# estCdf: Estimate cumulative distribution for D*M models In DstarM: Analyze Two Choice Reaction Time Data with the D*M Method

## Description

Estimate cumulative distribution for D*M models

## Usage

 `1` ```estCdf(x) ```

## Arguments

 `x` Any density function to calculate a cumulative distribution for. The code is designed for input of class `DstarM` but other input is also accepted. Other input can be either a matrix where columns represent densities or a single vector representing a density.

## Details

Cumulative distributions functions are calculated by: `cumsum(x) / sum(x)`. This method works well enough for our purposes. The example below shows that the `ecdf` functions seems to work slightly better. However, this estimates a cdf from raw data and does not transform a pdf into a cdf and is therefore not useful for D*M models.

## Value

Cumulative density function(s). If the input was a matrix, a matrix of cumulative density functions is returned.

## Examples

 ```1 2 3 4 5 6 7 8 9``` ```x = rnorm(1000) xx = seq(-5, 5, .1) approx1 = stats::ecdf(x)(xx) approx2 = estCdf(dnorm(xx, mean(x), sd(x))) trueCdf = pnorm(xx) matplot(xx, cbind(trueCdf, approx1, approx2), type = c('l', 'p', 'p'), lty = 1, col = 1:3, pch = 1, bty = 'n', las = 1, ylab = 'Prob') legend('topleft', legend = c('True Cdf', 'Stats Estatimation', 'DstarM Estimation'), col = 1:3, lty = c(1, NA, NA), pch = c(NA, 1, 1), bty = 'n') ```

DstarM documentation built on Aug. 29, 2020, 1:06 a.m.