3,10)
x <- runif(30, 0, 100)
y <- runif(30, 0, 100)
't
understand why. Is there another package I need to install when using
an older version of Jags? Thanks again, Florian
On Tue, Aug 31, 2010 at 12:38 AM, Ben Bolker wrote:
> Florian Weiler johnshopkins.it> writes:
>
>> First I create some data:
>> N <- 1000
>> x
ose and reopen the program.
I don't even start drawing samples from the model, it just crashes the
moment I try to call on JAGS.
Sorry for not providing the example right away,
Florian
On Mon, Aug 30, 2010 at 9:17 AM, Florian Weiler
wrote:
> Dear all,
>
> I have a question regarding
Dear all,
I have a question regarding using JAGS and R. My problem is that every
single time I want to call JAGS from R the latter crashes (i.e. it
turns pale and the Windows tells me "R has stopped working".
Strangely, if there is a mistake in my jags code, this will be
reported without a crash.
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