tests/tests/test-09-mirt.model.R
In xzhaopsy/MIRT: Multidimensional Item Response Theory
context('mirt.model')
test_that('syntax', {
data <- expand.table(LSAT7)
model0 <- 'F = 1-5'
model1 <- mirt.model('F = 1-5')
model2 <- mirt.model('F = 1-5
CONSTRAIN = (1,2,3-5,a1)')
model3 <- mirt.model('F = 1-5
CONSTRAIN = (2,3-5,a1)
PRIOR = (2,3-5, a1, lnorm, .2, .2), (1, d, norm, 0, 2)')
model4 <- mirt.model('F = 1-5
CONSTRAIN = (1-2, d)
CONSTRAINB = (2-4,5,a1), (1, a1)
PRIOR = (1-5, d, norm, 0, 2)')
model5 <- mirt.model('F = 1-5
CONSTRAIN = (1-5, d, male)
CONSTRAINB = (1-4,5,a1)
PRIOR = (1-5, d, norm, 0, 2, female)')
model6 <- 'F1 = 1-2
F2 = 3-5
CONSTRAIN = (3-5, a2), (1-2, a1)
COV = F1*F2'
model7 <- mirt.model('F1 = 1-2
F2 = 3-5
START = (2, a2, 1.5), (4,a1,-1)')
model8 <- mirt.model('F1 = 1-2
F2 = 3-5
CONSTRAIN = (1, 3, a1, a2), (5, 2, a2, a1), (1-3, d)')
model9 <- mirt.model('F1 = 1-5
LBOUND = (1-3, g, 0.2), (4,5, g, 0.2)')
model10 <- mirt.model('F1 = 1-5
START = (1,3-4, a1, 1)
FIXED = (1-3, a1)')
model11 <- mirt.model('F1 = 1-5
PRIOR = (1-5, g, expbeta, 10, 40)')
set.seed(1234)
group <- sample(c('male', 'female'), 1000, TRUE)
mod0 <- mirt(data, model0, verbose=FALSE, calcNull=FALSE)
expect_equal(mod2values(mod0)$value, c(0.987973787231699, 1.85608912732841, 0, 1, 1.08103954211169, 0.808007534786952, 0, 1, 1.70595475896956, 1.80426768080187, 0, 1, 0.765076394253259, 0.486005938565521, 0, 1, 0.735771996169788, 1.85448564531374, 0, 1, 0, 1),
tolerance = 1e-2)
mod1 <- mirt(data, model1, verbose=FALSE, calcNull=FALSE)
expect_equal(mod2values(mod1)$value, c(0.987973787231699, 1.85608912732841, 0, 1, 1.08103954211169, 0.808007534786952, 0, 1, 1.70595475896956, 1.80426768080187, 0, 1, 0.765076394253259, 0.486005938565521, 0, 1, 0.735771996169788, 1.85448564531374, 0, 1, 0, 1),
tolerance = 1e-2)
mod2 <- mirt(data, model2, verbose=FALSE, calcNull=FALSE)
expect_equal(mod2values(mod2)$value, c(1.01052705474606, 1.86793304554809, 0, 1, 1.01052705474606, 0.790899504740889, 0, 1, 1.01052705474606, 1.46073200902294, 0, 1, 1.01052705474606, 0.521457445159032, 0, 1, 1.01052705474606, 1.99261823763434, 0, 1, 0, 1),
tolerance = 1e-2)
mod3 <- mirt(data, model3, verbose=FALSE, calcNull=FALSE)
expect_equal(mod2values(mod3)$value, c(1.10784174676958, 1.91239633478765, 0, 1, 1.04684043398884, 0.797880409701628, 0, 1, 1.04684043398884, 1.47310584064986, 0, 1, 1.04684043398884, 0.526104887330641, 0, 1, 1.04684043398884, 2.00877254688793, 0, 1, 0, 1),
tolerance = 1e-2)
mod4 <- multipleGroup(data, model4, group=group, verbose = FALSE)
expect_equal(mod2values(mod4)$value, c(0.6325624,1.723712,0,1,2.969184,1.723712,0,1,1.184273,1.492652,0,1,0.5588478,0.4108658,0,1,0.5027873,1.840031,0,1,0,1,0.6325624,1.497525,0,1,2.969184,1.497525,0,1,1.184273,1.643479,0,1,0.5588478,0.546225,0,1,0.5027873,1.699658,0,1,0,1),
tolerance = 1e-2)
mod5 <- multipleGroup(data, model5, group=group, verbose = FALSE)
