Generic prediction method for various types of fitted models. predFit
can be used to obtain standard errors of fitted values and
adjusted/unadjusted confidence/prediction intervals for objects of class
"lm", "nls".
predFit(object, ...)# S3 method for default
predFit(object, ...)
# S3 method for nls
predFit(
object,
newdata,
se.fit = FALSE,
interval = c("none", "confidence", "prediction"),
level = 0.95,
adjust = c("none", "Bonferroni", "Scheffe"),
k,
...
)
An object that inherits from class "lm", "nls".
Additional optional arguments. At present, no optional arguments are used.
An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
A logical vaue indicating if standard errors are required.
Default is FALSE.
Type of interval to be calculated. Can be one of "none"
(default), "confidence", or "prediction". Default is "none".
A numeric scalar between 0 and 1 giving the confidence level for
the intervals (if any) to be calculated. Default is 0.95.
A logical value indicating if an adjustment should be made to
the critical value used in calculating the confidence interval. This is
useful for when the calibration curve is to be used multiple, say k, times.
Default is FALSE.
The number times the calibration curve is to be used for computing
a confidence/prediction interval. Only needed when
adjust = "Bonferroni".
No return value (called for side effects).
Greenwell B. M., and Schubert-Kabban, C. M. (2014) investr: An R Package for Inverse Estimation. The R Journal, 6(1), 90-100.