glsdrc-methods: gdrc methods

In daniel-gerhard/medrc: Mixed effect dose-response curves

Description

Methods for objects of class gdrc

Usage

## S3 method for class 'glsdrc'
df.residual(object, ...)
## S3 method for class 'glsdrc'
predict(object, ..., newdata = NULL)
## S3 method for class 'glsdrc'
residuals(object, ...)
## S3 method for class 'glsdrc'
vcov(object, ...)
## S3 method for class 'glsdrc'
summary(object, ...)
## S3 method for class 'glsdrc'
AIC(object, ..., k = 2)
## S3 method for class 'glsdrc'
BIC(object, ...)
## S3 method for class 'glsdrc'
logLik(object, REML = FALSE, ...)
## S3 method for class 'glsdrc'
print(x, ..., digits = max(3, getOption("digits") - 3))

Arguments

object	An object of class glsdrc
x	An object of class glsdrc
newdata	an optional data frame to be used for obtaining the predictions. All variables used in the fixed and random effects models, as well as the grouping factors, must be present in the data frame. If missing, the fitted values are returned.
k	numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC.
REML	an optional logical value. If TRUE the restricted log-likelihood is returned, else, if FALSE, the log-likelihood is returned. Defaults to FALSE.
digits	minimal number of significant digits
...	further arguments

Author(s)

Daniel Gerhard

daniel-gerhard/medrc documentation built on May 14, 2019, 3:38 p.m.