You can install it via:
install.packages("Zelig", repos = "http://gking.harvard.edu")
Cheers,
Kosuke
--
Department of Politics
Princeton University
http://imai.princeton.edu
On Sat, 31 Jan 2009, David Pain wrote:
> Hi Kosuke
>
> The CRAN site has the binary for 3.3.1 - the download link on the Zelig site hompage is to the source code, and I can't see any links on that site to the 3.4.1 binary.
>
> Could you point me in the right direction, please?
>
> Regards
>
> David Pain
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Dear All,
I am trying to estimate a model using Zelig on matched data (using MatchIt).
Some of the variables have large amount of missing values. In this
situation, what will be the ideal way to proceed? Imputing first then
matching and then modeling?
I would appreciate any suggestions and examples. Many thanks.
Best,
Shige
Hi everyone,
Zelig version 3.4-1 is online (see http://gking.harvard.edu/zelig)
Bugs fixed:
- Fixed predicted values in gam.* models
- Fixed plot functions in gam.* model
- Fixed zelig() signature to ensure that the formals() work properly and all
arguments remain documented. "save.data" and "cite" were not documented. (Thanks to Micah Altman)
- Changed the Zelig citation
As usually, to update/install this release:
- *nix / windows users:
install.packages("Zelig", repos="http://gking.harvard.edu" <http://gking.harvard.edu%22>)
- mac users:
install.packages("Zelig", repos="http://gking.harvard.edu"
<http://gking.harvard.edu%22>, type="source")
Please let us know if you run into problems.
thanks,
Ferdi
Happy new year Zelig people! :)
I am trying to use Zelig to simulate from a logit.gam model, however I
find that it gives me only expected values of 1, which is not correct
I think. The fitted model seems ok, but setx/sim somehow messes up the
scaling of the x variable. An example
x <- rnorm(1000)
y <- (x+rnorm(1000)) > 0
mydata <- data.frame(x=x,y=y)
z.out <- zelig(y~s(x),data=mydata,model="logit.gam")
x.out <- setx(z.out)
s.out <- sim(z.out,x.out)
summary(s.out)
#this is all nice what you would expect. Now let's rescale x.
mydata$x <- mydata$x + 10
z.out <- zelig(y~s(x),data=mydata,model="logit.gam")
x.out <- setx(z.out)
s.out <- sim(z.out,x.out)
summary(s.out)
this second model simulates only expected values around 1, although it
should be the same model as the first one, but with higher x values,
so if no x is specified in setx(), I would expect the same expected
values. Why does this happen?
I am using R 2.8.0, Zelig 3.4.0 and mgcv_1.4-1.1
Jeroen
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