# regSim: Regression Model Simulation In fRegression: Rmetrics - Regression Based Decision and Prediction

## Description

Simulates regression models.

## Usage

 ```1 2 3 4 5``` ```regSim(model = "LM3", n = 100, ...) LM3(n = 100, seed = 4711) LOGIT3(n = 100, seed = 4711) GAM3(n = 100, seed = 4711) ```

## Arguments

 `model` a character string defining the function name from which the regression model will be simulated. `n` an integer value setting the length, i.e. the number of records of the output series, an integer value. By default `n=100`. `seed` an integer value, the recommended way to specify seeds for random number generation. `...` arguments to be passed to the underlying function specified by the `model` argument.

## Details

The function `regSim` allows to simulate from various regression models defined by one of the three example functions `LM3`, `LOGIT3`, `GAM3` or by a user specified function.

The examples are defined in the following way:

`# LM3:`
`> y = 0.75 * x1 + 0.25 * x2 - 0.5 * x3 + 0.1 * eps `

`# LOGIT3:`
`> y = 1 / (1 + exp(- 0.75 * x1 + 0.25 * x2 - 0.5 * x3 + eps)) `

`# GAM3:`
`> y = scale(scale(sin(2 * pi * x1)) + scale(exp(x2)) + scale(x3)) `
`> y = y + 0.1 * rnorm(n, sd = sd(y))`

`"LM3"` models a liner regression model, `"LOGIT3"` a generalized linear regression model expressed by a logit model, and `"GAM"` an additive model. `x1`, `x2`, `x3`, and `eps` are random normal deviates of length `n`.

The `model` function should return an rectangular series defined as an object of class `data.frame`, `timeSeries` or `mts` which can be accepted from the parameter estimation functions `regFit` and `gregFit`.

## Value

The function `garchSim` returns an object of the same class as returned by the underlying function `match.fun(model)`. These may be objects of class `data.frame`, `timeSeries` or `mts`.

## Note

This function is still under development. For the future we plan, that the function `regSim` will be able to generate general regression models.

## Author(s)

Diethelm Wuertz for the Rmetrics R-port.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```## LM2 - # Data for a user defined linear regression model: LM2 = function(n){ x = rnorm(n) y = rnorm(n) eps = 0.1 * rnorm(n) z = 0.5 + 0.75 * x + 0.25 * y + eps data.frame(Z = z, X = x, Y = y) } for (FUN in c("LM2", "LM3")) { cat(FUN, ":\n", sep = "") print(regSim(model = FUN, n = 10)) } ```

fRegression documentation built on May 2, 2019, 11:10 a.m.