Attribute-wise agreement rate

Description

The function is used to compute the attribute-wise agreement rate between two sets of attribute profiles. They need to have the same dimensions.

Usage

AAR(x, y)

Arguments

Value

The function returns the attribute-wise agreement rate between two sets of attribute profiles.

See Also

PAR

Examples

# see examples used for GNPC.

Estimation of examinees' attribute profiles using the GNPC method

Description

Function GNPC is used to estimate examinees' attribute profiles using the general nonparametric classification (GNPC) method (Chiu et al., 2018; Chiu & Köhn, 2019). It can be used with data conforming to any cognitive diagnosis models (CDMs).

Usage

GNPC(
  Y,
  Q,
  fixed.w = FALSE,
  initial.dis = "hamming",
  initial.gate = "AND",
  max.iter = 1000,
  tol = 0.001,
  track.convergence = TRUE
)

Arguments

Value

The function returns a list with the following components:

att.est

A N \times K matrix of estimated attribute profiles for examinees

class

A vector of length N containing the estimated class memberships

ideal.response

A 2^K \times J matrix of weighted ideal responses

weight

A 2^K \times J matrix of weights used to compute the weighted ideal responses

convergence

(Only if track.convergence = TRUE) A list containing:

• iteration: Vector of iteration numbers

• prop.change: Proportion of examinees whose classification changed at each iteration

• total.distance: Total squared distance between observed and weighted ideal responses

• n.iter: Total number of iterations until convergence

• converged: Logical indicating whether the algorithm converged within max.iter

Details

A weighted ideal response \eta^{(w)}, defined as the convex combination of \eta^{(c)} and \eta^{(d)}, is used in the GNPC method to compute distances. Suppose item j requires K_{j}^* \leq {K} attributes that, without loss of generality, have been moved to the first K_{j}^* positions of the item attribute vector \boldsymbol{q_j}. For each item j and latent class \mathcal{C}_{l}, the weighted ideal response \eta_{lj}^{(w)} is defined as the convex combination \eta_{lj}^{(w)} = w _{lj} \eta_{lj}^{(c)}+(1-w_{lj})\eta_{lj}^{(d)} where 0\leq w_{lj}\leq 1. The distance between the observed responses to item j and the weighted ideal responses w_{lj}^{(w)} of examinees in \mathcal{C}_{l} is defined as the sum of squared deviations: d_{lj} = \sum_{i \in \mathcal {C}_{l}} (y_{ij} - \eta_{lj}^{(w)})^2. \hat{w}_{lj} can then be obtained by minimizing d_{lj}, which can then be used to compute \hat{\eta}_{lj}.

After all the \hat{\eta}_{lj} are obtained, examinees' attribute profiles \boldsymbol{\alpha} can be estimated by minimizing the loss function \hat{d}_{lj} = \sum_{i \in \mathcal{C}_{l}} (y_{ij} - \hat{\eta}_{lj}^{(w)})^2

The algorithm iteratively updates the weighted ideal responses and reclassifies examinees until convergence is achieved. The stopping criterion is based on the proportion of examinees whose classification changes between consecutive iterations: \frac{\sum_{i=1}^{N} I\left[\alpha_i^{(t)} \neq \alpha_i^{(t-1)}\right]}{N} < \epsilon where \epsilon is the tolerance level (default = 0.001).

The default initial values of \boldsymbol{\alpha} are obtained by using the NPC method. Chiu et al. (2018) suggested another viable alternative for obtaining initial estimates of the proficiency classes by using an ideal response with fixed weights defined as \eta_{lj}^{(fw)}=\frac{\sum_{k=1}^{K}\alpha_{k}q_{jk}}{K}\eta_{lj}^{(c)}+(1-\frac{\sum_{k=1}^{K}\alpha_{k}q_{jk}}{K})\eta_{lj}^{(d)}.

