Nothing
## Fitting the simple learning model (SLM) by MDML
slm <- function(K, N.R, method = c("MD", "ML", "MDML"), R = as.binmat(N.R),
beta = rep(0.1, nitems), eta = rep(0.1, nitems),
g = rep(0.1, nitems),
betafix = rep(NA, nitems), etafix = rep(NA, nitems),
betaequal = NULL, etaequal = NULL,
randinit = FALSE, incradius = 0,
tol = 1e-07, maxiter = 10000, zeropad = 16) {
K <- as.matrix(K)
N.R <- setNames(as.integer(N.R), names(N.R)) # convert to named int
N <- sum(N.R)
nitems <- ncol(K)
npat <- nrow(R)
nstates <- nrow(K)
Kof <- getKOfringe(K, nstates, nitems) # matrix of outer-fringe states
## Uniformly random initial values
if (randinit) {
beta <- runif(nitems) # constraint: beta + eta < 1
eta <- runif(nitems)
beta <- ifelse(beta + eta < 1, beta, 1 - beta)
eta <- ifelse(beta + eta < 1, eta, 1 - eta)
g <- runif(nitems)
}
## Parameter restrictions
betaeq <- etaeq <- diag(nitems)
if (!is.null(betaequal)) for (i in betaequal) betaeq[i, i] <- 1
if (!is.null( etaequal)) for (i in etaequal) etaeq[i, i] <- 1
beta[!is.na(betafix)] <- betafix[!is.na(betafix)] # overrides arguments
eta[!is.na( etafix)] <- etafix[!is.na( etafix)]
names(beta) <- names(eta) <-
if (is.null(colnames(K))) {
make.unique(c("a", letters[(seq_len(nitems) %% 26) + 1])[-(nitems + 1)],
sep = "")
} else colnames(K)
dimnames(betaeq) <- dimnames(etaeq) <- list(names(eta), names(eta))
## Assigning state K given response R
if(length(which(c(betafix, etafix) == 0))) {
d.RK <- apply(K, 1, function(k) {
RwoK <- t(R) & !k
idx <- which(RwoK, arr.ind=TRUE)
RwoK[idx[idx[, "row"] %in% which(etafix == 0), ]] <- NA
KwoR <- k & !t(R)
idx <- which(KwoR, arr.ind=TRUE)
KwoR[idx[idx[, "row"] %in% which(betafix == 0), ]] <- NA
colSums(RwoK) + colSums(KwoR)
})
PRKfun <- getPRK[["apply"]]
} else {
d.RK <- apply(K, 1, function(k) colSums(xor(t(R), k)))
PRKfun <- getPRK[["matmult"]]
}
d.min <- apply(d.RK, 1, min, na.rm = TRUE) # minimum discrepancy
i.RK <- (d.RK <= (d.min + incradius)) & !is.na(d.RK)
## Minimum discrepancy distribution
disc.tab <- xtabs(N.R ~ d.min)
disc <- as.numeric(names(disc.tab)) %*% disc.tab / N
## Call EM
method <- match.arg(method)
opt <- slmEM(beta = beta, eta = eta, g = g, K = K, Kof = Kof, R = R,
N.R = N.R, N = N, nitems = nitems, i.RK = i.RK,
PRKfun = PRKfun,
betafix = betafix, etafix = etafix, betaeq = betaeq,
etaeq = etaeq, method = method, tol = tol, maxiter = maxiter)
beta <- opt$beta
eta <- opt$eta
g <- opt$g
iter <- opt$iter
## If there are missing response patterns, create complete R and N.R
if(npat < 2^nitems && nitems <= zeropad) {
N.Rincomp <- N.R
R <- expand.grid(rep(list(0:1), nitems), KEEP.OUT.ATTRS=FALSE)
N.R <- setNames(integer(nrow(R)), as.pattern(R)) # named int filled w/zeros
R <- as.binmat(N.R) # named int again
N.R[names(N.Rincomp)] <- N.Rincomp
}
## Recompute predictions and likelihood
P.R.K <- do.call(PRKfun, list(beta, eta, K, R))
P.K <- getSlmPK(g, K, Kof)
names(P.K) <- if(is.null(rownames(K))) as.pattern(K) else rownames(K)
P.R <- as.numeric(P.R.K %*% P.K)
if (sum(P.R) < 1) P.R <- P.R/sum(P.R) # if no zero padding: normalize
