nordr {gnlm} | R Documentation |
nordr
fits arbitrary nonlinear regression functions (with
logistic link) to ordinal response data by proportional odds,
continuation ratio, or adjacent categories.
Nonlinear regression models can be supplied as formulae where
parameters are unknowns in which case factor variables cannot be used
and parameters must be scalars. (See finterp
.)
The printed output includes the -log likelihood (not the deviance), the corresponding AIC, the maximum likelihood estimates, standard errors, and correlations.
nordr(y, distribution="proportional", mu=NULL, linear=NULL, pmu, pintercept, weights=NULL, envir=parent.frame(), print.level=0, ndigit=10, gradtol=0.00001, steptol=0.00001, fscale=1, iterlim=100, typsiz=abs(p), stepmax=10*sqrt(p%*%p))
y |
A vector of ordinal responses, integers numbered from zero to
one less than the number of categories or an object of class,
response (created by restovec ) or repeated
(created by rmna ) or lvna ). If the
repeated data object contains more than one response variable,
give that object in envir and give the name of the response
variable to be used here. |
distribution |
The ordinal distribution: proportional odds, continuation ratio, or adjacent categories. |
mu |
User-specified function of pmu , and possibly linear ,
giving the logistic regression equation. This must contain the first
intercept. It may contain a linear part as the second argument to the
function. It may also be a formula beginning with ~, specifying a
logistic regression function for the location parameter, either a
linear one using the Wilkinson and Rogers notation or a general
function with named unknown parameters. If it contains unknown
parameters, the keyword linear may be used to specify a linear
part. If nothing is supplied, the location is taken to be constant
unless the linear argument is given. |
linear |
A formula beginning with ~ in W&R notation, specifying the linear part of the logistic regression function. |
pmu |
Vector of initial estimates for the regression parameters,
including the first intercept. If mu is a formula with unknown
parameters, their estimates must be supplied either in their order of
appearance in the expression or in a named list. |
pintercept |
Vector of initial estimates for the contrasts with the first intercept parameter (difference in intercept for successive categories): two less than the number of different ordinal values. |
weights |
Weight vector for use with contingency tables. |
envir |
Environment in which model formulae are to be
interpreted or a data object of class, repeated , tccov ,
or tvcov ; the name of the response variable should be given in
y . If y has class repeated , it is used as
the environment. |
others |
Arguments controlling nlm . |
A list of class nordr is returned that contains all of the relevant information calculated, including error codes.
J.K. Lindsey
finterp
, fmr
, glm
,
glmm
, gnlmm
,
gnlr
, gnlr3
,
nlr
, ordglm
# McCullagh (1980) JRSS B42, 109-142 # tonsil size: 2x3 contingency table y <- c(0:2,0:2) carrier <- c(rep(0,3),rep(1,3)) carrierf <- gl(2,3,6) wt <- c(19,29,24, 497,560,269) pmu <- c(-1,0.5) mu <- function(p) c(rep(p[1],3),rep(p[1]+p[2],3)) # proportional odds # with mean function nordr(y, dist="prop", mu=mu, pmu=pmu, weights=wt, pintercept=1.5) # using Wilkinson and Rogers notation nordr(y, dist="prop", mu=~carrierf, pmu=pmu, weights=wt, pintercept=1.5) # using formula with unknowns nordr(y, dist="prop", mu=~b0+b1*carrier, pmu=pmu, weights=wt, pintercept=1.5) # continuation ratio nordr(y, dist="cont", mu=mu, pmu=pmu, weights=wt, pintercept=1.5) # adjacent categories nordr(y, dist="adj", mu=~carrierf, pmu=pmu, weights=wt, pintercept=1.5) # # Haberman (1974) Biometrics 30, 589-600 # institutionalized schizophrenics: 3x3 contingency table y <- rep(0:2,3) fr <- c(43,6,9, 16,11,18, 3,10,16) length <- gl(3,3) # fit continuation ratio model with nordr and as a logistic model nordr(y, mu=~length, weights=fr, pmu=c(0,-1.4,-2.3), pint=0.13, dist="cont") # logistic regression with reconstructed table frcr <- cbind(c(43,16,3,49,27,13),c(6,11,10,9,18,16)) lengthord <- gl(3,1,6) block <- gl(2,3) summary(glm(frcr~lengthord+block,fam=binomial)) # note that AICs and deviances are different