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library(OpenMx)
library(rpf)
set.seed(9)
numItems <- 30
i1 <- rpf.drm(multidimensional=TRUE)
items <- list()
items[1:numItems] <- list(i1)
correct <- matrix(NA, 4, numItems)
for (x in 1:numItems) correct[,x] <- rpf.rparam(i1, version=1)
correct[1,] <- 1
correct[3,] <- logit(0)
correct[4,] <- logit(1)
ip.mat <- mxMatrix(name="item", nrow=4, ncol=numItems,
values=c(1,0, logit(0), logit(1)),
free=c(FALSE, TRUE, FALSE, FALSE))
colnames(ip.mat) <- paste("i", 1:numItems, sep="")
rownames(ip.mat) <- c('f1', 'b','g','u')
m.mat <- mxMatrix(name="mean", nrow=1, ncol=1, values=0, free=TRUE)
rownames(m.mat) <- 'f1'
cov.mat <- mxMatrix(name="cov", nrow=1, ncol=1, values=1, free=TRUE)
dimnames(cov.mat) <- list('f1','f1')
latent <- mxModel('latent', m.mat, cov.mat,
mxDataDynamic("cov", expectation="latentTest.expectation"),
mxExpectationNormal(covariance="cov", means="mean"),
mxFitFunctionML())
# We generate data in the "wrong" order to preserve compatibility
# with the previous version of the test.
data <- rpf.sample(500, items, correct, cov=matrix(5,1,1))
ldata <- rpf.sample(300, items, correct, mean=.5, cov=matrix(5,1,1))
ip.fix <- ip.mat
ip.fix$free[,] <- FALSE
ip.fix$values[,] <- correct
if (0) {
plan <- mxComputeEM('expectation', 'scores',
mxComputeGradientDescent(paste('latent',c('mean','cov'), sep="."),
fitfunction="latent.fitfunction"),
verbose=0L)
}
plan <- mxComputeIterate(list(
mxComputeOnce('expectation', 'scores'),
mxComputeGradientDescent(paste('latent',c('mean','cov'), sep="."),
fitfunction="latent.fitfunction"),
mxComputeOnce('expectation'),
mxComputeOnce('fitfunction', 'fit')))
m1 <- mxModel(model="latentTest", ip.fix, latent,
mxData(observed=ldata, type="raw"),
mxExpectationBA81(items, mean="latent.mean", cov="latent.cov"),
mxFitFunctionML(),
plan)
m1Fit <- mxRun(m1)
omxCheckCloseEnough(m1Fit$output$estimate, c(.46, 4.44), .1)
omxCheckCloseEnough(m1Fit$output$fit, 8251.09, .01)
latent.plan <- NULL
if (1) {
latent.plan <- mxComputeGradientDescent('latent.cov', fitfunction="latent.fitfunction")
} else {
latent.plan <-
mxComputeSequence(list(mxComputeOnce('expectation'),
mxComputeOnce('expectation', "latentDistribution", "copy"),
mxComputeOnce('fitfunction', "set-starting-values")),
freeSet='latent.cov')
}
latent$mean$free[,] <- FALSE
latent$data$expectation <- "drmmg.expectation"
m2 <- mxModel(model="drmmg", ip.mat, latent,
mxData(observed=data, type="raw"),
mxExpectationBA81(items, mean="latent.mean", cov="latent.cov"),
mxFitFunctionML(),
mxComputeEM('expectation', 'scores',
mxComputeSequence(list(
mxComputeNewtonRaphson(freeSet='item'),
latent.plan)),
verbose=0L))
m2 <- mxRun(m2)
omxCheckCloseEnough(m2$output$fit, 14129.94, .01)
omxCheckCloseEnough(m2$submodels$latent$matrices$cov$values[1,1], 4.377, .01)
emstat <- m2$compute$output
omxCheckCloseEnough(emstat$EMcycles, 29, 4)
#omxCheckCloseEnough(emstat$totalMstep, 763, 20) # includes the latent distribution
