# check.mono: Check that a detection function is monotone In mrds: Mark-Recapture Distance Sampling

 check.mono R Documentation

## Check that a detection function is monotone

### Description

Check that a fitted detection function is monotone non-increasing.

### Usage

```check.mono(
df,
strict = TRUE,
n.pts = 100,
tolerance = 1e-06,
plot = FALSE,
max.plots = 6
)
```

### Arguments

 `df` a fitted detection function object `strict` if `TRUE` (default) the detection function must be "strictly" monotone, that is that (`g(x[i])<=g(x[i-1])`) over the whole range (left to right truncation points). `n.pts` number of equally-spaced points between left and right truncation at which to evaluate the detection function (default 100) `tolerance` numerical tolerance for monotonicity checks (default 1e-6) `plot` plot a diagnostic highlighting the non-monotonic areas (default `FALSE`) `max.plots` when `plot=TRUE`, what is the maximum number of plots of non-monotone covariate combinations that should be plotted? Plotted combinations are a random sample of the non-monotonic subset of evaluations. No effect for non-covariate models.

### Details

Evaluates a series of points over the range of the detection function (left to right truncation) then determines:

1. If the detection function is always less than or equal to its value at the left truncation (`g(x)<=g(left)`, or usually `g(x)<=g(0)`). 2. (Optionally) The detection function is always monotone decreasing (`g(x[i])<=g(x[i-1])`). This check is only performed when `strict=TRUE` (the default). 3. The detection function is never less than 0 (`g(x)>=0`). 4. The detection function is never greater than 1 (`g(x)<=1`).

For models with covariates in the scale parameter of the detection function is evaluated at all observed covariate combinations.

Currently covariates in the shape parameter are not supported.

### Value

`TRUE` if the detection function is monotone, `FALSE` if it's not. `warning`s are issued to warn the user that the function is non-monotonic.

### Author(s)

David L. Miller

mrds documentation built on March 18, 2022, 5:26 p.m.