nplr (version 0.1-8)

getEstimates: Function to Estimate x Given y.

Description

This function takes as its first argument a model returned by nplr(). By inverting the logistic model, it estimates the x values corresponding to one (or a vector of) y target(s) provided. The standard error of the model, defined as the mean squared error on the fitted values, is used to estimate a confidence interval on the predicted x values, according to the specified conf.level. See Details.

Usage

# S4 method for nplr
getEstimates(object, targets = seq(.9, .1, by = -.1), B = 1e4, conf.level = .95)

Value

A data set containing:

y

the target value.

x.05

the lower bound of the estimated 95% confidence interval (default). If another value is passed to conf.level, x will be labelled as x.(1-conf.level)/2.

x

the estimated value.

x.95

the upper bound of the estimated 95% confidence interval (default). If another value is passed to conf.level, x will be labelled as x.1-(1-conf.level)/2.

Arguments

object

an object of class nplr.

targets

one, or a vector of, numerical value(s) for which the corresponding x has to be estimated. Default are target values from .9 to .1.

B

the length of the y distribution from which the x confidence interval is estimated.

conf.level

the estimated x confidence interval, bounded by (1-conf.level)/2 and 1 - (1-conf.level)/2 (by default .95, which gives x.025 and x.975).

Author

Frederic Commo, Brian M. Bot

Warning

Notice that, if any \(target \leq B\) or \(target \geq T\), in other words outside the 2 asymptotes, the maximal (or minimal) possible value the model can estimate is returned.

Details

In n-parameter logistic regressions, none of the parameters follow any particular distribution from which confidence intervals can be estimated. To overcome this issue, the standard error is used to generate a normal distribution of the target(s) passed to the function. The quantiles of that distribution are used in order to provide estimated bounds for the corresponding x value, with respect to conf.level. See also Warning.

See Also

nplr, plot.nplr, nplrAccessors

Examples

# Using the PC-3 data
  require(nplr)
  path <- system.file("extdata", "pc3.txt", package="nplr")
  pc3 <- read.delim(path)
  model <- nplr(x = pc3$CONC, y = pc3$GIPROP)
  getEstimates(model)
  getEstimates(model, c(.3, .6), conf.level = .9)