# slice

##### Slice the likelihood of a clm

Slice likelihood and plot the slice. This is usefull for illustrating the likelihood surface around the MLE (maximum likelihood estimate) and provides graphics to substantiate (non-)convergence of a model fit. Also, the closeness of a quadratic approximation to the log-likelihood function can be inspected for relevant parameters. A slice is considerably less computationally demanding than a profile.

- Keywords
- models

##### Usage

`slice(object, ...)`# S3 method for clm
slice(object, parm = seq_along(par), lambda = 3,
grid = 100, quad.approx = TRUE, ...)

# S3 method for slice.clm
plot(x, parm = seq_along(x),
type = c("quadratic", "linear"), plot.mle = TRUE,
ask = prod(par("mfcol")) < length(parm) && dev.interactive(), ...)

##### Arguments

- object
for the

`clm`

method an object of class`"clm"`

, i.e., the result of a call to`clm`

.- x
a

`slice.clm`

object, i.e., the result of`slice(clm.object)`

.- parm
for

`slice.clm`

a numeric or character vector indexing parameters, for`plot.slice.clm`

only a numeric vector is accepted. By default all parameters are selected.- lambda
the number of curvature units on each side of the MLE the slice should cover.

- grid
the number of values at which to compute the log-likelihood for each parameter.

- quad.approx
compute and include the quadratic approximation to the log-likelihood function?

- type
`"quadratic"`

plots the log-likelihood function which is approximately quadratic, and`"linear"`

plots the signed square root of the log-likelihood function which is approximately linear.- plot.mle
include a vertical line at the MLE (maximum likelihood estimate) when

`type = "quadratic"`

? Ignored for`type = "linear"`

.- ask
logical; if

`TRUE`

, the user is asked before each plot, see`par`

`(ask=.)`

.- …
further arguments to

`plot.default`

for the plot method. Not used in the slice method.

##### Value

The `slice`

method returns a list of `data.frame`

s with one
`data.frame`

for each parameter slice. Each `data.frame`

contains in the first column the values of the parameter and in the
second column the values of the (positive) log-likelihood
`"logLik"`

. A third column is present if `quad.approx = TRUE`

and contains the corresponding quadratic approximation to the
log-likelihood. The original model fit is included as the attribute
`"original.fit"`

.

The `plot`

method produces a plot of the likelihood slice for
each parameter.

##### Examples

```
# NOT RUN {
## fit model:
fm1 <- clm(rating ~ contact + temp, data = wine)
## slice the likelihood:
sl1 <- slice(fm1)
## three different ways to plot the slices:
par(mfrow = c(2,3))
plot(sl1)
plot(sl1, type = "quadratic", plot.mle = FALSE)
plot(sl1, type = "linear")
## Verify convergence to the optimum:
sl2 <- slice(fm1, lambda = 1e-5, quad.approx = FALSE)
plot(sl2)
# }
```

*Documentation reproduced from package ordinal, version 2019.12-10, License: GPL (>= 2)*