Computes the callback rates and their confidence intervals.
stat_mcr(x, level = 0.95)A list with class "stat_mcr" containing 8 components: level, props, cp,
wilson, student, t.fisher, t.pearson and t.student.
level: the level of the confidence intervals.
props: a data frame containing the following variables:
tests: number of tests.
p_callback: overall callback rate.
p_cand1: 1st candidate callback rate.
p_cand_dif: p_cand1-p_cand2.
cp: A data frame containing the Clopper-Pearson confidence intervals,
from binom.test().
inf_p_callback: overall callback rate, lower bound.
p_callback: overall callback rate.
sup_p_callback: overall callback rate, upper bound.
inf_p_cand1: 1st candidate callback rate, lower bound.
p_cand1: 1st candidate callback rate.
sup_p_cand1: 1st candidate callback rate, upper bound.
inf_p_cand2: 2nd candidate callback rate, lower bound.
p_cand2: 2nd candidate callback rate.
sup_p_cand2: 2nd candidate callback rate, upper bound.
inf_cand_dif: p_cand1-p_cand2, lower bound.
sup_cand_dif: p_cand1-p_cand2, upper bound.
wilson: a data frame containing the Wilson confidence intervals,
from prop.test().
inf_p_callback: overall callback rate, lower bound.
p_callback: overall callback rate.
sup_p_callback: overall callback rate, upper bound.
inf_p_cand1: 1st candidate callback rate, lower bound.
p_cand1: 1st candidate callback rate.
sup_p_cand1: 1st candidate callback rate, upper bound.
inf_p_cand2: 2nd candidate callback rate, lower bound.
p_cand2: 2nd candidate callback rate.
sup_p_cand2: 2nd candidate callback rate, upper bound.
inf_cand_dif: p_cand1-p_cand2, lower bound.
sup_cand_dif: p_cand1-p_cand2, upper bound.
student: a data frame containing the Student confidence intervals,
from t.test().
inf_p_callback: overall callback rate, lower bound.
p_callback: overall callback rate.
sup_p_callback: overall callback rate, upper bound.
inf_p_cand1: 1st candidate callback rate, lower bound.
p_cand1: 1st candidate callback rate.
sup_p_cand1: 1st candidate callback rate, upper bound.
inf_p_cand2: 2nd candidate callback rate, lower bound.
p_cand2: 2nd candidate callback rate.
sup_p_cand2: 2nd candidate callback rate, upper bound.
inf_cand_dif: p_cand1-p_cand2, lower bound.
sup_cand_dif: p_cand1-p_cand2, upper bound.
t.fisher: a data frame containing the statistics of the Fisher test.
p_cand_dif: callback proportion difference between the candidates.
p_Fisher: the p-value of the Fisher test.
s_Fisher: the significance code of the Fisher test.
t.pearson: a data frame containing the statistics of the Pearson test.
p_cand_dif: callback proportion difference between the candidates.
Pearson: the value of Pearson's chi-squared test statistic.
p_Pearson: the p-value of the Pearson test.
s_Pearson: the significance code of the Pearson test.
t.student: A data frame containing the statistics of the Student test.
p_cand_dif: callback proportion difference between the candidates.
Student: the value of Student's test statistic.
p_Student: the p-value of the Student test.
s_Student: the significance code of the Student test.
a callback object.
a number, containing the level of the confidence intervals (0.95 by default).
Emmanuel Duguet
Clopper, C. J. & Pearson, E. S. (1934). The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika, 26, 404–413. doi:10.2307/2331986.
Wilson, E.B. (1927). Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association, 22, 209–212. doi:10.2307/2276774.
data(labour1)
x <- callback(data=labour1,cluster="offer",candid="hist",callback="callback")
str(stat_mcr(x,level=0.9))
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