PWD_known: Weighted Deming Regression - general weights

In ppwdeming: Precision Profile Weighted Deming Regression

Weighted Deming Regression – general weights

Description

This code is used for the setting of known precision profiles implemented in user-provided R functions called gfun and hfun.

Usage

PWD_known(X, Y, gfun, hfun, gparms, hparms, epsilon=1e-10,
          MDL=NA, getCI=TRUE, printem=FALSE)

Arguments

X	the vector of predicate readings,
Y	the vector of test readings,
gfun	a function with two arguments, a vector of size n and a vector of parameters,
hfun	a function with two arguments, a vector of size n and a vector of parameters,
gparms	a numeric vector containing any parameters referenced by gfun,
hparms	a numeric vector containing any parameters referenced by hfun,
epsilon	optional convergence tolerance limit,
MDL	optional medical decision level(s),
getCI	optional - allows for jackknifed standard errors on the regression and MDL,
printem	optional - if TRUE, routine will print out results as a message.

Details

The functions gfun and hfun are allowed as inputs, to support flexibility in specification of the forms of these variance functions. The known precision profiles specified by the functions gfun and hfun, when provided with estimated vectors of \mu and \alpha + \beta\mu respectively and with any required parameters, will produce the vectors g and h. These vectors are then integrated into the iterative estimation of the slope and intercept of the linear relationship between predicate and test readings.

Value

A list containing the following components:

alpha	the fitted intercept
beta	the fitted slope
cor	the Pearson correlation between X and Y
fity	the vector of predicted Y
mu	the vector of estimated latent true values
resi	the vector of residuals
scalr	the vector of scaled residuals using the specified g and h
like	the -2 log likelihood L
sealpha	the jackknife standard error of alpha
sebeta	the jackknife standard error of beta
covar	the jackknife covariance between alpha and beta
preMDL	the predictions at the MDL(s)
preMDLl	the lower confidence limit(s) of preMDL
preMDLu	the upper confidence limit(s) of preMDL

Author(s)

Douglas M. Hawkins, Jessica J. Kraker [e-mail]

Examples

# library
library(ppwdeming)

# parameter specifications
alpha <- 1
beta  <- 1.1
true  <- 8*10^((0:99)/99)
truey <- alpha+beta*true
# forms of precision profiles
gfun    <- function(true, gparms) {
  gvals = gparms[1]+gparms[2]*true^gparms[3]
  gvals
}
hfun    <- function(true, hparms) {
  hvals = hparms[1]+hparms[2]*true^hparms[3]
  hvals
}

# Loosely motivated by Vitamin D data set
g     <- 4e-16+0.07*true^1.27
h     <- 6e-2+7e-5*truey^2.2
# simulate single sample - set seed for reproducibility
set.seed(1039)
# specifications for predicate method
X     <- true +sqrt(g)*rnorm(100)
# specifications for test method
Y     <- truey+sqrt(h)*rnorm(100)

# fit with to estimate linear parameters
pwd_known_fit <- PWD_known(X, Y, gfun, hfun,
                           gparms=c(4e-16, 0.07, 1.27),
                           hparms=c(6e-2, 7e-5, 2.2),
                           printem=TRUE)

ppwdeming documentation built on Sept. 9, 2025, 5:37 p.m.