difNLR (version 1.5.1-1)

genNLR: Generates data set based on generalized logistic regression DIF and DDF models.

Description

Generates dichotomous, nominal, and ordinal data based on generalized logistic regression models for DIF and DDF detection.

Usage

genNLR(N = 1000, ratio = 1, itemtype = "dich", a, b, c, d, mu = 0, sigma = 1)

Value

A data.frame containing N rows representing respondents and m + 1 columns representing m items. The last column is group membership variable with coding "0" for reference group and "1" for focal group.

Arguments

N

numeric: number of rows representing respondents. (default is 1000).

ratio

numeric: ratio of respondents number in reference and focal group.

itemtype

character: type of items to be generated. Options are "dich" (default) for dichotomous item based on non-linear regression model for DIF detection (see difNLR for details), "nominal" for nominal items based on multinomial model for DDF detection (see ddfMLR for detail), and "ordinal" for ordinal data based on adjacent category logit model (for details see difORD).

a

numeric: matrix representing discriminations with m rows (where m is number of items). Needs to be provided. See Details.

b

numeric: numeric: matrix representing difficulties with m rows (where m is number of items). Needs to be provided. See Details.

c

numeric: matrix representing guessings (lower asymptotes) with m rows (where m is number of items). Default is NULL. See Details.

d

numeric: matrix representing inattentions (upper asymptotes) with m rows (where m is number of items). Default is NULL. See Details.

mu

numeric: a mean vector of the underlying distribution. The first value corresponds to reference group, the second to focal group. Default is 0 value for both groups.

sigma

numeric: a standard deviation vector of the underlying distribution. The first value corresponds to reference group, the second to focal group. Default is 1 value for both groups.

Author

Adela Hladka (nee Drabinova)
Institute of Computer Science of the Czech Academy of Sciences
Faculty of Mathematics and Physics, Charles University

Patricia Martinkova
Institute of Computer Science of the Czech Academy of Sciences

Details

The a, b, c and d are numeric matrices with m rows (where m is number of items) representing parameters of regression models for DIF and DDF detection.

For option itemtype = "dich", matrices should have two columns. The first column represents parameters of the reference group and the second of the focal group. In case that only one column is provided, parameters are set to be the same for both groups.

For options itemtype = "nominal" and itemtype = "ordinal", matrices c and d are ignored. Matrices a and b contain parameters for distractors. For example, when item with 4 different choices is supposed to be generated, user provide matrices with 6 columns. First 3 columns correspond to distractors parameters for reference group and last three columns for focal group. The number of choices can differ for items. Matrices a and b need to consist of as many columns as is the maximum number of distractors. Items with less choices can contain NAs.

References

Drabinova, A. & Martinkova, P. (2017). Detection of differential item functioning with nonlinear regression: A non-IRT approach accounting for guessing. Journal of Educational Measurement, 54(4), 498--517, tools:::Rd_expr_doi("10.1111/jedm.12158").

See Also

difNLR, difORD, ddfMLR

Examples

# seed
set.seed(123)
# generating parameters for dichotomous data with DIF, 5 items
a <- matrix(runif(10, 0.8, 2), ncol = 2)
b <- matrix(runif(10, -2, 2), ncol = 2)
c <- matrix(runif(10, 0, 0.25), ncol = 2)
d <- matrix(runif(10, 0.8, 1), ncol = 2)
# generating dichotomous data set with 300 observations (150 each group)
genNLR(N = 300, a = a, b = b, c = c, d = d)
# generating dichotomous data set with 300 observations (150 each group)
# and different mean and standard deviation for underlying distribution
genNLR(N = 300, a = a, b = b, c = c, d = d, mu = c(1, 0), sigma = c(1, 2))
# generating dichotomous data set with 300 observations (250 reference group, 50 focal)
genNLR(N = 300, ratio = 5, a = a, b = b, c = c, d = d)

# generating parameters for nominal data with DDF, 5 items,
# each item 3 choices
a <- matrix(runif(20, 0.8, 2), ncol = 4)
b <- matrix(runif(20, -2, 2), ncol = 4)
# generating nominal data set with 300 observations (150 each group)
genNLR(N = 300, itemtype = "nominal", a = a, b = b)
# generating nominal data set with 300 observations (250 reference group, 50 focal)
genNLR(N = 300, itemtype = "nominal", ratio = 5, a = a, b = b)

# generating parameters for nominal data with DDF, 5 items,
# items 1 and 2 have 2 choices, items 3, 4 and 5 have 3 choices
a <- matrix(runif(20, 0.8, 2), ncol = 4)
a[1:2, c(2, 4)] <- NA
b <- matrix(runif(20, -2, 2), ncol = 4)
b[1:2, c(2, 4)] <- NA
# generating nominal data set with 300 observations (150 each group)
genNLR(N = 300, itemtype = "nominal", a = a, b = b)
# generating nominal data set with 300 observations (250 reference group, 50 focal)
genNLR(N = 300, itemtype = "nominal", ratio = 5, a = a, b = b)