ci.2x2.prop.bs | R Documentation |
Computes adjusted Wald confidence intervals and tests for the AB interaction effect, main effect of A, main effect of B, simple main effects of A, and simple main effects of B in a 2x2 between-subjects factorial design with a dichotomous response variable. The input vector of frequency counts is f = [ f11, f12, f21, f22 ], and the input vector of sample sizes is n = [ n11, n12, n21, n22 ] where the first subscript represents the levels of Factor A and the second subscript represents the levels of Factor B.
ci.2x2.prop.bs(alpha, f, n)
alpha |
alpha level for 1-alpha confidence |
f |
vector of frequency counts of participants who have the attribute |
n |
vector of sample sizes |
Returns a 7-row matrix (one row per effect). The columns are:
Estimate - adjusted estimate of effect
SE - standard error
z - z test statistic for test of null hypothesis
p - two-sided p-value
LL - lower limit of the adjusted Wald confidence interval
UL - upper limit of the adjusted Wald confidence interval
Price2004statpsych
f <- c(15, 24, 28, 23)
n <- c(50, 50, 50, 50)
ci.2x2.prop.bs(.05, f, n)
# Should return:
# Estimate SE z p LL UL
# AB: -0.27450980 0.13692496 -2.0048193 0.044982370 -0.54287780 -0.00614181
# A: -0.11764706 0.06846248 -1.7184165 0.085720668 -0.25183106 0.01653694
# B: -0.03921569 0.06846248 -0.5728055 0.566776388 -0.17339968 0.09496831
# A at b1: -0.25000000 0.09402223 -2.6589456 0.007838561 -0.43428019 -0.06571981
# A at b2: 0.01923077 0.09787658 0.1964798 0.844234654 -0.17260380 0.21106534
# B at a1: -0.17307692 0.09432431 -1.8349132 0.066518551 -0.35794917 0.01179533
# B at a2: 0.09615385 0.09758550 0.9853293 0.324462356 -0.09511021 0.28741790
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.