ci_rsquared | R Documentation |

This function calculates parametric CIs for the population `R^2`

.
It is based on CIs for the non-centrality parameter `\Delta`

of the F
distribution found by test inversion. Values of `\Delta`

are mapped to `R^2`

by `R^2 = \Delta / (\Delta + \textrm{df}_1 + \textrm{df}_2 + 1)`

,
where the `\textrm{df}_j`

are the degrees of freedom of the F test statistic.
A positive lower `(1 - \alpha) \cdot 100\%`

-confidence limit for the `R^2`

goes hand-in-hand with a significant F test at level `\alpha`

.

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

`x` |
The result of |

`df1` |
The numerator df. Only used if |

`df2` |
The denominator df. Only used if |

`probs` |
Lower and upper probabilities, by default |

According to `stats::pf()`

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

An object of class "cint", see `ci_mean()`

for details.

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))
```

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