References

programs: The general nonparametric classification method. Psychometrika, 83(2), 355–375. doi:10.1007/s11336-017-9595-4

classification method. Psychometrika, 84(3), 830–845. doi:10.1007/s11336-019-09660-x

Examples

## Not run: 
# Example 1: Basic usage
library(GDINA)
set.seed(123)
N <- 500
Q <- sim30GDINA$simQ
gs <- data.frame(guess = rep(0.2, nrow(Q)), slip = rep(0.2, nrow(Q)))
sim <- simGDINA(N, Q, gs.parm = gs, model = "DINA")
Y <- extract(sim, what = "dat")
alpha <- extract(sim, what = "attribute")

# Analyze data using GNPC
result <- GNPC(Y, Q, initial.dis = "hamming", initial.gate = "AND")

# View results
head(result$att.est)
table(result$class)

# Plot overall convergence 
plot(result)

# Plot individual examinee's convergence
plot(result, type = "individual", examinee.id = 1, true.alpha = alpha[1, ])

# Check attribute agreement rate
PAR(alpha, result$att.est)
AAR(alpha, result$att.est)

# Example 2: Without convergence tracking (Convergence tracking is only used for the GNPC plots.)
result2 <- GNPC(Y, Q, track.convergence = FALSE)

## End(Not run)

Estimation of examinees' attribute profiles using the NPC method

Description

The function estimates examinees' attribute profiles using the nonparametric classification (NPC) method (Chiu & Douglas, 2013). An examinee's attribute profile is estimated by minimizing the distance between the observed and ideal item responses.

Usage

NPC(
  Y,
  Q,
  distance = c("hamming", "whamming", "penalized"),
  gate = c("AND", "OR"),
  wg = 1,
  ws = 1
)

Arguments

Value

The function returns a series of outputs, including:

alpha.est

A N \times K matrix representing the estimated attribute profiles. 1 = examinee masters the attribute, 0 = examinee does not master the attribute.

est.ideal

A N \times J matrix indicating the estimated ideal response to all items from all examinees. 1 = correct, 0 = incorrect.

est.class

A N-dimensional vector showing the class memberships for all examinees.

n.tie

The number of ties in the Hamming distance among the candidate attribute profiles for each person. When ties occur, one of the tied attribute profiles is randomly chosen.

pattern

All possible attribute profiles in the latent space.

loss.matrix

A 2^K \times N matrix containing the values of the loss function (the distances) between each examinee's observed response vector and the 2^K ideal response vectors.

Details

The nonparametric classification (NPC) method (Chiu & Douglas, 2013) assigns examinees to the proficiency classes they belong to by comparing their observed item response patterns with each of the ideal item response patterns of the 2^K proficiency classes. When there is no data perturbation, an examinee's ideal response pattern corresponding to the examinee's true attribute pattern and his/her observed item response patterns are identical, and thus the distance between them is 0. When data perturbations are small, this ideal response pattern remains the one most similar to the observed response pattern, which is exactly the setup of data conforming to the DINA or DINO model. Hence, based on this rationale, an examinee's attribute profile is obtained by minimizing the distance between the observed and the ideal item response patterns. The nonparametric nature of the NPC method furthermore makes it suitable for data obtained from small-scale settings.

References

Chiu, C. Y., & Douglas, J. A. (2013). A nonparametric approach to cognitive diagnosis by proximity to ideal response patterns. Journal of Classification, 30(2), 225-250. doi:10.1007/s00357-013-9132-9

See Also

GNPC

Examples

## Not run: 
library(GDINA)
N <- 500
Q <- sim30GDINA$simQ
gs <- data.frame(guess = rep(0.2, nrow(Q)), slip = rep(0.2, nrow(Q)))
sim <- simGDINA(N, Q, gs.parm = gs, model = "DINA")
Y <- extract(sim, what = "dat")
alpha <- extract(sim, what = "attribute")

# Estimate attribute profiles using NPC
result <- NPC(Y, Q, distance = "hamming", gate = "AND")
print(result)
result$alpha.est

# Check attributed agreement rate
PAR(alpha, result$alpha.est)
AAR(alpha, result$alpha.est)

## End(Not run)

Pattern-wise agreement rate

Description

The function is used to compute the pattern-wise agreement rate between two sets of attribute profiles. They need to have the same dimensions.

