sirt (version 1.14-0)

sia.sirt: Statistical Implicative Analysis (SIA)

Description

This function is a simplified implementation of statistical implicative analysis (Gras & Kuntz, 2008) which aims at deriving implications $X_i \rightarrow X_j$. This means that solving item $i$ implies solving item $j$.

Usage

sia.sirt(dat, significance = 0.85)

Arguments

dat

Data frame with dochotomous item responses

significance

Minimum implicative probability for inclusion of an arrow in the graph. The probability can be interpreted as a kind of significance level, i.e. higher probabilities indicate more probable implications.

Value

A list with following entries

Details

The test statistic for selection an implicative relation follows Gras and Kuntz (2008). Transitive arrows (implications) are removed from the graph. If some implications are symmetric, then only the more probable implication will be retained.

References

Gras, R., & Kuntz, P. (2008). An overview of the statistical implicative analysis (SIA) development. In R. Gras, E. Suzuki, F. Guillet, & F. Spagnolo (Eds.). Statistical Implicative Analysis (pp. 11-40). Springer, Berlin Heidelberg.

See Also

See also the IsingFit package for calculating a graph for dichotomous item responses using the Ising model.

Examples

#############################################################################
# EXAMPLE 1: SIA for data.read
#############################################################################

data(data.read)
dat <- data.read

res <- sia.sirt(dat , significance=.85 )

#*** plot results with igraph package
library(igraph)
plot( res$igraph.obj ) # , vertex.shape="rectangle" , vertex.size=30 )

## Not run: 
# #*** plot results with qgraph package
# miceadds::library_install(qgraph)
# qgraph::qgraph( res$adj.matrix )
# 
# #*** plot results with Rgraphviz package
# # Rgraphviz can only be obtained from Bioconductor
# # If it should be downloaded, select TRUE for the following lines
# if (FALSE){
#      source("http://bioconductor.org/biocLite.R")
#      biocLite("Rgraphviz")
#             }
# # define graph
# grmatrix <- res$graph.matr
# res.graph <- new("graphNEL", nodes= res$graph.edges , edgemode="directed")
# # add edges
# RR <- nrow(grmatrix)
# for (rr in 1:RR){
#     res.graph <- Rgraphviz::addEdge(grmatrix[rr,1], grmatrix[rr,2], res.graph , 1)
#                     }                                                          
# # define cex sizes and shapes
# V <- length(res$graph.edges)
# size2 <- rep(16,V)
# shape2 <- rep("rectangle" , V )
# names(shape2) <- names(size2) <- res$graph.edges
# # plot graph
# Rgraphviz::plot( res.graph, nodeAttrs =list("fontsize" = size2 , "shape" = shape2) )
# ## End(Not run)