ci_rsquared: Confidence Interval for the Population R-Squared
In confintr: Confidence Intervals

Description Usage Arguments Details Value References See Also Examples

This function calculates parametric confidence intervals for the population R-squared. It is based on confidence intervals for the non-centrality parameter Delta of the F distribution, found by test inversion. Delta values are mapped to R-squared by R-squared = Delta / (Delta + df1 + df2 + 1), where df1 and df2 are the degrees of freedom of the F test statistic. A positive lower (1-alpha)*100%-confidence limit for the R-squared goes hand-in-hand with a significant F test at level alpha.

1	ci_rsquared(x, df1 = NULL, df2 = NULL, probs = c(0.025, 0.975))

`x`	The result of `stats::lm` or the F test statistic.
`df1`	The numerator degree of freedom. Only used if `x` is a test statistic.
`df2`	The denominator degree of freedom. Only used if `x` is a test statistic.
`probs`	Error probabilites. The default c(0.025, 0.975) gives a symmetric 95% confidence interval.

According to ?pf, the results might be unreliable for very large F values. Note that we do not provide bootstrap confidence intervals here to keep the input interface simple.

A list with class cint containing these components:

parameter: The parameter in question.
interval: The confidence interval for the parameter.
estimate: The estimate for the parameter.
probs: A vector of error probabilities.
type: The type of the interval.
info: An additional description text for the interval.

Smithson, M. (2003). Confidence intervals. Series: Quantitative Applications in the Social Sciences. New York, NY: Sage Publications.

ci_f_ncp.

fit <- lm(Sepal.Length ~ ., data = iris)
summary(fit)$r.squared
ci_rsquared(fit)
ci_rsquared(fit, probs = c(0.05, 1))
ci_rsquared(fit, probs = c(0, 0.95))
ci_rsquared(188.251, 5, 144)