Package {PP}

Type:	Package
Title:	Person Parameter Estimation
Version:	1.0.0
Date:	2026-10-14
Description:	Estimation of person parameters (abilities) for the 1, 2, 3 and 4 parameter logistic model, the (generalized) partial credit model and tests that mix dichotomous and polytomous items, under the assumption that the item parameters are known and fixed: maximum likelihood, weighted likelihood (Warm, 1989 <doi:10.1007/BF02294627>), maximum and expected a posteriori and robust estimation (Schuster and Yuan, 2011 <doi:10.3102/1076998610396890>). Further features are jackknife estimates (Wainer and Wright, 1980 <doi:10.1007/BF02293910>), plausible values, the person fit statistics lz, lz* (Snijders, 2001 <doi:10.1007/BF02294437>), infit and outfit, and the simulation of response data. Models fitted with the 'eRm' package can be passed on directly. The package is useful, e.g., when items of an item bank are given to a new sample of persons.
URL:	https://github.com/manuelreif/PP, https://manuelreif.github.io/PP/
BugReports:	https://github.com/manuelreif/PP/issues
License:	GPL-3
LazyData:	true
VignetteBuilder:	knitr, rmarkdown
Depends:	R (≥ 4.0)
Encoding:	UTF-8
Imports:	graphics, Rcpp (≥ 1.0.0), stats, tinyplot (≥ 0.8.0), utils
LinkingTo:	Rcpp
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown, eRm
NeedsCompilation:	yes
Config/testthat/edition:	3
Config/roxygen2/version:	8.1.0
Packaged:	2026-10-14 15:29:20 UTC; manuel
Author:	Manuel Reif [cre, aut], Jan Steinfeld [aut]
Maintainer:	Manuel Reif <manuel.reif@gmail.com>
Repository:	CRAN
Date/Publication:	2026-10-14 21:52:08 UTC

PP: Person Parameter Estimation

Description

The PP package estimates person parameters (abilities) in item response theory when the item parameters are known and fixed, e.g. for items from an item bank given to a new sample of persons.

Details

Models

the 1PL (Rasch), 2PL, 3PL and 4PL model (PP_4pl), the partial credit and generalized partial credit model (PP_gpcm) and tests that mix both (PPall).

Estimates

maximum likelihood (MLE), weighted likelihood (WLE; Warm, 1989), maximum a posteriori (MAP), expected a posteriori (EAP) and a robust Huber-type estimate, each with a standard error and a status per person.

Further analyses

jackknife estimates and standard errors (JKpp), plausible values (PV) and the person fit indices lz, lz*, infit and outfit (Pfit).

In one step

PPass estimates and computes the person fit for a data.frame with item responses and other person variables, or for a model fitted with the eRm package (RM, LLTM, RSM, PCM, LRSM, LPCM).

Simulation

sim_4pl, sim_gpcm and sim_mixed.

The numerical core is written in C++. Every person is estimated with a safeguarded Newton method, EAP estimates and plausible values come from the posterior on a grid. The vignettes give an introduction: vignette("intro_pp", package = "PP"), vignette("intro_pfit", package = "PP") and vignette("ppass_erm", package = "PP").

Author(s)

Maintainer: Manuel Reif manuel.reif@gmail.com

Authors:

• Manuel Reif manuel.reif@gmail.com

• Jan Steinfeld jan.d.steinfeld@gmail.com (ORCID)

References

Barton, M. A., & Lord, F. M. (1981). An upper asymptote for the three-parameter logistic item-response model. ETS Research Report Series, 1981(1), i–8.

Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee's ability. In F. M. Lord & M. R. Novick (Eds.), Statistical theories of mental test scores. Reading, MA: Addison-Wesley.

Drasgow, F., Levine, M. V., & Williams, E. A. (1985). Appropriateness measurement with polychotomous item response models and standardized indices. British Journal of Mathematical and Statistical Psychology, 38(1), 67–86.

Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 16, 159–176.

Schuster, C., & Yuan, K. H. (2011). Robust estimation of latent ability in item response models. Journal of Educational and Behavioral Statistics, 36(6), 720–735.

Snijders, T. A. B. (2001). Asymptotic null distribution of person fit statistics with estimated person parameter. Psychometrika, 66(3), 331–342.

Wainer, H., & Wright, B. D. (1980). Robust estimation of ability in the Rasch model. Psychometrika, 45(3), 373–391.

Warm, T. A. (1989). Weighted likelihood estimation of ability in item response theory. Psychometrika, 54, 427–450.

See Also

Useful links:

• https://github.com/manuelreif/PP

• https://manuelreif.github.io/PP/

• Report bugs at https://github.com/manuelreif/PP/issues

Examples

# 12 items of a 2PL test with known parameters, 100 persons
set.seed(1522)
b  <- seq(-3, 3, length = 12)
sl <- round(runif(12, 0.5, 1.5), 2)
x  <- sim_4pl(beta = b, alpha = sl, lowerA = 0, upperA = 1, theta = rnorm(100))

res <- PP_4pl(respm = x, thres = b, slopes = sl, type = "wle")
head(res$estimates)

# estimates and person fit in one table
PPass(data.frame(id = 1:100, x), items = 2:13, thres = b, slopes = sl)

Run a jackknife

Description

This function uses a jackknife approach to compute person parameters. The jackknife ability measure is based on primarily estimated models (PP_4pl(), PP_gpcm() or PPall()) - so the function is applied on the estimation objects, and jackknifed ability measures are returned.

Usage

JKpp(estobj, ...)

## S3 method for class 'ppeo'
JKpp(
  estobj,
  cmeth = "mean",
  maxsteps = NULL,
  exac = NULL,
  fullmat = FALSE,
  ...
)

## S3 method for class 'jk'
print(x, digits = 4, ...)

## S3 method for class 'jk'
summary(object, nrowmax = 15, digits = 4, ...)

## S3 method for class 'ppass'
JKpp(estobj, ...)

Arguments

Details

For each person, every answered item is left out once and the ability is estimated again from the remaining items, with the same estimation type, prior and range as in estobj (MLE, WLE, MAP and robust estimates start at the full estimate; EAP estimates use the posterior grid of the full estimate). Items a person did not answer are not left out: with L answered items and the leave-one-out estimates \hat\theta_{(-j)}, the pseudovalues are L\hat\theta - (L-1)\hat\theta_{(-j)}. The jackknife estimate is their mean, median or AMT (cmeth), the standard error is \sqrt{\sum_j (ps_j - \bar{ps})^2 / (L(L-1))} (Wainer & Wright, 1980).

A leave-one-out sample may have no finite estimate, e.g. with type = "mle" when the only wrong answer is left out. Such samples (and samples at the bound of range or not converged) are left out of the person's jackknife, counted in the column excluded, and a warning is given; WLE avoids this in most cases. A person whose full estimate has no solution (status "extreme", "no_response", "boundary" or "not_converged" in estobj$estimates) or who has fewer than two usable leave-one-out samples (status "too_few_items") gets NA.

