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# Bivariate random-intercepts model
# Originally from http://xxm.times.uh.edu/learn-xxm/xxm-tutorial/
library(OpenMx)
set.seed(1)
sData <- suppressWarnings(try(read.csv("models/passing/data/brim.student.csv"), silent=TRUE))
if (is(sData, "try-error")) sData <- read.csv("data/brim.student.csv")
sData$teacher <- as.integer(sData$teacher)
tMod <- mxModel("teacher", type="RAM",
latentVars = c("eta1", "eta2"),
mxData(type="raw", observed=data.frame(teacher=unique(sData$teacher)),
primaryKey = "teacher"),
mxPath(c("eta1", "eta2"), connect = "unique.pairs", arrows=2,
labels=paste0("psi", 1:3), values=c(.11,0,.12)))
sMod <- mxModel("student", type="RAM", tMod,
manifestVars = c("y1", "y2"),
mxData(type="raw", observed=sData),
mxPath("one", paste0("y",1:2), values=rnorm(2)),
mxPath(c("y1", "y2"), connect = "unique.pairs", arrows=2,
labels=paste0("theta", 1:3), values=c(1.1,0,1.2)),
mxPath(paste0("teacher.eta", 1:2), paste0("y",1:2),
free=FALSE, values=.98, joinKey = "teacher"))
if (1) {
sMod$expectation$.maxDebugGroups <- 2L
dist <- mxRun(mxModel(sMod,
mxComputeSequence(list(
mxComputeOnce('fitfunction', 'fit'),
mxComputeReportExpectation()))))
ed <- dist$expectation$debug
omxCheckEquals(ed$numGroups, 2L)
g1 <- ed$g1
omxCheckEquals(dim(g1$covariance), c(2,2))
omxCheckCloseEnough(as.matrix(g1$covariance), diag(c(1.1, 1.2)), 1e-3)
omxCheckCloseEnough(g1$mean, rep(0, 100), 1e-9)
omxCheckEquals(g1$numSufficientSets, 1L)
g2 <- ed$g2
omxCheckEquals(g2$numSufficientSets, 1L)
omxCheckCloseEnough(as.matrix(g2$covariance), diag(c(1.42, 1.55)), 1e-2)
omxCheckCloseEnough(g2$mean, c(-1.085, 0.318, rep(0, 48)), 1e-2)
omxCheckCloseEnough(dist$output$fit, 484.2459, 1e-2)
}
#sMod$expectation$verbose <- 2L
if (0) {
# Does multigroup work without rampart? Why not? TODO
#sMod$expectation$.rampart <- 0L
#sMod$expectation$.forceSingleGroup <- TRUE
dist <- mxRun(mxModel(sMod,
mxComputeSequence(list(
mxComputeOnce('expectation', 'distribution', 'flat'),
mxComputeReportExpectation()))))
#str(dist$expectation$output)
#str(dist$expectation$debug)
eo = dist$expectation$output
ed = dist$expectation$debug
names(ed$detail)
ed$layout[1:40,]
#print(ed$detail$g01$aIndex[1:10])
# ed$detail$g01$covariance[1:10,1:10]
print(round(ed$detail$g01$mean[1:20], 2))
# round(ed$detail$g01$dataVec[1:20], 2)
# print(ed$detail$g02$aIndex[1:10])
# ed$detail$g02$covariance[1:10,1:10]
#round(ed$detail$g02$mean[1:20],2)
# round(ed$detail$g02$dataVec[1:10], 2)
stop("here")
dist$expectation$output$covariance[1:10,1:10]
dist$expectation$output$covariance[120:130,120:130]
eigen(dist$expectation$output$covariance)$val
round(dist$expectation$debug$A[1:20,1:20], 2)
dist$expectation$debug$S[1:10,1:10]
}
#print(coef(sMod))
#sMod <- omxSetParameters(sMod, labels = c('psi1','theta3'), values = c(.5, .8))
sMod <- mxTryHard(sMod)
summary(sMod)
omxCheckCloseEnough(sMod$output$fit, 445.4331, .01)
omxCheckCloseEnough(sMod$expectation$debug$numGroups, 2)
omxCheckCloseEnough(sMod$expectation$debug$rampartUsage, 50)
#------------------------------------------------------------------------------
# Check that multilevel throws a reasonable error message when tried with WLS
sModW <- mxModel(sMod, mxFitFunctionWLS())
omxCheckError(mxRun(sModW), "Found Weighted Least Squares fit function in a multilevel model.\nWLS is not supported for multilevel models.")
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