ci_f_ncp: Confidence Interval for the Non-Centrality Parameter of the F...
In confintr: Confidence Intervals

Description Usage Arguments Details Value References See Also Examples

Based on the inversion principle, parametric confidence intervals for the non-centrality parameter Delta of the F distribution are calculated. Note that we do not provide bootstrap confidence intervals here to keep the input interface simple. A positive lower (1-alpha)*100%-confidence limit for the ncp goes hand-in-hand with a significant F test at level alpha.

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

`x`	The result of `lm` or the F test statistic.
`df1`	The numerator degree of freedom, e.g. the number of parameters (including the intercept) of a linear regression. Only used if `x` is a test statistic.
`df2`	The denominator degree of freedom, e.g. n - df1 - 1 in a linear regression. Only used if `x` is a test statistic.
`probs`	Error probabilites. The default c(0.025, 0.975) gives a symmetric 95% confidence interval.

Note that, according to ?pf, the results might be unreliable for very large F values.

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_rsquared.

fit <- lm(Sepal.Length ~ ., data = iris)
ci_f_ncp(fit)
ci_f_ncp(fit, probs = c(0.05, 1))
ci_f_ncp(fit, probs = c(0, 0.95))
ci_f_ncp(x = 188.251, df1 = 5, df2 = 144)