expect_equal(mod2values(mod5)$value, c(0.736972,1.863245,0,1,1.486771,0.9502583,0,1,1.059434,1.392984,0,1,1.271924,0.4727863,0,1,0.4711075,1.811339,0,1,0,1,0.736972,1.357013,0,1,1.486771,1.357013,0,1,1.059434,1.357013,0,1,1.271924,1.357013,0,1,0.4711075,1.357013,0,1,0,1),
tolerance = 1e-2)
mod6 <- mirt(data, model6, verbose=FALSE, calcNull=FALSE)
expect_equal(mod2values(mod6)$value, c(1.074887,0,1.902959,0,1,1.074887,0,0.8065663,0,1,0,1.00348,1.458001,0,1,0,1.00348,0.520351,0,1,0,1.00348,1.989104,0,1,0,0,1,0.939999,1),
tolerance = 1e-2)
mod7 <- mirt(data, model7, verbose=FALSE, calcNull=FALSE)
expect_equal(as.numeric(coef(mod7, simplify=TRUE)$items), c(-1.153815,-0.2728293,0,-1,0,0,1.5,1.740508,0.5660805,0.5774055,1.945502,0.9276134,1.818513,0.5439836,1.789067,0,0,0,0,0,1,1,1,1,1),
tolerance = 1e-2)
mod8 <- mirt(data, model8, verbose=FALSE, calcNull=FALSE)
expect_equal(as.numeric(coef(mod8, simplify=TRUE)$items), c(0.5501291,3.146379,0,0,0,0,0,0.5501291,0.4096282,3.146379,1.46666,1.46666,1.46666,0.4535099,3.685736,0,0,0,0,0,1,1,1,1,1),
tolerance = 1e-2)
mod9 <- mirt(data, model9, '3PL', verbose=FALSE, calcNull=FALSE)
expect_equal(as.numeric(coef(mod9, simplify=TRUE)$items), c(1.09262,1.819549,2.095646,0.8938963,0.8182848,1.587373,0.1118206,1.542427,0.0396478,1.595971,0.2,0.2901414,0.2,0.2,0.2,1,1,1,1,1),
tolerance = 1e-2)
mod10 <- mirt(data, model10, '3PL', pars = 'values')
expect_equal(mod10$value[mod10$name == 'a1'], c(1, 0.851, 1, 1, .851), tolerance = 1e-4)
expect_equal(mod10$est[mod10$name == 'a1'], c(FALSE, FALSE, FALSE, TRUE, TRUE))
mod11 <- mirt(data, model11, '3PL', verbose=FALSE)
expect_equal(as.vector(unname(coef(mod11)[[1]])),
c(1.0767651, 1.6027628, 0.1871268, 1.0000000), tolerance = 1e-4)
data(data.read, package = 'sirt')
dat <- data.read
# syntax with variable names
mirtsyn2 <- "
F1 = A1,B2,B3,C4
F2 = A1-A4,C2,C4
MEAN = F1
COV = F1*F1, F1*F2
CONSTRAIN=(A2-A4,a2),(A3,C2,d)
PRIOR = (C3,A2-A4,a2,lnorm, .2, .2),(B3,d,norm,0,.0001)"
# create a mirt model
mirtmodel <- mirt.model(mirtsyn2, itemnames=dat)
# or equivelently:
mirtmodel2 <- mirt.model(mirtsyn2, itemnames=colnames(dat))
expect_true(all(mirtmodel$x == mirtmodel2$x))
got <- matrix(c(c('F1', 'F2', "MEAN", 'COV', 'CONSTRAIN', 'PRIOR'),
c("1,6,7,12", "1-4,10,12","F1","F1*F1,F1*F2","(2-4,a2),(3,10,d)",
"(11,2-4,a2,lnorm,.2,.2),(7,d,norm,0,.0001)")), nrow = 6)
expect_true(all(mirtmodel$x == got))
mod <- mirt(dat, mirtsyn2, TOL = NaN)
sv <- mod2values(mod)
expect_true(all(sv$est == c(TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE,FALSE)))
expect_true(all(as.character(sv$prior.type) == c("none","none","none","none","none","none","lnorm","none","none","none","none","lnorm","none","none","none","none","lnorm","none","none","none","none","none","none","none","none","none","none","none","none","none","none","none","norm","none","none","none","none","none","none","none","none","none","none","none","none","none","none","none","none","none","none","lnorm","none","none","none","none","none","none","none","none","none","none","none","none","none")))
})
xzhaopsy/MIRT documentation built on May 29, 2019, 12:42 p.m.