loglik <- sum(log(P.R) * N.R, na.rm=TRUE)
## Mean number of errors
P.Kq <- numeric(nitems)
for(j in seq_len(nitems))
P.Kq[j] <- sum(P.K[K[, j] == 1])
nerror <- c("careless error" = sum(beta * P.Kq),
"lucky guess" = sum( eta * (1 - P.Kq)))
## Number of parameters
npar <- qr(betaeq)$rank - sum(!is.na(betafix)) +
qr( etaeq)$rank - sum(!is.na( etafix)) +
nitems
## Goodness of fit, df = number of patterns or persons
fitted <- setNames(N*P.R, names(N.R))
G2 <- 2*sum(N.R*log(N.R/fitted), na.rm=TRUE)
# df <- min(2^nitems - 1, N) - npar # number of patterns or persons
df <- min(if(nitems <= zeropad) 2^nitems - 1 else npat, N) - npar
gof <- c(G2=G2, df=df, pval = 1 - pchisq(G2, df))
z <- list(discrepancy=c(disc), P.K=P.K, beta=beta, eta=eta, g=g,
disc.tab=disc.tab, K=K, N.R=N.R, nitems=nitems, nstates=nstates,
npatterns=npat, ntotal=N, nerror=nerror, npar=npar,
method=method, iter=iter, loglik=loglik, fitted.values=fitted,
goodness.of.fit=gof)
class(z) <- c("slm", "blim")
z
}
## EM algorithm
slmEM <- function(beta, eta, g, K, Kof, R, N.R, N, nitems, i.RK, PRKfun,
betafix, etafix, betaeq, etaeq, method, tol, maxiter){
eps <- 1e-06
iter <- 0
maxdiff <- 2 * tol
em <- c(MD = 0, ML = 1, MDML = 1)[method]
md <- c(MD = 1, ML = 0, MDML = 1)[method]
beta.num <- beta.denom <- eta.num <- eta.denom <- g.of <- beta
while ((maxdiff > tol) && (iter < maxiter) &&
((md*(1 - em) != 1) || (iter == 0))) {
beta.old <- beta
eta.old <- eta
g.old <- g
P.R.K <- do.call(PRKfun, list(beta, eta, K, R)) # P(R|K)
P.K <- getSlmPK(g, K, Kof)
P.R <- as.numeric(P.R.K %*% P.K)
P.K.R <- P.R.K * outer(1/P.R, P.K) # prediction of P(K|R)
mat.RK <- i.RK^md * P.K.R^em
m.RK <- (mat.RK / rowSums(mat.RK)) * N.R # m.RK = E(M.RK) = P(K|R)*N(R)
## Careless error, guessing, and learning parameters
for(j in seq_len(nitems)) {
beta.num[j] <- sum(m.RK[R[, j] == 0, K[, j] == 1])
beta.denom[j] <- sum(m.RK[ , K[, j] == 1])
eta.num[j] <- sum(m.RK[R[, j] == 1, K[, j] == 0])
eta.denom[j] <- sum(m.RK[ , K[, j] == 0])
g.of[j] <- sum(m.RK[ , Kof[, j] == 1])
}
beta <- drop(betaeq %*% beta.num / betaeq %*% beta.denom)
eta <- drop( etaeq %*% eta.num / etaeq %*% eta.denom)
beta[is.na(beta) | beta < eps] <- eps # force 0 < beta, eta < 1
eta[is.na( eta) | eta < eps] <- eps
beta[beta > 1 - eps] <- 1 - eps
eta[ eta > 1 - eps] <- 1 - eps
beta[!is.na(betafix)] <- betafix[!is.na(betafix)] # reset fixed parameters
eta[!is.na( etafix)] <- etafix[!is.na( etafix)]
g <- beta.denom / (beta.denom + g.of)
maxdiff <- max(abs(c(beta, eta, g) - c(beta.old, eta.old, g.old)))
iter <- iter + 1
}
if(iter >= maxiter) warning("iteration maximum has been exceeded")
out <- list(beta = beta, eta = eta, g = g, iter = iter)
out
}
## Testing for closure under union
isKnowledgeSpace <- function(K) {
all(
sort(as.pattern(binary_closure(K == TRUE) + 0)) ==
sort(as.pattern(K))
)
}
## Obtain outer fringe for each state in K
getKOfringe <- function(K, nstates = nrow(K), nitems = ncol(K)) {
stopifnot(
is.numeric(K),
is.matrix(K),
isKnowledgeSpace(K)
)
## Get states K' with |K'| = |K| + 1
nItemsPerK <- rowSums(K)
