contingency

问题 The Fisher's Exact Test is related to the hypergeometric distribution, and I would expect that these two commands would return identical pvalues. Can anyone explain what I'm doing wrong that they do not match? #data (variable names chosen to match dhyper() argument names) x = 14 m = 20 n = 41047 k = 40 #Fisher test, alternative = 'greater' (fisher.test(matrix(c(x, m-x, k-x, n-(k-x)),2,2), alternative='greater'))$p.value #returns 2.01804e-39 #geometric distribution, lower.tail = F, i.e. P[X >

问题 The Fisher's Exact Test is related to the hypergeometric distribution, and I would expect that these two commands would return identical pvalues. Can anyone explain what I'm doing wrong that they do not match? #data (variable names chosen to match dhyper() argument names) x = 14 m = 20 n = 41047 k = 40 #Fisher test, alternative = 'greater' (fisher.test(matrix(c(x, m-x, k-x, n-(k-x)),2,2), alternative='greater'))$p.value #returns 2.01804e-39 #geometric distribution, lower.tail = F, i.e. P[X >

问题 I want to reshape my data from 4x3 to 2x2 in pyspark without aggregating. My current output is the following: columns = ['FAULTY', 'value_HIGH', 'count'] vals = [ (1, 0, 141), (0, 0, 140), (1, 1, 21), (0, 1, 12) ] What I want is a contingency table with the second column as two new binary columns ( value_HIGH_1 , value_HIGH_0 ) and the values from the count column - meaning: columns = ['FAULTY', 'value_HIGH_1', 'value_HIGH_0'] vals = [ (1, 21, 141), (0, 12, 140) ] 回答1: You can use pivot with

问题 I want to reshape my data from 4x3 to 2x2 in pyspark without aggregating. My current output is the following: columns = ['FAULTY', 'value_HIGH', 'count'] vals = [ (1, 0, 141), (0, 0, 140), (1, 1, 21), (0, 1, 12) ] What I want is a contingency table with the second column as two new binary columns ( value_HIGH_1 , value_HIGH_0 ) and the values from the count column - meaning: columns = ['FAULTY', 'value_HIGH_1', 'value_HIGH_0'] vals = [ (1, 21, 141), (0, 12, 140) ] 回答1: You can use pivot with

来源： https://stackoverflow.com/questions/64227592/table-in-r-to-be-weighted

来源： https://stackoverflow.com/questions/64227592/table-in-r-to-be-weighted

来源： https://stackoverflow.com/questions/64227592/table-in-r-to-be-weighted

来源： https://stackoverflow.com/questions/42225214/r-count-or-contingency-tables-produced-using-a-loop

问题 I used to create FlexTable-objects from ‘flat’ contingency tables (ftable, stats-package) based on the old packages reporteRs and rtable. Before these packages became obsolete and were removed from CRAN, there has been a function as.Flextable.ftable , which did the trick. --> See: https://rdrr.io/cran/rtable/man/as.FlexTable.ftable.html Is there a way to achieve this conversion for the new flextable package? I couldn't find a similar function yet. 回答1: that a very good question. The migration

问题 I used to create FlexTable-objects from ‘flat’ contingency tables (ftable, stats-package) based on the old packages reporteRs and rtable. Before these packages became obsolete and were removed from CRAN, there has been a function as.Flextable.ftable , which did the trick. --> See: https://rdrr.io/cran/rtable/man/as.FlexTable.ftable.html Is there a way to achieve this conversion for the new flextable package? I couldn't find a similar function yet. 回答1: that a very good question. The migration