gspline1 {glmmAK} | R Documentation

## Density and random number generation from a univariate G-spline (penalized Gaussian mixture)

### Description

Univariate G-spline (penalized Gaussian mixture) is distributed as

alpha + sum[j=-K][K]w[j]N(tau*mu[j], tau^2*sigma[j]^2).

### Usage

    rgspline1(n, mu, sigma, weight, intcpt=0, scale=1, logw=TRUE)

    dgspline1(x, mu, sigma, weight, intcpt=0, scale=1, logw=TRUE)

### Arguments

`n` | number of observations to be generated
`x` | grid of values at which we evaluate the G-spline values
`mu` | a vector with G-spline knots mu[j] (j=-K,...,K)(means of basis G-splines)
`sigma` | basis standard deviation(s) sigma[j] (j=-K,...,K). If a single number is supplied then it is assumed that all basis G-splines have the same standard deviation. Alternatively a vector of the same length as `mu` can be given in which case the basis G-splines do not necessarily have the same standard deviations
`weight` | a vector with G-spline (log-)weights. It should have the same length as `mu`
`intcpt` | G-spline intercept value alpha)
`scale` | G-spline scale value tau)
`logw` | logical indicating whether logarithmic weights are supplied in `weight`

### Value

Values of the density or generated random numbers.

### Author(s)

Arnošt Komárek arnost.komarek[AT]mff.cuni.cz

### Examples

    knots <- c(-2, 0, 2)
    sigma <- 1
    weight <- c(0.3, 0.1, 0.6)
    intcpt <- 3
    scale <- 0.2
    xgrid <- seq(1.8, 4.2, length=300)

    dx <- dgspline1(xgrid, mu=knots, sigma=sigma, weight=weight,
       intcpt=intcpt, scale=scale, logw=FALSE)
    x <- rgspline1(100, mu=knots, sigma=sigma, weight=weight,
       intcpt=intcpt, scale=scale, logw=FALSE)
    hist(x, col="seagreen2", prob=TRUE, xlim=range(xgrid), xlab="x", ylab="g(x)")
    lines(xgrid, dx, col="red", lwd=2)

[Package glmmAK version 1.3-1 Index]