mr.sig.cell | R Documentation |
This function performs for each pair of category and response option a multiple-response hypergeometric test as defined in Mahieu, Schlich, Visalli, and Cardot (2021) using random hypergeometric samplings to estimate the null distribution
mr.sig.cell(data, nsample = 2000, nbaxes.sig = Inf, two.sided = TRUE)
data |
A data.frame of observations in rows whose first column is a factor (the categories) and subsequent columns are binary numeric or integer, each column being a response option |
nsample |
Number of randomly sampled datasets to estimate the distribution of the value under the null hypothesis. See details |
nbaxes.sig |
The number of significant axes retuned by |
two.sided |
Logical. Should the tests be two-sided or not? |
nsample: The distribution of the value under the null hypothesis of no associations between categories and response options is estimated using nsample datasets generated thanks to random hypergeometric samplings of the response vectors along observations.
nbaxes.sig: If nbaxes.sig is lower than the total number of axes then the tests are performed on the derived contingency table corresponding to significant axes (Mahieu, Schlich, Visalli, & Cardot, 2021). This table is obtained by using the reconstitution formula of MR-CA on the first nbaxes.sig axes.
A list with the following elements:
Observed number of times each category chosen each response option
Within each category, percentage of observations where the response options were chosen
Expected number of times each category chosen each response option under the null hypothesis
P-values of the tests per cell fdr adjusted by response option
The derived contingency table corresponding to nbaxes.sig axes
Within each category, percentage of observations where the response options were chosen in the derived contingency table corresponding to nbaxes.sig axes
Loughin, T. M., & Scherer, P. N. (1998). Testing for Association in Contingency Tables with Multiple Column Responses. Biometrics, 54(2), 630-637.
Mahieu, B., Schlich, P., Visalli, M., & Cardot, H. (2021). A multiple-response chi-square framework for the analysis of Free-Comment and Check-All-That-Apply data. Food Quality and Preference, 93.
nb.obs=200
nb.response=5
nb.category=5
vec.category=paste("C",1:nb.category,sep="")
right=matrix(rbinom(nb.response*nb.obs,1,0.25),nb.obs,nb.response)
category=sample(vec.category,nb.obs,replace = TRUE)
dset=cbind.data.frame(category,right)
dset$category=as.factor(dset$category)
res=mr.sig.cell(dset)
plot(res)
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