# Compute Output from an Estimated Latent Variable Model A function to compute different quantities for latent variable models estimated with lamle. Currently supports computing . ```r lamle.compute(x, compute = "prob", ...) ``` ## Arguments - `x`: An estimated model from function lamle. - `compute`: A character vector indicating what to compute. Options are 'prob' for observed variable category probabilities, 'expectedscore' for observed variable expected score functions, 'TCC' for the test characteristic curve (i.e. the expected score for the sum of all observed variables), 'info' for observed variable information functions and 'testinfo' for test information functions (i.e. the information function for the sum of all observed variables). - `...`: Arguments to pass to the internal functions. Argument 'variables' specifies which variables to compute things for, argument 'group' indicates which group to compute things for and argument 'z' specifies the latent variable values to use in the computations. ## Returns A numeric vector with fit statistics. ## Author(s) Björn Andersson and Shaobo Jin . ## References Andersson, B., and Xin, T. (2021). Estimation of latent regression item response theory models using a second-order Laplace approximation. **Journal of Educational and Behavioral Statistics**, 46(2) 244-265. Jin, S., Noh, M., and Lee, Y. (2018). H-Likelihood Approach to Factor Analysis for Ordinal Data. **Structural Equation Modeling: A Multidisciplinary Journal**, 25(4) , 530-540. Shun, Z., and McCullagh, P. (1995). Laplace approximation of high dimensional integrals. **Journal of the Royal Statistical Society: Series B (Methodological)**, 57(4) , 749-760. ## Examples ```r ##### Load required package. library(mvtnorm) ##### Generate ordinal data from the GPCM ##### ##### Item parameter generation set.seed(123) GPCMa <- runif(10, 0.8, 2) GPCMb <- vector("list", 10) for(i in 1:10) GPCMb[[i]] <- -c(runif(1, -3, -2), runif(1, -1.5, -0.5), runif(1, 0, 1), runif(1, 1.5, 2.5)) GMCMa2d <- matrix(0, nrow = 10, ncol = 2) GMCMa2d[1:5, 1] <- GPCMa[1:5] GMCMa2d[6:10, 2] <- GPCMa[6:10] ##### Latent variables in two groups n <- 200 set.seed(1234) covmat2d <- diag(rep(1, 2)) covmat2d[!diag(2)] <- 0.6 latmat2dA <- rmvnorm(n, c(0, 0), covmat2d) latmat2dB <- rmvnorm(n, c(1, 1), covmat2d) ##### Observed data set.seed(12345) dataGPCM2d <- matrix(NA, nrow = 2 * n, ncol = 10) dataGPCM2d[1:n, ] <- DGP(GMCMa2d, GPCMb, rep("GPCM", 10), latmat2dA) dataGPCM2d[(n + 1):(2 * n), ] <- DGP(GMCMa2d, GPCMb, rep("GPCM", 10), latmat2dB) ##### Setup two-dimensional independent-clusters model mydim2d <- matrix(NA, nrow = 10, ncol = 2) mydim2d[1:5, 1] <- 1 mydim2d[6:10, 2] <- 1 ##### Lap(2) estLap2 <- lamle(y = dataGPCM2d[1:n,], model = mydim2d, modeltype = rep(c("GPCM", "GRM"), 5), method = "lap", accuracy = 2, optimizer = "BFGS", inithess = "crossprod") probsDim1Lap2 <- lamle.compute(estLap2, compute = "prob", variables = 1:5) ```

lamle.compute function