Check that a fitted detection function is monotone non-increasing.
check.mono(
df,
strict = TRUE,
n.pts = 100,
tolerance = 1e-08,
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 points between left and right truncation at which to evaluate the detection function (default 100)
numerical tolerance for monotonicity checks (default 1e-8)
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, Felix Petersma
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.