Predict textmodel_wordscores

Predict textmodel_wordscores

# S3 method for textmodel_wordscores
predict(
  object,
  newdata = NULL,
  se.fit = FALSE,
  interval = c("none", "confidence"),
  level = 0.95,
  rescaling = c("none", "lbg", "mv"),
  force = TRUE,
  ...
)

Arguments

object	a fitted Wordscores textmodel
newdata	dfm on which prediction should be made
se.fit	if TRUE, return standard errors as well
interval	type of confidence interval calculation
level	tolerance/confidence level for intervals
rescaling	"none" for "raw" scores; "lbg" for LBG (2003) rescaling; or "mv" for the rescaling proposed by Martin and Vanberg (2007). See References.
force	make the feature set of newdata conform to the model terms. The default of TRUE means that a fitted model can be applied to scale a dfm that does not contain a 1:1 match of features in the training and prediction data.
...	not used

Value

predict.textmodel_wordscores() returns a named vector of predicted document scores ("text scores" \(S_{vd}\) in LBG 2003), or a named list if se.fit = TRUE consisting of the predicted scores ($fit) and the associated standard errors ($se.fit). When interval = "confidence", the predicted values will be a matrix. This behaviour matches that of predict.lm.

Examples

tmod <- textmodel_wordscores(data_dfm_lbgexample, c(seq(-1.5, 1.5, .75), NA))
predict(tmod)
#>            R1            R2            R3            R4            R5 
#> -1.317931e+00 -7.395598e-01 -8.673617e-18  7.395598e-01  1.317931e+00 
#>            V1 
#> -4.480591e-01 
predict(tmod, rescaling = "mv")
#> Warning: More than two reference scores found with MV rescaling; using only min, max values.
#>         R1         R2         R3         R4         R5         V1 
#> -1.5000000 -0.8417280  0.0000000  0.8417280  1.5000000 -0.5099572 
predict(tmod, rescaling = "lbg")
#>          R1          R2          R3          R4          R5          V1 
#> -1.58967683 -0.88488724  0.01632248  0.91753220  1.62232179 -0.52967149 
predict(tmod, se.fit = TRUE)
#> $fit
#>            R1            R2            R3            R4            R5 
#> -1.317931e+00 -7.395598e-01 -8.673617e-18  7.395598e-01  1.317931e+00 
#>            V1 
#> -4.480591e-01 
#> 
#> $se.fit
#> [1] 0.006699613 0.011433605 0.012005250 0.011433605 0.006699613 0.011897667
#> 
predict(tmod, se.fit = TRUE, interval = "confidence")
#> $fit
#>              fit         lwr         upr
#> R1 -1.317931e+00 -1.33106234 -1.30480034
#> R2 -7.395598e-01 -0.76196925 -0.71715035
#> R3 -8.673617e-18 -0.02352986  0.02352986
#> R4  7.395598e-01  0.71715035  0.76196925
#> R5  1.317931e+00  1.30480034  1.33106234
#> V1 -4.480591e-01 -0.47137808 -0.42474008
#> 
#> $se.fit
#> [1] 0.006699613 0.011433605 0.012005250 0.011433605 0.006699613 0.011897667
#> 
predict(tmod, se.fit = TRUE, interval = "confidence", rescaling = "lbg")
#> $fit
#>            fit         lwr        upr
#> R1 -1.58967683 -1.60567795 -1.5736757
#> R2 -0.88488724 -0.91219485 -0.8575796
#> R3  0.01632248 -0.01235043  0.0449954
#> R4  0.91753220  0.89022458  0.9448398
#> R5  1.62232179  1.60632067  1.6383229
#> V1 -0.52967149 -0.55808746 -0.5012555
#> 
#> $se.fit
#> [1] 0.02448647 0.03025520 0.03095179 0.03025520 0.02448647 0.03082069
#>