search for: unobserv

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...

...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 b...

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 non-linear. Thanks Samiul Hasan -- View this...

...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 par...

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...

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 determine what is the problem. Can anybody recommend a good textbook abou...

...served. I see no prediction functionality anywhere, and am not sure what the best way to formulate something like is with the package without reinventing the wheel. I once i have a fitted model, i would like to apply it to sequences where the response variables on the right end of the sequence are unobserved, and get the prediction for those (conditioned on observed covariates for the response) using the filtering or smoothing distributions. I could ultimately pull out the relevant parameters of the conditional distribution of the response in each hidden state, the transition probabilities, rightmo...

...ed rm(.Random.seed); runif(1); .Random.seed and all seems to work nicely. But two questions remain: - was this my fault, is it intended, or are corrections inside R necessary ? - are there any other objects in R, which unintentionally are not initialized at start? (This may lead to unobserved errors.) Thank you for a great System !! Best wishes - Hartmut Oldenbürger -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[u...

...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...

...s in the data where s_i is a draw from this binomial distribution. That is, the problem with the data is that I don't observe r_i but s_i. Simple montecarlo experiments confirm my intuition that standard errors should be larger when using the "noisy" information s_i/p instead of (the unobserved) r_i. My guess is that I can consistently estimate any quantile of the number of doctors' patients AND THEIR STANDARD ERRORS using the quantreg's rq command: rq(I(s/p)~1, ...) and the summary.rq() command with option se="nid". Am I correct? I am greatful for any help on this...

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

...ght forward. I uploaded my specify.model script and my data covariance matrix here too so I wouldn't clutter this email with the entire model (20 observed variables, 5 factors). Could this error message be from the data itself and not from my path model? I have my observed variables X and my unobserved variables F. I have ONLY exogenous latent variables (i.e. they never appear on the right side of the single head arrow ->). I include all possible factor covariances FjFk, and the only constraints I've made was to restrict the Factor variances to 1. My model follows in this basic format...

...correct in this case. Let me see ask some further questions just to confirm. Let me attempt to replay what I believe is happening here: 1. There is an observably linear chain of calls to malloc() in the example, and because of the structure of the example, the compiler can see that there are no *unobserved* calls to malloc() in the program. 2. After all inlining of malloc() has proceeded, the compiler is free to observe that all calls succeed by partial evaluation (thus the non-zero return value) and that after this is done there is no witness to the internal heap limit variable. 3. Progressive a...

...e lowest Frequency for the n value in SEM,because n is different if i have NA's in data and use "pairwise.complete.obs" ? (2.) I'm little confused about the notation for the parameters compcar and personcar are the independent latent variable mesurementModel satisfaction is the unobserved dependend var. what is measure with loyalty and i assume here is no error and fix it by 1 . thanks for advance christian h.semModel <- matrix(c( + 'nemploy -> compcar','lamx11', NA, + 'sales ->...

...> some further questions just to confirm. > > > Let me attempt to replay what I believe is happening here: > > 1. There is an observably linear chain of calls to malloc() in the > example, and because of the structure of the example, the compiler can > see that there are no *unobserved* calls to malloc() in the program. (You meant "no *observed*", right?) > 2. After all inlining of malloc() has proceeded, the compiler is > free to > observe that all calls succeed by partial evaluation (thus the non- > zero > return value) and that after this is don...

...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 on...

...b*(U[t] - UN[t]) + e[t] (see?http://chart.apis.google.com/chart?cht=tx&chl=%5Cpi_t=a%5Cpi_{t-1}%2bb%28U_t-U^N_{t}%29%2Be_t for a pretty version) with pi and U?observed, a and b fixed coefficients, and e a well-behaved error term (gaussian, say, variance unknown). The object of interest is the unobserved?and time-varying component?UN which evolves according to UN[t] = UN[t-1] + w[t] (see http://chart.apis.google.com/chart?cht=tx&chl=U%5EN_%7Bt%7D%20=%20U%5EN_%7Bt-1%7D%20%2B%20%5Cepsilon_t for a pretty version) that is, a random walk with well-behaved error term?with?var(w)?known (or assumed...

...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* tri...