search for: unobserved

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 st...

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

...ry analysis suggest that (the right part of) a Poisson distribution might be reasonable for all real observations, the lack of observations with count==0 bothers me. Is there a way to cajole glm (and lmer, by the way) into modelling these data to an "incomplete Poisson" model, i. e. with unobserved "0" values ? Sincerely, Emmanuel Charpentier

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

...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, rightmost...

...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 "[un]...

...f 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 following: y_{it} = \alpha_i + \beta_1(T_{it}) + f(continuous.vars_{it}) + e_{it} To control for unobserved time-invariant heterogeneity, I want to de-mean the data as follows: y_{it}-\bar{y_i} = \beta_1(T_{it}-\bar{T_i}) + f(continuous.vars_{it}-\bar{continuous.vars_i}) + e_{it} - \bar{e}_i Fitting the demeaned model should give me coefficient estimates equal to the non de-meaned model, including c...

...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 is...

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 (a...

...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 app...

...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 -> c...

...> 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 done...

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

...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)....

----- 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