# Marginal Effect Plots

`mep.Rd`

Scatterplot of marginal effects based on fitted model objects.

## Arguments

- object
a fitted model object.

- which
numeric, logical, or character. Indices for the variables in the model frame if only one or a subset is desired.

- link
character accepted by

`make.link`

, optional argument to determine scaling. It is guessed when value cannot be determined based on`family(object)$link`

(see Details).- level
numeric [0, 1], the confidence level required.

- unique, digits
numeric, the number of unique points above which bins are used. If the number of unique values is less than or equal to this number, unique values are used without binning. Unique values are subject to rounding to

`digits`

.- n, minbucket
number of bins (

`n`

) to be used in quantile estimation when variable is not treated as unique points.`minbucket`

is the minimum number of points within each bin.`n`

is decreased until`minbucket`

condition is satisfied.- col.points, pch
color and type of points to be plotted.

- col.lines, lty, lwd
color, type, and width of quantile lines to be plotted. The 1st value correspond to the median, the 2nd value to the upper and lower quantiles, respectively.

- ask
logical. If

`TRUE`

, the user is asked before each plot, see`par(ask=.)`

.- subset
an optional vector specifying a subset of the data to be used for plotting.

- ylab
character or expression, optional y axis label.

- ...
other possible arguments passed to graphical functions.

## Details

The input object must have a `fitted`

and `model.frame`

method, and possibly a well identifiable family/link component
(`family(object)$link`

).
In the absence of family/link information, the range of the fitted
value will be used to guess the scaling (identity, log, or logit)
unless directly supplied via the `link`

argument.

Fitted values (f(x) = f(x_1,...,x_i,...,x_p); i = 1,...,p) are plotted against x_i.
The visual display is determined by the type of x_i (un-ordered factor, ordered
factor, unique numeric values, binned numeric values).
For each unique vale or bin, the median and confidence intervals
(quantiles corresponding to `level`

) of f(x) are calculated.
Binned values are smoothed by `lowess`

unless `n`

< 3.

Jitter is added to factor and unique value types.
Jitter is calculated based on kernel `density`

.

The model frame includes the response variable as well. Plotting f(x) as a function of the observations might be a useful visualization too to indicate goodness of fit or the lack of it.

## Value

The produces one or several marginal plots as a side effect. Returns a list of quantiles of fitted values corresponding to binned/unique values of variables in the input object.

## References

Avgar, T., Lele, S. R., Keim, J. L. & Boyce, M. S. (2017) Relative Selection Strength: Quantifying effect size in habitat- and step-selection inference. Ecology and Evolution 7, 5322--5330.

## See also

`kdepairs`

for 2D kernel density estimates and contours.

`fitted`

for fitted values and
`model.frame`

for model frames.

## Examples

```
data(goats)
goats$ELEVATION <- goats$ELEVATION/1000
goats$TASPc <- cut(goats$TASP, 3, ordered_result=FALSE)
goats$SLOPEc <- cut(goats$SLOPE, 3, ordered_result=TRUE)
fit <- rspf(STATUS ~ TASPc + SLOPEc + ELEVATION + I(ELEVATION^2), goats, m=0, B=0)
op <- par(mfrow=c(2,2))
mep(fit, which=1:4)#, subset=sample.int(nrow(goats), 10^4))
par(op)
```