R: Fit Cumulative Link Models

clm.fit {ordinal}

R Documentation

Fit Cumulative Link Models

Description

A direct fitter of cumulative link models.

Usage


clm.fit(y, X, S, N, weights = rep(1, nrow(X)),
  offset = rep(0, nrow(X)), S.offset = rep(0, nrow(X)),
  control = list(), start,
  link = c("logit", "probit", "cloglog", "loglog", "cauchit"),
  threshold = c("flexible", "symmetric", "symmetric2", "equidistant"))

Arguments

`y`	the response variable; a factor, preferably and ordered factor.
`X, S, N`	optional design matrices for the regression parameters, scale parameters and nominal parameters respectively.
`weights`	optional case weights.
`offset`	an optional offset.
`S.offset`	an optional offset for the scale part of the model.
`control`	a list of control parameters, optionally a call to `clm.control`.
`start`	an optional list of starting values of the form `c(alpha, beta, zeta)` for the thresholds and nominal effects (`alpha`), regression parameters (`beta`) and scale parameters (`zeta`).
`link`	the link function.
`threshold`	the threshold structure, see further at `clm`.

Details

This function does almost the same thing that clm does: it fits a cumulative link model. The main differences are that clm.fit does not setup design matrices from formulae and only does minimal post processing after parameter estimation.

Compared to clm, clm.fit does little to warn the user of any problems with data or model. However, clm.fit will attempt to identify column rank defecient designs. Any unidentified parameters are indicated in the aliased component of the fit.

In contrast to clm, clm.fit allows non-positive weights.

Value

A list with the following components:

`par`	the maximum likelihood estimate of the model parameters on the form `c(alpha, beta, zeta)` for the thresholds and nominal effects (`alpha`), regression parameters (`beta`) and scale parameters (`zeta`).
`gradient`	gradient of the log-likelihood function for the parameters at the optimum.
`Hessian`	the Hessian of the parameters at the optimum.
`logLik`	the value of the log-likelihood function at the optimum.
`convergence`	a convergence code; 1: successful convergence, 2: iteration limit reached, 3: step factor reduced below minimum.
`message`	convergence message.
`maxGradient`	the maximum absolute gradient: `max(abs(gradient))`.
`niter`	depends on the optimizer. For the default Newton optimizer it is a vector of length 2: the number of iteration and the number of step halvings respectively.
`fitted`	the fitted values (probabilities).
`coef.names`	a list of vectors of names of the coefficients as inferred from the column names of the design matrices.
`aliased`	a list of logical vectors; `TRUE` is a parameter is not identified due to column rank deficiency in the design matrices and `FALSE` otherwise.

Author(s)

Rune Haubo B Christensen

Examples


## A simple example:
fm1 <- clm(rating ~ contact + temp, data=wine)
summary(fm1)
## get the model frame containing y and X:
mf1 <- update(fm1, method="model.frame")
clm.fit(mf1$y, mf1$X)

[Package ordinal version 2013.9-30 Index]