plausible_values: Draw plausible values

In dexter: Data Management and Analysis of Tests

Draw plausible values

Description

Draws plausible values based on test scores

Usage

plausible_values(
  dataSrc,
  parms = NULL,
  predicate = NULL,
  covariates = NULL,
  nPV = 1,
  use_draw = NULL,
  prior.dist = c("normal", "mixture"),
  merge_within_persons = FALSE
)

Arguments

dataSrc	a connection to a dexter database, a matrix, or a data.frame with columns: person_id, item_id, item_score
parms	An object returned by function fit_enorm containing parameter estimates. If parms are provided, item parameters are considered known. If parms = NULL, plausible values are marginalized over the posterior distribution of the item parameters and uncertainty of the item parameters is taken into account.
predicate	an expression to filter data. If missing, the function will use all data in dataSrc
covariates	name or a vector of names of the variables to group the populations used to improve the prior. A covariate must be a discrete person property (e.g. not a float) that indicates nominal categories, e.g. gender or school. If dataSrc is a data.frame, it must contain the covariate.
nPV	Number of plausible values to draw per person.
use_draw	When the ENORM was fitted with a Gibbs sampler, this specifies the use of a particular sample of item parameters used to generate the plausible value(s). If NULL, the posterior means are used. If outside range, the last iteration will be used.
prior.dist	use a normal prior or a mixture of two normals
merge_within_persons	If a person took multiple booklets, this indicates whether plausible values are generated per person (TRUE) or per booklet (FALSE)

Value

A data.frame with columns booklet_id, person_id, booklet_score and nPV plausible values named PV1...PVn.

References

Marsman, M., Maris, G., Bechger, T. M., and Glas, C.A.C. (2016) What can we learn from plausible values? Psychometrika. 2016; 81: 274-289. See also the vignette.

Examples

db = start_new_project(verbAggrRules, ":memory:", 
   person_properties=list(gender="<unknown>"))
add_booklet(db, verbAggrData, "agg")
add_item_properties(db, verbAggrProperties)

f=fit_enorm(db)
pv_M=plausible_values(db,f,(mode=="Do")&(gender=="Male"))
pv_F=plausible_values(db,f,(mode=="Do")&(gender=="Female"))

par(mfrow=c(1,2))

plot(ecdf(pv_M$PV1), 
   main="Do: males versus females", xlab="Ability", col="red")
lines(ecdf(pv_F$PV1), col="green")
legend(-2.2,0.9, c("female", "male") , 
   lty=1, col=c('green', 'red'), bty='n', cex=.75)

pv_M=plausible_values(db,f,(mode=="Want")&(gender=="Male"))
pv_F=plausible_values(db,f,(mode=="Want")&(gender=="Female"))

plot(ecdf(pv_M$PV1), 
   main="Want: males versus females", xlab=" Ability", col="red")
lines(ecdf(pv_F$PV1),col="green")
legend(-2.2,0.9, c("female", "male") , 
   lty=1, col=c('green', 'red'), bty='n', cex=.75)
   
close_project(db)    

dexter documentation built on Nov. 10, 2022, 5:15 p.m.