# R/gdist.R In ordinal: Regression Models for Ordinal Data

#### Documented in gcauchyglogisgnorm

```## This file contains:
## Gradients of densities of common distribution functions on the form
## g[dist], where "dist" can be one of "logis", "norm", and
## "cauchy". These functions are used in Newton-Raphson algorithms
## when fitting CLMs and CLMMs in clm(), clm2(), clmm() and
## clmm2(). Similar gradients are implemented for the gumbel,
## log-gamma, and Aranda-Ordaz distributions.

glogis <- function(x)
.C("glogis",
x = as.double(x),
length(x),
NAOK = TRUE)\$x

gnorm <- function(x)
### gradient of dnorm(x) wrt. x
.C("gnorm",
x = as.double(x),
length(x),
NAOK = TRUE)\$x

gcauchy <- function(x)
### gradient of dcauchy(x) wrt. x
.C("gcauchy",
x = as.double(x),
length(x),
NAOK = TRUE)\$x

glogisR <- function(x) {
### glogis in R
res <- rep(0, length(x))
isFinite <- !is.infinite(x)

x <- x[isFinite]
isNegative <- x < 0
q <- exp(-abs(x))
q <- 2*q^2*(1 + q)^-3 - q*(1 + q)^-2
q[isNegative] <- -q[isNegative]
res[isFinite] <- q
res
}

gnormR <- function(x)
### gnorm in R
-x * dnorm(x)

gcauchyR <- function(x)
### gcauchy(x) in R
-2*x/pi*(1+x^2)^-2
```

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ordinal documentation built on May 2, 2019, 5:47 p.m.