# profile-methods

##### Profile method for merMod objects

Methods for `profile`

() of [ng]`lmer`

fitted
models.

The `log()`

method and the more flexible `logProf()`

utility transform a `lmer`

profile into one where logarithms of standard deviations
are used, while `varianceProf`

converts from the
standard-deviation to the variance scale; see Details.

- Keywords
- methods

##### Usage

```
# S3 method for merMod
profile(fitted, which = NULL, alphamax = 0.01,
maxpts = 100, delta = NULL,
delta.cutoff = 1/8, verbose = 0, devtol = 1e-09,
maxmult = 10, startmethod = "prev", optimizer = NULL,
control=NULL, signames = TRUE,
parallel = c("no", "multicore", "snow"),
ncpus = getOption("profile.ncpus", 1L), cl = NULL,
prof.scale = c("sdcor","varcov"),
…)
# S3 method for thpr
as.data.frame (x, ...)
# S3 method for thpr
log(x, base = exp(1))
logProf(x, base = exp(1), ranef = TRUE,
sigIni = if(ranef) "sig" else "sigma")
varianceProf(x, ranef = TRUE)
```

##### Arguments

- fitted
a fitted model, e.g., the result of

`lmer(..)`

.- which
NULL value, integer or character vector indicating which parameters to profile: default (NULL) is all parameters. For integer, i.e., indexing, the parameters are ordered as follows:

- (1)
random effects (theta) parameters; these are ordered as in

`getME(.,"theta")`

, i.e., as the lower triangle of a matrix with standard deviations on the diagonal and correlations off the diagonal.- (2)
residual standard deviation (or scale parameter for GLMMs where appropriate).

- (3)
fixed effect (beta) parameters.

Alternatively,

`which`

may be a character, containing`"beta_"`

or`"theta_"`

denoting the fixed or random effects parameters, respectively, or also containing parameter names, such as`".sigma"`

or`"(Intercept)"`

.- alphamax
a number in \((0,1)\), such that

`1 - alphamax`

is the maximum alpha value for likelihood ratio confidence regions; used to establish the range of values to be profiled.- maxpts
maximum number of points (in each direction, for each parameter) to evaluate in attempting to construct the profile.

- delta
stepping scale for deciding on next point to profile. The code uses the local derivative of the profile at the current step to establish a change in the focal parameter that will lead to a step of

`delta`

on the square-root-deviance scale. If`NULL`

, the`delta.cutoff`

parameter will be used to determine the stepping scale.- delta.cutoff
stepping scale (see

`delta`

) expressed as a fraction of the target maximum value of the profile on the square-root-deviance scale. Thus a`delta.cutoff`

setting of`1/n`

will lead to a profile with approximately`2*n`

calculated points for each parameter (i.e.,`n`

points in each direction, below and above the estimate for each parameter).- verbose
level of output from internal calculations.

- devtol
tolerance for fitted deviances less than baseline (supposedly minimum) deviance.

- maxmult
maximum multiplier of the original step size allowed, defaults to 10.

- startmethod
method for picking starting conditions for optimization (STUB).

- optimizer
(character or function) optimizer to use (see

`lmer`

for details); default is to use the optimizer from the original model fit.- control
a

`list`

of options controlling the profiling (see`lmerControl`

): default is to use the control settings from the original model fit.- signames
logical indicating if abbreviated names of the form

`.sigNN`

should be used; otherwise, names are more meaningful (but longer) of the form`(sd|cor)_(effects)|(group)`

. Note that some code for profile transformations (e.g.,`log()`

or`varianceProf`

) depends on`signames==TRUE`

.- …
potential further arguments for various methods.

- x
an object of class

`thpr`

(i.e., output of`profile`

)- base
the base of the logarithm. Defaults to natural logarithms.

- ranef
logical indicating if the sigmas of the random effects should be

`log()`

transformed as well. If false, only \(\sigma\) (standard deviation of errors) is transformed.- sigIni
character string specifying the initial part of the sigma parameters to be log transformed.

- parallel
The type of parallel operation to be used (if any). If missing, the default is taken from the option

`"profile.parallel"`

(and if that is not set,`"no"`

).- ncpus
integer: number of processes to be used in parallel operation: typically one would choose this to be the number of available CPUs.

- cl
An optional parallel or snow cluster for use if

`parallel = "snow"`

. If not supplied, a cluster on the local machine is created for the duration of the`profile`

call.- prof.scale
whether to profile on the standard deviation-correlation scale (

`"sdcor"`

) or on the variance-covariance scale (`"varcov"`

)

##### Details

The `log`

method and the more flexible `logProf()`

function transform the profile into one where \(\log(\sigma)\) is
used instead of \(\sigma\).
By default all sigmas including the standard deviations of the random
effects are transformed i.e., the methods return a profile with all
of the `.sigNN`

parameters replaced by `.lsigNN`

. If `ranef`

is false, only
`".sigma"`

, the standard deviation of the errors, is transformed
(as it should never be zero, whereas random effect standard
deviations (`.sigNN`

) can be reasonably be zero).

