# estQdf: Estimate quantiles of distribution In DstarM: Analyze Two Choice Reaction Time Data with the D*M Method

## Description

Estimate quantiles of distribution

## Usage

 `1` ```estQdf(p, x, cdf) ```

## Arguments

 `p` A vector of probabilities. `x` The x-axis values corresponding to the cumulative distribution function. `cdf` A cumulative distributions function, i.e. output of `estCdf`.

## Details

Quantiles are obtained in the following manner. For p = 0 and p = 1, the minimum and maximum of x is used. For other probabilities the quantiles are obtained via `q[i] = uniroot(x, cdf - p[i])\$root`. Y values are interpolated via `approxfun`.

## Value

Quantiles of cumulative distribution function(s). If the input was a matrix of cumulative distributions functions, a matrix of quantiles is returned.

## Examples

 ```1 2 3 4 5 6 7 8``` ```x = seq(-9, 9, .1) # x-grid d = dnorm(x) # density functions p = seq(0, 1, .2) # probabilities of interest cEst = estCdf(d) # estimate cumulative distribution functions qEst = estQdf(p = p, x = x, cdf = cEst) # estimate quantiles plot(x, cEst, bty = 'n', las = 1, type = 'l', ylab = 'Probability') # plot cdf abline(h = p, v = qEst, col = 1:6, lty = 2) # add lines for p and for obtained quantiles points(x = qEst, y = p, pch = 18, col = 1:6, cex = 1.75) # add points for intersections ```

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