Give the S3 Methods for CDF-Quantile Distribution Models
# S3 method for cdfqr
summary(object, ...)# S3 method for cdfqr
print(x, digits = max(3, getOption("digits") - 3), ...)
# S3 method for cdfqr
coef(object, type = "full", ...)
# S3 method for cdfqr
vcov(object, type = "full", ...)
# S3 method for cdfqr
update(object, formula., zero.fo., one.fo., mu.fo., ..., evaluate = TRUE)
# S3 method for cdfqr
confint(object, parm, level = 0.95, submodel = "full", ...)
# S3 method for cdfqr
formula(x, ...)
# S3 method for cdfqr
nobs(object, ...)
# S3 method for cdfqr
deviance(object, ...)
# S3 method for cdfqrH
logLik(object, ...)
# S3 method for cdfqrH
confint(
object,
parm,
level = 0.95,
type = c("full", "mean", "sigma", "zero", "one"),
...
)
# S3 method for cdfqrFT
confint(object, parm, level = 0.95, submodel = "full", ...)
Pass onto other functions or currently ignored
The fitted cdfqr model.
Number of digits to be retained in printed output.
The parts of coefficients or variance-covariance matrix to be extracted.Can be "full", "mean",or "sigma".
Changes to the formula. See update.Formula
for details.
Changes to the formulas for zero/one component for hurdle models, and for location submodel for finite-tailed models.
If true evaluate the new updated model else return the call for the new model.
a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.
the confidence level required.
data(cdfqrExampleData)
fit <- cdfquantreg(crc99 ~ vert | confl, 't2','t2', data = JurorData)
summary(fit)
print(fit)
logLik(fit)
coef(fit)
deviance(fit)
vcov(fit)
confint(fit)
#Update the model
fit2 <- update(fit, crc99 ~ vert*confl | confl)
summary(fit2)
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