The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
This vignette includes some templates for hypothesis objects that can be altered for your specific use case.
The templates currently include:
The templates are described in some more detail in the respective sections. We are open for your suggestions to include further templates.
We test the null hypothesis that the parameters adhere to the Rasch model against the alternative hypothesis that the parameters are better described by a 2PL model.
h_1PLvs2PL <- list(res = function(altpars, nullpars = NULL) {
n.items <- length(altpars[[1]])
re <- list(n.items = n.items, itemtype = "2PL",
Amat = c(1, 0, -1, 0, rep(0, (n.items - 1) *
2)) |>
(function(x) rep(x, n.items - 2))() |>
c(1, 0, -1, 0) |>
matrix(ncol = n.items * 2, byrow = TRUE),
cvec = 0, model = mirt::mirt.model(paste("F = 1-",
n.items, "
CONSTRAIN = (1-",
n.items, ", a1)")))
return(re)
}, unres = function(altpars) {
re <- list(parsets = altpars, model = 1, itemtype = "2PL",
longpars = pars.long(pars = altpars, itemtype = "2PL"))
return(re)
}, maximizeL = function(hyp) {
# Hypothesis-specific algorithm to find the
# maximum likelihood restricted parameter set
maxlpreload <- function(pars, funs) {
# returns the density for each response
# pattern under the model parameters pars
patterns <- as.matrix(expand.grid(lapply(seq_len(length(pars$a)),
function(x) c(0, 1))))
pre <- c()
for (i in seq_len(nrow(patterns))) {
pre[i] <- funs$g(patterns[i, ], pars)
}
return(pre)
}
maxl <- function(x, pars, pre, funs) {
# calculates the likelihood of parameters
# x given model 'pars'
patterns <- as.matrix(expand.grid(lapply(seq_len(length(pars$a)),
function(x) c(0, 1))))
x <- list(a = rep(x[1], length(pars$a)), d = x[2:length(x)])
res <- c()
for (i in seq_len(nrow(patterns))) {
px <- pre[i]
qx <- funs$g(patterns[i, ], x)
res[i] <- {
px * log(qx)
}
}
re <- -sum(res)
}
resmod <- hyp$resmod
unresmod <- hyp$unresmod
pars <- unresmod$parsets
funs <- load.functions(unresmod$itemtype)
startval <- c(mean(pars$a), as.numeric(pars$d))
maxlpre <- maxlpreload(pars, funs)
optpar <- stats::optim(startval, function(x) {
maxl(x, pars, maxlpre, funs)
}, method = "BFGS")
re <- pars
re$a <- rep(optpar$par[1], length(pars$a))
re$d <- optpar$par[2:length(optpar$par)]
return(re)
})We test for differential item functioning in the 2PL model. Our alternative hypothesis is that certain item parameters differ in two known participant groups. Which item parameters differ can be determined in the altpars object, see the “demo” vignette for an example.
h_DIF2PL <- list(res = function(altpars, nullpars = NULL) {
n.items <- length(altpars[[1]][[1]])
reA <- altpars[[1]]
reB <- altpars[[2]]
hyp_a <- which(reA$a != reB$a)
hyp_d <- which(reA$d != reB$d)
Amat <- matrix(0, nrow = length(c(hyp_a, hyp_d)),
ncol = n.items * 2)
i <- 1
for (j in hyp_a) {
Amat[i, j * 2 - 1] <- 1
i <- i + 1
}
for (j in hyp_d) {
Amat[i, j * 2] <- 1
i <- i + 1
}
Amat <- cbind(Amat, -Amat)
delcols <- (colSums(Amat) == 0) & (1:(n.items *
2 * 2)) > 2 * n.items
relpars <- colSums(Amat[, 1:(2 * n.items)]) ==
1
Amat <- Amat[, !delcols]
re <- list(n.items = n.items, itemtype = "2PL",
Amat = Amat, cvec = 0, model = mirt::mirt.model(paste("F = 1-",
