Description Usage Arguments Value Note References Examples
The function calibrate
computes the maximum likelihood estimate and a
condfidence interval for the unknown predictor value that corresponds to an
observed value of the response (or vector thereof) or specified value of the
mean response. See the reference listed below for more details.
#' @rdname calibrate #' @export #' @method calibrate lm calibrate.lm < function(object, ...) calibrate(formula(object), data = eval(object$call$data), ...)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  calibrate(object, ...)
## Default S3 method:
calibrate(object, y0, interval = c("inversion", "Wald",
"none"), level = 0.95, mean.response = FALSE, adjust = c("none",
"Bonferroni", "Scheffe"), k, ...)
## S3 method for class 'formula'
calibrate(formula, data = NULL, ..., subset,
na.action = na.fail)
## S3 method for class 'lm'
calibrate(object, y0, interval = c("inversion", "Wald", "none"),
level = 0.95, mean.response = FALSE, adjust = c("none", "Bonferroni",
"Scheffe"), k, ...)

object 
An object that inherits from class 
... 
Additional optional arguments. At present, no optional arguments are used. 
y0 
The value of the observed response(s) or specified value of the mean response. 
interval 
The method to use for forming a confidence interval. 
level 
A numeric scalar between 0 and 1 giving the confidence level for the interval to be calculated. 
mean.response 
Logicial indicating whether confidence intervals should
correspond to an observed response(s) ( 
adjust 
A logical value indicating if an adjustment should be made to the critical value used in calculating the confidence interval. This useful for when the calibration curve is to be used multiple, say k, times. 
k 
The number times the calibration curve is to be used for computing a
confidence interval. Only needed when 
formula 
A formula of the form 
data 
an optional data frame, list or environment (or object coercible
by 
subset 
An optional vector specifying a subset of observations to be used in the fitting process. 
na.action 
a function which indicates what should happen when the data
contain 
An object of class "invest"
containing the following
components:
estimate
The estimate of x0.
lwr
The lower confidence limit for x0.
upr
The upper confidence limit for x0.
se
An estimate of the standard error (Wald interval only).
interval
The method used for calculating lower
and
upper
(only used by print
method).
The function invest
is more general, but based on numerical
techniques to find the solution. When the underlying model is that of the
simple linear regression model with normal errors, closedform expressions
exist which are utilized by the function calibrate
.
Graybill, F. A., and Iyer, H. K. (1994) Regression analysis: Concepts and Applications. Duxbury Press.
Miller, R. G. (1981) Simultaneous Statistical Inference. SpringerVerlag.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31  #
# Arsenic example (simple linear regression with replication)
#
# Inverting a prediction interval for an individual response
arsenic.lm < lm(measured ~ actual, data = arsenic)
plotFit(arsenic.lm, interval = "prediction", shade = TRUE,
col.pred = "lightblue")
(cal < calibrate(arsenic.lm, y0 = 3, interval = "inversion"))
abline(h = 3)
segments(cal$estimate, 3, cal$estimate, par()$usr[3])
arrows(cal$lower, 3, cal$lower, par()$usr[3])
arrows(cal$upper, 3, cal$upper, par()$usr[3])
#
# Crystal weight example (simple linear regression)
#
# Inverting a confidence interval for the mean response
crystal.lm < lm(weight ~ time, data = crystal)
plotFit(crystal.lm, interval = "confidence", shade = TRUE,
col.conf = "lightblue")
(cal < calibrate(crystal.lm, y0 = 8, interval = "inversion",
mean.response = TRUE))
abline(h = 8)
segments(cal$estimate, 8, cal$estimate, par()$usr[3])
arrows(cal$lower, 8, cal$lower, par()$usr[3])
arrows(cal$upper, 8, cal$upper, par()$usr[3])
# Wald interval and approximate standard error based on the delta method
calibrate(crystal.lm, y0 = 8, interval = "Wald", mean.response = TRUE)

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