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
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
|
dataRep <- function(formula, data, subset, na.action)
{
call <- match.call()
nact <- NULL
y <- match.call(expand=FALSE)
if(missing(na.action))
y$na.action <- na.delete
y[[1]] <- as.name("model.frame")
X <- eval(y, sys.parent())
nact <- attr(X,"na.action")
n <- nrow(X)
nam <- names(X)
p <- length(nam)
types <- character(p)
parms <- character(p)
pctl <- vector('list',p)
margfreq <- vector('list',p)
Xu <- vector('list',p)
for(j in 1:p) {
namj <- nam[j]
xj <- X[[j]]
if(is.character(xj))
xj <- as.factor(xj)
if(is.factor(xj)) {
parms[[j]] <- paste(levels(xj),collapse=' ')
types[j] <- 'exact categorical'
} else if(inherits(xj,'roundN')) {
atr <- attributes(xj)
nam[j] <- atr$name
types[j] <- 'round'
parms[j] <- paste('to nearest',format(atr$tolerance))
if(length(w <- atr$clip))
parms[j] <- paste(parms[j],', clipped to [',
paste(format(w),collapse=','),']',sep='')
pctl[[j]] <- atr$percentiles
} else {
types[j] <- 'exact numeric'
parms[j] <- ''
pctl[[j]] <- quantile(xj, seq(0,1,by=.01))
}
margfreq[[j]] <- table(xj)
Xu[[j]] <- sort(unique(xj))
X[[j]] <- xj
}
names(types) <- names(parms) <- names(pctl) <- names(margfreq) <-
names(Xu) <- nam
Xu <- expand.grid(Xu)
m <- nrow(Xu)
count <- integer(m)
for(i in 1:m) {
matches <- rep(TRUE,n)
for(j in 1:p)
matches <- matches & (as.character(X[[j]]) ==
as.character(Xu[[j]][i]))
count[i] <- sum(matches)
}
if(any(count==0)) {
s <- count > 0
Xu <- Xu[s,]
count <- count[s]
m <- sum(s)
}
structure(list(call=call, formula=formula, n=n, names=nam,
types=types, parms=parms, margfreq=margfreq,
percentiles=pctl, X=Xu, count=count, na.action=nact),
class='dataRep')
}
roundN <- function(x, tol=1, clip=NULL)
{
pct <- quantile(x, seq(0,1,by=.01), na.rm=TRUE)
name <- deparse(substitute(x))
lab <- attr(x, 'label')
if(!length(lab))
lab <- name
if(!missing(clip))
x <- pmin(pmax(x,clip[1]),clip[2])
structure(as.single(tol*round(x/tol)), tolerance=tol, clip=clip,
percentiles=pct, name=name, label=lab, class='roundN')
}
if(.R.)
as.data.frame.roundN <- as.data.frame.vector
'[.roundN' <- function(x, i, ...)
{
atr <- attributes(x)
x <- oldUnclass(x)[i]
attributes(x) <- atr
x
}
print.dataRep <- function(x, long=FALSE, ...)
{
cat("\n")
cat("Data Representativeness n=",x$n,"\n\n", sep='')
dput(x$call)
cat("\n")
if(length(z <- x$na.action))
naprint(z)
specs <- data.frame(Type=x$types,
Parameters=x$parms,
row.names=x$names)
cat('Specifications for Matching\n\n')
print.data.frame(specs)
X <- x$X
if(long) {
X$Frequency <- x$count
cat('\nUnique Combinations of Descriptor Variables\n\n')
print.data.frame(X)
} else cat('\n',nrow(X),
'unique combinations of variable values were found.\n\n')
invisible()
}
predict.dataRep <- function(object, newdata, ...)
{
n <- object$n
count <- object$count
if(missing(newdata))
return(count)
pctl <- object$percentiles
margfreq <- object$margfreq
p <- length(margfreq)
m <- nrow(newdata)
nam <- object$names
types <- object$types
X <- object$X
##Xn <- if(length(model.frame.default$Des)) 3Aug02
## model.frame(object$formula, newdata, na.action=na.keep, Des=FALSE) else
Xn <- model.frame(object$formula, newdata, na.action=na.keep)
names(Xn) <- nam
worst.margfreq <- rep(1e8, m)
pct <- matrix(NA, m, p, dimnames=list(row.names(Xn),nam))
for(j in 1:p) {
xj <- Xn[[j]]
freq <- margfreq[[nam[j]]][as.character(xj)]
freq[is.na(freq)] <- 0
pct[,j] <- if(types[j]=='exact categorical')
100*freq/n
else
approx(pctl[[nam[j]]], seq(0,100,by=1),
xout=newdata[[nam[j]]], rule=2)$y
worst.margfreq <- pmin(worst.margfreq, freq)
}
cnt <- integer(m)
for(i in 1:m) {
matches <- rep(TRUE,nrow(X))
for(j in 1:p) {
matches <- matches & (as.character(X[[j]]) == as.character(Xn[[j]][i]))
}
s <- sum(matches)
if(s > 1)
warning('more than one match to original data combinations')
cnt[i] <- if(s)
count[matches]
else
0
}
if(any(cnt > worst.margfreq))
warning('program logic error')
structure(list(count=cnt, percentiles=pct, worst.margfreq=worst.margfreq,
newdata=newdata), class='predict.dataRep')
}
print.predict.dataRep <- function(x, prdata=TRUE, prpct=TRUE, ...)
{
if(prdata) {
dat <- x$newdata
dat$Frequency <- x$count
dat$Marginal.Freq <- x$worst.margfreq
cat('\nDescriptor Variable Values, Estimated Frequency in Original Dataset,\nand Minimum Marginal Frequency for any Variable\n\n')
print.data.frame(dat)
} else {
cat('\nFrequency in Original Dataset\n\n')
print(x$count)
cat('\nMinimum Marginal Frequency for any Variable\n\n')
print(x$worst.margfreq)
}
if(prpct) {
cat('\n\nPercentiles for Continuous Descriptor Variables,\nPercentage in Category for Categorical Variables\n\n')
print(round(x$percentiles))
}
invisible()
}
|
