ci.2x2.prop.mixed: Computes tests and confidence intervals of effects in a 2x2...
In statpsych: Statistical Methods for Psychologists
ci.2x2.prop.mixed R Documentation
Computes tests and confidence intervals of effects in a 2x2 mixed factorial
design for proportions
Description
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.
Usage
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)
# 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
statpsych documentation built on Sept. 11, 2024, 7:42 p.m.
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| ci.2x2.prop.mixed | R Documentation |
Computes tests and confidence intervals of effects in a 2x2 mixed factorial design for proportions
Description
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.
Usage
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)
# 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
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