# tests/clm.fit.R In ordinal: Regression Models for Ordinal Data

```library(ordinal)
data(wine)

## clm.fit with nominal and scale effects:

## get simple model:
fm1 <- clm(rating ~ temp, scale=~temp, nominal=~ contact,
data=wine, method="design")
str(fm1, give.attr=FALSE)
fm1\$control\$method <- "Newton"
res <- clm.fit(fm1)
names(res)
res\$Theta

## construct some weights and offsets:
set.seed(1)
off1 <- runif(length(fm1\$y))
set.seed(1)
off2 <- rnorm(length(fm1\$y))
set.seed(1)
wet <- runif(length(fm1\$y))

## Fit various models:
fit <- clm.fit(fm1\$y, fm1\$X, fm1\$S, fm1\$NOM, weights=wet)
Coef <-
c(-0.905224120279548, 1.31043498891987, 3.34235590523008,
4.52389661722693,      -3.03954652971192, -1.56922389038976,
-1.75662549320839, -1.16845464236365,      2.52988580848393,
-0.0261457032829033)
stopifnot(all.equal(coef(fit), Coef, check.attributes=FALSE, tol=1e-6))
str(fit)

fit <- clm.fit(fm1\$y, fm1\$X, fm1\$S, fm1\$NOM, offset=off1)
str(fit)

fit <- clm.fit(fm1\$y, fm1\$X, fm1\$S, fm1\$NOM, offset=off1,
S.offset=off2)
str(fit)

fit <- clm.fit(fm1\$y, fm1\$X, fm1\$S)
str(fit)

fit <- clm.fit(fm1\$y, fm1\$X)
str(fit)

fit <- clm.fit(fm1\$y)
coef(fit)
str(fit)

## Remember: compare with corresponding .Rout file
```

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