AnalyseBinQmet: Statistical analysis of binary Q-method data

In MargotBr/BinQmet: Statistical analysis of binary Q-method data (stimulus-oriented analysis and participant-oriented analysis)

Description

Performs the statistical analysis of binary Q-method data through a stimulus-oriented approach and a participant-oriented approach. The method is able to manage covariates providing informations about the participants and the stimuli to facilitate the interpretation of the results.

Usage

AnalyseBinQmet(dta, id.info.stim = NULL, type.info.stim = NULL, id.info.part = NULL, type.info.part = NULL, graph = TRUE, ext.dev.Rstudio = FALSE, ...)

Arguments

dta	A numeric matrix or data frame of dimensions S x P (S=number of stimuli, P=number of participants). This is the minimum needed. But dta can also contain covariates about the stimuli as extra columns and covariates about the participants as extra lines. See the pedagdata dataset for an example.
id.info.stim	A vector of integer elements composed of the identification of the columns containing the supplementary information (i.e. covariates) about the stimuli. This argument is optional and, by default, it is fixed to NULL, meaning that dta does not contain supplementary information about the stimuli.
type.info.stim	A vector of character elements composed of the type of the covariates about the stimuli. This vector must be of the same length that id.info.stim. A continuous covariate is associated to 'cont' and a categorical covariate is associated to 'cat'. This argument is optional and, by default, it is fixed to NULL, meaning that dta does not contain supplementary information about the stimuli.
id.info.part	A vector of integer elements composed of the identification of the lines containing the supplementary information (i.e. covariates) about the participants. This argument is optional and, by default, it is fixed to NULL, meaning that dta does not contain supplementary information about the participants.
type.info.part	A vector of character elements composed of the type of the covariates about the participants. This vector must be of the same length that id.info.part. A continuous covariate is associated to 'cont' and a categorical covariate is associated to 'cat'. This argument is optional and, by default, it is fixed to NULL, meaning that dta does not contain supplementary information about the participants.
graph	A boolean specifying if the graphical outputs should be plotted or not. By default, they are plotted.
ext.dev.Rstudio	A boolean specifying if the graphical outputs should be plotted in the Rstudio plot pane or not.
...	Supplementary arguments of the AgreeClustBin function of the AgreeClust package.

Value

BinQmet.data	A matrix of dimensions S x (R + 2) (S=number of stimuli, R=number of participants) corresponding to the multiple table necessary to analyse binary Q-method data. The first table is of dimensions S x R and contains information about the structure into groups of stimuli, and the second table is of dimensions S x 2 and contains information about the degree of association with the concept.
res.mfa	All the results of the MFA realized during the stimulus-oriented statistical analysis.
concept.surface	The response surface computed during the stimulus-oriented statistical analysis. The response (predicted % of participants assessing each point as representative of the concept) is expressed according to the coordinates of the points provided by MFA.
res.AgreeClust	All the results of the agreement-based clustering of the participants realized during the participant-oriented statistical analysis.

Author(s)

Margot Brard [e-mail]

See Also

plot.AnalyseBinQmet

Examples

# Small pedagogic data set
data(pedagdata)
res.pedag <- AnalyseBinQmet(pedagdata, id.info.stim = 21 : ncol(pedagdata), type.info.stim = c(rep("cont", 4), "cat"), id.info.part = 9 : nrow(pedagdata), type.info.part = c(rep("cat", 2), "cont"))
res.pedag
plot.AnalyseBinQmet(res.pedag, interact = TRUE)

# 'Perfumes' data set
data(perfumes)
res.perfumes <- AnalyseBinQmet(perfumes, id.info.stim = 121 : ncol(perfumes), type.info.stim = rep("cont", 27), id.info.part = 40 : nrow(perfumes), type.info.part = rep("cat", 2))
res.perfumes
plot.AnalyseBinQmet(res.perfumes, interact = TRUE)

MargotBr/BinQmet documentation built on May 29, 2019, 10:52 a.m.