Pearson's chi-squared test for contingency tables

When we test for homogeneity of rates in a k-armed trial with binary endpoints, the test statistic is chi-squared distributed with \(k-1\) degrees of freedom under the null. Under the alternative, the statistic is chi-squared distributed with a non-centrality parameter \(\lambda\). The function get_tau_Pearson2xk then computes \(\tau\), such that \(\lambda\) is given as \(n \cdot \tau\), where \(n\) is the number of subjects per group. In adoptr, \(\tau\) is used in the same way as \(\theta\) in the case of the normally distributed test statistic.

Usage

Pearson2xK(n_groups)

get_tau_Pearson2xK(p_vector)

Arguments

n_groups

number of groups considered for testing procedure

p_vector

vector denoting the event rates per group

Examples

pearson <- Pearson2xK(3)


H1 <- PointMassPrior(get_tau_Pearson2xK(c(.3, .25, .4)), 1)