Performs the chi-squared goodness-of-fit test for the particular case
of an uniform distribution in the unit interval
(by grouping data into bins, y = floor(nclass*x) + 1,
and calling chisq.test(y)).
Used for testing random number generators.
Arguments
- x
numeric vector containing the observed values (or the pseudorandom numbers).
- nclass
number of bins (to partition the unit interval).
Value
A list with class "htest" returned by chisq.test().
See also
Examples
set.rng(321, "lcg", a = 5, c = 1, m = 512) # set.seed(321)
nsim <- 500
res <- freq.test(rng(nsim))
res # Pearson's Chi-squared test
#>
#> Chi-squared test for given probabilities
#>
#> data: rng(nsim)
#> X-squared = 2.2, df = 49, p-value = 1
#>
# Pearson's Chi-squared test table
sapply(res[c("observed", "expected", "residuals", "stdres")], as.vector)
#> observed expected residuals stdres
#> [1,] 11 10 0.3162278 0.3194383
#> [2,] 9 10 -0.3162278 -0.3194383
#> [3,] 10 10 0.0000000 0.0000000
#> [4,] 10 10 0.0000000 0.0000000
#> [5,] 11 10 0.3162278 0.3194383
#> [6,] 10 10 0.0000000 0.0000000
#> [7,] 9 10 -0.3162278 -0.3194383
#> [8,] 10 10 0.0000000 0.0000000
#> [9,] 10 10 0.0000000 0.0000000
#> [10,] 10 10 0.0000000 0.0000000
#> [11,] 10 10 0.0000000 0.0000000
#> [12,] 9 10 -0.3162278 -0.3194383
#> [13,] 11 10 0.3162278 0.3194383
#> [14,] 9 10 -0.3162278 -0.3194383
#> [15,] 10 10 0.0000000 0.0000000
#> [16,] 10 10 0.0000000 0.0000000
#> [17,] 11 10 0.3162278 0.3194383
#> [18,] 10 10 0.0000000 0.0000000
#> [19,] 10 10 0.0000000 0.0000000
#> [20,] 9 10 -0.3162278 -0.3194383
#> [21,] 11 10 0.3162278 0.3194383
#> [22,] 10 10 0.0000000 0.0000000
#> [23,] 9 10 -0.3162278 -0.3194383
#> [24,] 10 10 0.0000000 0.0000000
#> [25,] 10 10 0.0000000 0.0000000
#> [26,] 11 10 0.3162278 0.3194383
#> [27,] 10 10 0.0000000 0.0000000
#> [28,] 10 10 0.0000000 0.0000000
#> [29,] 10 10 0.0000000 0.0000000
#> [30,] 11 10 0.3162278 0.3194383
#> [31,] 10 10 0.0000000 0.0000000
#> [32,] 9 10 -0.3162278 -0.3194383
#> [33,] 9 10 -0.3162278 -0.3194383
#> [34,] 11 10 0.3162278 0.3194383
#> [35,] 10 10 0.0000000 0.0000000
#> [36,] 10 10 0.0000000 0.0000000
#> [37,] 9 10 -0.3162278 -0.3194383
#> [38,] 11 10 0.3162278 0.3194383
#> [39,] 10 10 0.0000000 0.0000000
#> [40,] 10 10 0.0000000 0.0000000
#> [41,] 10 10 0.0000000 0.0000000
#> [42,] 11 10 0.3162278 0.3194383
#> [43,] 10 10 0.0000000 0.0000000
#> [44,] 10 10 0.0000000 0.0000000
#> [45,] 9 10 -0.3162278 -0.3194383
#> [46,] 11 10 0.3162278 0.3194383
#> [47,] 10 10 0.0000000 0.0000000
#> [48,] 9 10 -0.3162278 -0.3194383
#> [49,] 10 10 0.0000000 0.0000000
#> [50,] 10 10 0.0000000 0.0000000
# Plot observed and expected counts.
plot(res$observed)
abline(h = res$expected[1], lty = 2)
