search for: unobservant

Hello, I'm interested in correcting for and measuring unobserved heterogeneity ("missing variables") using R. In particular, I'm searching for a simple way to measure the amount of unobserved heterogeneity remaining in a series of increasingly complex models (adding additional variables to each new model) on the same data. I have a static database of 400,000 or

Hi Has anyone used R to conduct confirmatory factor analysis? This email pertains to use of SEM. For context consider an example: the basic idea is that there are a bunch of observables variables (say study habbits, amount of time reading in the bus, doing homework, helping other do homework, doing follow-up on errors etc.) and one believes that all these variables maybe measured by two or

Hi Can anyone please suggest me a package where I can estimate a non-linear regression model? One of the independent variables is latent or unobserved. I have an indicator variable for this unobserved variable; however the relationship is known to be non-linear also. In terms of equations my problem is y=f(latent, fixed) q=g(latent) where q is the indicator variable For me both f and g are

Dear list, I have the following problem : I want to model a series of observations of a given hospital activity on various days under various conditions. among my "outcomes" (dependent variables) is the number of patients for which a certain procedure is done. The problem is that, when no relevant patient is hospitalized on said day, there is no observation (for which the "number

Wrap "copy/nbd-ops.c" at 80 characters. I couldn't find a way to test that this change is unobservable. Bugzilla: https://bugzilla.redhat.com/show_bug.cgi?id=2172516 Signed-off-by: Laszlo Ersek <lersek at redhat.com> --- copy/nbd-ops.c | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/copy/nbd-ops.c b/copy/nbd-ops.c index d3e50864125f..843b7c1746e3

Dear list, this is not a particular R question but perhaps someone can help. I am running a maximum likelihood estimation (competing risk duration model with unobserved heterogeneity) on 30 different datasets. The problem is that on 2 datasets the model does not converge. I am interested if there are any methods, based on the gradients or (an approximation of) the hessian which helps to

I am getting started with using the depmixS4 package. First, I would like to see I am very impressed with its speed and flexibility. The question I have is regarding predicting on new data. I want to fit the model on some sequences with observed responses, and then make predictions on the right end of the sequences where the responses are not observed. I see no prediction functionality anywhere,

Dear R-Developers and -Community, after compiling and installing R 1.2.2., I started 'demo(graphics)' for a test, and got: 'Error in rnorm(0): .Random.seed(0) is not a Normal type' Obviously .Random.seed was not initialized appropriately. Following the the documentation for '.Random.seed' [see below 'Examples'], this can be fixed rm(.Random.seed);

Dear List, I'm just teaching myself semi-parametric techniques. Apologies in advance for the long post. I've got observational data and a longitudinal, semi-parametric model that I want to fit in GAM (or potentially something equivalent), and I'm not sure how to do it. I'm posting this to ask whether it is possible to do what I want to do using "canned" commands

Dear all, My question might be more of a statistics question than a question on R, although it's on how to apply the 'quantreg' package. Please accept my apologies if you believe I am strongly misusing this list. To be very brief, the problem is that I have data on only a random draw, not all of doctors' patients. I am interested in the, say, median number of patients of

dear all, I am a new comer to R and statistic. Now I have a little confuse about the package pls. I have to use 5 components to form a model. There are strong relationship between some of the components, which leads to the changes of the sign of each coeficeince, of course this is unwanted when using the normal regression way. So I choose the way of PLS, which is good at solve this kind of

Hello. I started using the sem package in R and after a lot of searching and trying things I am still having difficulty. I get the following error message when I use the sem() function: Warning message: In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, : Could not compute QR decomposition of Hessian. Optimization probably did not converge. I started with a

On Wed, 2008-04-30 at 18:26 -0400, David Vandevoorde wrote: > > Daveed: > > > > Good to know that I was looking at the correct section. I do not agree > > that your interpretation follows the as-if rule, because I do not > > agree > > with your interpretation of the C library specification of malloc(). > > > Before I go on, let me state that this is

Hi, (1.) How is it possible to get automatic a "lower triangle of correlation matrix" ? h.cor <- cor(dat,use="pairwise.complete.obs") zz <- lower.tri(h.cor,diag=T) ### that's not what i wish and "wrong" ? results <- matrix(unlist(h.cor[upper.tri(h.cor,diag=T)])) results <- matrix(unlist(h.cor[upper.tri(h.cor,diag=T)]),5) Must i take the lowest

On Apr 30, 2008, at 9:35 PM, Jonathan S. Shapiro wrote: > On Wed, 2008-04-30 at 18:26 -0400, David Vandevoorde wrote: >>> Daveed: >>> >>> Good to know that I was looking at the correct section. I do not >>> agree >>> that your interpretation follows the as-if rule, because I do not >>> agree >>> with your interpretation of the C

Hi All, I’ve been thinking about both O1 and Og optimization levels and have a proposal for an improved O1 that I think overlaps in functionality with our desires for Og. The design goal is to rewrite the O1 optimization and code generation pipeline to include the set of optimizations that minimizes build and test time while retaining our ability to debug. This isn’t to minimize efforts around

This question pertains to setting up a model in the package "dlm" (dynamic linear models, http://cran.r-project.org/web/packages/dlm/index.html I have read both the vignette and?"An R Package for Dynamic Linear Models" (http://www.jstatsoft.org/v36/i12/paper), both of which are very helpful. There is also some discussion at

----- Original Message ----- > From: "Chandler Carruth" <chandlerc at google.com> > To: "Hal Finkel" <hfinkel at anl.gov>, "Sanjoy Das" <sanjoy at playingwithpointers.com> > Cc: "cfe-dev at cs.uiuc.edu Developers" <cfe-dev at cs.uiuc.edu>, "LLVM Developers Mailing List" <llvmdev at cs.uiuc.edu> > Sent:

During the review of https://reviews.llvm.org/D59978 we got to the question whether we can optimize based on the assumption that stack overflow is undefined behavior. While I think it is according to the C++ standard, Windows Structured Exception Handling and signal handlers might allow programs to recover/exit gracefully. Concretely, the patch D59978 wants add the noreturn attribute to functions

Hi Michael, Please keep in mind non-C/C++ frontends. For example, in Rust, we promise to avoid all undefined behavior in safe code. There is no reasonable compositional analysis that can statically detect stack overflows (I know safety-critical systems are subjected such analyses, but those could not reasonably be enforced on all Rust code -- most of them just forbid recursion, for example), so