search for: fiml

Does anyone know if someone is developing full-information maximum likelihood (FIML) estimation algorithms for basic regression functions, like glm()? I think that having FIML options for workhorse functions that typically use ML would give R an edge over other statistical software, given how well FIML performs in missing data situations compared to ML. While my current level of...

Hi I was asked if lme can use FIML (Full Information Maximum Likelihood) instead of REML or ML but I don't know the answer. Does anybody know if this is implemented in R? Thanks Francisco

Dear R users/developers I requested help to solve the problem of formulating Multivariate Sample selection model by using Full Information Maximum Likelihood (FIML)estimation method. I could not get any response. I formulated the following code of FIML to analyse univariate sample selection problem. Would you please advise me where is my problem library (sem) library(nrmlepln) Selection equation ws = c(w1, w2, w3) # values of dependent variables in selecti...

Is there a way to use full information maximum likelihood (FIML) to estimate missing data in the sem package? For example, suppose I have a dataset with complete information on X1-X3, but missing data (MAR) on X4. Is there a way to use FIML in this case? I know lavaan and openmx allow you to do it, but I couldn't find anything in the documentation for the s...

Is there an equivalent to MPlus's Full Information Maximum Likelihood (FIML) missing data estimator for R? If so, is there a way to take covariance structures produced by such a package and perform multiple regression with these? If you are unfamiliar with Mplus' FIML below is a link to their manual. Their estimation technology is discussed on page 25. I have asked th...

...noretired + retired + ds + ministrytime + hrswork + nomoralescore.c + negint.c + cong.conflict.c + nomoraleXjoin + nomoraleXleave + nomoraleXalways + negintXjoin + negintXleave + negintXalways + conflictXjoin + conflictXleave + conflictXalways ' mod1 = sem(pathmod, data=sampledat, missing="fiml", se="robust") At the time, the model ran fine. Now, using version 0.4-14, the model returns all 0's for coefficients. This does not happen, however, when I run the model using listwise deletion for missing data. Any idea what is happening, or how I can fix it? For those wis...

Dear R experts/ users Full Information Maximum Likelihood (FIML) estimation approach is considered robust over Seemingly Unrelated Regression (SUR) approach for analysing data of multivariate sample selection problem. The zero cases in my dependent variables are resulted from three sources: Irreverent options, not choosing due to negative utility and not used i...

...but I would prefer to use the sem package, because OpenMx does not work on 64 bit versions of R for Windows x64 and is not available from CRAN presently. Obviously it is not difficult to write out the model, but I am hoping to bundle this in a function that for some arbitrary data, will return the FIML estimated covariance (and correlation matrix). Alternately, if there are any functions/packages that just return FIML estimates of a covariance matrix from raw data, that would be great (but googling and using findFn() from the sos package did not turn up good results). Thanks! Josh -- Joshua...

Dear all, I am trying to estimate simultaneous equation model concerning growth in russian regions. I run the analysis by means of FIML in R sem package. I am not familiar with SEM yet, but I've just got several suitable estimated specifications. Nevertheless, sometimes R gives the following warning message: Warning message: Negative parameter variances. Model is probably underidentified. in: sem.default(ram = ram, S = S, N =...

Hi,I have a fundamental questions that I'm a bit confused. If any guru from this circle could help me out, I would really appreciate.I have a system of equations in which some of the endogs appear on right hand sides of some equations. To solve this, one needs a technique like 2SLS or FIML to circumvent inconsistency of the estimated coefficients. My question is that if I apply the nonlinear regression like SVM regression. Do I still need to worry about endogeneity? Meaning, what I only need to care is the 1st step of 2SLS. That would mean that I only need to carry out the SVM regres...

