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: 0x122e9cef0>
## <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 22 19 24 16 12 9 6 8 10 3
## W02 20 23 18 14 10 9 18 8 7 5
## W03 20 24 9 19 18 18 6 6 7 4
## W04 15 14 19 26 10 15 10 8 4 6
## W05 13 20 24 11 23 16 9 5 8 9
## W06 4 10 10 12 12 11 19 18 12 29
## W07 5 4 9 8 11 17 13 26 14 24
## W08 3 8 8 11 12 24 13 17 24 21
## W09 5 7 9 11 12 12 18 21 25 18
## W10 2 7 7 16 5 16 22 25 20 24
## W11 57 48 59 35 34 34 24 20 21 15
## W12 64 57 39 36 31 29 25 18 13 14
## W13 61 66 58 52 46 38 27 23 20 10
## W14 65 54 55 47 35 26 26 29 19 12
## W15 75 49 51 39 36 30 34 19 18 13
## W16 14 13 19 34 37 53 54 48 53 50
## W17 17 20 19 31 46 39 35 56 56 63
## W18 10 19 17 25 36 35 43 57 56 61
## W19 15 14 23 34 36 32 43 49 51 56
## W20 13 24 23 23 38 37 55 39 62 63
##
## $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 -1.59643 0.11669 -1.82514 -1.3677
## D02 -1.10452 0.10145 -1.30336 -0.9057
## D03 -0.91268 0.09718 -1.10314 -0.7222
## D04 -0.38643 0.08992 -0.56267 -0.2102
## D05 -0.06874 0.08857 -0.24233 0.1048
## D06 0.18899 0.08911 0.01435 0.3636
## D07 0.49786 0.09170 0.31814 0.6776
## D08 0.87749 0.09787 0.68567 1.0693
## D09 1.03342 0.10140 0.83467 1.2322
## D10 1.46943 0.11455 1.24492 1.6939