# getEstimates: Function to Estimate x Given y. In fredcommo/nplr: N-Parameter Logistic Regression

## 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

 ```1 2``` ``` ## S4 method for signature 'nplr' getEstimates(object, targets = seq(.9, .1, by = -.1), B = 1e4, conf.level = .95) ```

## Arguments

 `object` : an object of class `nplr`. `targets` : one, of 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).

## 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`.

## 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.

## Warning

Notice that, if any target<=B or target>=T, in other words outside the 2 asymptotes, the maximal (or minimal) possible value the model can estimates is returned.

## Note

The data used in the examples are samples from the NCI-60 Growth Inhibition Data: https://wiki.nci.nih.gov/display/NCIDTPdata/NCI-60+Growth+Inhibition+Data, except for multicell.tsv which are simulated data.

## Author(s)

Frederic Commo, Brian M. Bot

`nplr`, `plot.nplr`, , `nplrAccessors`
 ```1 2 3 4 5 6 7``` ```# 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) ```