Up to version 0.6.4 the standard error mixed the leave-one-out estimates with the formula for the pseudovalues and was too small by a factor of about L-1, all items counted as answered, and leave-one-out samples without a finite estimate were dropped silently.

AMT-robustified jackknife: When choosing cmeth = "AMT", the pseudovalues are combined as in scheme 4 of Wainer & Wright (1980): they are split into the pseudovalues of the right and of the wrong answers (polytomous items: answers at or above the expected score at \hat\theta and below it), each group is summarised by Andrews' sine M-estimator (AMT), one of the winners in the Princeton Robustness Study (Andrews et al., 1972), and the two values are averaged with the group sizes as weights. Within a group, T solves \sum_j \psi((ps_j - T)/s) = 0 with \psi(x) = \sin(x/2.1) for |x| \le 2.1\pi and 0 otherwise, where s is the median absolute deviation of the group's pseudovalues from their median; pseudovalues further than 2.1 \pi s from T get no weight. Up to version 0.6.4 the AMT had a sign error, no scale and maximised the sum of the sines.

Value

An object of class jk, a list with

Author(s)

Manuel Reif

References

Wainer, H., & Wright, B. D. (1980). Robust estimation of ability in the Rasch model. Psychometrika, 45(3), 373-391.

Andrews, D. F., Bickel, P. J., Hampel, F. R., Huber, P. J., Rogers, W. H., & Tukey, J. W. (1972). Robust estimates of location: Survey and advances. Princeton University Press.

See Also

PP_gpcm, PP_4pl, PPall

Examples

set.seed(1623)
b  <- seq(-3, 3, length = 12)
sl <- round(runif(12, 0.5, 1.5), 2)
x  <- sim_4pl(beta = b, alpha = sl, lowerA = 0, upperA = 1, theta = rnorm(20, sd = 1.5))

res <- PP_4pl(respm = x, thres = b, slopes = sl, type = "wle")
jk <- JKpp(res)
summary(jk)
# WLE and jackknife side by side
head(cbind(res$estimates[, c("estimate", "SE")], jk = jk$estimates[, c("estimate", "SE")]))

# robust centering: the AMT of Wainer & Wright (1980)
head(JKpp(res, cmeth = "AMT")$estimates)

# partial credit model, EAP
thres <- rbind(0, c(-1, -0.5, 0, 0.4), c(0.5, 0.8, 1, 1.5))
xg <- sim_gpcm(thres = thres, alpha = 1, theta = rnorm(20))
JKpp(PP_gpcm(respm = xg, thres = thres, type = "eap"))

Estimate Person Parameters for the 4-PL model

Description

Compute Person Parameters for the 1/2/3/4-PL model and choose between five common estimation techniques: ML, WL, MAP, EAP and a robust estimation. All item parameters are treated as fixed.

Usage

PP_4pl(
  respm,
  thres,
  slopes = NULL,
  lowerA = NULL,
  upperA = NULL,
  theta_start = NULL,
  mu = NULL,
  sigma2 = NULL,
  type = "wle",
  maxsteps = 100,
  exac = 0.001,
  H = 1,
  ctrl = list(),
  range = c(-6, 6),
  verbose = FALSE
)

Arguments

Details

With this function you can estimate:

• 1-PL model (Rasch model) by submitting: the data matrix, item difficulties and nothing else, since the 1-PL model is merely a 4-PL model with: any slope = 1, any lower asymptote = 0 and any upper asymptote = 1!

• 2-PL model by submitting: the data matrix, item difficulties and slope parameters. Lower and upper asymptotes are automatically set to 0 and 1 respectively.

• 3-PL model by submitting anything except the upper asymptote parameters

• 4-PL model by submitting all parameters.

The probability function of the 4-PL model is:

P(x_{ij} = 1 | \hat \alpha_i, \hat\beta_i, \hat\gamma_i, \hat\delta_i, \theta_j ) = \hat\gamma_i + (\hat\delta_i-\hat\gamma_i) \frac{exp(\hat \alpha_i (\theta_{j} - \hat\beta_{i}))}{\,1 + exp(\hat\alpha_i (\theta_{j} - \hat\beta_{i}))}

In our case \theta is to be estimated, and the four item parameters are assumed as fixed (usually these are estimates of a former scaling procedure).

The 3-PL model is the same, except that \delta_i = 1, \forall i.

In the 2-PL model \delta_i = 1, \gamma_i = 0, \forall i.

In the 1-PL model \delta_i = 1, \gamma_i = 0, \alpha_i = 1, \forall i.

The robust estimation method, applies a Huber-type estimator (Schuster & Yuan, 2011), which downweights responses to items which provide little information for the ability estimation. First a residuum is estimated and on this basis, the weight for each observation is computed.

residuum:

r_i = \alpha_i(\theta - \beta_i)

weight:

w(r_i) = 1 \rightarrow if\, |r_i| \leq H

w(r_i) = H/|r| \rightarrow if\, |r_i| > H

Value

An object of class ppeo (and fourpl, gpcm or gpcm4pl), a list with

estimates

A data.frame with one row per person, in the order of respm: estimate, SE, nsteps (Newton steps of the person; 0 for eap) and status, a factor with the levels

• "ok": converged inside range,

• "extreme": mle or robust estimate of a person with all answers in the lowest or highest category, the estimate is -Inf or Inf and the SE NA,

• "boundary": the estimating equation has no root inside range, the estimate is the bound,

• "no_response": the person answered no item, estimate and SE are NA (eap: prior mean and SD),

• "not_converged": no convergence within maxsteps.

A warning reports persons with status "boundary" or "not_converged".

type, call

The estimation type and the call.

model

The checked and prepared inputs.

resPP

The result in the layout of earlier versions: resPP$resPP is the persons x 2 matrix of estimates and SEs, resPP$nsteps the largest number of steps.

Use coef() for the estimates, as.data.frame() for the table and plot() for the measurement precision, the person fit map or person profiles (see pp-plots).

Author(s)

Manuel Reif

References

Baker, Frank B., and Kim, Seock-Ho (2004). Item Response Theory - Parameter Estimation Techniques. CRC-Press.

Barton, M. A., & Lord, F. M. (1981). An Upper Asymptote for the Three-Parameter Logistic Item-Response Model.

Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee's ability. In Lord, F.M. & Novick, M.R. (Eds.), Statistical theories of mental test scores. Reading, MA: Addison-Wesley.

Magis, D. (2013). A note on the item information function of the four-parameter logistic model. Applied Psychological Measurement, 37(4), 304-315.

Samejima, Fumiko (1993). The bias function of the maximum likelihood estimate of ability for the dichotomous response level. Psychometrika, 58, 195-209.

Samejima, Fumiko (1993). An approximation of the bias function of the maximum likelihood estimate of a latent variable for the general case where the item responses are discrete. Psychometrika, 58, 119-138.

Schuster, C., & Yuan, K. H. (2011). Robust estimation of latent ability in item response models. Journal of Educational and Behavioral Statistics, 36(6), 720-735.

Warm, Thomas A. (1989). Weighted Likelihood Estimation Of Ability In Item Response Theory. Psychometrika, 54, 427-450.

