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

## Description

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.

## Usage

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

## Arguments

 `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.

## Details

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

## Value

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.

## References

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

## See Also

`ci_rsquared`.

## Examples

 ```1 2 3 4 5``` ```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) ```

confintr documentation built on July 2, 2020, 1:51 a.m.