Usage

PAR(x, y)

Arguments

Value

The function returns the pattern-wise agreement rate between two sets of attribute profiles.

See Also

AAR

Examples

# see examples used for GNPC.

Check the completeness status of a binary Q-matrix

Description

Q.completeness is used to examine whether a given Q-matrix is complete when data conform to a specified CDM. A Q-matrix is said to be complete if it allows for the unique identification of all possible attribute profiles among examinees. So far, the function can only be used for a binary Q-matrix with binary responses.

Usage

Q.completeness(raw.Q, model = NULL)

Arguments

Details

The conditions for one Q-matrix completeness are model-dependent: a Q-matrix may be complete for one CDM but incomplete for another. This function implements the theoretical work developed by Chiu et al. (2009) and Köhn and Chiu (2017).

For DINA and DINO models:

A Q-matrix is complete if and only if it contains all K single-attribute items (Chiu et al., 2009).

For More General CDMs:

The function implements a sequential procedure based on Theorems 3-4 and Propositions 1-2 in the work by Köhn and Chiu (2017).

1. If Q contains all K single-attribute items, it is complete (Proposition 1).

2. If Q has rank < K, it is incomplete (Theorem 3).

3. For full-rank Q-matrices without all single-attribute items, the function examines non-nested attribute pairs using indicator vectors to determine if distinct expected response patterns \mathbf{S}(\boldsymbol{\alpha}) can be guaranteed.

The theoretical framework establishes the sufficient conditions for Q completeness, which means completeness implies distinct expected item response patterns for all 2^K possible attribute profiles.

Value

A list of class "Qcompleteness" containing:

The function also prints the status message to the console as a side effect.

References

Chiu, C.-Y., Douglas, J. A., & Li, X. (2009). Cluster analysis for cognitive diagnosis: Theory and applications. Psychometrika, 74(4), 633-665. doi:10.1007/s11336-009-9125-0

Köhn, H.-F., & Chiu, C.-Y. (2017). A procedure for assessing the completeness of the Q-matrices of cognitively diagnostic tests. Psychometrika, 82(1), 112-132. doi:10.1007/s11336-016-9536-7

Köhn, H.-F., & Chiu, C.-Y. (2018). How to build a complete Q-matrix for a #' cognitively diagnostic test. Journal of Classification, 35(2), 273-299. doi:10.1007/s00357-018-9255-0

Examples

## Not run: 
# Example 1: Complete Q-matrix for DINA model
# (contains all 3 single-attribute items)
Q1 <- matrix(c(1, 0, 0,
               0, 1, 0,
               0, 0, 1,
               1, 1, 0,
               1, 0, 1), ncol = 3, byrow = TRUE)
result1 <- Q.completeness(Q1, model = "DINA")
print(result1$is_complete)  # TRUE

# Example 2: Incomplete Q-matrix for DINA model
# (missing single-attribute items)
Q2 <- matrix(c(1, 1, 0,
               1, 0, 1,
               0, 1, 1), ncol = 3, byrow = TRUE)
result2 <- Q.completeness(Q2, model = "DINA")
print(result2$is_complete)  # FALSE

# Example 3: Check completeness for general CDM
Q3 <- matrix(c(1, 0, 0,
               0, 1, 0,
               0, 0, 1,
               1, 1, 0,
               1, 0, 1,
               0, 1, 1), ncol = 3, byrow = TRUE)
result3 <- Q.completeness(Q3, model = "General")

## End(Not run)

Generation of dichotomous Q-matrix

Description

The function generates a complete Q-matrix based on a pre-specified probability that each q-entry equals 1.

Usage

Q.generate(K, J, p, single.att = TRUE)

Arguments

Value

The function returns a dichotomous Q-matrix. A complete Q-matrix is the default unless single.att = F is specified.