Yen, Y.-C., Ho, R.-G., Liao, W.-W., Chen, L.-J., & Kuo, C.-C. (2012). An empirical evaluation of the slip correction in the four parameter logistic models with computerized adaptive testing. Applied Psychological Measurement, 36, 75-87.

See Also

PPass, PPall, PP_gpcm, JKpp, PV

Examples

## real data: an adaptive intelligence test with known item difficulties (1PL)
data(pp_amt)
d <- as.matrix(pp_amt$daten_amt[, -(1:7)])
res <- PP_4pl(respm = d, thres = pp_amt$betas[, 2], type = "wle")
summary(res)

## simulated data: 2PL, 3PL and 4PL
set.seed(1522)
b  <- seq(-3, 3, length = 12)
sl <- round(runif(12, 0.5, 1.5), 2)
la <- round(runif(12, 0, 0.25), 2)
ua <- round(runif(12, 0.8, 1), 2)
x  <- sim_4pl(beta = b, alpha = sl, lowerA = la, upperA = ua, theta = rnorm(50))

res2pl <- PP_4pl(respm = x, thres = b, slopes = sl, type = "mle")
res3pl <- PP_4pl(respm = x, thres = b, slopes = sl, lowerA = la, type = "wle")
res4pl <- PP_4pl(respm = x, thres = b, slopes = sl, lowerA = la, upperA = ua, type = "map")

# persons with all answers right (or wrong) have no finite ML estimate
table(res2pl$estimates$status)
head(res3pl$estimates)
coef(res4pl)[1:5]

## EAP with person-specific priors
res_eap <- PP_4pl(respm = x, thres = b, slopes = sl, type = "eap",
                  mu = rep(c(-0.5, 0.5), 25), sigma2 = 0.8)

## robust estimation: the example of Schuster & Yuan (2011)
AWM <- matrix(0, 7, 7)
diag(AWM) <- 1
coef(PP_4pl(respm = AWM, thres = -3:3, type = "mle"))
coef(PP_4pl(respm = AWM, thres = -3:3, type = "robust"))

Estimate Person Parameters for the GPCM

Description

Compute person parameters for the GPCM and choose between five common estimation techniques: ML, WL, MAP, EAP and a robust estimation. All item parameters are treated as fixed.

Usage

PP_gpcm(
  respm,
  thres,
  slopes = NULL,
  theta_start = NULL,
  mu = NULL,
  sigma2 = NULL,
  type = "wle",
  maxsteps = 100,
  exac = 0.001,
  H = 1,
  ctrl = list(),
  range = c(-6, 6),
  verbose = FALSE
)

Arguments

Details

Please note, that robust estimation with (Huber ability estimate) polytomous items is still experimental!

The probability choosing the k-th category is as follows:

P(x_{ij} = k | \hat \alpha_i, \hat\beta_{iv}, \theta_j) = \frac{exp(\sum_{v=0}^{(k-1)}\hat \alpha_{i}(\theta_j - \hat \beta_{iv}))}{\,\sum_{c=0}^{m_i - 1}exp(\sum_{v=0}^{c}\hat \alpha_{i}(\theta_j - \hat \beta_{iv})))}

In our case \theta is to be estimated. The item parameters are assumed as fixed (usually these are estimates of a former scaling procedure).

The model simplifies to the Partial Credit Model by setting \alpha_{i} = 1, \forall i.

Value

An object of class ppeo (and fourpl, gpcm or gpcm4pl), a list with

estimates

A data.frame with one row per person, in the order of respm: estimate, SE, nsteps (Newton steps of the person; 0 for eap) and status, a factor with the levels

• "ok": converged inside range,

• "extreme": mle or robust estimate of a person with all answers in the lowest or highest category, the estimate is -Inf or Inf and the SE NA,

• "boundary": the estimating equation has no root inside range, the estimate is the bound,

• "no_response": the person answered no item, estimate and SE are NA (eap: prior mean and SD),

• "not_converged": no convergence within maxsteps.

A warning reports persons with status "boundary" or "not_converged".

type, call

The estimation type and the call.

model

The checked and prepared inputs.

resPP

The result in the layout of earlier versions: resPP$resPP is the persons x 2 matrix of estimates and SEs, resPP$nsteps the largest number of steps.

Use coef() for the estimates, as.data.frame() for the table and plot() for the measurement precision, the person fit map or person profiles (see pp-plots).

Author(s)

Manuel Reif

References

Baker, Frank B., and Kim, Seock-Ho (2004). Item Response Theory - Parameter Estimation Techniques. CRC-Press.

Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47(2), 149-174.

Muraki, Eiji (1992). A Generalized Partial Credit Model: Application of an EM Algorithm. Applied Psychological Measurement, 16, 159-176.

Muraki, Eiji (1993). Information Functions of the Generalized Partial Credit Model. Applied Psychological Measurement, 17, 351-363.

Samejima, Fumiko (1993). The bias function of the maximum likelihood estimate of ability for the dichotomous response level. Psychometrika, 58, 195-209.

Samejima, Fumiko (1993). An approximation of the bias function of the maximum likelihood estimate of a latent variable for the general case where the item responses are discrete. Psychometrika, 58, 119-138.

Schuster, C., & Yuan, K. H. (2011). Robust estimation of latent ability in item response models. Journal of Educational and Behavioral Statistics, 36(6), 720-735.

Wang, S. and Wang, T. (2001). Precision of Warm's Weighted Likelihood Estimates for a Polytomous Model in Computerized Adaptive Testing. Applied Psychological Measurement, 25, 317-331.

Warm, Thomas A. (1989). Weighted Likelihood Estimation Of Ability In Item Response Theory. Psychometrika, 54, 427-450.

See Also

PPass, PPall, PP_4pl, JKpp, PV

Examples

# six items with three categories: one column of thresholds per item
thres <- matrix(c(-2, -1.23, 1.11, 3.48, 1, 2, -1, -0.2, 0.5, 1.3, -0.8, 1.5), nrow = 2)
sl <- c(0.5, 1, 1.5, 1.1, 1, 0.98)
set.seed(1750)
x <- sim_gpcm(thres = thres, alpha = sl, theta = rnorm(30))

# GPCM, weighted likelihood
resgpcm <- PP_gpcm(respm = x, thres = thres, slopes = sl, type = "wle")
summary(resgpcm)

# partial credit model: all slopes 1 (the default), EAP
respcm <- PP_gpcm(respm = x, thres = thres, type = "eap")
head(respcm$estimates)

# items with a different number of categories: NA below the last threshold
thres4 <- rbind(thres, c(NA, NA, NA, NA, 1.7, 1))
x4 <- sim_gpcm(thres = thres4, alpha = sl, theta = rnorm(30))
head(PP_gpcm(respm = x4, thres = thres4, slopes = sl, type = "map")$estimates)

Estimate Person Parameters

Description

Compute person parameters for the 1,2,3,4-PL model and for the GPCM. Choose between ML, WL, MAP, EAP and robust estimation. Use this function if 4-PL items and GPCM items are mixed for each person.

