ci_f_ncp {confintr} R Documentation

## Confidence Interval for the Non-Centrality Parameter of the F Distribution

### 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

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.

ci_rsquared.
fit <- lm(Sepal.Length ~ ., data = iris)