library("mirt") ## mirt() example data <- expand.table(LSAT7) (mod1 <- mirt(data, 1)) coef(mod1) summary(mod1) residuals(mod1) residuals(mod1, restype = "exp") (mod2 <- mirt(data, 2)) summary(mod2, rotate = "oblimin", suppress = 0.25) anova(mod1, mod2) ## bfactor() example data("SAT12") data <- key2binary(SAT12, key = c(1, 4, 5, 2, 3, 1, 2, 1, 3, 1, 2, 4, 2, 1, 5, 3, 4, 4, 1, 4, 3, 3, 4, 1, 3, 5, 1, 3, 1, 5, 4, 5)) specific <- c(2, 3, 2, 3, 3, 2, 1, 2, 1, 1, 1, 3, 1, 3, 1, 2, 1, 1, 3, 3, 1, 1, 3, 1, 3, 3, 1, 3, 2, 3, 1, 2) guess <- rep(0.1, 32) b_mod1 <- bfactor(data, specific, guess) coef(b_mod1) b_mod2 <- bfactor(data, specific, guess, par.prior = list(int = c(0, 4), int.items = 32)) coef(b_mod2) ## polymirt() example #simulated data parameters from ?simdata a <- matrix(c( .7471, .0250, .1428, .4595, .0097, .0692, .8613, .0067, .4040, 1.0141, .0080, .0470, .5521, .0204, .1482, 1.3547, .0064, .5362, 1.3761, .0861, .4676, .8525, .0383, .2574, 1.0113, .0055, .2024, .9212, .0119, .3044, .0026, .0119, .8036, .0008, .1905,1.1945, .0575, .0853, .7077, .0182, .3307,2.1414, .0256, .0478, .8551, .0246, .1496, .9348, .0262, .2872,1.3561, .0038, .2229, .8993, .0039, .4720, .7318, .0068, .0949, .6416, .3073, .9704, .0031, .1819, .4980, .0020, .4115,1.1136, .2008, .1536,1.7251, .0345, .1530, .6688, .0020, .2890,1.2419, .0220, .1341,1.4882, .0050, .0524, .4754, .0012, .2139, .4612, .0063, .1761,1.1200, .0870),30,3,byrow=TRUE) d <- matrix(c(.1826,-.1924,-.4656,-.4336,-.4428,-.5845,-1.0403, .6431,.0122,.0912,.8082,-.1867,.4533,-1.8398,.4139, -.3004,-.1824,.5125,1.1342,.0230,.6172,-.1955,-.3668, -1.7590,-.2434,.4925,-.3410,.2896,.006,.0329),ncol=1) simdata1 <- simdata(a, d, 2000) p_mod <- polymirt(simdata1, 3) p_mod coef(p_mod) ## confmirt() example #data from Appendix B a <- matrix(c(1.5, NA, 0.5, NA, 1, NA, 1, 0.5, NA, 1.5, NA, 0.5, NA, 1, NA, 1), ncol = 2, byrow = TRUE) d <- matrix(c(-1, NA, NA, -1.5, NA, NA, 1.5, NA, NA, 0, NA, NA, 3, 2, -0.5, 2.5, 1, -1, 2, 0, NA, 1, NA, NA), ncol = 3, byrow = TRUE) sigma <- diag(2) sigma[1, 2] <- sigma[2, 1] <- 0.4 simdata2 <- simdata(a, d, 2000, sigma) model.1 <- confmirt.model() F1 = 1-4 F2 = 4-8 COV = F1*F2 c_mod <- confmirt(simdata2, model.1, printcycles = FALSE) c_mod coef(c_mod) model.2 <- confmirt.model() F1 = 1-4 F2 = 4-8 COV = F1*F2 SLOPE = F1@1 eq 1.346, F1@3 eq F1@4, F2@7 eq F2@8 c_mod2 <- confmirt(simdata2, model.2, printcycles = FALSE) coef(c_mod2) anova(c_mod2, c_mod) ## Code from Appendix A plot(mod1) plot(mod2) itemplot(mod1, combine = 5, auto.key=list(space="right")) fscores(mod1) dataWithFS <- fscores(mod1, full.scores = TRUE) ## Code from Appendix B a <- matrix(c(1.5, NA, 0.5, NA, 1, NA, 1, 0.5, NA, 1.5, NA, 0.5, NA, 1, NA, 1), ncol = 2, byrow = TRUE) d <- matrix(c(-1, NA, NA, -1.5, NA, NA, 1.5, NA, NA, 0, NA, NA, 3, 2, -0.5, 2.5, 1, -1, 2, 0, NA, 1, NA, NA), ncol = 3, byrow = TRUE) sigma <- diag(2) sigma[1, 2] <- sigma[2, 1] <- 0.4 simdata2 <- simdata(a, d, 2000, sigma)