The forward and backward splines for the log-transformed parameters
are recalculated.
Note that correlation parameters are not handled sensibly at present
(i.e., they are logged rather than taking a more applicable
transformation such as an arc-hyperbolic tangent,
`atanh(x)`

=\(\log((1+x)/(1-x))/2\)).

The `varianceProf`

function works similarly, including
non-sensibility for correlation parameters, by squaring all
parameter values, changing the names by appending `sq`

appropriately (e.g. `.sigNN`

to `.sigsqNN`

).
Setting `prof.scale="varcov"`

in the original
`profile()`

call is a more computationally
intensive, but more correct, way to compute confidence
intervals for covariance parameters.

Methods for function `profile`

(package
stats), here for profiling (fitted) mixed effect models.

##### Value

`profile(<merMod>)`

returns an object of S3 class
`"thpr"`

,
which is `data.frame`

-like.
Notable methods for such a profile object
`confint()`

, which returns the
confidence intervals based on the profile,
and three plotting methods
(which require the lattice package),
`xyplot`

, `densityplot`

, and
`splom`

.

In addition, the
`log()`

(see above) and `as.data.frame()`

methods can transform `"thpr"`

objects in useful ways.

##### See Also

The plotting methods `xyplot`

etc, for class
`"thpr"`

.

For (more expensive) alternative confidence intervals:
`bootMer`

.

##### Examples

```
# NOT RUN {
fm01ML <- lmer(Yield ~ 1|Batch, Dyestuff, REML = FALSE)
system.time(
tpr <- profile(fm01ML, optimizer="Nelder_Mead", which="beta_")
)## fast; as only *one* beta parameter is profiled over
## full profiling (default which means 'all) needs
## ~2.6s (on a 2010 Macbook Pro)
system.time( tpr <- profile(fm01ML))
## ~1s, + possible warning about bobyqa convergence
(confint(tpr) -> CIpr)
# }
# NOT RUN {
<!-- % too much precision (etc). but just FYI: -->
stopifnot(all.equal(unname(CIpr),
array(c(12.1985292, 38.2299848, 1486.4515,
84.0630513, 67.6576964, 1568.54849), dim = 3:2),
tol= 1e-07))# 1.37e-9 {64b}
# }
# NOT RUN {
library("lattice")
xyplot(tpr)
xyplot(tpr, absVal=TRUE) # easier to see conf.int.s (and check symmetry)
xyplot(tpr, conf = c(0.95, 0.99), # (instead of all five 50, 80,...)
main = "95% and 99% profile() intervals")
xyplot(logProf(tpr, ranef=FALSE),
main = expression("lmer profile()s"~~ log(sigma)*" (only log)"))
densityplot(tpr, main="densityplot( profile(lmer(..)) )")
densityplot(varianceProf(tpr), main=" varianceProf( profile(lmer(..)) )")
splom(tpr)
splom(logProf(tpr, ranef=FALSE))
doMore <- lme4:::testLevel() > 1
# }
# NOT RUN {
<!-- %% even more --> ../tests/profile.R -->
# }
# NOT RUN {
if(doMore) { ## not typically, for time constraint reasons
## Batch and residual variance only
system.time(tpr2 <- profile(fm01ML, which=1:2, optimizer="Nelder_Mead"))
print( xyplot(tpr2) )
print( xyplot(log(tpr2)) )# log(sigma) is better
print( xyplot(logProf(tpr2, ranef=FALSE)) )
## GLMM example
gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
data = cbpp, family = binomial)
## running ~ 10-12 seconds on a modern machine {-> "verbose" while you wait}:
print( system.time(pr4 <- profile(gm1, verbose=TRUE)) )
print( xyplot(pr4, layout=c(5,1), as.table=TRUE) )
print( xyplot(log(pr4), absVal=TRUE) ) # log(sigma_1)
print( splom(pr4) )
print( system.time( # quicker: only sig01 and one fixed effect
pr2 <- profile(gm1, which=c("theta_", "period2"))))
print( confint(pr2) )
## delta..: higher underlying resolution, only for 'sigma_1':
print( system.time(
pr4.hr <- profile(gm1, which="theta_", delta.cutoff=1/16)))
print( xyplot(pr4.hr) )
}
# }
# NOT RUN {
<!-- % doMore -->
# }
```

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