Usage

PPall(
  respm,
  thres,
  slopes = NULL,
  lowerA = NULL,
  upperA = NULL,
  theta_start = NULL,
  mu = NULL,
  sigma2 = NULL,
  type = "wle",
  model2est = NULL,
  maxsteps = 100,
  exac = 0.001,
  H = 1,
  ctrl = list(),
  range = c(-6, 6),
  verbose = FALSE
)

Arguments

Details

For a test with both: dichotomous and polytomous items which have been scaled under 1/2/3/4-PL model or the GPCM, use this function to estimate the person ability parameters. The model of each item follows from thres: an item with one threshold is a 4PL item (with slopes, lowerA and upperA), an item with several thresholds a GPCM item (see findmodel).

Please note, that robust estimation with (Huber ability estimate) polytomous items is still experimental!

Value

An object of class ppeo (and fourpl, gpcm or gpcm4pl), a list with

estimates

A data.frame with one row per person, in the order of respm: estimate, SE, nsteps (Newton steps of the person; 0 for eap) and status, a factor with the levels

• "ok": converged inside range,

• "extreme": mle or robust estimate of a person with all answers in the lowest or highest category, the estimate is -Inf or Inf and the SE NA,

• "boundary": the estimating equation has no root inside range, the estimate is the bound,

• "no_response": the person answered no item, estimate and SE are NA (eap: prior mean and SD),

• "not_converged": no convergence within maxsteps.

A warning reports persons with status "boundary" or "not_converged".

type, call

The estimation type and the call.

model

The checked and prepared inputs.

resPP

The result in the layout of earlier versions: resPP$resPP is the persons x 2 matrix of estimates and SEs, resPP$nsteps the largest number of steps.

Use coef() for the estimates, as.data.frame() for the table and plot() for the measurement precision, the person fit map or person profiles (see pp-plots).

Author(s)

Manuel Reif

References

Baker, Frank B., and Kim, Seock-Ho (2004). Item Response Theory - Parameter Estimation Techniques. CRC-Press.

Barton, M. A., & Lord, F. M. (1981). An Upper Asymptote for the Three-Parameter Logistic Item-Response Model.

Magis, D. (2013). A note on the item information function of the four-parameter logistic model. Applied Psychological Measurement, 37(4), 304-315.

Muraki, Eiji (1992). A Generalized Partial Credit Model: Application of an EM Algorithm. Applied Psychological Measurement, 16, 159-176.

Muraki, Eiji (1993). Information Functions of the Generalized Partial Credit Model. Applied Psychological Measurement, 17, 351-363.

Samejima, Fumiko (1993). The bias function of the maximum likelihood estimate of ability for the dichotomous response level. Psychometrika, 58, 195-209.

Samejima, Fumiko (1993). An approximation of the bias function of the maximum likelihood estimate of a latent variable for the general case where the item responses are discrete. Psychometrika, 58, 119-138.

Schuster, C., & Yuan, K. H. (2011). Robust estimation of latent ability in item response models. Journal of Educational and Behavioral Statistics, 36(6), 720-735.

Wang, S. and Wang, T. (2001). Precision of Warm's Weighted Likelihood Estimates for a Polytomous Model in Computerized Adaptive Testing. Applied Psychological Measurement, 25, 317-331.

Warm, Thomas A. (1989). Weighted Likelihood Estimation Of Ability In Item Response Theory. Psychometrika, 54, 427-450.

Yen, Y.-C., Ho, R.-G., Liao, W.-W., Chen, L.-J., & Kuo, C.-C. (2012). An empirical evaluation of the slip correction in the four parameter logistic models with computerized adaptive testing. Applied Psychological Measurement, 36, 75-87.

See Also

PPass, PP_gpcm, PP_4pl, JKpp, PV

Examples

# items 1 to 3 are 4PL items (one threshold), items 4 to 6 GPCM items
thres <- matrix(c(-2, -1.23, 1.11, 3.48, 1, 2, -1, -0.2, 0.5, 1.3, -0.8, 1.5), nrow = 2)
thres[2, 1:3] <- NA
sl <- c(0.5, 1, 1.5, 1.1, 1, 0.98)
la <- c(0.02, 0.1, 0, 0, 0, 0)
ua <- c(0.97, 0.91, 1, 1, 1, 1)
set.seed(1811)
x <- sim_mixed(thres = thres, alpha = sl, lowerA = la, upperA = ua, theta = rnorm(30))

res <- PPall(respm = x, thres = thres, slopes = sl, lowerA = la, upperA = ua, type = "wle")
summary(res)

res_eap <- PPall(respm = x, thres = thres, slopes = sl, lowerA = la, upperA = ua, type = "eap")
head(res_eap$estimates)

Person assessment: estimates and person fit in one step

Description

PPass (Person Parameter assessment) estimates the person parameters and computes the person fit in one call, and returns both in one table, next to the other person variables. It takes a data.frame or matrix with item responses and known item parameters, or a model fitted with the eRm package.

Usage

PPass(...)

## Default S3 method:
PPass(
  respdf,
  items = "all",
  fitindices = c("lz", "lzstar", "infit", "outfit"),
  SE = FALSE,
  ...
)

## S3 method for class 'eRm'
PPass(
  RMobj,
  fitindices = c("lz", "lzstar", "infit", "outfit"),
  SE = FALSE,
  ...
)

## S3 method for class 'ppass'
print(x, n = 10, digits = 3, ...)

## S3 method for class 'ppass'
summary(object, alpha = 0.05, digits = 3, ...)

## S3 method for class 'ppass'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

Details

Data with item parameters (PPass.default): the columns items of respdf are the responses, the other columns are kept as person variables. The item parameters are passed in ... (thres, slopes, lowerA, upperA), and the model follows from them: thres alone gives the 1PL model, with slopes the 2PL, with lowerA the 3PL and with upperA the 4PL model, estimated with PP_4pl(). A threshold matrix with more than one threshold for some item gives the partial credit model (all slopes 1) or the generalized partial credit model, estimated with PP_gpcm(). With such polytomous items and lowerA or upperA, the test is a mixed test (items with one threshold are 4PL items), estimated with PPall(). The arguments mod and model2est of earlier versions are no longer needed; they are ignored with a warning.

eRm models (PPass.eRm): all models of the eRm package are supported, the Rasch model (RM), the linear logistic test model (LLTM), the rating scale model (RSM), the partial credit model (PCM) and the linear models LRSM and LPCM, also with several time points (mpoints) and groups (groupvec). The responses and item parameters come from the fitted object. eRm stores easiness parameters per category, \beta_{ih} = -\sum_{k \le h} \delta_{ik}; they are turned into the thresholds \delta_{ih} = \beta_{i,h-1} - \beta_{ih}, so for the dichotomous models the difficulty is -\beta_i. With time points or groups every combination of item, time point and group is a virtual item with its own parameters; a person answers the virtual items of their group, and gets one ability over all time points. The person parameters are estimated with PP_4pl() (RM, LLTM) or PP_gpcm() with all slopes 1. The maximum likelihood estimates (type = "mle") equal those of eRm::person.parameter(); unlike eRm, PPass also gives estimates for models with group contrasts, and WLE, MAP, EAP and robust estimates, person fit, jackknife and plausible values. The eRm package is not needed to run PPass on such an object.

