Description Usage Arguments Details Value References See Also Examples
Generate simulated data from mixtures of Rasch models. The latent classes of the mixture can differ regarding size as well as item and person parameters of the corresponding Rasch model.
1  simRaschmix(design, extremes = FALSE, attributes = TRUE, ...)

design 
Type of data generating process. Can be provided as a character or a named list. See Details. 
extremes 
Logical. Should observations with none or all items solved be included in the data? 
attributes 
Logical. Should the true group membership as well as
true item and person parameters be attached to the data as attributes

... 
Currently not used. 
The design
of the data generating process (DGP) can be provided
in essentially three different ways.
If the design
argument is one of "rost1"
, "rost2"
or "rost3"
, responses from the three DGPs introduced in Rost
(1990) will be drawn.
Alternatively, the design
can be provided as a named list with
elements nobs
, weights
, ability
, and
difficulty
.
The weights
can be provided in three formats: If provided as a
vector of probabilities (summing to 1), class membership will be drawn
with these probabilities. If weights
is a vector of integer weights
(summing to nobs
, or an integer division thereof), Class sizes
will be either the weights directly or a multiple thereof. As a third
alternative, the weights
can be provided as a
function of the number of observations (nobs
).
The ability
specification can also be provided in three
formats: If provided as a matrix of dimension 2xk with mean and
standard deviation for each of the k clusters, the ability parameters
are drawn from a normal distribution with the corresponding parameters.
Second, ability
can be an array of dimension (., 2, k) with abilities
and corresponding weights/probabilities per cluster. Third, it can
also be provided as a list of k functions which take the number of
observations as an argument.
The specification of the item difficulty
can be
provided either as a matrix with k columns with the item
difficulties per cluster or as a matrix with nobs
rows with the item
difficulties per subject.
As a third option, design
may also be a named list containing a
vector of ability
parameters and a matrix
difficulty
of dimension (number of observation x number of items).
A matrix of item responses with dimension (number of observations x
number of items).
If attributes = TRUE
, the matrix has attributes cluster
,
ability
, and difficulty
. The class memberships
cluster
are only returned when not provided implicitly through
and a vector of abilties and a difficulty matrix with entries for each
subject.
Frick, H., Strobl, C., Leisch, F., and Zeileis, A. (2012). Flexible Rasch Mixture Models with Package psychomix. Journal of Statistical Software, 48(7), 1–25. http://www.jstatsoft.org/v48/i07/.
Rost, J. (1990). Rasch Models in Latent Classes: An Integration of Two Approaches to Item Analysis. Applied Psychological Measurement, 14(3), 271–282.
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## Rost's DGPs ##
#################
suppressWarnings(RNGversion("3.5.0"))
set.seed(1990)
## DGP 1 with just one latent class
r1 < simRaschmix(design = "rost1")
## less than 1800 observations because the extreme scorers have been removed
table(attr(r1, "ability"))
table(rowSums(r1))
## DGP 2 with 2 equally large latent classes
r2 < simRaschmix(design = "rost2", extreme = TRUE)
## exactly 1800 observations including the extreme scorers
table(attr(r2, "ability"))
table(rowSums(r2))
## DGP 3 with 3 latent classes
r3 < simRaschmix(design = "rost3")
## item parameters in the three latent classes
attr(r3, "difficulty")
####################################
## flexible specification of DGPs ##
####################################
suppressWarnings(RNGversion("3.5.0"))
set.seed(482)
## number of observations
nobs < 8
## relative weights
weights < c(1/4, 3/4)
## exact weights: either summing to nobs or an integer division thereof
weights < c(2, 6)
weights < c(1, 3)
## weights as function
## here the result is the same as when specifying relative weights
weights < function(n) sample(size = n, 1:2, prob = c(1/4, 3/4), replace
= TRUE)
## class 1: only ability level 0
## class 2: normally distributed abilities with mean = 2 and sd = 1
ability < cbind(c(0, 0), c(2, 1))
## class 1: 3 ability levels (1, 0, 1); class 2: 2 ability levels (0.5, 0.5)
## with equal probabilities and frequencies, repectively
ability < array(c(cbind(1:1, rep(1/3, 3)), cbind(1:1/2, c(0.5, 0, 0.5))),
dim = c(3, 2, 2))
ability < array(c(cbind(1:1, rep(1, 3)), cbind(1:1/2, c(1, 0, 1))),
dim = c(3, 2, 2))
## ability as function
ability < list(
function(n) rnorm(n, mean = 0, sd = 0.5),
function(n) sample(c(0.5, 0.5), size = n, replace = TRUE)
)
## difficulty per latent class
difficulty < cbind(c(1,1,rep(0,8)), c(rep(0,8),1,1))
## simulate data
dat < simRaschmix(design = list(nobs = nobs, weights = weights,
ability = ability, difficulty = difficulty))
## inspect attributes and raw scores
table(attr(dat, "cluster"))
hist(attr(dat, "ability"))
barplot(table(rowSums(dat)))
attr(dat, "difficulty")
## specification of DGP only via ability and difficulty
## one vector of abilities of all subjects
ability < c(rnorm(4, mean = 0, sd = 0.5), sample(c(0.5, 0.5), size = 4,
replace = TRUE))
## difficulty per subject
difficulty < matrix(c(rep(c(1,1,rep(0,8)), 4), rep(c(rep(0,8),1,1), 4)),
nrow = 8, byrow = TRUE)
## simulate data
dat < simRaschmix(design = list(ability = ability, difficulty = difficulty))
## inspect attributes and raw scores
hist(attr(dat, "ability"))
barplot(table(rowSums(dat)))
attr(dat, "difficulty")

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