R: prop.test returns different values based on whether matrix or vectors are passed to it -

why r's prop.test function (documentation here) return different results based on whether pass matrix or vectors?

here pass vectors:

> prop.test(x = c(135, 47), n = c(1781, 1443))      2-sample test equality of proportions     continuity correction  data:  c(135, 47) out of c(1781, 1443) x-squared = 27.161, df = 1, p-value = 1.872e-07 alternative hypothesis: two.sided 95 percent confidence interval:  0.02727260 0.05918556 sample estimates:     prop 1     prop 2  0.07580011 0.03257103  

here create matrix , pass in instead:

> table <- matrix(c(135, 47, 1781, 1443), ncol=2) > prop.test(table)      2-sample test equality of proportions     continuity correction  data:  table x-squared = 24.333, df = 1, p-value = 8.105e-07 alternative hypothesis: two.sided 95 percent confidence interval:  0.02382527 0.05400606 sample estimates:     prop 1     prop 2  0.07045929 0.03154362  

why different results? expect same results both scenarios returned.

when x , n entered separate vectors, treated, respectively, number of successes , total number of trials. when enter matrix, first column treated number of successes , second number of failures. prop.test:

x    vector of counts of successes, one-dimensional table 2      entries, or two-dimensional table (or matrix) 2 columns, giving      counts of successes , failures, respectively. 

so, same result matrix, need convert second column of matrix number of failures (assuming in example x number of successes , n number of trials).

x = c(135, 47) n = c(1781, 1443)  prop.test(x, n)  # x = successes; n = total trials 

  2-sample test equality of proportions continuity correction  data:  x out of n x-squared = 27.161, df = 1, p-value = 1.872e-07 alternative hypothesis: two.sided 95 percent confidence interval:  0.02727260 0.05918556 sample estimates:     prop 1     prop 2  0.07580011 0.03257103 

prop.test(cbind(x, n - x)) # x = successes; convert n number of failures 

  2-sample test equality of proportions continuity correction  data:  cbind(x, n - x) x-squared = 27.161, df = 1, p-value = 1.872e-07 alternative hypothesis: two.sided 95 percent confidence interval:  0.02727260 0.05918556 sample estimates:     prop 1     prop 2  0.07580011 0.03257103