Check that a fitted detection function is monotone non-increasing.
check.mono(
df,
strict = TRUE,
n.pts = 100,
tolerance = 1e-06,
plot = FALSE,
max.plots = 6
)TRUE if the detection function is monotone, FALSE if
it's not. warnings are issued to warn the user that the function is
non-monotonic.
a fitted detection function object
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).
number of equally-spaced points between left and right truncation at which to evaluate the detection function (default 100)
numerical tolerance for monotonicity checks (default 1e-6)
plot a diagnostic highlighting the non-monotonic areas (default
FALSE)
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.
David L. Miller
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.