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Christoph Kiefer
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update tests and summary
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‎DESCRIPTION

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Package: lavacreg
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Type: Package
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Title: Latent Variable Count Regression Models
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Version: 0.1-0
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Date: 2021-01-20
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Version: 0.1-1
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Date: 2021-03-01
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Authors@R: c(person("Christoph", "Kiefer",
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email = "[email protected]",
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role = c("cre", "aut"),

‎NAMESPACE

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@@ -13,6 +13,7 @@ importFrom(stats,as.formula)
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importFrom(stats,dpois)
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importFrom(stats,na.omit)
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importFrom(stats,nlminb)
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importFrom(stats,pnorm)
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importFrom(stats,terms.formula)
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importFrom(utils,combn)
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useDynLib(lavacreg)

‎NEWS.md

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# lavacreg 0.1-1
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* skipping two tests failing on MacOS (i.e. applications in which only one latent covariate is considered)
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* lowered numerical precision for test on Poisson regression with three manifest covariates (for Solaris)
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* added a more detailed summary-function
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# lavacreg 0.1-0
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* Added a `NEWS.md` file to track changes to the package.

‎R/00documentation.R

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#' A first example to illustrate
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#' A first example dataset to illustrate the use of lavacreg
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#'
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#' A dataset containing ... .
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#' A dataset containing 9 variables: a dependent variable dv, a group variable treat
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#' and 7 indicators for 3 latent covariates.
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#'
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#' @format A data frame with 871 rows and 9 variables:
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#' \describe{

‎R/00methods.R

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#' @param object lavacreg object
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#' @return Function prints the parameter table of an estimated model, which
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#' includes the parameter estimates and standard errors.
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#' @importFrom stats pnorm
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setMethod("summary", signature(object="lavacreg"),
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function(object) {
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input <- object@input
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dataobj <- object@dataobj
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pt <- object@fit$pt
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pt$SE <- NULL
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SE <- sqrt(diag(object@fit$vcov_fit))
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pt$pval <- pt$zval <- pt$SE <- NA
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vcov_fit <- object@fit$vcov_fit
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if (object@input@se & !is.null(vcov_fit)){
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SE <- sqrt(diag(object@fit$vcov_fit))
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pt$SE[as.logical(pt$par_free)] <- SE
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pt$zval <- pt$par/pt$SE
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pt$pval <- 2*(1-pnorm(abs(pt$zval)))
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}
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for (g in 1:dataobj@no_groups){
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pt_g <- pt[pt$group == g,]
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cat(paste0("\n\n--------------------- Group ",g," --------------------- \n\n"))
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# Print regression coefficients
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cat("Regression:\n")
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res <- subset(pt_g, pt_g$dest == "regcoef", select = c("rhs", "par", "SE", "zval", "pval"))
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rownames(res) <- res$rhs
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res <- res[,-1]
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names(res) <- c("Estimate", "SE", "Est./SE", "p-value")
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print(res, digits=3, print.gap=3)
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if (input@family != "poisson"){
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# Print overdispersion parameter if it exists
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res <- subset(pt_g, pt_g$type == "size", select = c("par", "SE", "zval", "pval"))
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rownames(res) <- "Dispersion"
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names(res) <- c("Estimate", "SE", "Est./SE", "p-value")
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print(res, digits=3, print.gap=3)
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}
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if (dataobj@no_z | dataobj@no_lv){
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# Print means and variances of the covariates
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cat("\nMeans:\n")
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res <- subset(pt_g, pt_g$type == "mean", select = c("lhs", "par", "SE", "zval", "pval"))
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rownames(res) <- res$lhs
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res <- res[,-1]
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names(res) <- c("Estimate", "SE", "Est./SE", "p-value")
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print(res, digits=3, print.gap=3)
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cat("\nVariances:\n")
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res <- subset(pt_g, pt_g$type == "var", select = c("lhs", "par", "SE", "zval", "pval"))
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rownames(res) <- res$lhs
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res <- res[,-1]
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names(res) <- c("Estimate", "SE", "Est./SE", "p-value")
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print(res, digits=3, print.gap=3)
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}
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if (dataobj@no_z + dataobj@no_lv >= 2){
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# Print covariances of covariates
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cat("\nCovariances:\n")
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res <- subset(pt_g, pt_g$type == "cov" | pt_g$type == "cov_z_lv",
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select = c("lhs", "op", "rhs", "par", "SE", "zval", "pval"))
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rownames(res) <- paste(res$lhs, res$op, res$rhs)
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res <- res[,-c(1:3)]
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names(res) <- c("Estimate", "SE", "Est./SE", "p-value")
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print(res, digits=3, print.gap=3)
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}
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if (dataobj@no_lv){
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# Print measurement model
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cat("\nMeasurement Model:\n")
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res <- subset(pt_g, (pt_g$dest == "mm" | pt_g$type == "veps") & pt_g$par_free > 0,
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select = c("lhs", "op", "rhs", "par", "SE", "zval", "pval"))
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rownames(res) <- paste(res$lhs, res$op, res$rhs)
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res <- res[,-c(1:3)]
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names(res) <- c("Estimate", "SE", "Est./SE", "p-value")
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print(res, digits=3, print.gap=3)
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}
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}
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pt$SE[as.logical(pt$par_free)] <- SE
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print(pt)
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}
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)