#print(m2$matrices$item$values)
#print(correct.mat)
mask <- is.finite(correct)
got <- cor(c(m2$matrices$item$values[mask]),
c(correct[mask]))
omxCheckCloseEnough(got, .994, .01)
if (1) {
ip.mat <- mxMatrix(name="item", nrow=4, ncol=numItems,
values=c(1,0, logit(0), logit(1)),
free=c(TRUE, TRUE, FALSE, FALSE))
colnames(ip.mat) <- paste("i", 1:numItems, sep="")
rownames(ip.mat) <- c('f1', 'b','g','u')
ip.mat$labels[1,] <- 'a1'
m2 <- mxModel(model="drmmg", ip.mat,
mxData(observed=data, type="raw"),
mxExpectationBA81(ItemSpec=items),
mxFitFunctionML(),
mxComputeSequence(steps=list(
mxComputeOnce('expectation', 'scores'),
mxComputeOnce('fitfunction', c('gradient', 'hessian', 'ihessian')),
mxComputeReportDeriv()
)))
deriv <- mxRun(m2, silent=TRUE)
omxCheckCloseEnough(deriv$output$ihessian, solve(deriv$output$hessian), 1e-4)
if (0) {
m3 <- mxModel(m2, mxComputeSequence(list(
mxComputeOnce('fitfunction', 'fit'),
mxComputeNumericDeriv(parallel=FALSE, iterations=2L),
mxComputeReportDeriv())))
deriv <- mxRun(m3)
stop("ok")
}
m2 <- mxModel(model="drmmg", ip.mat,
mxData(observed=data, type="raw"),
mxExpectationBA81(ItemSpec=items),
mxFitFunctionML(),
mxComputeEM('expectation', 'scores',
mxComputeNewtonRaphson(freeSet='item')))
m2 <- mxRun(m2)
emstat <- m2$compute$output
omxCheckCloseEnough(emstat$EMcycles, 21, 1)
omxCheckCloseEnough(emstat$totalMstep, 56, 5)
omxCheckCloseEnough(m2$fitfunction$result, 14129.04, .01)
omxCheckCloseEnough(m2$matrices$item$values[1,], rep(2.133, numItems), .002)
# correct values are from flexMIRT
est <- c(-0.838622, -1.02653, -0.0868472, -0.251784, 0.953364, 0.735258, 0.606918,
1.04239, 0.466055, -2.05196, -0.0456446, -0.320668, -0.362073, 2.02502,
0.635298, -0.0731132, -2.05196, -0.0456446, -1.17429, 0.880002, -0.838622,
-0.838622, 1.02747, 0.424094, -0.584298, 0.663755, 0.663755, 0.064287, 1.38009,
1.01259 )
omxCheckCloseEnough(m2$matrices$item$values[2,], est, .002)
}
if (0) {
library(mirt)
rdata <- sapply(data, unclass)-1
# for flexMIRT, write CSV
#write.table(rdata, file="ifa-drm-mg.csv", quote=FALSE, row.names=FALSE, col.names=FALSE)
pars <- mirt(rdata, 1, itemtype="2PL", D=1, quadpts=49, pars='values')
pars[pars$name=="a1",'value'] <- 1
pars[pars$name=="a1",'est'] <- FALSE
pars[pars$name=="COV_11",'est'] <- TRUE
fit <- mirt(rdata, 1, itemtype="2PL", D=1, quadpts=49, pars=pars)
# LL -7064.519 * -2 = 14129.04
got <- coef(fit)
print(got$GroupPars)
# COV 4.551
got$GroupPars <- NULL
round(m2$matrices$item$values - simplify2array(got), 2)
# MH-RM takes forever, not run
pars <- confmirt(rdata, 1, itemtype="2PL", D=1, quadpts=49, pars='values')
pars[pars$name=="a1",'value'] <- 1
pars[pars$name=="a1",'est'] <- FALSE
pars[pars$name=="COV_11",'est'] <- TRUE
fit <- confmirt(rdata, 1, itemtype="2PL", D=1, quadpts=49, pars=pars)
got <- coef(fit)
got$GroupPars <- NULL
round(m2$matrices$item$values - sapply(got, function(l) l[1,]), 2)
}
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