Person fit is computed by Pfit() and needs an estimate of type "mle", "wle" or "map". For type = "eap" and "robust" PPass returns the estimates only, with a message.

Value

A data.frame of class ppass with one row per person: the person variables (the columns of respdf that are not items; for eRm models with groups the column group), estimate, SE and status of the estimate (see PP_4pl()) and the columns of the chosen person fit indices (see Pfit()). It can be used like any data.frame. The estimation object is kept as attribute, so JKpp() and PV() can be applied to the result directly; print() and summary() show an overview with the persons flagged by the person fit, plot() draws the person fit map, the measurement precision or person profiles (see pp-plots), and as.data.frame() returns a plain data.frame.

Author(s)

Manuel Reif, Jan Steinfeld

See Also

PP_4pl, PP_gpcm, PPall, Pfit, JKpp, PV, pp-plots

Examples

## data with item parameters

set.seed(1337)
# difficulties and slopes of 15 items
diffpar <- seq(-3, 3, length = 15)
sl <- round(runif(15, 0.5, 1.5), 2)
# responses of 200 persons, plus an id and a group variable
awm <- sim_4pl(beta = diffpar, alpha = sl, lowerA = rep(0, 15), upperA = rep(1, 15),
               theta = rnorm(200))
dat <- data.frame(id = 1:200, group = rep(c("a", "b"), 100), awm)

# 2PL, WLE estimates and all person fit indices; id and group are kept
res <- PPass(dat, items = 3:17, thres = diffpar, slopes = sl)
res
summary(res)

# person fit map, flagged persons labelled with their id
plot(res, label = "id")

# a plain data.frame for further use
head(as.data.frame(res))

# jackknife directly from the result
jk <- JKpp(res)


## real data (adaptive test, 1PL)

data(pp_amt)
d <- pp_amt$daten_amt
rd_res <- PPass(d, items = 8:ncol(d), thres = pp_amt$betas[, 2], fitindices = "lz")
head(rd_res)


## models fitted with eRm

if (requireNamespace("eRm", quietly = TRUE)) {

  # Rasch model
  my_rm <- eRm::RM(eRm::raschdat1)
  res_rm <- PPass(my_rm)
  summary(res_rm)

  # the maximum likelihood estimates equal eRm::person.parameter()
  res_mle <- PPass(my_rm, type = "mle", fitindices = "lzstar")

  # partial credit model
  set.seed(2751)
  thres <- rbind(0, matrix(c(-2, -1, 0.5, -1.2, 0.3, NA, -0.5, 1, NA, 0, 1.5, 2), 3))
  simdat <- sim_gpcm(thres = thres, alpha = rep(1, 4), theta = rnorm(300))
  res_pcm <- PPass(eRm::PCM(simdat))

  # linear partial credit model with two time points and two groups
  simdat2 <- sim_gpcm(thres = thres, alpha = rep(1, 4), theta = rnorm(300, 0.5))
  my_lpcm <- eRm::LPCM(cbind(simdat, simdat2), mpoints = 2, groupvec = rep(1:2, 150))
  res_lpcm <- PPass(my_lpcm)
  head(res_lpcm)

  # plausible values need an EAP or MAP estimate
  pv <- PV(PPass(my_rm, type = "map"))
}

Person-Fit statistics

Description

Compute the person fit statistics lz, lz*, infit and outfit for the 1-PL, 2-PL, 3-PL, 4-PL model, the GPCM and mixed tests, at the person estimates of PP_4pl(), PP_gpcm() or PPall().

Usage

Pfit(pp, fitindices = c("lz", "lzstar", "infit", "outfit"), SE = FALSE)

## S3 method for class 'ppeo'
Pfit(pp, fitindices = c("lz", "lzstar", "infit", "outfit"), SE = FALSE)

## Default S3 method:
Pfit(pp, fitindices = c("lz", "lzstar", "infit", "outfit"), SE = FALSE)

## S3 method for class 'PPfit'
print(x, digits = 4, ...)

## S3 method for class 'PPfit'
summary(object, alpha = 0.05, digits = 4, ...)

Arguments

Details

All statistics are computed at the estimate \hat\theta of each person, over the items the person answered (missing responses are left out of every sum). With the observed response x_i, the expected score E_i, its variance V_i and fourth central moment C_i of item i, the residual is Y_i = x_i - E_i, and for the n answered items:

• infit (weighted mean square, Wright & Masters, 1982): \sum Y_i^2 / \sum V_i, with variance q^2 = \sum (C_i - V_i^2) / (\sum V_i)^2.

• outfit (unweighted mean square): \sum (Y_i^2 / V_i) / n, with variance q^2 = \sum (C_i / V_i^2) / n^2 - 1/n.

• Both mean squares MS are standardised by the Wilson-Hilferty transformation t = (MS^{1/3} - 1) (3/q) + q/3. chisq is \sum Y_i^2 / V_i with df = n - 1 and the p-value of the \chi^2 distribution.

• lz (Drasgow, Levine & Williams, 1985): the log-likelihood l_0 = \sum \log P_i(x_i), standardised by its expectation and variance under the model, (l_0 - \sum E \log P_i) / \sqrt{\sum Var \log P_i}. lz_unst is l_0 / n.

• lz* (Snijders, 2001) corrects lz for the estimated \theta: with the item information I, c_n = \sum Cov(\log P_i, s_i) / I (s_i the score of item i) and the term r_0 of the estimating equation (mle: 0, wle: J / (2I), map: (\mu - \theta) / \sigma^2), lz* = (l_0 - \sum E \log P_i + c_n r_0) / \sqrt{\sum Var \log P_i - c_n^2 I}. For dichotomous items this is Snijders' lz*, for polytomous items and mixed tests the generalisation of Sinharay (2016).

Person fit needs an estimate of type "mle", "wle" or "map". Persons without an estimate (status other than "ok" in pp$estimates, e.g. an extreme score with mle) get NA. With a single answered dichotomous item lz* is not defined (the corrected variance is 0) and the \chi^2 p-value has no degree of freedom; both are NA.

With SE = TRUE every statistic gets a jackknife standard error: for each answered item j, \theta is estimated without item j (the leave-one-out estimates of JKpp()) and the statistic f_{(-j)} is computed on the remaining items. With L answered items and m leave-one-out samples with a finite estimate, SE = (L - 1) \sqrt{\sum_j (f_{(-j)} - \bar f)^2 / (m (m - 1))}, which is \sqrt{(L-1)/L \sum_j (f_{(-j)} - \bar f)^2} when no sample is left out (Efron & Stein, 1981).

Up to version 0.6.4, lz* summed over all items also for persons with missing responses, the GPCM infit and outfit used the observed instead of the possible categories and ignored the slopes, mixed tests were not supported, the jackknife standard error of lz used the prior SD as variance, and all results were rounded. The responses were passed again as first argument respm; since 1.0.0 they are taken from pp, and respm is gone: call Pfit(res) instead of Pfit(respm = X, pp = res).

