9 Week 5 Demo
9.2 Wordscores
We can inspect the function for the wordscores model by Laver et al. (2003) in the following way:
classic.wordscores## function (wfm, scores)
## {
## if (!is.wfm(wfm))
## stop("Function not applicable to this object")
## if (length(scores) != length(docs(wfm)))
## stop("There are not the same number of documents as scores")
## if (any(is.na(scores)))
## stop("One of the reference document scores is NA\nFit the model with known scores and use 'predict' to get virgin score estimates")
## thecall <- match.call()
## C.all <- as.worddoc(wfm)
## C <- C.all[rowSums(C.all) > 0, ]
## F <- scale(C, center = FALSE, scale = colSums(C))
## ws <- apply(F, 1, function(x) {
## sum(scores * x)
## })/rowSums(F)
## pi <- matrix(ws, nrow = length(ws))
## rownames(pi) <- rownames(C)
## colnames(pi) <- c("Score")
## val <- list(pi = pi, theta = scores, data = wfm, call = thecall)
## class(val) <- c("classic.wordscores", "wordscores", class(val))
## return(val)
## }
## <bytecode: 0x1265783c8>
## <environment: namespace:austin>We can then take some example data included in the austin package.
And here our reference documents are all those documents marked with an “R” for reference; i.e., columns one to five.
ref## docs
## words R1 R2 R3 R4 R5
## A 2 0 0 0 0
## B 3 0 0 0 0
## C 10 0 0 0 0
## D 22 0 0 0 0
## E 45 0 0 0 0
## F 78 2 0 0 0
## G 115 3 0 0 0
## H 146 10 0 0 0
## I 158 22 0 0 0
## J 146 45 0 0 0
## K 115 78 2 0 0
## L 78 115 3 0 0
## M 45 146 10 0 0
## N 22 158 22 0 0
## O 10 146 45 0 0
## P 3 115 78 2 0
## Q 2 78 115 3 0
## R 0 45 146 10 0
## S 0 22 158 22 0
## T 0 10 146 45 0
## U 0 3 115 78 2
## V 0 2 78 115 3
## W 0 0 45 146 10
## X 0 0 22 158 22
## Y 0 0 10 146 45
## Z 0 0 3 115 78
## ZA 0 0 2 78 115
## ZB 0 0 0 45 146
## ZC 0 0 0 22 158
## ZD 0 0 0 10 146
## ZE 0 0 0 3 115
## ZF 0 0 0 2 78
## ZG 0 0 0 0 45
## ZH 0 0 0 0 22
## ZI 0 0 0 0 10
## ZJ 0 0 0 0 3
## ZK 0 0 0 0 2This matrix is simply a series of words (here: letters) and reference texts with word counts for each of them.
We can then look at the wordscores for each of the words, calculated using the reference dimensions for each of the reference documents.
ws <- classic.wordscores(ref, scores=seq(-1.5,1.5,by=0.75))
ws## $pi
## Score
## A -1.5000000
## B -1.5000000
## C -1.5000000
## D -1.5000000
## E -1.5000000
## F -1.4812500
## G -1.4809322
## H -1.4519231
## I -1.4083333
## J -1.3232984
## K -1.1846154
## L -1.0369898
## M -0.8805970
## N -0.7500000
## O -0.6194030
## P -0.4507576
## Q -0.2992424
## R -0.1305970
## S 0.0000000
## T 0.1305970
## U 0.2992424
## V 0.4507576
## W 0.6194030
## X 0.7500000
## Y 0.8805970
## Z 1.0369898
## ZA 1.1846154
## ZB 1.3232984
## ZC 1.4083333
## ZD 1.4519231
## ZE 1.4809322
## ZF 1.4812500
## ZG 1.5000000
## ZH 1.5000000
## ZI 1.5000000
## ZJ 1.5000000
## ZK 1.5000000
##
## $theta
## [1] -1.50 -0.75 0.00 0.75 1.50
##
## $data
## docs
## words R1 R2 R3 R4 R5
## A 2 0 0 0 0
## B 3 0 0 0 0
## C 10 0 0 0 0
## D 22 0 0 0 0
## E 45 0 0 0 0
## F 78 2 0 0 0
## G 115 3 0 0 0
## H 146 10 0 0 0
## I 158 22 0 0 0
## J 146 45 0 0 0
## K 115 78 2 0 0
## L 78 115 3 0 0
## M 45 146 10 0 0
## N 22 158 22 0 0
## O 10 146 45 0 0
## P 3 115 78 2 0
## Q 2 78 115 3 0
## R 0 45 146 10 0
## S 0 22 158 22 0
## T 0 10 146 45 0
## U 0 3 115 78 2
## V 0 2 78 115 3
## W 0 0 45 146 10
## X 0 0 22 158 22
## Y 0 0 10 146 45
## Z 0 0 3 115 78
## ZA 0 0 2 78 115
## ZB 0 0 0 45 146
## ZC 0 0 0 22 158
## ZD 0 0 0 10 146
## ZE 0 0 0 3 115
## ZF 0 0 0 2 78
## ZG 0 0 0 0 45
## ZH 0 0 0 0 22
## ZI 0 0 0 0 10
## ZJ 0 0 0 0 3
## ZK 0 0 0 0 2
##
## $call
## classic.wordscores(wfm = ref, scores = seq(-1.5, 1.5, by = 0.75))
##
## attr(,"class")
## [1] "classic.wordscores" "wordscores" "list"And here we see the thetas contained in this wordscores object, i.e., the reference dimensions for each of the reference documents and the pis, i.e., the estimated wordscores for each word.
