| cops | R Documentation |
Returns a vector similar to coef(object),
comprising the centre of the parameter space (COPS)
values,
given a fitted VGLM.
cops(object, ...)
copsvglm(object, level = 0.999,
beta.range = confint(object, level = level),
muxrange = 4, iter.max = 8, tol = 1e-5, subset = NULL,
do1 = TRUE, slowtrain = FALSE, ...)
object |
A |
level |
Fed into |
beta.range |
Numeric.
Interval for the numerical search.
It is common for the COPS to be relatively
near the estimated regression coefficients.
Argument |
muxrange, iter.max |
Numeric.
Used to obtain the interval endpoints that
cover each COPS value.
The initial confidence intervals are linearly
widened by multiplication by |
tol |
Numeric.
Fed into |
do1 |
Logical. Include the intercepts?
This operates in conjunction with |
slowtrain |
Logical. Used internally, not important. |
subset |
Same as in |
... |
currently unused but may be used in the future for further arguments passed into other methods. |
For many models, some COPS values will be
Inf or -Inf
so that manual checking is needed,
for example, poissonff.
The answers returned by this function only
make sense if the COPSs are in the
interior of the parameter space.
This function was written specifically for
logistic regression but has much wider
applicability.
Currently the result returned depends critically
on beta.range,
muxrange and iter.max
so that the answer ought to be checked.
Yee (2025) shows that not all VGLMs have a
finite COPS. For example, an intercept-only
propodds with second
cumulative probability less than 0.25 will
not have a finite COPS for the first
intercept.
For now, this function should probably
only applied to
binomialff models
with the four most common link functions.
A named vector, similar to coefvlm.
This function could be made more robust in the future. Of course, the function will not return finite results if the COPS is not finite, e.g., a Poisson regression.
Thomas W. Yee.
Yee, T. W. (2025). The centre of the parameter space and its mapping with the WSDM function: A diagnostic for logistic regression and beyond. In preparation.
hdeff,
wsdm,
cops3.glm,
binomialff,
coefvlm,
coef.
## Not run: data("xs.nz", package = "VGAMdata")
data1 <- na.omit(xs.nz[, c("age", "cancer", "sex")])
fit1 <- vglm(cancer ~ age + sex, binomialff, data1)
cops(fit1)
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.