similar to: Full Information Maximum Likelihood estimation method for multivariate sample selection problem

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

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 the developer of the

Hi, I am new to R for solving optimization problems, I have set of communication channels with limited capacity with two types of costs, fixed and variable cost. Each channel has expected gain for a single communication. I want to determine optimal number of communications for each channel maximizing ROI)return on investment) with overall budget as constraint.60000 is the budget allocated.

Hi everone, I have a question on using lme on a mixed effects model. The linear mixed model is in the form of: y = bX +Zu + e where "X" and "Z" are the matrices, "b" is the coefficient vector of fixed effects, "u" is the coefficient vector of random effects, and e is an error vector. I would like to use "lme" function to fit the model and

Dear guRus: I am using lmer for a mixed model that includes a random intercept for a set of effects that have the same distribution, Normal(0, sig2b). This set of effects is of variable size, so I am using an as.formula statement to create the formula for lmer. For example, if the set of random effects has dimension 8, then the lmer call is: Zs<-

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 programming

I am trying to do a surface profile plot. data is X                  Y(1)             Z(1) 1-jan-02       2002        number 2-jan-02       2002        number . . . 1-jan-03       2003 (Y2)     number Z(2) 2-jan-03       2003 (Y2)     number Z(2) . . . until dec 31 2007.   I used the plot3d funtions to build a scatter point plot. Call rinterface.rrun("library(rgl)") Call

Dear all, I have a set of data with 4 inputs (independent variables) and one output (dependent variable). I want to perform a regression analysis in order to fit these data to a regression model, however due to the non-linearity of the model I do not have a clue which equation to use. I am thinking of starting with a very general equation including ^3 terms and interactions between the variables

Hola a todos, Me gustaria pedir vuestra ayuda a encontrar el error que no consigo encontrar en este archivo. He revisado todo mil veces y probado y no doy con ello.Adjunto el archivo con Google drive porque es muy grande. ? monicap_50.csv <https://docs.google.com/file/d/0B8o2KrPEgG7ATlBMc19lTVk1d3M/edit?usp=drive_web> ? Este es el script, y lo que no entiendo que pasa es que tengo 592044

Hi all, I have a data frame with "clustered" rows as follows: Cu1 x1 y1 z1 ... Cu1 x2 y2 z2 ... Cu1 x3 y3 z3 ... # end of first cluster Cu1 Cu2 x4 y4 z4 ... Cu2 x5 y5 z5 Cu2 ... # end of second cluster Cu2 Cu3 ... ... "cluster"-size is 3 in the example above (rows making up a cluster are always consecutive). Is there any faster way to sample n clusters (with

Hi List, Am trying to customize a legend in trellis: Draws 2x5 lines in 5 colors and 2 linetypes. I would like to add two more items to the legend showing the key for the line types above the colored legend. Any suggestions welcome - thanks Herry ############################# #Following example code: library(gregmisc) trellis.device(bg="white") i1=0 i2=-1.89767506 i3=-1.17087085

I have a function called nnmf which takes in one matrix and returns two matrices. for example, > X [,1] [,2] [,3] [,4] [1,] 1 4 7 10 [2,] 2 5 8 11 [3,] 3 6 9 12 > z=nnmf(X,2) > z$W [,1] [,2] [1,] 0.8645422 0.6643681 [2,] 1.7411863 0.5377504 [3,] 2.6179287 0.4111063 > z$H [,1] [,2] [,3] [,4] [1,] 1.14299486

[mod: Forwarded by Jeff Uphoff. I tried to mangle the headers that it appears as the original post: with an invalid return address. -- REW] A few months back, a very alpha version of bliss got posted. That shouldn''t have happened, but, it was pretty much ignored so I didn''t worry about it. But now it seems there''s a bit of a fuss about this. I''ll post the

Dear All, I am trying to perform MANOVA. I have table with 504 columns(species) and 36 rows) with two grouping (season and location) Zx <- Z[c(4:504)] Zxm <- as.matrix(Z) m<- manova(Zxm~Season*location, data=Z) when I do summary.aov, I get respond for each species but summary.manova summary.manova(m) :" residuals have rank" 24<501. What can it be the reason for this error

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 sem package. Thanks! -- Dustin Fife

Hat tip to the irreverent Boing Boing group blog: http://www.boingboing.net/2006/11/02/hilarious_piechartvi.html Dirk -- Hell, there are no rules here - we're trying to accomplish something. -- Thomas A. Edison

On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > Ralf, et al., > > Attached is the latest version of my autovectorization patch. llvmdev > has been CC'd (as had been suggested to me); this e-mail contains > additional benchmark results. > > First, these are preliminary results because I did not do the things > necessary to make them real (explicitly quiet the

Hi, I am wondering if anyone knows of an easy way to fit a saturated model using the sem package on raw data? Say the data were: mtcars[, c("mpg", "hp", "wt")] The model would estimate the three means (intercepts) of c("mpg", "hp", "wt"). The variances of c("mpg", "hp", "wt"). The covariance of mpg with

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.

Hello! I am trying to reproduce (for a publication) analyses that I ran several months ago using lavaan, I'm not sure which version, probably 0.4-12. A sample model is given below: pathmod='mh30days.log.w2 ~ mh30days.log + joingroup + leavegroup + alwaysgroup + grp.partic.w2 + black + age + bivoc + moved.conf + local.noretired + retired + ds + ministrytime + hrswork + nomoralescore.c +