R/pugesek.R

In gustavobio/semeco: Examples of Structural Equation Modelling Usage in Ecology

#' Bumpus house sparrow data and model.
#'
#' Pugesek and Tomer reanalyse this classic dataset using structural
#' equation modeling.
#'
#' @docType data
#' @format Two data frames, one untransformed and one log-transformed, with 136
#' observations on 13 variables. Also, two character vectors with the
#' syntaxes of two of the models described in the paper.
#' @note
#' According to the authors, they used log-transformed data in their
#' analyses, so there are two datasets: \code{bumpus} and
#' \code{bumpus.log}. Not all traits measured were used by the authors in
#' the paper. All analyses used sex as the grouping variable. The
#' one-factor model is in page 392, figure 3 and table 1. The
#' three-factor model is in page 393 and 394, figure 5 and table 2. The
#' survival model in page 395 could not be fitted as there is no support
#' for categorical variables in either \code{\link{lavaan}}.
#'
#' Three-factor models 2 and 3 converge and we get somewhat similar
#' loadings, but there are negative variances, which indicate Heywood
#' cases. This might be caused by the equality constraints we imposed in
#' some of the loadings to try to mimic the results from the paper.
#'
#' @usage
#' data(pugesek)
#' bumpus
#' bumpus.log
#' pugesek.onefactor.model
#' pugesek.threefactors.model
#'
#' @name pugesek
#'
#' @source
#' Article:
#' \url{https://springerlink3.metapress.com/content/m48860525r801047/resource-secured/?target=fulltext.pdf}
#' Data:
#' \url{http://fm1.fieldmuseum.org/aa/staff_page.cgi?staff=lowther&id=432}
#'
#' @references
#' Pugesek, BH and Tomer, A. 1996. The Bumpus house sparrow data: a
#' reanalysis using structural equation models. Evolutionary Ecology 10:
#' 387-404.
#'
#' @examples
#' \dontrun{
#' data(pugesek)
#' cat(pugesek.onefactor.model)
#' cat(pugesek.threefactors.model)
#'
#' pugesek.onefactor.1 <- sem(pugesek.onefactor.model, data = bumpus.log, fixed.x = FALSE, group =
#'                              "sex", std.lv = 1, estimator = "MLM")
#' pugesek.onefactor.2 <- sem(pugesek.onefactor.model, data = bumpus.log, fixed.x = FALSE, group =
#'                              "sex", std.lv = 1, group.equal = "loadings")
#' pugesek.onefactor.3 <- sem(pugesek.onefactor.model, data = bumpus.log, fixed.x = FALSE, group =
#'                              "sex", std.lv = 1, group.equal = c("loadings",
#'                                                                 "residuals"))
#' pugesek.threefactors.1 <- sem(pugesek.threefactors.model, data = bumpus.log, fixed.x = FALSE, group =
#'                                 "sex", orthogonal = TRUE,
#'                               std.lv = 1)
#' pugesek.threefactors.2 <- sem(pugesek.threefactors.model, data = bumpus.log, fixed.x = FALSE, group =
#'                                 "sex", orthogonal = TRUE,
#'                               std.lv = 1, group.equal = c("loadings"))
#' pugesek.threefactors.3 <- sem(pugesek.threefactors.model, data = bumpus.log,
#'                               fixed.x = FALSE, std.lv = TRUE, orthogonal = TRUE)
#'
#' summary(pugesek.onefactor.1, standardize = TRUE)
#' summary(pugesek.onefactor.2, standardize = TRUE)
#' summary(pugesek.onefactor.3, standardize = TRUE)
#'
#' summary(pugesek.threefactors.1, standardize = TRUE)
#' summary(pugesek.threefactors.2, standardize = TRUE)
#' summary(pugesek.threefactors.3, standardize = TRUE)
#' }
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