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context('mirt.model')
test_that('syntax', {
data <- expand.table(LSAT7)
model0 <- 'F = 1-5'
model1 <- mirt.model('F = 1-5')
model2 <- mirt.model('F = 1-5
CONSTRAIN = (1,2,3-5,a1)')
model3 <- mirt.model('F = 1-5
CONSTRAIN = (2,3-5,a1)
PRIOR = (2,3-5, a1, lnorm, .2, .2), (1, d, norm, 0, 2)')
model4 <- mirt.model('F = 1-5
CONSTRAIN = (1-2, d)
CONSTRAINB = (2-4,5,a1), (1, a1)
PRIOR = (1-5, d, norm, 0, 2)')
model5 <- mirt.model('F = 1-5
CONSTRAIN = (1-5, d, male)
CONSTRAINB = (1-4,5,a1)
PRIOR = (1-5, d, norm, 0, 2, female)')
model6 <- 'F1 = 1-2
F2 = 3-5
CONSTRAIN = (3-5, a2), (1-2, a1)
COV = F1*F2'
model7 <- mirt.model('F1 = 1-2
F2 = 3-5
START = (2, a2, 1.5), (4,a1,-1)')
model8 <- mirt.model('F1 = 1-2
F2 = 3-5
CONSTRAIN = (1, 3, a1, a2), (5, 2, a2, a1), (1-3, d)')
model9 <- mirt.model('F1 = 1-5
LBOUND = (1-3, g, 0.2), (4,5, g, 0.2)')
model10 <- mirt.model('F1 = 1-5
START = (1,3-4, a1, 1)
FIXED = (1-3, a1)')
model11 <- mirt.model('F1 = 1-5
PRIOR = (1-5, g, expbeta, 10, 40)')
set.seed(1234)
group <- sample(c('male', 'female'), 1000, TRUE)
mod0 <- mirt(data, model0, verbose=FALSE, calcNull=FALSE)
expect_equal(mod2values(mod0)$value, c(0.987973787231699, 1.85608912732841, 0, 1, 1.08103954211169, 0.808007534786952, 0, 1, 1.70595475896956, 1.80426768080187, 0, 1, 0.765076394253259, 0.486005938565521, 0, 1, 0.735771996169788, 1.85448564531374, 0, 1, 0, 1),
tolerance = 1e-2)
mod1 <- mirt(data, model1, verbose=FALSE, calcNull=FALSE)
expect_equal(mod2values(mod1)$value, c(0.987973787231699, 1.85608912732841, 0, 1, 1.08103954211169, 0.808007534786952, 0, 1, 1.70595475896956, 1.80426768080187, 0, 1, 0.765076394253259, 0.486005938565521, 0, 1, 0.735771996169788, 1.85448564531374, 0, 1, 0, 1),
tolerance = 1e-2)
mod2 <- mirt(data, model2, verbose=FALSE, calcNull=FALSE)
expect_equal(mod2values(mod2)$value, c(1.01052705474606, 1.86793304554809, 0, 1, 1.01052705474606, 0.790899504740889, 0, 1, 1.01052705474606, 1.46073200902294, 0, 1, 1.01052705474606, 0.521457445159032, 0, 1, 1.01052705474606, 1.99261823763434, 0, 1, 0, 1),
tolerance = 1e-2)
mod3 <- mirt(data, model3, verbose=FALSE, calcNull=FALSE)
expect_equal(mod2values(mod3)$value, c(1.10784174676958, 1.91239633478765, 0, 1, 1.04684043398884, 0.797880409701628, 0, 1, 1.04684043398884, 1.47310584064986, 0, 1, 1.04684043398884, 0.526104887330641, 0, 1, 1.04684043398884, 2.00877254688793, 0, 1, 0, 1),
tolerance = 1e-2)
mod4 <- multipleGroup(data, model4, group=group, verbose = FALSE)
expect_equal(mod2values(mod4)$value, c(0.6325624,1.723712,0,1,2.969184,1.723712,0,1,1.184273,1.492652,0,1,0.5588478,0.4108658,0,1,0.5027873,1.840031,0,1,0,1,0.6325624,1.497525,0,1,2.969184,1.497525,0,1,1.184273,1.643479,0,1,0.5588478,0.546225,0,1,0.5027873,1.699658,0,1,0,1),
tolerance = 1e-2)
mod5 <- multipleGroup(data, model5, group=group, verbose = FALSE)