See Also

Q.completeness

Examples

## Not run: 
# Example 1: A complete Q-matrix with items requiring fewer attributes.
Q1 = Q.generate(3, 20, 0.5, single.att = TRUE)

# Example 2: A Q-matrix with items requiring more attributes but completeness is not guaranteed.
Q2 = Q.generate(5, 30, 0.6, single.att = FALSE)

## End(Not run)

Generation of a Q-matrix that contains implausible MC items

Description

This function turns a proper and plausible Q-matrix into an implausible Q-matrix with the user-specified number of implausible distractors.

Usage

Q.implausible(Q, n.implausible)

Arguments

Value

The function returns

Q.implausible

The generated Q-matrix

item.implausible

The ID of the items that have implausible distractors.

References

Chiu, C.-Y., Köhn, H. F. & Wang, Y. (Online first). Plausible and proper multiple-choice items for diagnostic classification. Psychometrika. doi:10.1017/psy.2025.10074

Generation of a Q-matrix with improper MC items that are not proper based on a 'plausible and proper' Q

Description

This function turns a proper and plausible Q-matrix into an improper Q-matrix with the user-specified number of improper distractors.

Usage

Q.improper(Q, n.improper)

Arguments

Value

The function returns

Q.improper

The improper Q-matrix generated from Q

item.improper

The ID of the items that are improper.

References

Chiu, C.-Y., Köhn, H. F. & Wang, Y. (Online first). Plausible and proper multiple-choice items for diagnostic classification. Psychometrika. doi:10.1017/psy.2025.10074

Q-matrix refinement method

Description

The QR function refines a provisional Q-matrix by minimizing the residual sum of squares (RSS) between the observed and ideal item responses across all possible q-vectors, given the estimates of examinees' attribute profiles.

Usage

QR(Y, Q, gate = c("AND", "OR"), max.ite = 50)

Arguments

Details

This function implements the Q-matrix refinement (QR) method developed by Chiu (2013). The NPC method (Chiu & Douglas, 2013) is first used to classify examinees and the best q-vector for an item is identified by minimizing its RSS. Specifically, the RSS of item j for examinee i is defined as

RSS_{j} =\sum_{m=1}^{2^K} \sum_{i \in C_{m}} (Y_{ij} - \eta_{jm})^2,

where C_m for m = 1, \ldots, 2^K is the mth proficiency class, and N is the number of examinees. Chiu (2013) proved that the expected value of RSS_j corresponding to the correct q-vector is the minimum among the 2^K - 1 candidates.

Value

A list containing:

References

Chiu, C. Y. (2013). Statistical Refinement of the Q-matrix in Cognitive Diagnosis. Applied Psychological Measurement, 37(8), 598-618. doi:10.1177/0146621613488436

See Also

NPC

Examples

## Not run: 
## Generate data
library(GDINA)
N = 500
Q = sim30GDINA$simQ
J = nrow(Q)
K= ncol(Q)
gs = data.frame(guess = rep(0.2,J), slip = rep(0.2,J))
sim = simGDINA(N, Q, gs.parm = gs, model = "DINA")
Y = extract(sim,what = "dat")

## Randomly generate a misspecified Q with 20% of misspecifications
mis.Q = matrix(0, J, K)
while (any(rowSums(mis.Q)==0)==T){
  mis.q = sample(J*K, J*K*0.2) ## percentage of misspecified q
  ind = arrayInd(mis.q, dim(Q))
  mis.Q = Q
  mis.Q[ind] = 1-mis.Q[ind]
}

## Refine the misspecified Q-matrix
ref = QR(Y, mis.Q)
ref.Q = ref$modified.Q

## Compute the entry-wise and item-wise recovery rates
rr = RR(ref.Q, Q)
rr$entry.wise
rr$item.wise

## Compute the retention rate
retention.rate(ref.Q, mis.Q, Q)

## Compute the correction rate
correction.rate(ref.Q, mis.Q, Q)

## End(Not run)

Q-Matrix from Ozaki (2015)

Description

A Q-matrix for 30 multiple-choice items measuring 5 attributes, originally used in Ozaki (2015) to demonstrate structured MC-DINA models. Items 1–10 are single-attribute items with no coded distractors. Items 11–20 have one coded distractor each, and items 21–30 have two or three coded distractors.

