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Confidence interval for squared multiple correlation

Source: R/statpsych2.R
ci.rsqr.Rd

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)
#>  R-squared adj R-squared         SE         LL        UL
#>      0.241     0.2206696 0.06752263 0.09819599 0.3628798

# Should return:
# R-squared adj R-squared         SE         LL        UL  
#     0.241     0.2206696 0.06752263 0.09819599 0.3628798