n.items, "
CONSTRAINB = (1-",
n.items, ", d), (1-", n.items, ", a1)")),
multigroup = TRUE, delcols = delcols, relpars = relpars)
return(re)
}, unres = function(altpars) {
n.items <- length(altpars[[1]][[1]])
reA <- altpars[[1]]
reB <- altpars[[2]]
reA$itemtype <- reB$itemtype <- "2PL"
reA$longpars <- pars.long(pars = reA, itemtype = "2PL")
reB$longpars <- pars.long(pars = reB, itemtype = "2PL")
constrain_a <- which(reA$a == reB$a)
constrain_d <- which(reA$d == reB$d)
hyp_a <- which(reA$a != reB$a)
hyp_d <- which(reA$d != reB$d)
Amat <- matrix(0, nrow = length(c(hyp_a, hyp_d)),
ncol = n.items * 2)
i <- 1
for (j in hyp_a) {
Amat[i, j * 2 - 1] <- 1
i <- i + 1
}
for (j in hyp_d) {
Amat[i, j * 2] <- 1
i <- i + 1
}
Amat <- cbind(Amat, -Amat)
delcols <- (colSums(Amat) == 0) & (1:(n.items *
2 * 2)) > 2 * n.items
longpars <- c(reA$longpars, reB$longpars)[!delcols]
re <- list(parsets = list(reA, reB), model = mirt::mirt.model(paste("F = 1-",
n.items, "
CONSTRAINB = (",
paste(constrain_d, collapse = ","), ", d), (",
paste(constrain_a, collapse = ","), ", a1)")),
longpars = longpars, multigroup = TRUE, itemtype = "2PL",
delcols = delcols)
return(re)
}, maximizeL = function(hyp) {
# Hypothesis-specific algorithm to find the
# maximum likelihood restricted parameter set
# L Optimizer
maxl <- function(x, pars1, pars2, i) {
px1 <- function(th) {
funs$f(th, pars1$a[i], pars1$d[i], 1)
}
px2 <- function(th) {
funs$f(th, pars2$a[i], pars2$d[i], 1)
}
qx <- function(th) {
funs$f(th, x[1], x[2], 1)
}
kl <- function(th) {
px1(th) * log(qx(th)) + (1 - px1(th)) *
log(1 - qx(th)) + px2(th) * log(qx(th)) +
(1 - px2(th)) * log((1 - qx(th)))
}
re <- -spatstat.random::gauss.hermite(kl, order = 20)
}
resmod <- hyp$resmod
unresmod <- hyp$unresmod
pars <- unresmod$parsets
pars1 <- pars[[1]]
pars2 <- pars[[2]]
funs <- load.functions(pars1$itemtype)
re <- pars1
for (i in seq_len(length(pars1$a))) {
startval <- c(re$a[i], re$d[i])
optpar <- stats::optim(startval, function(x) {
maxl(x, pars1, pars2, i)
}, method = "BFGS")
re$a[i] <- optpar$par[1]
re$d[i] <- optpar$par[2]
}
return(re)
})We test the null hypothesis that the parameters adhere to the partial credit model against the alternative hypothesis that the parameters are better described by the generalized partial credit model.
h_PCMvsGPCM <- list(res = function(altpars, nullpars = NULL) {
n.items <- length(altpars[[1]])
nkat <- ncol(altpars$d)
re <- list(n.items = n.items, itemtype = "gpcm",
Amat = c(1, rep(0, nkat - 1), -1, rep(0, nkat -
1), rep(0, (n.items - 1) * nkat)) |>
(function(x) rep(x, n.items - 2))() |>
c(1, rep(0, nkat - 1), -1, rep(0, nkat -
1)) |>
matrix(ncol = n.items * nkat, byrow = TRUE),
cvec = 0, model = mirt::mirt.model(paste("F = 1-",
n.items, "
CONSTRAIN = (1-",
n.items, ", a1)")))
return(re)
}, unres = function(altpars) {
re <- list(parsets = altpars, model = 1, itemtype = "gpcm",
longpars = pars.long(pars = altpars, itemtype = "gpcm"))
return(re)
}, maximizeL = function(hyp) {
# Hypothesis-specific algorithm to find the
# maximum likelihood restricted parameter set
maxlpreload <- function(pars) {
# returns the density for each response
# pattern under the model parameters pars
n.items <- length(pars$a)
