R/stat_cross.R
augment_chisq_add_phi.RdAugment a chi-squared test and compute phi coefficients
augment_chisq_add_phi(x)a chi-squared test as returned by stats::chisq.test()
A tibble.
Phi coefficients are a measurement of the degree of association between two binary variables.
A value between -1.0 to -0.7 indicates a strong negative association.
A value between -0.7 to -0.3 indicates a weak negative association.
A value between -0.3 to +0.3 indicates a little or no association.
A value between +0.3 to +0.7 indicates a weak positive association.
A value between +0.7 to +1.0 indicates a strong positive association.
stat_cross(), GDAtools::phi.table() or psych::phi()
tab <- xtabs(Freq ~ Sex + Class, data = as.data.frame(Titanic))
augment_chisq_add_phi(chisq.test(tab))
#> # A tibble: 8 × 13
#> Sex Class .observed .prop .row.prop .col.prop .expected .resid .std.resid
#> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Male 1st 180 0.0818 0.104 0.554 256. -4.73 -11.1
#> 2 Female 1st 145 0.0659 0.309 0.446 69.4 9.07 11.1
#> 3 Male 2nd 179 0.0813 0.103 0.628 224. -3.02 -6.99
#> 4 Female 2nd 106 0.0482 0.226 0.372 60.9 5.79 6.99
#> 5 Male 3rd 510 0.232 0.295 0.722 555. -1.92 -5.04
#> 6 Female 3rd 196 0.0891 0.417 0.278 151. 3.68 5.04
#> 7 Male Crew 862 0.392 0.498 0.974 696. 6.29 17.6
#> 8 Female Crew 23 0.0104 0.0489 0.0260 189. -12.1 -17.6
#> # ℹ 4 more variables: .row.observed <dbl>, .col.observed <dbl>,
#> # .total.observed <dbl>, .phi <dbl>