# getNormFromCI: Find the best-fit normal / Gaussian distribution for a given... In bootComb: Combine Parameter Estimates via Parametric Bootstrap

## Description

Finds the best-fit normal distribution for a given confidence interval; returns the corresponding density, distribution, quantile and sampling functions.

## Usage

 `1` ```getNormFromCI(qLow, qUpp, alpha = 0.05, initPars = c(0, 1), maxiter = 1000) ```

## Arguments

 `qLow` The observed lower quantile. `qUpp` The observed upper quantile. `alpha` The confidence level; i.e. the desired coverage is 1-alpha. Defaults to 0.05. `initPars` A vector of length 2 giving the initial parameter values (mean & sd) to start the optimisation; defaults to c(0,1). `maxiter` Maximum number of iterations for `optim`. Defaults to 1e3. Set to higher values if convergence problems are reported.

## Value

A list with 5 elements:

 `r` The sampling function. `d` The density function. `p` The distribution function. `q` The quantile function. `pars` A vector of length 2 giving the mean and standard deviation for the best-fit normal distribution (`mean` and `sd` as in `rnorm`, `dnorm`, `pnorm`, `qnorm`).

## See Also

`identifyNormPars`, `optim`, `dnorm`

## Examples

 ```1 2 3 4 5 6 7 8 9``` ```n<-getNormFromCI(qLow=1.08,qUpp=8.92) print(n\$pars) # the fitted parameter values (mean & sd) n\$r(10) # 10 random values from the fitted normal distribution n\$d(6) # the probability density at x=6 for the normal distribution n\$p(4.25) # the cumulative density at x=4.25 for the fitted normal distribution n\$q(c(0.25,0.5,0.75)) # the 25th, 50th (median) and 75th percentiles of the fitted distribution x<-seq(0,10,length=1e3) y<-n\$d(x) plot(x,y,type="l",xlab="",ylab="density") # density plot for the fitted normal distribution ```

bootComb documentation built on Nov. 19, 2020, 1:07 a.m.