n.kat <- max(ncol(pars$d), 2)
patterns <- as.matrix(expand.grid(lapply(1:n.items,
function(x) 0:(n.kat - 1))))
pre <- c()
for (i in seq_len(nrow(patterns))) {
pre[i] <- funs$g(patterns[i, ], pars)
}
return(pre)
}
maxl <- function(x, pars, pre) {
# calculates the likelihood of parameters
# x given model 'pars'
n.items <- length(pars$a)
n.kat <- max(ncol(pars$d), 2)
patterns <- as.matrix(expand.grid(lapply(1:n.items,
function(x) 0:(n.kat - 1))))
x <- list(a = rep(x[1], n.items), d = matrix(c(rep(0,
n.items), x[2:length(x)]), ncol = ncol(pars$d)))
res <- c()
for (i in seq_len(nrow(patterns))) {
px <- pre[i]
qx <- funs$g(patterns[i, ], x)
res[i] <- {
px * log(qx)
}
}
re <- -sum(res)
}
resmod <- hyp$resmod
unresmod <- hyp$unresmod
pars <- unresmod$parsets
funs <- load.functions(unresmod$itemtype)
n.kat <- max(ncol(pars$d), 2)
n.items <- length(pars$a)
startval <- c(mean(pars$a), as.numeric(pars$d[,
2:n.kat]))
maxlpre <- maxlpreload(pars)
optpar <- stats::optim(startval, function(x) {
maxl(x, pars, maxlpre)
}, method = "BFGS")
re <- pars
re$a <- rep(optpar$par[1], n.items)
re$d <- matrix(c(rep(0, n.items), optpar$par[2:length(optpar$par)]),
ncol = ncol(pars$d))
return(re)
})The null hypothesis states that the first item has difficulty 0. The underlying model is the 2PL model.
h_2PL_basic <- list(res = function(altpars, nullpars = NULL) {
n.items <- length(altpars[[1]])
re <- list(n.items = n.items, itemtype = "2PL",
Amat = c(0, 1, rep(0, (n.items - 1) * 2)) |>
(function(x) matrix(x, ncol = n.items *
2, byrow = TRUE))(), cvec = 0, model = mirt::mirt.model(paste("F = 1-",
n.items, "
FIXED = (1, d)
START = (1,d,0)")))
return(re)
}, unres = function(altpars) {
re <- list(parsets = altpars, model = 1, itemtype = "2PL",
longpars = pars.long(pars = altpars, itemtype = "2PL"))
return(re)
}, maximizeL = function(hyp) {
# Hypothesis-specific algorithm to find the
# maximum likelihood restricted parameter set
maxlpreload <- function(pars) {
# returns the density for each response
# pattern under the model parameters pars
patterns <- as.matrix(expand.grid(lapply(seq_len(length(pars$a)),
function(x) c(0, 1))))
pre <- c()
for (i in seq_len(nrow(patterns))) {
pre[i] <- funs$g(patterns[i, ], pars)
}
return(pre)
}
maxl <- function(x, pars, pre) {
# calculates the likelihood of parameters
# x given model 'pars'
patterns <- as.matrix(expand.grid(lapply(seq_len(length(pars$a)),
function(x) c(0, 1))))
x <- list(a = c(x, pars$a[2:length(pars$a)]),
d = c(0, pars$d[2:length(pars$d)]))
res <- c()
for (i in seq_len(nrow(patterns))) {
px <- pre[i]
qx <- funs$g(patterns[i, ], x)
res[i] <- {
px * log(qx)
}
}
re <- -sum(res)
}
resmod <- hyp$resmod
unresmod <- hyp$unresmod
pars <- unresmod$parsets
funs <- load.functions(unresmod$itemtype)
startval <- pars$a[1]
maxlpre <- maxlpreload(pars)
optpar <- stats::optim(startval, function(x) {
maxl(x, pars, maxlpre)
}, method = "BFGS")
re <- pars
re$a <- c(optpar$par[1], pars$a[2:length(pars$a)])
re$d <- c(0, pars$d[2:length(pars$d)])
return(re)
})We test the null hypothesis that the first item parameters are (1,0,.2) for the a,d, and g parameters respectively. The underlying model is the 3PL model.