We can now use these to score the so-called “virgin” texts as follows.
#get "virgin" documents
vir <- getdocs(lbg, 'V1')
vir## docs
## words V1
## A 0
## B 0
## C 0
## D 0
## E 0
## F 0
## G 0
## H 2
## I 3
## J 10
## K 22
## L 45
## M 78
## N 115
## O 146
## P 158
## Q 146
## R 115
## S 78
## T 45
## U 22
## V 10
## W 3
## X 2
## Y 0
## Z 0
## ZA 0
## ZB 0
## ZC 0
## ZD 0
## ZE 0
## ZF 0
## ZG 0
## ZH 0
## ZI 0
## ZJ 0
## ZK 0
# predict textscores for the virgin documents
predict(ws, newdata=vir)## 37 of 37 words (100%) are scorable
##
## Score Std. Err. Rescaled Lower Upper
## V1 -0.448 0.0119 -0.448 -0.459 -0.4379.3 Wordfish
If we wish, we can inspect the function for the wordscores model by Slapin and Proksch (2008) in the following way. This is a much more complex algorithm, which is not printed here, but you can inspect on your own devices.
wordfishWe can then simulate some data, formatted appropriately for wordfiash estimation in the following way:
dd <- sim.wordfish()
dd## $Y
## docs
## words D01 D02 D03 D04 D05 D06 D07 D08 D09 D10
## W01 14 16 24 18 17 14 8 8 5 5
## W02 25 27 25 22 10 9 11 6 7 4
## W03 26 20 21 18 18 10 10 5 10 4
## W04 16 15 13 17 18 9 5 12 7 3
## W05 21 18 20 13 17 15 11 3 6 4
## W06 6 7 11 14 8 15 16 13 17 22
## W07 3 9 10 10 10 14 20 23 23 20
## W08 4 9 5 9 15 13 15 15 18 24
## W09 5 4 6 7 9 18 16 33 19 28
## W10 6 9 6 7 14 10 15 16 22 20
## W11 58 54 34 46 50 41 31 25 16 11
## W12 58 63 58 51 40 32 31 23 20 15
## W13 57 59 46 58 33 30 23 25 17 12
## W14 48 57 47 43 47 33 31 26 10 15
## W15 69 55 57 46 31 37 28 23 27 17
## W16 18 24 18 18 30 36 44 36 56 60
## W17 17 12 21 20 38 36 55 41 58 60
## W18 14 14 25 30 35 37 51 58 44 52
## W19 16 16 30 33 32 41 48 56 61 55
## W20 19 12 23 20 28 50 31 53 57 69
##
## $theta
## [1] -1.4863011 -1.1560120 -0.8257228 -0.4954337 -0.1651446 0.1651446
## [7] 0.4954337 0.8257228 1.1560120 1.4863011
##
## $doclen
## D01 D02 D03 D04 D05 D06 D07 D08 D09 D10
## 500 500 500 500 500 500 500 500 500 500
##
## $psi
## [1] 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1
##
## $beta
## [1] 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1
##
## attr(,"class")
## [1] "wordfish.simdata" "list"Here we can see the document and word-level FEs, as well as the specified range of the thetas to be estimates.
Then estimating the document positions is simply a matter of implementing this algorithm.
## Call:
## wordfish(wfm = dd$Y)
##
## Document Positions:
## Estimate Std. Error Lower Upper
## D01 -1.2876 0.10926 -1.50176 -1.07345
## D02 -1.2137 0.10717 -1.42379 -1.00368
## D03 -0.8169 0.09816 -1.00930 -0.62453
## D04 -0.6986 0.09617 -0.88709 -0.51013
## D05 -0.2206 0.09124 -0.39945 -0.04182
## D06 0.2022 0.09093 0.02394 0.38040
## D07 0.5068 0.09306 0.32438 0.68918
## D08 0.8551 0.09795 0.66312 1.04707
## D09 1.1158 0.10337 0.91324 1.31845
## D10 1.5565 0.11616 1.32880 1.78414