expect_equal(mod2values(mod5)$value, c(0.736972,1.863245,0,1,1.486771,0.9502583,0,1,1.059434,1.392984,0,1,1.271924,0.4727863,0,1,0.4711075,1.811339,0,1,0,1,0.736972,1.357013,0,1,1.486771,1.357013,0,1,1.059434,1.357013,0,1,1.271924,1.357013,0,1,0.4711075,1.357013,0,1,0,1),
tolerance = 1e-2)
mod6 <- mirt(data, model6, verbose=FALSE, calcNull=FALSE)
expect_equal(mod2values(mod6)$value, c(1.074887,0,1.902959,0,1,1.074887,0,0.8065663,0,1,0,1.00348,1.458001,0,1,0,1.00348,0.520351,0,1,0,1.00348,1.989104,0,1,0,0,1,0.939999,1),
tolerance = 1e-2)
mod7 <- mirt(data, model7, verbose=FALSE, calcNull=FALSE)
expect_equal(as.numeric(coef(mod7, simplify=TRUE)$items), c(-1.153815,-0.2728293,0,-1,0,0,1.5,1.740508,0.5660805,0.5774055,1.945502,0.9276134,1.818513,0.5439836,1.789067,0,0,0,0,0,1,1,1,1,1),
tolerance = 1e-2)
mod8 <- mirt(data, model8, verbose=FALSE, calcNull=FALSE)
expect_equal(as.numeric(coef(mod8, simplify=TRUE)$items), c(0.5501291,3.146379,0,0,0,0,0,0.5501291,0.4096282,3.146379,1.46666,1.46666,1.46666,0.4535099,3.685736,0,0,0,0,0,1,1,1,1,1),
tolerance = 1e-2)
mod9 <- mirt(data, model9, '3PL', verbose=FALSE, calcNull=FALSE)
expect_equal(as.numeric(coef(mod9, simplify=TRUE)$items), c(1.09262,1.819549,2.095646,0.8938963,0.8182848,1.587373,0.1118206,1.542427,0.0396478,1.595971,0.2,0.2901414,0.2,0.2,0.2,1,1,1,1,1),
tolerance = 1e-2)
mod10 <- mirt(data, model10, '3PL', pars = 'values')
expect_equal(mod10$value[mod10$name == 'a1'], c(1, 0.851, 1, 1, .851), tolerance = 1e-4)
expect_equal(mod10$est[mod10$name == 'a1'], c(FALSE, FALSE, FALSE, TRUE, TRUE))
mod11 <- mirt(data, model11, '3PL', verbose=FALSE)
expect_equal(as.vector(unname(coef(mod11)[[1]])),
c(1.0767651, 1.6027628, 0.1871268, 1.0000000), tolerance = 1e-4)
data(data.read, package = 'sirt')
dat <- data.read
# syntax with variable names
mirtsyn2 <- "
F1 = A1,B2,B3,C4
F2 = A1-A4,C2,C4
MEAN = F1
COV = F1*F1, F1*F2
CONSTRAIN=(A2-A4,a2),(A3,C2,d)
PRIOR = (C3,A2-A4,a2,lnorm, .2, .2),(B3,d,norm,0,.0001)"
# create a mirt model
mirtmodel <- mirt.model(mirtsyn2, itemnames=dat)
# or equivelently:
mirtmodel2 <- mirt.model(mirtsyn2, itemnames=colnames(dat))
expect_true(all(mirtmodel$x == mirtmodel2$x))
got <- matrix(c(c('F1', 'F2', "MEAN", 'COV', 'CONSTRAIN', 'PRIOR'),
c("1,6,7,12", "1-4,10,12","F1","F1*F1,F1*F2","(2-4,a2),(3,10,d)",
"(11,2-4,a2,lnorm,.2,.2),(7,d,norm,0,.0001)")), nrow = 6)
expect_true(all(mirtmodel$x == got))
mod <- mirt(dat, mirtsyn2, TOL = NaN)
sv <- mod2values(mod)
expect_true(all(sv$est == c(TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE,FALSE)))
expect_true(all(as.character(sv$prior.type) == c("none","none","none","none","none","none","lnorm","none","none","none","none","lnorm","none","none","none","none","lnorm","none","none","none","none","none","none","none","none","none","none","none","none","none","none","none","norm","none","none","none","none","none","none","none","none","none","none","none","none","none","none","none","none","none","none","lnorm","none","none","none","none","none","none","none","none","none","none","none","none","none")))
})
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