Value

An object of class PPfit: a list with one matrix per statistic (persons in rows, in the order of the response matrix):

• lz: lz, lz_unst

• lzstar: lzstar

• infit: infit (mean square), in_t, in_chisq, in_df, in_pv

• outfit: outfit (mean square), ou_t, ou_chisq, ou_df, ou_pv

• with SE = TRUE also lz_se, lzs_se, in_se, ou_se.

summary() gives, per statistic, the number of persons, mean, SD, range and the number of persons flagged at level alpha.

Author(s)

Jan Steinfeld, Manuel Reif

References

Armstrong, R. D., Stoumbos, Z. G., Kung, M. T. & Shi, M. (2007). On the performance of the lz person-fit statistic. Practical Assessment, Research & Evaluation, 12(16).

De La Torre, J., & Deng, W. (2008). Improving Person-Fit Assessment by Correcting the Ability Estimate and Its Reference Distribution. Journal of Educational Measurement, 45(2), 159-177.

Drasgow, F., Levine, M. V. & Williams, E. A. (1985) Appropriateness measurement with polychotomous item response models and standardized indices. British Journal of Mathematical and Statistical Psychology, 38(1), 67–86.

Efron, B., & Stein, C. (1981). The jackknife estimate of variance. The Annals of Statistics, 9(3), 586-596.

Karabatsos, G. (2003) Comparing the Aberrant Response Detection Performance of Thirty-Six Person-Fit Statistics. Applied Measurement In Education, 16(4), 277–298.

Magis, D., Raiche, G. & Beland, S. (2012) A didactic presentation of Snijders's lz* index of person fit with emphasis on response model selection and ability estimation. Journal of Educational and Behavioral Statistics, 37(1), 57–81.

Meijer, R. R. & Sijtsma, K. (2001) Methodology review: Evaluating person fit. Applied Psychological Measurement, 25(2), 107–135.

Molenaar, I. W. & Hoijtink, H. (1990) The many null distributions of person fit indices. Psychometrika, 55(1), 75–106.

Reise, S. P. (1990). A comparison of item-and person-fit methods of assessing model-data fit in IRT. Applied Psychological Measurement, 14(2), 127-137.

Sinharay, S. (2016). Asymptotically correct standardization of person-fit statistics beyond dichotomous items. Psychometrika, 81(4), 992–1013.

Snijders, T. A. B. (2001) Asymptotic null distribution of person fit statistics with estimated person parameter. Psychometrika, 66(3), 331–342.

Wright, B. D. & Masters, G. N. (1990). Computation of OUTFIT and INFIT Statistics. Rasch Measurement Transactions, 3:4, 84-85.

Wright, B. D., & Masters, G. N. (1982). Rating Scale Analysis. Rasch Measurement. MESA Press, 5835 S. Kimbark Avenue, Chicago, IL 60637.

See Also

PPall, PP_4pl, PP_gpcm, PPass, JKpp

Examples

## dichotomous items

set.seed(1337)
diffpar <- seq(-3, 3, length = 15)
sl      <- round(runif(15, 0.5, 1.5), 2)
la      <- round(runif(15, 0, 0.25), 2)
awm     <- sim_4pl(beta = diffpar, alpha = sl, lowerA = la, upperA = rep(1, 15),
                   theta = rnorm(100))

# 3PL, WLE
res3plwle <- PP_4pl(respm = awm, thres = diffpar, slopes = sl, lowerA = la, type = "wle")

# the responses are taken from the estimation object
fit3pl <- Pfit(res3plwle, fitindices = c("lz", "lzstar", "infit", "outfit"))
head(fit3pl$lzstar)
summary(fit3pl)

# with missing responses and jackknife standard errors
awm_na <- awm
awm_na[sample(length(awm), 300)] <- NA
res3plna <- PP_4pl(respm = awm_na, thres = diffpar, slopes = sl, lowerA = la, type = "wle")
head(Pfit(res3plna, fitindices = c("lz", "infit"), SE = TRUE)$lz)

## GPCM

thres <- rbind(0, c(-1, -0.5, 0), c(0.5, 0.8, 1), c(1.5, 2, 2.5))
slg   <- c(0.8, 1.2, 1)
respg <- sim_gpcm(thres = thres, alpha = slg, theta = rnorm(50))
resgwle <- PP_gpcm(respm = respg, thres = thres, slopes = slg, type = "wle")
summary(Pfit(resgwle))

## GPCM and 4PL mixed

THRESx <- matrix(c(-2, -1.23, 1.11, 3.48, 1, 2, -1, -0.2, 0.5, 1.3, -0.8, 1.5), nrow = 2)
THRESx[2, 1:3] <- NA
slm <- c(0.5, 1, 1.5, 1.1, 1, 0.98)
lam <- c(0.02, 0.1, 0, 0, 0, 0)
uam <- c(0.97, 0.91, 1, 1, 1, 1)
awmatrix <- matrix(c(1, 0, 1, 0, 1, 1, 1, 0, 0, 1,
                     2, 0, 0, 0, 0, 0, 0, 0, 0, 1,
                     1, 2, 2, 1, 1, 1, 1, 0, 0, 1), byrow = TRUE, nrow = 5)
resmixed <- PPall(respm = awmatrix, thres = THRESx, slopes = slm, lowerA = lam,
                  upperA = uam, type = "wle")
Pfit(resmixed, fitindices = c("lz", "lzstar"))

The model of each item in a mixed test

Description

This function shows which model PPall() uses for each item: an item with one threshold is a 4PL item, an item with several thresholds a GPCM item.

Usage

findmodel(thres)

Arguments

Details

Up to version 0.6.4-1 its result was passed to PPall() as model2est. This is no longer needed: PPall() derives the model of each item from thres in the same way.

Value

A character vector with one entry per item (column of thres): "4PL" for items with one threshold, "GPCM" for items with more.

Author(s)

Manuel Reif

See Also

PPall

Examples

thres <- rbind(0, c(-1, 0.5, -0.2), c(NA, 1.2, 0.4))
findmodel(thres)

Simulated data set

Description

This dataset contains the responses of 60 persons to 12 items of a 3PL test, simulated with the sim_4pl() function, together with an id and the sex of each person. The item parameters are stored as attributes.

Format

A data.frame with 60 rows and 14 columns:

id

person id

sex

sex of the person ("m" or "w")

Item1, ..., Item12

the responses (0 or 1, NA = not answered)

The item parameters are the attributes diffpar (difficulties), slopes, lowerA (lower asymptotes) and upperA (upper asymptotes, all 1), e.g. attr(fourpl_df, "diffpar").

Source

simulation

See Also

sim_4pl

Plots of person estimates and person fit

Description

Three plots for the results of PPass(), PP_4pl(), PP_gpcm() and PPall(): a person fit map, the measurement precision of the test, and the response profiles of single persons.