h_3PL_basic <- list(res = function(altpars, nullpars = NULL) {
n.items <- length(altpars[[2]])
re <- list(n.items = n.items, itemtype = "3PL",
Amat = c(1, 0, 0, rep(0, (n.items - 1) * 3),
0, 1, 0, rep(0, (n.items - 1) * 3), 0,
0, 1, rep(0, (n.items - 1) * 3)) |>
matrix(ncol = n.items * 3, byrow = TRUE),
cvec = c(1, 0, 0.2), model = mirt::mirt.model(paste("F = 1-",
n.items, "
FIXED = (1, d), (1,a1), (1,g)
START = (1,d,0),(1,a1,1),(1,g,.2)")))
return(re)
}, unres = function(altpars) {
n.items <- length(altpars[[2]])
re <- list(parsets = altpars, model = 1, itemtype = "3PL",
longpars = pars.long(pars = altpars, itemtype = "3PL"))
return(re)
}, maximizeL = function(hyp) {
# Hypothesis-specific algorithm to find the
# maximum likelihood restricted parameter set
maxlpreload <- function(pars) {
# returns the density for each response
# pattern under the model parameters pars
patterns <- as.matrix(expand.grid(lapply(seq_len(length(pars$a)),
function(x) c(0, 1))))
pre <- c()
for (i in seq_len(nrow(patterns))) {
pre[i] <- funs$g(patterns[i, ], pars)
}
return(pre)
}
maxl <- function(x, pars, pre) {
# calculates the likelihood of parameters
# x given model 'pars'
patterns <- as.matrix(expand.grid(lapply(seq_len(length(pars$a)),
function(x) c(0, 1))))
x <- list(a = c(x, pars$a[2:length(pars$a)]),
d = c(0, pars$d[2:length(pars$d)]))
res <- c()
for (i in seq_len(nrow(patterns))) {
px <- pre[i]
qx <- funs$g(patterns[i, ], x)
res[i] <- {
px * log(qx)
}
}
re <- -sum(res)
}
resmod <- hyp$resmod
unresmod <- hyp$unresmod
pars <- unresmod$parsets
funs <- load.functions(unresmod$itemtype)
startval <- pars$a[1]
maxlpre <- maxlpreload(pars)
optpar <- stats::optim(startval, function(x) {
maxl(x, pars, maxlpre)
}, method = "BFGS")
re <- pars
re$a <- c(optpar$par[1], pars$a[2:length(pars$a)])
re$d <- c(0, pars$d[2:length(pars$d)])
return(re)
})We test the null hypothesis that the difficulty of the first two item parameters is equal. The underlying model is the two-dimensional 2PL model. Only the sampling-based approach is available for now.
h_multi_basic <- list(res = function(altpars, nullpars = NULL) {
n.items <- length(altpars[[2]])
re <- list(n.items = n.items, itemtype = "2PL",
Amat = c(0, 0, 1, 0, 0, -1, rep(0, (n.items -
3) * 3 + 2)) |>
matrix(ncol = n.items * 3 - 1, byrow = TRUE),
cvec = 0, model = mirt::mirt.model(paste("F1 = 1-",
n.items, "
F2 = 1-",
n.items - 1, "
CONSTRAIN = (1-2, d")))
return(re)
}, unres = function(altpars) {
n.items <- length(altpars[[2]])
re <- list(parsets = altpars, model = mirt::mirt.model(paste("F1 = 1-",
n.items, "
F2 = 1-",
n.items - 1, "")), itemtype = "2PL", longpars = pars.long(pars = altpars,
itemtype = "2PL"))
return(re)
}, maximizeL = function(hyp) {
# Hypothesis-specific algorithm to find the
# maximum likelihood restricted parameter set
# not written yet, only sampling-based
# available for now
})We test the null hypothesis that the difficulty of the first item is equal to 2. The underlying model is the two-dimensional 2PL model. Only the sampling-based approach is available for now.
h_multi_basic2 <- list(res = function(altpars, nullpars = NULL) {
n.items <- length(altpars[[2]])
re <- list(n.items = n.items, itemtype = "2PL",
Amat = c(0, 0, 1, rep(0, (n.items - 2) * 3 +
2)) |>
matrix(ncol = n.items * 3 - 1, byrow = TRUE),
cvec = 2, model = mirt::mirt.model(paste("F1 = 1-",
n.items, "
F2 = 1-",
n.items - 1, "
FIXED = (1, d)
START = (1,d,2)")))
return(re)
}, unres = function(altpars) {
n.items <- length(altpars[[2]])
re <- list(parsets = altpars, model = mirt::mirt.model(paste("F1 = 1-",
n.items, "
F2 = 1-",
n.items - 1, "")), itemtype = "2PL", longpars = pars.long(pars = altpars,
itemtype = "2PL"))
return(re)
}, maximizeL = function(hyp) {
# Hypothesis-specific algorithm to find the
# maximum likelihood restricted parameter set
# not written yet, only sampling-based
# available for now
})These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.