Displaying 20 results from an estimated 800 matches similar to: "New Package for Multivariate and Propensity Score Matching"
2004 Feb 20
0
New Package: multinomRob
We would like to announce the availability on CRAN of a new package
multinomRob. It does robust estimation of overdispersed multinomial
regression models. The package is also able to estimate overdispersed
grouped multinomial logistic and multivariate-t logistic models. The
code is relatively general; for example, it allows for equality
constraints across parameters and it can handle datasets in
2004 Feb 20
0
New Package: multinomRob
We would like to announce the availability on CRAN of a new package
multinomRob. It does robust estimation of overdispersed multinomial
regression models. The package is also able to estimate overdispersed
grouped multinomial logistic and multivariate-t logistic models. The
code is relatively general; for example, it allows for equality
constraints across parameters and it can handle datasets in
2008 Sep 18
5
propensity score adjustment using R
Hi all,
i am looking to built a simple example of a very basic propensity
score adjustment, just using the estimated propensity scores as
inverse probability weights (respectively 1-estimated weights for the
non-treated). As far as i understood, MLE predictions of a logit model
can directly be used as to estimates of the propensity score.
I already considered the twang package and the
2012 Jun 05
0
propensity score matching estimates?
I'm using the "Match" package to do propensity score matching. Here's some
example code that shows the problem that I'm having (much of this code is
taken from the Match package documentation):
*data(lalonde)
glm1 <- glm(treat~age + I(age^2) + educ + I(educ^2) + black +
hisp + married + nodegr + re74 + I(re74^2) + re75 + I(re75^2)
+
u74 + u75,
2006 Sep 18
0
Propensity score modeling using machine learning methods. WAS: RE: LARS for generalized linear models
There may be benefits to having a machine learning method that
explicitly targets covariate balance. We have experimented with
optimizing the weights directly to obtain the best covariate balance,
but got some strange solutions for simple cases that made us wary of
such methods.
Machine learning methods that yield calibrated probability estimates
should do well (e.g. those that optimize the
2005 Apr 05
1
exclusion rules for propensity score matchng (pattern rec)
Dear R-list,
i have 6 different sets of samples. Each sample has about 5000 observations,
with each observation comprised of 150 baseline covariates (X), 125 of which
are dichotomous. Roughly 20% of the observations in each sample are "treatment"
and the rest are "control" units.
i am doing propensity score matching, i have already estimated propensity
scores(predicted
2008 Nov 09
1
[OT] propensity score implementation
Dear All,
My question is more a statistical question than a R question. The reason I
am posting here is that there are lots of excellent statistician on this
list, who can always give me good advices.
Per my understanding, the purpose of propensity score is to reduce the bias
while estimating the treatment effect and its implementation is a 2-stage
model.
1) First of all, if we assume that T =
2011 Feb 03
1
rgenoud for multiple chips: does a more recent special version of "snow" exist?
Dear everyone,
I am trying to run rgenoud on several chips simultaneusly. I used the
instructions provided on Jasjeet Sekhon's Homepage
(http://sekhon.berkeley.edu/rgenoud/multiple_cpus.html).
However, I have the newer version of R (R 2.12) installed - for a
64-bit machine. So, when I tried to install the special version of
"snow" from a zip file provided by Jasjeet on his page, R
2006 Sep 22
1
Propensity score and three treatments
Dear All,
I would like to find something ( references, code,..) to implement a
comparison of three
treatments in an observational study using the 'Propensity Score'.
Any help is much appreciated. Thanks!
Giovanni
--
dr. Giovanni Parrinello
Department of Biotecnologies
Medical Statistics Unit
University of Brescia
Viale Europa, 11 25123 Brescia
email: parrinel at med.unibs.it
Phone:
2013 Sep 25
0
error when using ps() function on categorical variables - re propensity score matching
Dear List,
I am having difficulty running the ps() function when variables are stored
as factors and was hoping someone could provide some advice on how to
proceed.
