| ci.rsqr {statpsych} | R Documentation |
Confidence interval for squared multiple correlation
Description
Computes an approximate confidence interval for a population squared multiple correlation in a linear model with random predictor variables. This function uses the scaled central F approximation method. An approximate standard error is recovered from the confidence interval.
Usage
ci.rsqr(alpha, r2, s, n)
Arguments
alpha |
alpha value for 1-alpha confidence |
r2 |
estimated unadjusted squared multiple correlation |
s |
number of predictor variables |
n |
sample size |
Value
Returns a 1-row matrix. The columns are:
R-squared - estimate of unadjusted R-squared (from input)
adj R-squared - bias adjusted R-squared estimate
SE - recovered standard error
LL - lower limit of the confidence interval
UL - upper limit of the confidence interval
References
Helland IS (1987). “On the interpretation and use of R2 in regression analysis.” Biometrics, 43(1), 61–69. doi:10.2307/2531949.
Examples
ci.rsqr(.05, .241, 3, 116)
# Should return:
# R-squared adj R-squared SE LL UL
# 0.241 0.2206696 0.06752263 0.09819599 0.3628798