# confint.merMod

##### Compute Confidence Intervals for Parameters of a [ng]lmer Fit

Compute confidence intervals on the parameters of a `*lmer()`

model fit (of class`"'>merMod"`

).

##### Usage

```
# S3 method for merMod
confint(object, parm, level = 0.95,
method = c("profile", "Wald", "boot"), zeta,
nsim = 500,
boot.type = c("perc","basic","norm"),
FUN = NULL, quiet = FALSE,
oldNames = TRUE, ...)
# S3 method for thpr
confint(object, parm, level = 0.95,
zeta, non.mono.tol=1e-2,
...)
```

##### Arguments

- object
a fitted [ng]lmer model or profile

- parm
parameters for which intervals are sought. Specified by an integer vector of positions,

`character`

vector of parameter names, or (unless doing parametric bootstrapping with a user-specified bootstrap function)`"theta_"`

or`"beta_"`

to specify variance-covariance or fixed effects parameters only: see the`which`

parameter of`profile`

.- level
confidence level \(< 1\), typically above 0.90.

- method
a

`character`

string determining the method for computing the confidence intervals.- zeta
(for

`method = "profile"`

only:) likelihood cutoff (if not specified, as by default, computed from`level`

).- nsim
number of simulations for parametric bootstrap intervals.

- FUN
bootstrap function; if

`NULL`

, an internal function that returns the fixed-effect parameters as well as the random-effect parameters on the standard deviation/correlation scale will be used. See`bootMer`

for details.- boot.type
bootstrap confidence interval type, as described in

`boot.ci`

. (Methods ‘stud’ and ‘bca’ are unavailable because they require additional components to be calculated.)- quiet
(logical) suppress messages about computationally intensive profiling?

- oldNames
(logical) use old-style names for variance-covariance parameters, e.g.

`".sig01"`

, rather than newer (more informative) names such as`"sd_(Intercept)|Subject"`

? (See`signames`

argument to`profile`

).- non.mono.tol
tolerance for detecting a non-monotonic profile and warning/falling back to linear interpolation

- …
additional parameters to be passed to

`profile.merMod`

or`bootMer`

, respectively.

##### Details

Depending on the `method`

specified, `confint()`

computes
confidence intervals by

`"profile"`

:computing a likelihood profile and finding the appropriate cutoffs based on the likelihood ratio test;

`"Wald"`

:approximating the confidence intervals (of fixed-effect parameters only; all variance-covariance parameters CIs will be returned as

`NA`

) based on the estimated local curvature of the likelihood surface;`"boot"`

:performing parametric bootstrapping with confidence intervals computed from the bootstrap distribution according to

`boot.type`

(see`bootMer`

,`boot.ci`

).

##### Value

a numeric table (`matrix`

with column and row names) of
confidence intervals; the confidence intervals are computed on the
standard deviation scale.

##### Note

The default method `"profile"`

amounts to

confint(profile(object, which=parm), signames=oldNames, ...), level, zeta)

where the `profile`

method `profile.merMod`

does almost all the computations. Therefore it is typically
advisable to store the profile(.) result, say
in `pp`

, and then use `confint(pp, level=*)`

e.g., for
different levels.

##### Examples

```
# NOT RUN {
fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
fm1W <- confint(fm1, method="Wald")# very fast, but ....
fm1W
(fm2 <- lmer(Reaction ~ Days + (Days || Subject), sleepstudy))
(CI2 <- confint(fm2, maxpts = 8)) # method = "profile"; 8: to be much faster
# }
# NOT RUN {
testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1
if(interactive() || testLevel >= 3) {
## ~20 seconds, MacBook Pro laptop
system.time(fm1P <- confint(fm1, method="profile", ## default
oldNames = FALSE))
## ~ 40 seconds
system.time(fm1B <- confint(fm1, method="boot",
.progress="txt", PBargs=list(style=3)))
} else
load(system.file("testdata","confint_ex.rda",package="lme4"))
fm1P
fm1B
# }
```

*Documentation reproduced from package lme4, version 1.1-20, License: GPL (>= 2)*