Computes tests and confidence intervals of effects in a 2x2 mixed factorial design for proportions

Source: R/statpsych3.R

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 mixed factorial design with a dichotomous response variable where Factor A is a within-subjects factor and Factor B is a between-subjects factor. The 4x1 vector of frequency counts for Factor A within each group is f00, f01, f10, f11 where fij is the number of participants with a response of i = 0 or 1 at level 1 of Factor A and a response of j = 0 or 1 at level 2 of Factor A.

ci.2x2.prop.mixed(alpha, group1, group2)

Arguments

alpha

alpha level for 1-alpha confidence

group1

vector of frequency counts from 2x2 contingency table in group 1

group2

vector of frequency counts from 2x2 contingency table in group 2

Value

Returns a 7-row matrix (one row per effect). The columns are:

  • Estimate - adjusted estimate of effect

  • SE - standard error of estimate

  • z - z test statistic

  • p - two-sided p-value

  • LL - lower limit of the adjusted Wald confidence interval

  • UL - upper limit of the adjusted Wald confidence interval

Examples

group1 <- c(125, 14, 10, 254)
group2 <- c(100, 16, 9, 275)
ci.2x2.prop.mixed (.05, group1, group2)
#>              Estimate          SE          z          p          LL
#> AB:       0.007555369 0.017716073  0.4264697 0.66976559 -0.02716750
#> A:       -0.013678675 0.008858036 -1.5442107 0.12253730 -0.03104011
#> B:       -0.058393219 0.023032656 -2.5352360 0.01123716 -0.10353640
#> A at b1: -0.009876543 0.012580603 -0.7850612 0.43241768 -0.03453407
#> A at b2: -0.017412935 0.012896543 -1.3502018 0.17695126 -0.04268969
#> B at a1: -0.054634236 0.032737738 -1.6688458 0.09514794 -0.11879902
#> B at a2: -0.062170628 0.032328556 -1.9230871 0.05446912 -0.12553343
#>                    UL
#> AB:       0.042278234
#> A:        0.003682758
#> B:       -0.013250043
#> A at b1:  0.014780985
#> A at b2:  0.007863824
#> B at a1:  0.009530550
#> B at a2:  0.001192177

# Should return:
#              Estimate          SE          z          p          LL           UL
# AB:       0.007555369 0.017716073  0.4264697 0.66976559 -0.02716750  0.042278234
# A:       -0.013678675 0.008858036 -1.5442107 0.12253730 -0.03104011  0.003682758
# B:       -0.058393219 0.023032656 -2.5352360 0.01123716 -0.10353640 -0.013250043
# A at b1: -0.009876543 0.012580603 -0.7850612 0.43241768 -0.03453407  0.014780985
# A at b2: -0.017412935 0.012896543 -1.3502018 0.17695126 -0.04268969  0.007863824
# B at a1: -0.054634236 0.032737738 -1.6688458 0.09514794 -0.11879902  0.009530550
# B at a2: -0.062170628 0.032328556 -1.9230871 0.05446912 -0.12553343  0.001192177