I am running propensity score matching as outlined in:
Greg Ridgeway, Dan McCarey, Andrew Morral, Lane Burgette and Beth Ann
Grin (May 3, 2013) Toolkit for Weighting and Analysis of Nonequivalent
Groups: A tutorial for the twang
2008 Aug 25
0
selection bias adjustment via propensity score
Hi all,
i am wondering if there?s any other method to adjust for selection
related bias of estimates except propensity scoring and heckit / mills
ratio approach? i also read documentation of Match and twang package
so far, so i don?t speak of any ATE / ATT related methods,
respectively any methods that match or stratify... Is there something
else ?
thx in advance
2005 Mar 02
1
Rounding parameter values in genoud(), Rgenoud package
I would like to limit the significant figures of the calibrated
parameters determined by genoud() in the Rgenoud package. Below is some
example output, where column 1 is model run number, columns 2-7 are the
parameter values, and columns 8-12 are model fit statistics. I would
like genoud to internally limit parameters to 4 decimal places as shown
in this output. It is clear that the function is
2011 Mar 25
1
Matching package - Match function
Hi.
I am using the Matching package for propensity score matching. For each
treated unit, I want to find all control units whose propensity scores lie
within a certain distance from the treated unit. The sample code is as
follows:
> library(Matching)
> x <- rnorm(100000)
> y <- rnorm(100000)
> z <- rbinom(100000,1,0.002)
> logit.reg <-
2020 Nov 10
0
Help propensity score
Hola chic en s, alguien con experiencia en propensión score matching?
Planteo duda: Clasicamente el PSM se ha utilizado en un intento de homogeneizar cohortes de enfermos quienes han estado ?expuestos? a un tratamiento x Vs aquellos que no han estado expuestos (no expuestos). Esto aplica para medicamentos o procedimientos quirúrgicos o no.
Bien, En algún articulo he leído que el PSM se puede
2003 Jun 03
1
Logistic regression problem: propensity score matching
Hello all.
I am doing one part of an evaluation of a mandatory welfare-to-work
programme in the UK.
As with all evaluations, the problem is to determine what would have
happened if the initiative had not taken place.
In our case, we have a number of pilot areas and no possibility of
random assignment.
Therefore we have been given control areas.
My problem is to select for survey individuals in
2020 Nov 12
1
Propensity Score Matching
Hola chic en s, alguien con experiencia en propensión score matching?
Planteo duda: Clasicamente el PSM se ha utilizado en un intento de homogeneizar cohortes de enfermos quienes han estado ?expuestos? a un tratamiento x Vs aquellos que no han estado expuestos (no expuestos). Esto aplica para medicamentos o procedimientos quirúrgicos o no.
Bien, En algún articulo he leído que el PSM se puede
2003 Aug 26
4
R on Linux/Opteron?
Dear R-help:
Has anyone tried using R on the the AMD Opteron in either 64- or 32-bit
mode? If so, any good/bad experiences, comments, etc? We are considering
getting this hardware, and would like to know if R can run smoothly on such
a beast. Any comment much appreciated.
Best,
Andy
Andy Liaw, PhD
Biometrics Research PO Box 2000, RY33-300
Merck Research Labs Rahway, NJ
2006 Mar 13
1
Parallel computing with the snow package: external file I/O possible?
Hello,
I am trying to do model autocalibration using the snow and rgenoud
packages. The function I want to run in task-parallel fashion across
multiple machines is one that pre- and post-processes data and runs an
external model code. My problem is that external file I/O is happening
only in the master node and not in the slaves. I have followed Jasjeet
Sekhon's suggestion to test the
2007 May 09
3
Increasing precision of rgenoud solutions
Dear All
I am using rgenoud to solve the following maximization problem:
myfunc <- function(x) {
x1 <- x[1]
x2 <- x[2]
if (x1^2+x2^2 > 1)
return(-9999999)
else x1+x2
}
genoud(myfunc, nvars=2,
Domains=rbind(c(0,1),c(0,1)),max=TRUE,boundary.enforcement=2,solution.tolerance=0.000001)
How can one increase the precision of the solution
$par
[1] 0.7072442 0.7069694
?
I
2012 Dec 08
1
imputation in mice
Hello! If I understand this listserve correctly, I can email this address
to get help when I am struggling with code. If this is inaccurate, please
let me know, and I will unsubscribe.
I have been struggling with the same error message for a while, and I can't
seem to get past it.
Here is the issue:
I am using a data set that uses -1:-9 to indicate various kinds of missing
data. I changed