search for: unobservable

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

...or 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 more unobservable constructs... say ability to work hard and ability to follow instructions. If one has empirical evidence from earlier studies which relates similar observable to similar unobservables one wants to do a confirmatory factor analysis to check if the posited relationship holds in the current data being...

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

...talized on said day, there is no observation (for which the "number of patients" item would be 0). My goal is to model this number of patients as a function of the "various conditions" described by my independant variables, mosty of them observed but uncontrolled, some of them unobservable (random effects). I am tempted to model them along the lines of : glm(NoP~X+Y+..., data=MyData, family=poisson(link=log)) or (accounting for some random effects) : lmer(NoP~X+Y....+(X|Center)), data=Mydata, family=poisson(link=log)) While the preliminary analysis suggest that (the right part of...

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 100644 --- a/copy/nbd...

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);

...possible to do what I want to do using "canned" commands and plotting routines. If not, I'd probably have to spend some time programming from scratch. The response is modeled as a function of a few dummy variables and several continuous variables. To control for time-invariant unobservable characteristics, I'm also including a "fixed effect" in the econometrics sense of the term -- an individual-specific intercept. I also want to model the continuous variables flexibly -- I have no good priors on the proper specification for the function form. The model is the fol...

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

...emoving branches and dead code paths and not including commoning and speculation Basic Scalar Optimizations - Constant propagation including SCCP and IPCP - Constant merging - Instruction Combining - Inlining: always_inline and normal inlining passes - Memory to register promotion - CSE of “unobservable” operations - Reassociation of expressions - Global optimizations - try to fold globals to constants Loop Optimizations Loop optimizations have some problems around debuggability and observability, but a suggested set of passes would include optimizations that remove abstractions and not ones t...

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

...eful if later inlined), and now we have a second reason. Regardless, because we don't actively remove @llvm.assume, I'm not convinced the current state of things is currently broken. -Hal > > This would, in turn, mean that the store in Reid's "@f" function is > an unobservable in the case that %a == %b through some dynamic > property, whatever that may be. And as a consequence, the > store-forwarding is a correct transformation. > > > -Chandler > > > > > > -Hal > > > > > -- Sanjoy > > ________________________...

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

...uard against UB exploited by the compiler. So, in that case it would become basically impossible to compile a safe language to LLVM, as far as I can see. > Two aspects that I forgot to mention in the RFC mail: > > First: In some situations, we already assume that stack overflows are > unobservable behaviours. For instance, optimizations of stack > variables cause the stack frames to be smaller, such that the stack > overflow does not occur where it would have in an unoptimized program. That's fine. Basically, the abstract machine can be specified to *non-deterministically* trigger...