Usage

## S3 method for class 'ppass'
plot(
  x,
  which = c("fit", "precision", "profile"),
  person = NULL,
  index = NULL,
  alpha = 0.05,
  label = NULL,
  curve = NULL,
  ...
)

## S3 method for class 'ppeo'
plot(
  x,
  which = c("precision", "fit", "profile"),
  person = NULL,
  index = NULL,
  alpha = 0.05,
  curve = NULL,
  ...
)

Arguments

Details

Person fit map (which = "fit"): the person fit statistic index against the estimate, one point per person with status "ok". The dashed lines are the critical values \pm z_{1-\alpha/2}; persons beyond them are flagged (the same rule as in summary() of a PPass result), and the at most 10 most extreme ones are labelled.

Measurement precision (which = "precision"): the upper panel shows the distribution of the estimates, the lower panel the standard error of each person against the estimate. Because the item parameters are known, the standard error of the whole test follows from the test information I(\theta), the sum of the item informations: 1/\sqrt{I(\theta)}, and 1/\sqrt{I(\theta) + 1/\sigma^2} for MAP and EAP estimates with the prior variance \sigma^2. This curve is drawn when every person answered every item (curve = NULL); in incomplete or adaptive designs each person has an own set of items, and the curve of all items holds for nobody. The ticks at the bottom mark the location of each item, the ability at which it gives the most information (for the 1PL and 2PL its difficulty). The plot shows where the test measures accurately and whether the persons are located there. For robust estimates the curve is the model standard error without the Huber weights; for EAP estimates the standard error is the posterior SD.

Person profile (which = "profile"): one panel per person in person. Each answered item is a point: the expected score at the estimate (x axis, relative to the maximum score of the item, easy items on the left) against the observed score (y axis, relative to the maximum score). Responses in line with the model lie close to the diagonal. Items with a standardised residual |z| = |x - E|/\sqrt{V} above z_{1-\alpha/2} are marked as unexpected and labelled with the item name: a wrong answer to an easy item at the bottom left, a right answer to a hard item at the top right. The profile shows which responses make a person conspicuous. The panel title gives the estimate, its standard error and the fit statistic index.

The plots are drawn with the tinyplot package, so a theme set with tinyplot::tinytheme() applies to them.

Value

The plotted data, invisibly: for "fit" a data.frame with one row per plotted person (person, label, estimate, value of index, flagged); for "precision" a list with the data.frames persons (person, estimate, SE), curve (theta, info, SE; NULL without curve) and items (item, location); for "profile" a data.frame with one row per answered item and person (person, item, response, max, expected, z, unexpected).

Author(s)

Manuel Reif

See Also

PPass, Pfit, PP_4pl, PP_gpcm, PPall

Examples

set.seed(1337)
diffpar <- seq(-3, 3, length = 20)
sl <- round(runif(20, 0.5, 1.5), 2)
awm <- sim_4pl(beta = diffpar, alpha = sl, lowerA = rep(0, 20), upperA = rep(1, 20),
               theta = rnorm(300))
# 10 persons answer at random
awm[1:10, ] <- matrix(rbinom(200, 1, 0.5), 10)
dat <- data.frame(id = sprintf("P%03d", 1:300), awm)

res <- PPass(dat, items = 2:21, thres = diffpar, slopes = sl)

# who does not fit?
plot(res, label = "id")

# where does the test measure accurately, and are the persons there?
plot(res, which = "precision")

# which responses make the flagged persons conspicuous?
plot(res, which = "profile", label = "id")

# the same plots for an estimation result
est <- PP_4pl(respm = awm, thres = diffpar, slopes = sl, type = "map")
plot(est)
plot(est, which = "profile", person = 1:2)

Adaptive Matrices Test data

Description

This dataset contains real data from the 'Adaptive Matrices Test' (AMT), which is a computer-administered test. This power test assesses logical reasoning as an indicator of general intelligence. The ability to identify regularities and draw logical conclusions is a very good predictor of long-term success at work. The dataset is sparse, because the test tailors a specific set of items for each examinee's ability level. (More information about adaptive testing: https://en.wikipedia.org/wiki/Computerized_adaptive_testing)

Format

A list with two data.frames. The first data.frame 'daten_amt' contains 298 columns and 710 rows. Each row contains responses from one examinee. The second data.frame 'betas' contains the difficulty parameter (1PL) (These parameters came with the raw-score extraction.).

Details

The data are provided from the University of Vienna, Faculty of Psychology, Department of Psychological Assessment. Thanks to Schuhfried.

Source

Division of Psychological Assessment and Applied Psychometrics, Faculty of Psychology, University of Vienna

• ID: id of person

• AGE: age in years (with ages below 18 and above 34 are collapsed)

• TE_GA:

  • TE: self-assessment. To pass a psychological assessment course, the students have to complete several hours self assessment on a bunch of tests, to get familiar with them.

  • GA: testing for an assessment report. The students also have to test other people (not psychologists nor psychology students) in order to write an assessment report.

• FORM: There are several different versions of this test, which differ in test length, duration etc ...

• TIME1: start time

• TIME2: end time

• REL: reliability for each person

• i: items

References

• Hornke, L. F., Etzel, S., & Rettig, K. (2003). Manual Adaptive Matrices Test (AMT). Moedling: SCHUHFRIED GmbH.

See Also

PPass

Methods for estimation results

Description

Print, summarise and extract the person estimates of PP_4pl(), PP_gpcm() and PPall() (objects of class ppeo). The plots are described in pp-plots.

Usage

## S3 method for class 'ppeo'
print(x, digits = 4, ...)

## S3 method for class 'ppeo'
summary(object, nrowmax = 15, digits = 4, ...)

## S3 method for class 'ppeo'
coef(object, ...)

## S3 method for class 'ppeo'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

Value

coef() returns the estimates as a vector (persons in the order of the response matrix), as.data.frame() the data.frame $estimates (estimate, SE, nsteps, status). print() and summary() return the object invisibly.

See Also

PP_4pl, PP_gpcm, PPall, pp-plots

Draw Plausible values

Description

This function draws npv plausible values for each person from their posterior density.

Usage

## S3 method for class 'pv'
print(x, digits = 4, ...)

## S3 method for class 'pv'
summary(object, nrowmax = 15, digits = 4, ...)

## S3 method for class 'ppass'
PV(estobj, ...)

PV(estobj, ...)

## S3 method for class 'ppeo'
PV(estobj, npv = 10, approx = TRUE, ...)

Arguments

Details

Plausible values need a prior, so estobj must come from an estimation with type = "eap" or type = "map". The posterior of each person combines the likelihood of the answered items with the normal prior N(mu, sigma2) of the person, as given to PP_4pl(), PP_gpcm() or PPall() (default N(0, 1)). It is computed on a grid of ability values, the same as for type = "eap": one grid for all persons, and a finer grid around the posterior mode for persons whose posterior is very narrow or lies outside the range of the prior. Both types give the same plausible values, since they share the posterior; the point estimate itself is not used. MLE, WLE and robust estimates have no prior, and PV() stops with an error for them. Missing responses are skipped; a person without any response gets draws from the prior.

With approx = FALSE each value is drawn from the posterior on the grid: a grid point is chosen with its posterior probability, then the value is drawn uniformly within its cell. The draws are independent. With approx = TRUE (default) the draws come from the normal distribution with the posterior mean and the posterior SD of the person (the EAP estimate and its SE).