Usage

Q_Ozaki

Format

A data frame with 66 rows and 7 columns:

V1

Item index (1–30).

V2

Option index. For each item, the first row is the key; subsequent rows are coded distractors.

V3

Attribute 1 indicator (0 or 1).

V4

Attribute 2 indicator (0 or 1).

V5

Attribute 3 indicator (0 or 1).

V6

Attribute 4 indicator (0 or 1).

V7

Attribute 5 indicator (0 or 1).

References

Ozaki, K. (2015). DINA models for multiple-choice items with few parameters: Considering incorrect answers. Applied Psychological Measurement, 39, 431–447.

Entry-wise and vector-wise agreement rate between two Q-matrices

Description

RR is used to compute the agreement rate between two Q-matrices with identical dimensions.

Usage

RR(Q1, Q2)

Arguments

Value

The function returns

entry.wise

The entry-wise agreement rate

item.wise

The item-wise agreement rate

See Also

See the examples for the QR and TSQE functions.

Two-step Q-matrix estimation method

Description

The function estimates the Q-matrix based on the response data using the two-step Q-matrix estimation method.

Usage

TSQE(
  Y,
  K,
  input.cor = c("tetrachoric", "pearson"),
  ref.method = c("QR", "GDI"),
  GDI.model = c("GDINA", "DINA", "ACDM", "RRUM"),
  cutoff = 0.8
)

Arguments

Value

The function returns the estimated Q-matrix.

Details

The TSQE method estimates a Q-matrix by integrating the provisional attribute extraction (PAE) algorithm with a Q-matrix refinement-and-validation method, such as the Q-Matrix Refinement (QR) method and the G-DINA Model Discrimination Index (GDI). Specifically, the PAE algorithm relies on classic exploratory factor analysis (EFA) combined with a unique stopping rule for identifying a provisional Q-matrix, and the resulting provisional Q-Matrix is "polished" by a refinement method to derive the finalized estimation of Q-matrix.

The PAE Algorithm starts with computing the inter-item tetrachoric correlation matrix. The reason for using tetrachoric correlation is that the examinee responses are binary, so it is more appropriate than the Pearson product moment correlation coefficient. See Köhn et al. (2025) for details. The next step is to use factor analysis on the item-correlation matrix, and treat the extracted factors as proxies for the latent attributes. The third step concerns the identification of specific attributes required for each item. The detailed algorithm is described below:

(1)

Initialize the item index as j = 1.

(2)

Let l_{jk} denote the loading of item j on factor k, where k = 1,2,...,K.

(3)

Arrange the loadings in descending order. Define a mapping function f(k) = t, where t is the order index. Hence, l_{j(1)} will indicate the maximum loading, while l_{j(K)} will indicate the minimum loading.

(4)

Define

p_j(t) = \frac{\sum_{h=1}^t l_{j(h)}^2}{\sum_{k=1}^K l_{jk}^2}

as the proportion of the communality of item j accounted for by the first t factors.

(5)

Define

K_j = \min \{ t \mid p_j(t) \geq \lambda \}

, where \lambda is the cut-off value for the desired proportion of item variance-accounted-for. Then, the ordered entries of the provisional q-vector of item j are obtained as

q_{j(t)}^* = \begin{cases} 1 & \text{if } t \leq K_j \\ 0 & \text{if } t > K_j \end{cases}

.

(6)

Identify q_j^* = (q_{j1}^*,q_{j2}^*,...,q_{jK}^*) by rearranging the ordered entries of the q-vector using the inverse function k = f^{-1}(t).

