*Hi R Users,
*
*I was trying to write a simple function for data imputation. I need to
index the results from each step and combine it with some data of the
original dataset for the next step, until the end. I attached a sample data
and the work-well R code function I wrote, but the function is rather
applied easily for all kind of data. Can somebody generalize my
'pace.imp'
function?
*
*
run the attached sample data using the following function:
pred<-matrix(c(rep(0, 11), rep(1,7), rep(0, 72)), 9, 9, byrow=T)
rownames(pred)<-colnames(pred)<-paste("PR", 1:9)
impace<-function(data, time, imp, i, pred, m){
library(mice)
if (sum(is.na(data[data[, time]<=i, imp]))>=1)
{compl<-complete(mice(data[data[, time]<=i, ], pred=pred, m=m))}
else {compl=data[data[, time]<=i, ]}
return(compl)
}
pace.imp<-function(data, time, imp, pred, m){
C1<-impace(data, time, imp, 1, pred, m)
R1<-rbind(C1, data[data[, time]==(1+1),])
C2<-impace(R1, time, imp, 2, pred, m)
R2<-rbind(C2, data[data[, time]==(1+2),])
C3<-impace(R2, time, imp, 3, pred, m)
R3<-rbind(C3, data[data[, time]==(1+3),])
C4<-impace(R3, time, imp, 4, pred, m)
R4<-rbind(C4, data[data[, time]==(1+4),])
C5<-impace(R4, time, imp, 5, pred, m)
R5<-rbind(C5, data[data[, time]==(1+5),])
C6<-impace(R5, time, imp, 6, pred, m)
R6<-rbind(C6, data[data[, time]==(1+6),])
C7<-impace(R6, time, imp, 7, pred, m)
R7<-rbind(C7, data[data[, time]==(1+7),])
C8<-impace(R7, time, imp, 8, pred, m)
R8<-rbind(C8, data[data[, time]==(1+8),])
C9<-impace(R8, time, imp, 9, pred, m)
R9<-rbind(C9, data[data[, time]==(1+9),])
C10<-impace(R9, time, imp, 10, pred, m)
R10<-rbind(C10, data[data[, time]==(1+10),])
C11<-impace(R10, time, imp, 11, pred, m)
return(C11)
}
Pace<-pace.imp(test, time=4, imp=2, pred, 10)
*
*Thanks,
*
*David
*
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"PR 1" "PR 2" "PR 3" "PR 4" "PR
5" "PR 6" "PR 7" "PR 8" "PR 9"
"694" 1196 1 2 1 237 3 4 3 4
"731" 1225 NA 1 1 281 1 2 2 3
"739" 1228 0 2 1 261 2 2 1 1
"758" 1024 NA 1 1 462 1 2 1 1
"766" 1083 0 1 1 307 2 3 2 2
"88" 1067 NA 1 2 412 2 2 1 1
"119" 1090 NA 1 2 347 1 2 1 1
"196" 1132 0 2 2 319 3 2 1 1
"222" 1139 0 1 2 318 3 3 2 2
"251" 1153 0 2 2 329 2 2 2 2
"358" 1199 NA 2 2 201 1 2 1 1
"397" 1214 NA 1 2 290 1 2 1 1
"458" 1007 NA 2 2 317 1 2 2 3
"479" 1018 1 1 2 259 2 3 3 4
"496" 1022 0 1 2 410 1 1 1 1
"528" 1045 NA 1 2 304 1 2 1 1
"571" 1085 NA 1 2 308 1 2 2 3
"593" 1117 0 1 2 444 2 2 1 1
"601" 1119 0 2 2 344 3 2 1 1
"644" 1147 0 1 2 253 4 4 1 1
"659" 1167 0 1 2 224 2 2 1 1
"680" 1181 0 1 2 303 3 2 3 2
"695" 1196 NA 2 2 252 1 3 1 1
"732" 1225 NA 1 2 321 1 2 1 1
"740" 1228 0 2 2 348 2 2 1 1
"759" 1024 0 1 2 482 2 2 3 2
"767" 1083 NA 1 2 356 1 2 1 1
"120" 1090 0 1 3 446 4 2 2 2
"197" 1132 0 2 3 330 3 2 1 1
"223" 1139 0 1 3 332 2 4 3 3
"252" 1153 0 2 3 348 1 2 1 1
"359" 1199 NA 2 3 235 1 2 3 2
"398" 1214 0 1 3 332 1 2 1 1
"459" 1007 0 2 3 326 2 3 1 1
"480" 1018 0 1 3 272 2 2 3 3
"497" 1022 NA 1 3 431 1 1 1 1
"529" 1045 0 1 3 377 1 2 1 1
"572" 1085 0 1 3 326 2 2 1 1
"602" 1119 0 2 3 375 2 2 1 1
"645" 1147 0 1 3 303 1 2 1 1
"660" 1167 1 1 3 246 2 3 1 1
"681" 1181 NA 1 3 366 2 2 1 1
"696" 1196 0 2 3 274 3 2 1 1
"741" 1228 0 2 3 368 1 2 1 1
"768" 1083 0 1 3 375 1 2 2 2
"198" 1132 NA 2 4 422 1 2 1 1
"224" 1139 NA 1 4 345 1 2 1 1
"253" 1153 0 2 4 386 1 2 2 2
"360" 1199 NA 2 4 258 1 2 1 1
"399" 1214 0 1 4 348 1 2 1 1
"460" 1007 NA 2 4 335 1 3 2 4
"481" 1018 0 1 4 319 2 2 2 2
"498" 1022 0 1 4 467 1 2 1 1
"530" 1045 NA 1 4 500 2 2 2 2
"573" 1085 0 1 4 383 1 2 1 1
"603" 1119 0 2 4 457 2 2 1 1
"646" 1147 0 1 4 326 2 3 1 1
"661" 1167 0 1 4 255 1 3 1 1
"697" 1196 NA 2 4 288 1 2 1 1
"769" 1083 1 1 4 479 1 2 1 1
"225" 1139 NA 1 5 442 1 2 1 1
"254" 1153 0 2 5 406 2 2 1 1
"361" 1199 NA 2 5 280 1 2 1 1
"400" 1214 NA 1 5 357 1 2 1 1
"461" 1007 0 2 5 340 2 3 2 2
"482" 1018 0 1 5 326 1 2 1 1
"574" 1085 0 1 5 472 4 2 1 1
"647" 1147 0 1 5 387 2 3 1 1
"662" 1167 0 1 5 284 2 3 1 1
"698" 1196 0 2 5 296 2 2 1 1
"362" 1199 0 2 6 304 3 2 2 2
"462" 1007 1 2 6 396 2 3 3 2
"483" 1018 0 1 6 344 4 3 4 2
"663" 1167 0 1 6 296 2 2 1 1
"699" 1196 NA 2 6 311 1 2 1 1
"363" 1199 NA 2 7 334 1 2 1 1
"484" 1018 0 1 7 367 1 4 3 2
"664" 1167 0 1 7 314 2 2 1 1
"700" 1196 0 2 7 373 2 2 1 1
"485" 1018 0 1 8 386 2 2 1 1
"665" 1167 0 1 8 359 2 2 1 1
"701" 1196 NA 2 8 386 1 2 1 1
"486" 1018 0 1 9 411 2 2 1 1
"666" 1167 NA 1 9 401 1 2 1 1
"487" 1018 0 1 10 432 2 2 3 3
"488" 1018 0 1 11 500 2 2 1 1