‎R/RcppExports.R

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.Call('_lavacreg_compute_groupcond_logl', PACKAGE = 'lavacreg', x, muy, sigmayw, muwz, sigmaz, ghweight, detvarz, dims)
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}
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creg_group_logl_cpp <- function(datalist, modellist) {
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.Call('_lavacreg_creg_group_logl_cpp', PACKAGE = 'lavacreg', datalist, modellist)
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}
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‎R/creg_loglikelihood.R

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#' @keywords internal
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#' @noRd
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creg_loglikelihood_function <- function(datalist, modellist) {
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# browser()
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kappas <- modellist$groupw
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n_cell <- modellist$n_cell
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no_groups <- length(kappas)
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family <- modellist$family
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obj.group <- sum(dpois(n_cell, exp(kappas), log = TRUE))
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# obj.ingroups <- creg_group_logl_cpp(datalist, modellist$modellist_g)
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obj.ingroups <- mapply(function(data, modellist_g) {
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#data <- datalist[[g]]
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#modellist_g <- modellist$modellist_g[[g]]
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muy <- modellist_g$muy
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sigmayw <- modellist_g$sigmayw
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muwz <- modellist_g$muwz
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sigmaz <- modellist_g$sigmaz
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ghweight <- modellist_g$ghweight
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detvarz <- modellist_g$detvarz
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dims <- modellist_g$dims
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if (any(!is.na(sigmaz))){
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if (any(diag(solve(sigmaz)) <= 0)) return(-Inf)
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}
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}
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if (any(sigmayw[-1] <= 0)) return(-Inf)
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if (family == "nbinom" & sigmayw[1] <= 0) return(-Inf)
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obj.i <- compute_groupcond_logl(x = data, muy = muy, sigmayw = sigmayw, muwz = muwz,
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sigmaz = sigmaz, ghweight = ghweight, detvarz = detvarz, dims = dims)
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obj.i <- compute_groupcond_logl(x = data, muy = muy, sigmayw = sigmayw, muwz = muwz,
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sigmaz = sigmaz, ghweight = ghweight, detvarz = detvarz,
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dims = dims)
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if(is.na(obj.i)) return(-Inf)
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return(obj.i)
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}, data = datalist, modellist_g = modellist$modellist_g, SIMPLIFY = TRUE)
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obj <- -(obj.group + sum(obj.ingroups))/sum(n_cell)
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if (is.na(obj)) browser()
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if (is.na(obj)) return(+Inf)
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return(obj)
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}

‎docs/404.html

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‎docs/articles/index.html

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