if(any(sort(unique(nItemsPerK)) != c(0, seq_len(nitems))))
stop("K not wellgraded")
## List of matrices containing the K' states
Kplus <- vector(mode = "list", length = nstates)
for(i in seq_len(nstates)) {
Kplus[[i]] <- K[nItemsPerK == nItemsPerK[i] + 1, , drop = FALSE]
}
getOfringeItems <- function(k, kplusStates) {
x <- xor(k, t(kplusStates)) # symm set diff with the K' states
rowSums(x[, colSums(x) == 1, drop = FALSE]) # keep single-element diffs
}
of <- t(mapply(FUN = getOfringeItems,
split(K, seq_len(nstates)),
Kplus))
rownames(of) <- rownames(K)
mode(of) <- "integer"
of
}
## Compute P(K) from g parameters
getSlmPK <- function(g, K, Kof) {
# foreach k in K:
# prod(g[q-in-K]) * prod(1 - g[q-in-Ks-Ofringe])
apply(K == TRUE, 1, function(k) prod(g[k])) * # use K as index
apply(Kof == TRUE, 1, function(k) prod(1 - g[k]))
}
print.slm <- function(x, P.Kshow = FALSE, parshow = TRUE,
digits=max(3, getOption("digits") - 2), ...){
cat("\nSimple learning models (SLMs)\n")
cat("\nNumber of knowledge states:", x$nstates)
cat("\nNumber of response patterns:", x$npatterns)
cat("\nNumber of respondents:", x$ntotal)
method <- switch(x$method,
MD = "Minimum discrepancy",
ML = "Maximum likelihood",
MDML = "Minimum discrepancy maximum likelihood")
cat("\n\nMethod:", method)
cat("\nNumber of iterations:", x$iter)
G2 <- x$goodness.of.fit[1]
df <- x$goodness.of.fit[2]
pval <- x$goodness.of.fit[3]
cat("\nGoodness of fit (2 log likelihood ratio):\n")
cat("\tG2(", df, ") = ", format(G2, digits=digits), ", p = ",
format(pval, digits=digits), "\n", sep="")
cat("\nMinimum discrepancy distribution (mean = ",
round(x$discrepancy, digits=digits), ")\n", sep="")
disc.tab <- x$disc.tab
names(dimnames(disc.tab)) <- NULL
print(disc.tab)
cat("\nMean number of errors (total = ",
round(sum(x$nerror), digits=digits), ")\n", sep="")
print(x$nerror)
if(P.Kshow){
cat("\nDistribution of knowledge states\n")
printCoefmat(cbind("P(K)"=x$P.K), digits=digits, cs.ind=1, tst.ind=NULL,
zap.ind=1)
}
if(parshow){
cat("\nError, guessing, and learning parameters\n")
printCoefmat(cbind(beta=x$beta, eta=x$eta, g=x$g), digits=digits,
cs.ind=1:2, tst.ind=NULL, zap.ind=1:2)
}
cat("\n")
invisible(x)
}
# ## Simulate responses from SLM
# simulate.slm <- function(object, nsim = 1, seed = NULL, ...){
# beta <- object$beta
# eta <- object$eta
# g <- object$g
# P.K <- getSlmPK(g, object$K, getKOfringe(object$K))
# tK <- t(as.matrix(object$K))
# N <- object$ntotal
# nitems <- nrow(tK)
#
# state.id <- sample(seq_along(P.K), N, replace=TRUE, prob=P.K) # draw states
#
# P.1.K <- tK*(1 - beta) + (1 - tK)*eta # P(resp = 1 | K)
# R <- matrix(0, N, nitems) # response matrix
# for(i in seq_len(N))
# R[i,] <- rbinom(nitems, 1, P.1.K[, state.id[i]]) # draw a response
#
# as.pattern(R, freq = TRUE)
# }
coef.slm <- function(object, ...){
c(setNames(object$beta, paste("beta", names(object$beta), sep=".")),
setNames(object$eta, paste( "eta", names(object$eta), sep=".")),
setNames(object$g, paste( "g", names(object$g), sep=".")))
}
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