Sorry if this question has been asked previously, I searched but found little. There also doesn't seem to be a dedicated SEM list-serv so hopefully this will find its way to the appropriate audience. In discussing SEM with a colleague I mentioned that a model they were fitting in AMOS was equivalent to a linear regression and that the coefficients would be the same. This of course was the

...ong as they are independent. But what if the models were to be written: Y1.plr <- polr(Y1 ~ Y2 + X1 + X2) Y2.plr <- polr(Y2 ~ Y1 + X3 + X4) Are estimates of the coefficients for Y1 and Y2 biased, as they would be in a linear model? I think yes. Do I need some equivalent of 2SLS or FIML? It is not entirely clear to me if, in this example, the input Y1 or Y2 is conceptualized as the 5 point scale or rather if it is thought of as a continuous variable which is observed with error. Is there an email list besides r-help where I should be asking questions like this? I understand i...

...ct, elegant and useR-friendly way - lavaan is robust and reliable: there are no convergence problems and numerical results are very close (if not identical) to the commercial package Mplus - many different estimators are available: ML, GLS, WLS, robust ML using Satorra-Bentler corrections, and FIML for data with missing values - full support for meanstructures and multiple groups - user friendly output including standardized solutions, fit measures, modification indices and more To get a first impression of how the 'lavaan model syntax' looks like, below is the full R code for fi...

...ged. Since the challenge to convergence (many levels of wk and ID/fam/pop) was the same as it had been before, I went back and tried model.A on the other analysis, and it also ran. I then started checking results for everything I'd done in the past three weeks in packages that use ML methods (FIML, REML)--and got different outcomes. I've quadruple-checked to be sure I'm using the same code and the same data (I use .csv files for simplicity), and see no differences. However, results from nlme and sem packages are both different. I had not saved detailed output, but had recorded par...

...ct, elegant and useR-friendly way - lavaan is robust and reliable: there are no convergence problems and numerical results are very close (if not identical) to the commercial package Mplus - many different estimators are available: ML, GLS, WLS, robust ML using Satorra-Bentler corrections, and FIML for data with missing values - full support for meanstructures and multiple groups - user friendly output including standardized solutions, fit measures, modification indices and more To get a first impression of how the 'lavaan model syntax' looks like, below is the full R code for fi...

...or multinormal full-information maximum likelihood and for generalized least squares; and three optimizers are provided, based on the standard R nlm(), optim(), and nlminb() optimizers. The user can add objective functions and optimizers. o Analytic standard errors are provided by default for the FIML estimator (standard errors based on the numeric Hessian are now optional), and robust standard errors and tests are optionally available. o The ability to fit a model to a data frame, as a preferred alternative to computing a covariance or moment matrix in an intermediate step. The original data...

...or multinormal full-information maximum likelihood and for generalized least squares; and three optimizers are provided, based on the standard R nlm(), optim(), and nlminb() optimizers. The user can add objective functions and optimizers. o Analytic standard errors are provided by default for the FIML estimator (standard errors based on the numeric Hessian are now optional), and robust standard errors and tests are optionally available. o The ability to fit a model to a data frame, as a preferred alternative to computing a covariance or moment matrix in an intermediate step. The original data...

Hi all, I'm trying to estimate model parameters in R for a pretty simple system of equations, but I'm having trouble. Here is the system of equations (all derivatives): eqAlgae <- (u_Amax * C_A) * (1 - (Q_Amin / Q_A)) eqQuota <- (p_max * R_V) / (K_p + R_V) - ((Q_A-Q_Amin)*u_Amax) eqResource <- -C_A * (p_max * R_V) / (K_p + R_V) eqSystem <- list(C_A = eqAlgae, Q_A = eqQuota,

Hello- I am using R to build my initial models, but need to use AMOS to compare the models of two groups (adults vs. kids). The problem is I am getting different results with R and AMOS for the initial models of the separate groups (and the R results make more sense). The parameter estimates (path coefficients and variances) from both programs are nearly identical, but the model chi-squares

...ider GRP growth, migration and investment to be endogenous and all others vars to be exogenous to the model. The data are over 77 regions (n=77) and 8 years (t = 1997..2004), so that I have total 616 observations. Actually, the final goal of the study is to estimate a model 1) simultaneously by FIML 2) with Mundlak (1978, 1981) specification of panels to capture for fixed and between effects 3) with spatial lag on GRP growth. As for spatial lag - the model needs to be estimated by maximum likelihood (as it is shown in Anselin 1988, for example) The main problems with Mundlak are increasing nu...