# confint

##### Confidence Intervals for Model Parameters

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"`

.

- Keywords
- models

##### Usage

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

##### Details

`confint`

is a generic function. The default method assumes
asymptotic 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.

There are stub methods in package `"glm"`

and `"nls"`

which call those in package

##### 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%).

##### See Also

`confint.glm`

and
`confint.nls`

in package

##### Examples

`library(stats)`

```
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) # needs MASS to be installed
confint.default(glm.D93) # based on asymptotic normality
```

*Documentation reproduced from package stats, version 3.3, License: Part of R 3.3*

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