se.ave.mean2.dep {vcmeta} | R Documentation |
Computes the standard error for the average of 2-group mean differences from two parallel measurement response variables in the same sample
Description
In a study that reports a 2-group mean difference for two response variables that satisfy the conditions of parallel measurments, this function can be used to compute the standard error of the average of the two mean differences using the two estimated means, estimated standard deviations, estimated within-group correlation between the two response variables, and the two sample sizes. The average mean difference and standard error output from this function can then be used as input in the meta.ave.gen, meta.lc.gen, and meta.lm.gen functions in a meta-analysis where some studies have used one of the two parallel response variables and other studies have used the other parallel response variable. Equality of variances is not assumed.
Usage
se.ave.mean2.dep(m1A, m2A, sd1A, sd2A, m1B, m2B, sd1B, sd2B, rAB, n1, n2)
Arguments
m1A |
estimated mean for variable A in group 1 |
m2A |
estimated mean for variable A in group 2 |
sd1A |
estimated standard deviation for variable A in group 1 |
sd2A |
estimated standard deviation for variable A in group 2 |
m1B |
estimated mean for variable B in group 1 |
m2B |
estimated mean for variable B in group 2 |
sd1B |
estimated standard deviation for variable B in group 1 |
sd2B |
estimated standard deviation for variable B in group 2 |
rAB |
estimated within-group correlation between variables A and B |
n1 |
sample size for group 1 |
n2 |
sample size for group 2 |
Value
Returns a one-row matrix:
Estimate - estimated average mean difference
SE - standard error
VAR(A) - variance of mean difference for variable A
VAR(B) - variance of mean difference for variable B
COV(A,B) - covariance of mean differences for variables A and B
Examples
se.ave.mean2.dep(21.9, 16.1, 3.82, 3.21, 24.8, 17.1, 3.57, 3.64, .785, 40, 40)
# Should return:
# Estimate SE VAR(A) VAR(B) COV(A,B)
# Average mean difference: 6.75 0.7526878 0.6224125 0.6498625 0.4969403