similar to: marginal structural models

I am looking for study materisl?on how to conduct 'small area estimation' using structural equations models in R for both longtudinal and repeated cross-section data.?Tried google did not work. There are other regression technique my interest is on structural equation models.?

Dear all, I am modeling the incidence of recreational anglers along a stretch of coastline, and with a vary large proportion of zeros (>80%) have chosen to use a zero inflated negative binomial (ZINB) distribution. I am using the same variables for both parts of the model, can anyone help me with R code to compute overall marginal effects of each variable? My model is specified as follows:

I've used Anova() from the car package to get marginal (aka type II) sum-of-squares and tests for linear models with categorical variables. Is it possible to get marginal SSs also for continuous variables, when the model includes powers of the continuous variables? For instance, if A and B are categorical ("factor"s) and x is continuous ("numeric"), Anova (lm (y ~ A*B +

Dear R users, The OpenMx developer team takes great pride in announcing the availability of OpenMx 1.0. The team would like to express its gratitude to the large number of beta-testers who have helped us improve the code. Thank you. OpenMx is a free suite of R functions and a estimation back-end that supports fitting a wide variety of structural equation models including multiple groups, full

Dear R users, The OpenMx developer team takes great pride in announcing the availability of OpenMx 1.0. The team would like to express its gratitude to the large number of beta-testers who have helped us improve the code. Thank you. OpenMx is a free suite of R functions and a estimation back-end that supports fitting a wide variety of structural equation models including multiple groups, full

Listers, I have created a cross-tab matrix using the following code: S <- c(1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,1,2,3,1,1,2,3,1,2,2) F <- c(1,2,3,1,2,3,1,1,1,1,2,3,1,1,3,2,2,2,1,2,3,1,1,1,1,2,3,3,1,3,1,3,1,1,2,3,1,1,3,2,3,2,1,1,1,2,3,1,1,2) table(S,F) fF <-factor(F, labels = c("Dem","Ind","Rep")) fS <-factor(S,

Dear Helplist, I want to produce forecasts from a structural equation model. With the SEM package the model setup and its estimation is possible. However, I have not figured out how to obtain ex ante forecasts, i.e. applying the Gauss-Seidel algorithm to the estimated structural equations for provided values of the exogenous variables (i.e.: y_t = -inv(A)*B*x_t). Does anyone know if the there is

Hello useRs, I`m running structural equation models with R, but for one of my models the below error message apears. I`m trying to change startvalues but without success. The manual for sem package did not help me. Does anyone knows how to change startvalues for iteration in sem package? Or it can be another problem with the model? Error in startvalues(S, ram, debug = debug, tol = start.tol)

Hello, Is there a package or sample code that shows how to do ex ante forecasts with a macroeconomic structural equations model? I looked at the "sem" package, which lets you estimate e.g. Klein's model, but I'm not sure how to make simulations using the full set of equations, including the identities. Thank you, Ronaldo Carpio rncarpio at yahoo.com

Hi, I am a stata user trying to transition to R. Typically I compute marginal effects plots for (example) probit models by drawing simulated betas by using the coefficient/standard error estimates after I run a probit model. I then use these simulated betas to compute first difference marginal effects. My question is, can I do this in R? Specifically, I was wondering if anyone knows how R

Yesterday I posted the following question to the help list. Thanks to John Fox (copied below) who pointed out the solution. Original question: I have come across a result that I cannot explain, and am hopingthat someone else can provide an answer. A student fitted a mixed model usingthe lme function: out<- lme(fixed=Y~A+B+A:B, random=~1|Site). Y is a continuous variable while A and

Dear R-users, I am running a large number of simulations and estimating a structural equation model for each one using the SEM package. Each run of my program has around 8000 simulations. Most of the time the program completes all of them correctly but sometimes I get a segmentation fault in the sem routine and my program stops with the following error message: > *** caught

Hi Yves lavaan looks like a very nice package. From the tutorial introduction I see you create path diagrams for some of the models you describe. How did you do this? I don't see a function for this in the package. I know there is a path.diagram function in the sem package that uses dot to draw the diagram, but I've always found the layouts from dot somewhat strange for path diagrams

Dear R-Team, Am I right supposing that the "breakpoints()" function in the strucchange package is an implementation of the pure structural change model proposed by Bai and Perron (1997, 2003)? My question relates to a partial structural change model that Bai and Perron formulate in their 2003 paper, e.g. formulated as y = x' beta + z' delta_j + epsilon, where beta and delta

In that case, I can't work out why the first model fails but not the second. I would start looking at "Data" to see what it contains. if: object2 <- polr(Inc ~ Training ,Data,Hess = T,method = "logistic" ) works, the problem may be with the "Adopt" variable. Jim On Tue, Mar 20, 2018 at 10:55 AM, Willy Byamungu <wmulimbi at email.uark.edu> wrote: >

Dear all, I wonder if it is possible to obtain marginal tests for effects in generalized linear models. Indeed, the anova function produces sequential tests and it doesn't have any "type" argument to specify that we would like marginal tests instead, as in the similar anova function for lme objects. Thanks a lot for your help! Eve CORDA Office national de la chasse et de la faune

Thank you to all who made very helpful suggestions to get started with R. Duncan Murdoch raised an excellent question, asking about my background and reason for using R. I'm an epidemiologist, applying the marginal structural models approach (inverse probability of treatment weights) in a Cox proportional hazards analysis. The statistical program which I had been using does not have

Dear R-help list members, I forgot to copy my reply to the r-help list. Here's most of it. John -------------------------------- John Fox Department of Sociology McMaster University Hamilton, Ontario Canada L8S 4M4 905-525-9140x23604 http://socserv.mcmaster.ca/jfox -------------------------------- -----Original Message----- From: John Fox [mailto:jfox at mcmaster.ca] Sent: Sunday,

Hi John Thanks a lot for the reply. Could you suggest how I can correct this problem? I tried using a correlation matrix instead of raw moments, but still got the same error. I also fixed parameters v1,v2,v3,a1 at 1; then it gave me the error that the system is exatly singular. To answer the points that you raised: 1. x1-x6 are not causes; they are just indicatiors. Does that change my

Dear all: When I am trying to get the marginal effects: summary(result7,adv_inc_ratio=mean(m9201 $adv_inc_ratio),adv_price_ratio=mean(m9201$adv_price_ratio), ...(SOME MORE CONTINUOUS AND DISCRETE VARIABLES BUT I AM NOT LISTING)... regW=c (0,mean(m9201$regW),1), regWM=c(0,mean(m9201$regWM),1)) It gave out an error message: Error in summary.Design(result7, adv_inc_ratio = mean(m9201