Description Usage Arguments Details Value References Examples
View source: R/200.OS.Reversal.Association.Pattern.R
Given an input matrix, we can split it into smaller sub-matrix (min 2x2) and then find the Chi-squared test for each sub-matrix. The smaller matrix can "support" or "oppose" (have a different conclusion at 95 test value of the full input matrix. We count the number of times each cell supports or opposes the overall Chi-squared test. We also generate the possible list of sub-matrix.
1 |
mat |
- matrix for which the sub-matrix is to be generated |
This can be used as an outlier detection method as well as observing the individual cells within an IxJ table
A dataframe with
Dimention.row |
Number of rows |
Dimention.col |
Number of columns |
Chi-sq-test.value |
Chi squared test value |
Significance at .95 |
Test for significance at .95 confidence level |
[1] J.Berkson Some difficulties of interpretation encountered in the application of the chi-square test Journal of the American Statistical Association. 33, 1938, 526-536. [2] H.W,Norton Calculation of chi-square for complex contingency tables Journal of the American Statistical Association. 40, 1945, 251-258. [3] C.R. Blyth On Simpsons paradox and the sure thing principle. Journal of the American Statistical Association. 67, 1972, 364-366. [4] A.Agresti Categorical Data Analysis (New York: Wiley & Sons 1990) pp 51-54.
1 2 3 4 5 6 | ## Most influential school of Psychiatric thought and ascribed origin of schizophrenia- Agresti 1992
Eclectic=c(90, 12, 78) # Example from [reference 4]
Medical=c(13, 1, 6)
Psychoanalytic=c(19, 13, 50)
mat=rbind(Eclectic,Medical,Psychoanalytic)
Reversal.Association.Finder(mat)
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