# get.t.par: Fitting parameter of a Student's t distribution from one or... In rriskDistributions: Fitting Distributions to Given Data or Known Quantiles

## Description

`get.t.par` returns the parameters of a Student's t distribution where the `p`th percentiles match with the quantiles `q`.

## Usage

 ```1 2``` ```get.t.par(p = c(0.025, 0.5, 0.975), q, show.output = TRUE, plot = TRUE, tol = 0.001, fit.weights = rep(1, length(p)), scaleX = c(0.1, 0.9), ...) ```

## Arguments

 `p` numeric, single value or vector of probabilities. `q` numeric, single value or vector of quantiles corresponding to p. `show.output` logical, if `TRUE` the `optim` result will be printed (default value is `TRUE`). `plot` logical, if `TRUE` the graphical diagnostics will be plotted (default value is `TRUE`). `tol` numeric, single positive value giving the absolute convergence tolerance for reaching zero (default value is `0.001`). `fit.weights` numerical vector of the same length as a probabilities vector `p` containing positive values for weighting quantiles. By default all quantiles will be weighted by 1. `scaleX` numerical vector of the length 2 containing values (from the open interval (0, 1)) for scaling quantile-axis (relevant only if `plot = TRUE`). The smaller the left value, the further the graph is extrapolated within the lower percentile, the greater the right value, the further it goes within the upper percentile. `...` further arguments passed to the functions `plot` and `points` (relevant only if `plot = TRUE`).

## Details

The number of probabilities and the number of quantiles must be identical and should be at least one. Using the default `p`, the three corresponding quantiles are the 2.5th percentile, the median and the 97.5th percentile, respectively. `get.t.par` uses the R function `optim` with the method `L-BFGS-B`. If this method fails the optimization method `BFGS` will be invoked.

If `show.output = TRUE` the output of the function `optim` will be shown. The item `convergence` equal to 0 means the successful completion of the optimization procedure, otherwise it indicates a convergence error. The item `value` displays the achieved minimal value of the functions that were minimized.

The estimated distribution parameters returned by the function `optim` are accepted if the achieved value of the minimized function (output component `value` of `optim`) is smaller than the argument `tol`.

The items of the probability vector `p` should lie between 0 and 1.

The items of the weighting vector `fit.weights` should be positive values.

The function which will be minimized is defined as a sum of squared differences between the given probabilities and the theoretical probabilities of the specified distribution evaluated at the given quantile points (least squares estimation).

## Value

Returns fitted parameters of a Student's t distribution or missing values (`NA`'s) if the distribution cannot fit the specified quantiles.

## Note

It should be noted that there might be deviations between the estimated and the theoretical distribution parameters in certain circumstances. This is because the estimation of the parameters is based on a numerical optimization method and depends strongly on the initial values. In addition, one must always keep in mind that a distribution for different combinations of parameters may look very similar. Therefore, the optimization method cannot always find the "right" distribution, but a "similar" one.

If the function terminates with the error massage "convergence error occurred or specified tolerance not achieved", one may try to set the convergence tolerance to a higher value. It is yet to be noted, that good till very good fits of parameters could only be obtained for tolerance values that are smaller than 0.001.

## Author(s)

Matthias Greiner [email protected] (BfR),
Katharina Schueller [email protected] (STAT-UP Statistical Consulting),
Natalia Belgorodski [email protected] (STAT-UP Statistical Consulting)

See `stats::pt` for distribution implementation details.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34``` ```q <- stats::qt(p = c(0.025, 0.5, 0.975), df = 10) old.par <- graphics::par(mfrow = c(2, 3)) get.t.par(q = q) get.t.par(q = q, fit.weights = c(100, 1, 100)) get.t.par(q = q, fit.weights = c(10, 1, 10)) get.t.par(q = q, fit.weights = c(1, 100, 1)) get.t.par(q = q, fit.weights = c(1, 10, 1)) graphics::par(old.par) q <- stats::qt(p = c(0.025, 0.5, 0.975), df = 0.1) old.par <- graphics::par(mfrow = c(2, 3)) get.t.par(q = q, scaleX = c(0.5, 0.5)) get.t.par(q = q, fit.weights = c(100, 1, 100), scaleX = c(0.5, 0.5)) get.t.par(q = q, fit.weights = c(10, 1, 10), scaleX = c(0.5, 0.5)) get.t.par(q = q, fit.weights = c(1, 100, 1), scaleX = c(0.5, 0.5)) get.t.par(q = q, fit.weights = c(1, 10, 1), scaleX = c(0.5, 0.5)) graphics::par(old.par) q <- stats::qt(p = c(0.025, 0.5, 0.975), df = 1) old.par <- graphics::par(mfrow = c(2, 3)) get.t.par(q = q, scaleX = c(0.5, 0.5)) get.t.par(q = q, fit.weights = c(100, 1, 100), scaleX = c(0.5, 0.5)) get.t.par(q = q, fit.weights = c(10, 1, 10), scaleX = c(0.5, 0.5)) get.t.par(q = q, fit.weights = c(1, 100, 1), scaleX = c(0.5, 0.5)) get.t.par(q = q, fit.weights = c(1, 10, 1), scaleX = c(0.5, 0.5)) graphics::par(old.par) ## example with only one quantile q <- stats::qt(p = c(0.025), df = 3) old.par <- graphics::par(mfrow = c(1, 3)) get.t.par(p = c(0.025), q = q) get.t.par(p = c(0.025), q = q, fit.weights = 10) get.t.par(p = c(0.025), q = q, fit.weights = 100) graphics::par(old.par) ```