(7)

Set j = j + 1 and repeat (2) to (6) until j = J. Then denote the provisional Q-matrix as \mathbf{Q}^*.

The provisional Q-matrix \mathbf{Q}^* is then refined by using either the QR or GDI method.

References

Chiu, C. Y. (2013). Statistical Refinement of the Q-matrix in Cognitive Diagnosis. Applied Psychological Measurement, 37(8), 598-618. doi:10.1177/0146621613488436

de la Torre, J., & Chiu, C.-Y. (2016). A general method of empirical Q-matrix validation. Psychometrika, 81, 253-73. doi:10.1007/s11336-015-9467-8

Köhn, H. F., Chiu, C.-Y., Oluwalana, O., Kim, H. & Wang, J. (2025). A two-step Q-matrix estimation method, Applied Psychological Measurement, 49(1-2), 3-28. doi:10.1177/01466216241284418

See Also

QR

Examples

## Not run: 
library(GDINA)
N = 1000
Q = sim30GDINA$simQ
J = nrow(Q)
K= ncol(Q)
gs = data.frame(guess=rep(0.2,J),slip=rep(0.2,J))
sim = simGDINA(N,Q,gs.parm = gs,model = "DINA")
Y = extract(sim,what = "dat")

## Run TSQE method with QR
est.Q = TSQE(Y, K, input.cor = "tetrachoric", ref.method = "QR", cutoff = 0.8)

## If the recovery rate is to be computed, the columns of the estimated Q-matrix 
## should be permuted so that they align with those of the true Q-matrix. 
best.est.Q = bestQperm(est.Q, Q)

## Compute the recovery rate
RR(best.est.Q, Q)

## End(Not run)

Column permutation of a Q-matrix with respect to a benchmark Q-matrix.

Description

Function bestQperm is used to permute the columns of a Q-matrix so that the order of the columns best matches that of the benchmark Q-matrix. This function is useful in a Q-matrix estimation process.

Usage

bestQperm(Q, bench.Q)

Arguments

Value

The function returns a Q-matrix in which the order of the columns best matches that of the benchmark Q-matrix.

Examples

# See examples used for TSQE.

Correction rate of a Q-matrix refinement method

Description

This function computes the proportion of corrected q-entries that were originally misspecified in the provisional Q-matrix. This function is used only when the true Q-matrix is known.

Usage

correction.rate(ref.Q = ref.Q, mis.Q = mis.Q, true.Q = true.Q)

Arguments

Value

The function returns a value between 0 and 1 representing the proportion of corrected q-entries in ref.Q that were originally misspecified in mis.Q.

Examples

# See examples used for QR function.

Detect the implausible and improper distractors in a Q-Matrix for multiple-choice items

Description

Function distractor.check is used to assess whether the distractors of a given Q-matrix for multiple-choice items are plausible and/or proper.

Usage

distractor.check(Q = Q, key = NULL)

Arguments

Value

A list with class "distractor.check" containing:

not.plausible

A matrix indicating items and options that are not plausible, or NULL if all items are plausible.

not.proper

A vector of item IDs that are not proper, or NULL if all items are proper.

all.plausible

Logical; TRUE if all items are plausible.

all.proper

Logical; TRUE if all items are proper.

References

Chiu, C.-Y., Köhn, H. F. & Wang, Y. (Online first). Plausible and proper multiple-choice items for diagnostic classification. Psychometrika.

Examples

## Not run: 
library(NPCDTools)
Q1 <- Q_Ozaki
distractor.check(Q1)

Q2 <- GDINA::sim10MCDINA2$simQ
key <- c(1, 2, 4, 1, 1, 3, 2, 4, 1, 4)
distractor.check(Q2, key)

## End(Not run)

Plot Diagnostics for GNPC

Description

This function gives two types of diagnostic plots for the outcomes of the GNPC algorithm.

The type = "convergence" option gives two graphs: The upper panel displays the trajectory of the proportion of membership switches and the lower panel shows the total squared distance along with the iterations. They illustrate the detailed information about how and whether the algorithm has converged.

