confint {stats} | R Documentation |

## Confidence Intervals for Model Parameters

### Description

Computes confidence intervals for one or more parameters in a fitted
model. There is a default and a method for objects inheriting from class
`"lm"`

.

### Usage

```
confint(object, parm, level = 0.95, ...)
## Default S3 method:
confint(object, parm, level = 0.95, ...)
## S3 method for class 'lm'
confint(object, parm, level = 0.95, ...)
## S3 method for class 'glm'
confint(object, parm, level = 0.95, trace = FALSE, test=c("LRT", "Rao"), ...)
## S3 method for class 'nls'
confint(object, parm, level = 0.95, ...)
```

### Arguments

`object` |
a fitted model object. |

`parm` |
a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered. |

`level` |
the confidence level required. |

`trace` |
logical. Should profiling be traced? |

`test` |
use Likelihood Ratio or Rao Score test in profiling. |

`...` |
additional argument(s) for methods. |

### Details

`confint`

is a generic function. The default method assumes
normality, and needs suitable `coef`

and
`vcov`

methods to be available. The default method can be
called directly for comparison with other methods.

For objects of class `"lm"`

the direct formulae based on `t`

values are used.

Methods for classes `"glm"`

and `"nls"`

call the appropriate profile method,
then find the confidence intervals by interpolation in the profile
traces. If the profile object is already available it can be used
as the main argument rather than the fitted model object itself.

### Value

A matrix (or vector) with columns giving lower and upper confidence limits for each parameter. These will be labelled as (1-level)/2 and 1 - (1-level)/2 in % (by default 2.5% and 97.5%).

### References

Venables, W. N. and Ripley, B. D. (2002)
*Modern Applied Statistics with S.* Fourth edition. Springer.

### See Also

Original versions: `confint.glm`

and
`confint.nls`

in package MASS.

### Examples

```
fit <- lm(100/mpg ~ disp + hp + wt + am, data = mtcars)
confint(fit)
confint(fit, "wt")
## from example(glm)
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3, 1, 9); treatment <- gl(3, 3)
glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())
confint(glm.D93)
confint.default(glm.D93) # based on asymptotic normality
```

*stats*version 4.4.0 Index]