search for: estimands

Dear R-Users, I have a problem on extracting T-Stat and P-Value. I have written R-code below library("Matching") data("lalonde") attach(lalonde) names(lalonde) Y <- lalonde$re78 Tr <- lalonde$treat glm1 <- glm(Tr~age+educ+black+hisp+married+nodegr+re74+re75,family=binomial,data=lalonde) pscore.predicted <- predict(glm1) rr1 <-

In the next version of the survival package I intend to make a non-upwardly compatable change to the survfit object.? With over 600 dependent packages this is not something to take lightly, and I am currently undecided about the best way to go about it.? I'm looking for advice. The change: 20+ years ago I had decided not to include the initial x=0,y=1 data point in the survfit object

> On Jun 1, 2019, at 12:59 PM, Peter Langfelder <peter.langfelder at gmail.com> wrote: > > On Sat, Jun 1, 2019 at 3:22 AM Therneau, Terry M., Ph.D. via R-devel > <r-devel at r-project.org> wrote: >> >> In the next version of the survival package I intend to make a non-upwardly compatable >> change to the survfit object. With over 600 dependent packages

...of univariate and multivariate tests to determine if balance has been obtained by the matching procedure. These tests can also be used to determine if an experiment or quasi-experiment is balanced on baseline covariates. The functions provide valid standard errors and allow one to estimate various estimands. For documentation and further details see: http://jsekhon.fas.harvard.edu/matching Cheers, Jas. ====================================== Jasjeet S. Sekhon Associate Professor Harvard University Center for Basic Research in the Social Sciences jasjeet_sekhon at harvard.edu http://jsekhon.fas.h...

Dear all, I am trying to program an estimator which maximizes a likelihood type objective function which is basically just lots of sums of indicator functions of data and parameters. In order to make the optimization I would like to smooth these functions. Since they are either 0 or 1, one possibility is to use the normal cdf. I am wondering whether anyone is aware of a less arbitrary choice

...of univariate and multivariate tests to determine if balance has been obtained by the matching procedure. These tests can also be used to determine if an experiment or quasi-experiment is balanced on baseline covariates. The functions provide valid standard errors and allow one to estimate various estimands. For documentation and further details see: http://jsekhon.fas.harvard.edu/matching Cheers, Jas. ====================================== Jasjeet S. Sekhon Associate Professor Harvard University Center for Basic Research in the Social Sciences jasjeet_sekhon at harvard.edu http://jsekhon.fas.h...

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,

On Sat, Jun 1, 2019 at 3:22 AM Therneau, Terry M., Ph.D. via R-devel <r-devel at r-project.org> wrote: > > In the next version of the survival package I intend to make a non-upwardly compatable > change to the survfit object. With over 600 dependent packages this is not something to > take lightly, and I am currently undecided about the best way to go about it. I'm looking

On 6/1/19 1:32 PM, Marc Schwartz wrote: > >> On Jun 1, 2019, at 12:59 PM, Peter Langfelder <peter.langfelder at gmail.com> wrote: >> >> On Sat, Jun 1, 2019 at 3:22 AM Therneau, Terry M., Ph.D. via R-devel >> <r-devel at r-project.org> wrote: >>> In the next version of the survival package I intend to make a non-upwardly compatable >>> change

...etailed case study is in http://biostat.mc.vanderbilt.edu/CourseBios330 Chapter 15. Ordinal regression is now a direct competitor to linear models, and far more robust, even allowing spikes in the distribution of Y. * More generality and ease of obtaining bootstrap confidence limits for all estimands (predictions, contrasts). The basic bootstrap is now implemented and tends to work better than the percentile bootstrap in terms of confidence coverage. * Change of Surv to Srv * Added tk/tcl progress bars for bootstrap and other repeated calculations. This can be turned off by specify...