Seriously?
Did you not receive the reply to the same question from Uwe Ligges at 12:31pm
today?
You are overfishing the common pool, bro.
2009/6/19 Uwe Ligges :
Most of the times it is advisable to get a good book about the statistical
concepts (multivariate statistics or data-mining) and anoth
Seriously?
Did you not receive the reply to the same question from Uwe Ligges at 12:31pm
today?
You are overfishing the common pool, bro.
2009/6/19 Uwe Ligges :
Most of the times it is advisable to get a good book about the statistical
concepts (multivariate statistics or data-mining) and anoth
Seriously?
Did you not receive the reply to the same question from Uwe Ligges at 12:31pm
today?
You are overfishing the common pool, bro.
2009/6/19 Uwe Ligges :
Most of the times it is advisable to get a good book about the statistical
concepts (multivariate statistics or data-mining) and anoth
Might as well answer myself in case anyone has this problem again...
To save a variance-covariance matrix from Stata as a CSV file that can be read
into R, it's something like:
regress mpg weight foreign
matrix V=e(V)
svmat V,names(vvector)
outsheet vvector* using vv1.csv, replace
> -Origi
Before you flame me, the reason I am using Stata is that I didn't get a
response to my query below, so I have my cluster robust covariance matrix in
Stata [one line of code], but now I need to take all those parameter estimates
and put them back in R so I can simulate properly.
Anyone done this
If I wanted to fit a logit model and account for clustering of observations, I
would do something like:
library(Design)
f <- lrm(Y1 ~ X1 + X2, x=TRUE, y=TRUE, data=d)
g <- robcov(f, d$st.year)
What would I do if I wanted to do the same thing with a probit model?
?robcov says the input model mus
Dear list,
I am trying to replicate some Stata results but having a tough time doing it in
R. The goal is to obtain a difference-in-difference estimate in a model with
simple state fixed effects. The "state" variable is a factor, but some levels
are missing. It appears that Stata automaticall
Can someone please show me how to smooth time series data that I have in the
form of a zoo object?
I have a monthly economies series and all I really need is to see a less jagged
line when I plot it.
If I do something like
s <- smooth.spline(d.zoo$Y, spar = 0.2)
plot(predict(s,index(d.zoo)),
t;- paste("d$density", st, sep=".") # easier than mapply etc.
>
> more importantly, in the for loop you should not be incrementing i
> manually (as in a while loop), it's already taken care of by the for{}
> construct.
>
>
>
> HTH,
>
> baptiste
I have a data frame containing monthly observations of the 'density' of each US
state, recorded in variables named "density.AL", "density.AK", "density.AZ",
and so on for all 50 states. The data frame (called d) also contains a variable
called "Date" which is encoded as a string in the format "J
Not 100% sure what you are looking for, but have a look at the Generalized
Event Count model in the Zelig package. It will also let you fit a Poisson and
other event count models by MLE.
> -Original Message-
> From: azam...@isrt.ac.bd
> Sent: Tue, 24 Mar 2009 23:26:59 +0600
> To: r-help
What's the neat way to create a dummy from a list?
The code below is not replicable, but hopefully self-explanatory...
d$treatment<-rep(1,length(d))
notreat<-c("AR", "DE", "MS", "NY", "TN", "AK", "LA", "MD", "NC", "OK", "UT",
"VA")
#i would really like this to work:
d$treatment[d$st==any(notre
Sure, you can do that. Check out wireframe() in the lattice library.
> -Original Message-
> From: fwa...@gmail.com
> Sent: Wed, 11 Mar 2009 20:35:22 -0700 (PDT)
> To: r-help@r-project.org
> Subject: [R] can I draw 3D plot like this using R?
>
> hi, all
>
> I am looking at R package RGL
Dear list,
I have an irregular time series saved and exported as a zoo object. What is the
trick to force zoo to ignore the missing dates when reading it back in? Thanks.
> str(g)
‘zoo’ series from 1948-11-02 to 2012-11-06
Data: num [1:14881, 1:8] 1 0 0 0 0 0 0 0 0 0 ...
- attr(*, "dimnames")
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