The type = "individual" option returns a sequence of squared distances for a single examinee and the estimates of the examinee's attribute profile along with the iterations. The plots allow users to investigate how the algorithm arrives at its final classifications. In simulation studies, the true attribute profile can be provided as a reference.

Usage

## S3 method for class 'GNPC'
plot(
  x,
  type = c("convergence", "individual"),
  examinee.id = NULL,
  true.alpha = NULL,
  top.n.pattern = NULL,
  ...
)

Arguments

Details

For "individual" plots, the visual elements are:

• Red line: the attribute pattern ultimately selected by GNPC.

• Black line: the true attribute pattern (only when true.alpha is provided). A small vertical jitter is applied when it overlaps with the red line.

• Other colored lines: the most competitive candidate patterns, selected by proximity at the final iteration.

• Filled circle at each iteration: indicates which attribute profile GNPC assigned to the examinee at each iteration, drawn in the corresponding line's color. The line for the true attribute profile always has black circles at every iteration as a fixed reference.

See Also

GNPC

Examples

## Not run: 
library(GDINA)
set.seed(123)
N <- 500
Q <- sim30GDINA$simQ
gs <- data.frame(guess = rep(0.2, nrow(Q)), slip = rep(0.2, nrow(Q)))
sim <- simGDINA(N, Q, gs.parm = gs, model = "DINA")
Y <- extract(sim, what = "dat")
alpha <- extract(sim, what = "attribute")

# Analyze data using GNPC
result <- GNPC(Y, Q, initial.dis = "hamming", initial.gate = "AND")

# Convergence
plot(result)

# Individual with true attribute profile (simulation)
plot(result, type = "individual", examinee.id = 1, true.alpha = alpha[1, ])

# Individual without true attribute profile (real data)
plot(result, type = "individual", examinee.id = 1)

## End(Not run)

Print the Summary of a GNPC Object

Description

Prints a summary of the GNPC estimation results.

Usage

## S3 method for class 'GNPC'
print(x, ...)

Arguments

Print the Summary of an NPC Object

Description

Prints a summary of NPC classification results.

Usage

## S3 method for class 'NPC'
print(x, ...)

Arguments

Print Summary of a Qcompleteness Object

Description

Print method for objects of class "Qcompleteness".

Usage

## S3 method for class 'Qcompleteness'
print(x, ...)

Arguments

Print Summary of the Q-Matrix Refinement Result

Description

Prints a summary of the Q-matrix refinement process, including which entries were modified and the final refined Q-matrix.

Usage

## S3 method for class 'Qrefine'
print(x, ...)

Arguments

Print the Summary of Distractor Check Results

Description

Prints a summary of whether the distractors of a given Q-matrix for multiple-choice items are plausible and/or proper.

Usage

## S3 method for class 'distractor.check'
print(x, ...)

Arguments

Retention rate of a Q-matrix refinement method

Description

This function computes the proportion of correctly specified q-entries in a provisional Q-matrix that remain correctly specified after a Q-matrix refinement procedure is applied. This function is used only when the true Q-matrix is known.

Usage

retention.rate(ref.Q = ref.Q, mis.Q = mis.Q, true.Q = true.Q)

Arguments

Value

The function returns a value between 0 and 1 indicating the proportion of correctly specified q-entries in mis.Q that remain correctly specified in ref.Q after a Q-matrix refinement procedure is applied to mis.Q.

See Also

See examples used for QR.

Launch the GNPC Shiny app

Description

Launches the interactive demo shipped with the package (can be found in inst/shiny/GNPC_app). The app demonstrates NPC, GNPC, and G-DINA workflows and the ECPE real-data example.

Usage

run_gnpc_app(
  launch.browser = interactive(),
  host = "127.0.0.1",
  port = NULL,
  ...
)

Arguments

Examples

## Not run: 
  run_gnpc_app()

## End(Not run)