1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
|
library(ordinal)
## source("test.clm.predict.R")
## library(devtools)
## r2path <- "/Users/rhbc/Documents/Rpackages/ordinal/pkg/ordinal"
## clean_dll(pkg = r2path)
## load_all(r2path)
cy <- with(wine, which(temp == "cold" & contact == "yes"))
options("contrasts" = c("contr.treatment", "contr.poly"))
getOption("contrasts")
## Example model
wine1.clm <- clm(rating ~ temp*contact, subset = -cy, data = wine)
summary(wine1.clm)
names(wine1.clm)
wine.clm <- clm(rating~temp*contact, data=wine)
summary(wine.clm)
names(wine.clm)
## Make sure the same elements are present with a rank deficient model
## fit:
stopifnot(all(names(wine1.clm) == names(wine.clm)))
## With treatment contrasts:
options("contrasts" = c("contr.treatment", "contr.poly"))
wine.clm <- clm(rating~temp*contact, data=wine)
coef(summary(wine.clm))
head(model.matrix(wine.clm)$X)
wine.clm$contrasts
head(pred1 <- predict(wine.clm)$fit)
## With sum contrasts:
options("contrasts" = c("contr.sum", "contr.poly"))
wine.clm <- clm(rating~temp*contact, data=wine)
coef(summary(wine.clm))
head(model.matrix(wine.clm)$X)
wine.clm$contrasts
head(pred2 <- predict(wine.clm)$fit)
## Mixture of sum and treatment contrasts:
options("contrasts" = c("contr.treatment", "contr.poly"))
wine.clm <- clm(rating~temp*contact, data=wine,
contrasts=list(temp="contr.sum"))
coef(summary(wine.clm))
head(model.matrix(wine.clm)$X)
wine.clm$contrasts
head(pred3 <- predict(wine.clm)$fit)
stopifnot(isTRUE(all.equal(pred1, pred2)))
stopifnot(isTRUE(all.equal(pred1, pred3)))
#################################
### Now for a rank deficient fit:
#################################
cy <- with(wine, which(temp == "cold" & contact == "yes"))
options("contrasts" = c("contr.treatment", "contr.poly"))
wine1.clm <- clm(rating ~ temp*contact, subset = -cy, data = wine)
coef(summary(wine1.clm))
attributes(model.matrix(wine1.clm)$X)$contrasts
wine1.clm$contrasts
head(pred4 <- predict(wine1.clm)$fit)
options("contrasts" = c("contr.sum", "contr.poly"))
wine1.clm <- clm(rating ~ temp*contact, subset = -cy, data = wine)
attributes(model.matrix(wine1.clm)$X)$contrasts
options("contrasts" = c("contr.treatment", "contr.poly"))
attributes(model.matrix(wine1.clm)$X)$contrasts
## Notice that the contrasts change in the attributes of the fit!!!
coef(summary(wine1.clm))
wine1.clm$contrasts
head(pred5 <- predict(wine1.clm)$fit)
head(cbind(pred4, pred5))
stopifnot(isTRUE(all.equal(pred4, pred5)))
options("contrasts" = c("contr.treatment", "contr.poly"))
wine1.clm <- clm(rating ~ temp*contact, subset = -cy, data = wine,
contrasts=list(temp="contr.sum"))
coef(summary(wine1.clm))
head(model.matrix(wine1.clm)$X)
attributes(model.matrix(wine1.clm)$X)$contrasts
wine1.clm$contrasts
head(pred6 <- predict(wine1.clm)$fit)
head(cbind(pred4, pred5, pred6))
stopifnot(isTRUE(all.equal(pred4, pred6)))
##################################################################
## Compare equality of fitted values for models with different contrasts:
options("contrasts" = c("contr.treatment", "contr.poly"))
fm1 <- clm(rating ~ temp + contact, data=wine)
fitted(fm1)
options("contrasts" = c("contr.sum", "contr.poly"))
fm2 <- clm(rating ~ temp + contact, data=wine)
fitted(fm2)
options("contrasts" = c("contr.treatment", "contr.poly"))
fm3 <- clm(rating ~ temp + contact, data=wine,
contrasts=list(contact="contr.sum"))
fitted(fm3)
stopifnot(isTRUE(all.equal(fitted(fm1), fitted(fm2))))
stopifnot(isTRUE(all.equal(fitted(fm1), fitted(fm3))))
##################################################################
## Compare equality of fitted values for models with different
## contrasts in face of aliased coefficients:
options("contrasts" = c("contr.treatment", "contr.poly"))
cy <- with(wine, which(temp == "cold" & contact == "yes"))
Wine <- subset(wine, subset=!(temp == "cold" & contact == "yes"))
fm1 <- clm(rating ~ temp + contact, data=Wine)
options("contrasts" = c("contr.sum", "contr.poly"))
fm2 <- clm(rating ~ temp + contact, data=Wine)
options("contrasts" = c("contr.treatment", "contr.poly"))
fm3 <- clm(rating ~ temp + contact, data=Wine,
contrasts=list(contact="contr.sum"))
stopifnot(isTRUE(all.equal(fitted(fm1), fitted(fm2))))
stopifnot(isTRUE(all.equal(fitted(fm1), fitted(fm3))))
stopifnot(isTRUE(all.equal(predict(fm1)$fit, predict(fm2)$fit)))
stopifnot(isTRUE(all.equal(predict(fm1)$fit, predict(fm3)$fit)))
#################################
## Does this also happen if the wine data has changed?
options("contrasts" = c("contr.treatment", "contr.poly"))
Wine <- subset(wine, subset=!(temp == "cold" & contact == "yes"))
fm1 <- clm(rating ~ temp + contact, data=Wine)
fit1 <- fitted(fm1)
pred1 <- predict(fm1)$fit
Wine <- wine
pred2 <- predict(fm1)$fit
stopifnot(isTRUE(all.equal(fit1, pred1)))
stopifnot(isTRUE(all.equal(fit1, pred2)))
## What if weights, say, is an expression?
## Notice that updating the model object changes it:
set.seed(123)
fm1 <- clm(rating ~ temp + contact, data=wine,
weights=runif(nrow(wine), .5, 1.5))
fm2 <- update(fm1)
stopifnot(isTRUE(all.equal(fitted(fm1), predict(fm1)$fit)))
stopifnot(!isTRUE(all.equal(fitted(fm1), fitted(fm2))))
#################################
## Test equality of fits and predictions of models with:
## 'x + I(x^2)' and 'poly(x, 2)':
## December 25th 2014, RHBC.
data(wine)
set.seed(1)
x <- rnorm(nrow(wine), sd=2) + as.numeric(wine$rating)
range(x)
## Comparison of 'x + I(x^2)' and 'poly(x, 2)':
fm3 <- clm(rating ~ temp + x + I(x^2), data=wine)
fm4 <- clm(rating ~ temp + poly(x, 2), data=wine)
## Same model fits, but different parameterizations:
stopifnot(
!isTRUE(all.equal(coef(fm3), coef(fm4), check.names=FALSE))
)
stopifnot(isTRUE(all.equal(logLik(fm3), logLik(fm4))))
newData <- expand.grid(temp = levels(wine$temp),
x=seq(-1, 7, 3))
predict(fm3, newdata=newData)$fit
predict(fm4, newdata=newData)$fit
stopifnot(isTRUE(all.equal(fitted(fm3), fitted(fm4))))
stopifnot(isTRUE(
all.equal(predict(fm3, newdata=newData)$fit,
predict(fm4, newdata=newData)$fit)))
#################################
|