Up to version 0.6.4 PV() accepted any estimation type, approx = FALSE used a Metropolis-Hastings algorithm and approx = TRUE drew from the normal distribution around the estimate of estobj with its SE. For an MLE or WLE estimate this is not a posterior and the plausible values were too widely spread. The arguments thinning, burnin and mult of the sampler are no longer used.

Value

An object of class pv, a list with pvdraws, a matrix with one row per person (in the order of respm) and npv columns, and call.

Author(s)

Manuel Reif

References

Mislevy, R. J. (1991). Randomization-based inference about latent variables from complex samples. Psychometrika, 56(2), 177-196.

Von Davier, M., Gonzalez, E., & Mislevy, R. (2009). What are plausible values and why are they useful. IERI monograph series, 2, 9-36.

See Also

PP_gpcm, PP_4pl, PPall, JKpp

Examples

## 2PL model

set.seed(1522)
# difficulties and slopes
diffpar <- seq(-3, 3, length = 12)
sl      <- round(runif(12, 0.5, 1.5), 2)

# response matrix
awm <- sim_4pl(beta = diffpar, alpha = sl, lowerA = rep(0, 12), upperA = rep(1, 12),
               theta = rnorm(10))

# plausible values need a prior: EAP (or MAP) estimation, here with the default N(0, 1)
res2pl <- PP_4pl(respm = awm, thres = diffpar, slopes = sl, type = "eap")

# 10 plausible values per person, normal approximation of the posterior
res_pv <- PV(res2pl)
summary(res_pv)

# 200 draws from the posterior itself; their means approximate the EAP
res_pv2 <- PV(res2pl, npv = 200, approx = FALSE)
cbind(pv_mean = rowMeans(res_pv2$pvdraws),
      eap = res2pl$estimates$estimate)


## GPCM model with person-specific priors

THRES  <- matrix(c(-2,-1.23,1.11,3.48,1
                   ,2,-1,-0.2,0.5,1.3,-0.8,1.5),nrow=2)
sl     <- c(0.5,1,1.5,1.1,1,0.98)
awmatrix <- matrix(c(1,0,2,0,1,1,1,0,0,1
                     ,2,0,0,0,0,0,0,0,0,1,1,2,2,1,1,1,1,0,0,1),byrow=TRUE,nrow=5)

resgpcm <- PP_gpcm(respm = awmatrix, thres = THRES, slopes = sl, type = "eap",
                   mu = c(0, 0, 1, 1, 1), sigma2 = 0.5)
PV(resgpcm, npv = 5, approx = FALSE)


## GPCM and 4PL items mixed

THRESx <- THRES
THRESx[2,1:3] <- NA
la     <- c(0.02,0.1,0,0,0,0)
ua     <- c(0.97,0.91,1,1,1,1)
awmatrix <- matrix(c(1,0,1,0,1,1,1,0,0,1
                     ,2,0,0,0,0,0,0,0,0,1
                     ,1,2,2,1,1,1,1,0,0,1),byrow=TRUE,nrow=5)

respcm <- PPall(respm = awmatrix, thres = THRESx, slopes = sl, lowerA = la, upperA = ua,
                type = "eap")
res_mixedpv <- PV(respcm, npv = 200, approx = FALSE)

# quantiles of the draws per person
apply(res_mixedpv$pvdraws, 1, quantile)

Simulate data for 1/2/3/4-pl model

Description

This function returns a dichotomous matrix of simulated responses under given item and person parameters.

Usage

sim_4pl(beta, alpha, lowerA, upperA, theta)

Arguments

Details

alpha, lowerA and upperA may also be single numbers, which are then used for every item. A person with NA as person parameter gets a row of NA.

Value

An integer matrix with one row per person and one column per item.

Author(s)

Manuel Reif

See Also

sim_gpcm, sim_mixed, PP_4pl

Examples

set.seed(1700)
theta <- rnorm(200)
b <- seq(-2, 2, length.out = 7)

# 1PL (Rasch) model: slopes 1, no asymptotes
x1 <- sim_4pl(beta = b, alpha = 1, lowerA = 0, upperA = 1, theta = theta)
colMeans(x1)

# 4PL model
x4 <- sim_4pl(beta = b, alpha = c(1, 1.1, 0.9, 0.9, 1.5, 1.1, 1),
              lowerA = c(0, 0, 0.2, 0.15, 0.04, 0, 0.21),
              upperA = c(0.9, 0.98, 0.97, 1, 1, 1, 0.97), theta = theta)
head(x4)

Simulate data for the gpcm model

Description

This function returns an integer matrix of simulated responses under given item and person parameters.

Usage

sim_gpcm(thres, alpha, theta)

Arguments

Details

alpha may also be a single number, which is then used for every item. A person with NA as person parameter gets a row of NA.

Value

An integer matrix with one row per person and one column per item.

See Also

sim_4pl, sim_mixed, PP_gpcm

Examples

set.seed(1750)
# thresholds of six items with three categories (the row of zeroes is optional)
thres <- rbind(0, matrix(c(-2, -1.23, 1.11, 3.48, 1, 2, -1, -0.2, 0.5, 1.3, -0.8, 1.5),
                         nrow = 2))
x <- sim_gpcm(thres = thres, alpha = c(0.5, 1, 1.5, 1.1, 1, 0.98), theta = rnorm(100))
head(x)

# the last two items get a fourth category
thres4 <- rbind(thres, c(NA, NA, NA, NA, 1.7, 1))
apply(sim_gpcm(thres = thres4, alpha = 1, theta = rnorm(100)), 2, max)

Simulate data for mixed tests (4PL and GPCM items)

Description

This function returns an integer matrix of simulated responses for a test with dichotomous (1/2/3/4-PL) and polytomous (GPCM) items, with the item parameters in the layout of PPall.

Usage

sim_mixed(thres, alpha = NULL, lowerA = NULL, upperA = NULL, theta)

Arguments

Details

alpha, lowerA and upperA may also be single numbers, which are then used for every item. lowerA and upperA only apply to the 4PL items. A person with NA as person parameter gets a row of NA. The data of a pure 4PL or GPCM test are the same as those of sim_4pl or sim_gpcm with the same seed.

Value

An integer matrix with one row per person and one column per item.

See Also

sim_4pl, sim_gpcm, PPall

Examples

set.seed(1800)
# four 3PL items and two GPCM items with three categories
thres <- rbind(0, c(-1, -0.3, 0.4, 1.2, -0.5, 0.2), c(NA, NA, NA, NA, 0.8, 1.4))
x <- sim_mixed(thres = thres, alpha = c(1, 1.2, 0.8, 1.1, 1, 0.9),
               lowerA = c(0.2, 0.1, 0.15, 0.2, 0, 0), theta = rnorm(300))
head(x)

# and back: estimate the persons with the same parameters
res <- PPall(x, thres = thres, slopes = c(1, 1.2, 0.8, 1.1, 1, 0.9),
             lowerA = c(0.2, 0.1, 0.15, 0.2, 0, 0))
summary(res$estimates$estimate)