print.RdPrint concise, user-friendly summaries of objects generated by the Qval package.
Supports objects of classes CDM, validation, sim.data,
as well as their corresponding summary objects.
# S3 method for class 'CDM'
print(x, ...)
# S3 method for class 'validation'
print(x, ...)
# S3 method for class 'sim.data'
print(x, ...)
# S3 method for class 'summary.CDM'
print(x, ...)
# S3 method for class 'summary.validation'
print(x, ...)
# S3 method for class 'summary.sim.data'
print(x, ...)An object of the appropriate class (e.g., CDM,
validation, sim.data, or their summaries).
Currently unused. Additional arguments are ignored.
Invisibly returns x.
The print methods provide an at-a-glance view of key information:
displays sample size, item and attribute counts, and package information.
shows suggested modifications to the Q-matrix, marking changed entries with an asterisk.
reports dimensions of simulated data and offers guidance on extraction.
prints fitted model details and alpha-pattern distribution from a summary.CDM object.
prints suggested Q-matrix changes or a message if none are recommended.
prints attribute-pattern frequencies and proportions from summary.sim.data.
print(CDM): Print method for CDM objects
print(validation): Print method for validation objects
print(sim.data): Print method for sim.data objects
print(summary.CDM): Print method for summary.CDM objects
print(summary.validation): Print method for summary.validation objects
print(summary.sim.data): Print method for summary.sim.data objects
set.seed(123)
library(Qval)
# \donttest{
################################################################
# Example 1: print a CDM object #
################################################################
Q <- sim.Q(3, 20)
IQ <- list(P0 = runif(20, 0.0, 0.2), P1 = runif(20, 0.8, 1.0))
data.obj <- sim.data(Q = Q, N = 500, IQ = IQ,
model = "GDINA", distribute = "horder")
#> distribute = horder
#> model = GDINA
#> number of attributes: 3
#> number of items: 20
#> num of examinees: 500
#> average of P0 = 0.083
#> average of P1 = 0.894
#> theta_mean = -0.055 , theta_sd = 0.996
#> a = 1.5 1.5 1.5
#> b = -1.5 1.5 0
CDM.obj <- CDM(data.obj$dat, Q, model = "GDINA",
method = "EM", maxitr = 2000, verbose = 1)
#>
Iter = 1 Max. abs. change = 0.57951 Deviance = 10238.69
Iter = 2 Max. abs. change = 0.09509 Deviance = 8501.84
Iter = 3 Max. abs. change = 0.03995 Deviance = 8478.24
Iter = 4 Max. abs. change = 0.01792 Deviance = 8477.13
Iter = 5 Max. abs. change = 0.02604 Deviance = 8477.02
Iter = 6 Max. abs. change = 0.01142 Deviance = 8476.97
Iter = 7 Max. abs. change = 0.01740 Deviance = 8476.96
Iter = 8 Max. abs. change = 0.00686 Deviance = 8476.95
Iter = 9 Max. abs. change = 0.00466 Deviance = 8476.94
Iter = 10 Max. abs. change = 0.00253 Deviance = 8476.94
Iter = 11 Max. abs. change = 0.00030 Deviance = 8476.94
Iter = 12 Max. abs. change = 0.00014 Deviance = 8476.94
Iter = 13 Max. abs. change = 0.00016 Deviance = 8476.94
Iter = 14 Max. abs. change = 0.00760 Deviance = 8476.94
Iter = 15 Max. abs. change = 0.00246 Deviance = 8476.94
Iter = 16 Max. abs. change = 0.00502 Deviance = 8476.94
Iter = 17 Max. abs. change = 0.00017 Deviance = 8476.94
Iter = 18 Max. abs. change = 0.00109 Deviance = 8476.94
Iter = 19 Max. abs. change = 0.00004 Deviance = 8476.94
print(CDM.obj)
#> Qval version1.2.2(2025-05-02)
#> ==============================================
#> Number of items = 20
#> Number of attributes = 3
#> Number of individuals = 500
#> To extract components, use the method extract.
################################################################
# Example 2: print a validation object #
################################################################
set.seed(123)
MQ <- sim.MQ(Q, 0.1)
#> rate of mis-specifications = 0.1
#> rate of over-specifications = 0.07
#> rate of under-specifications = 0.03
CDM.obj <- CDM(data.obj$dat, MQ)
#>
Iter = 1 Max. abs. change = 0.47926 Deviance = 11325.04
Iter = 2 Max. abs. change = 0.13995 Deviance = 9168.03
Iter = 3 Max. abs. change = 0.07841 Deviance = 9097.21
Iter = 4 Max. abs. change = 0.02452 Deviance = 9091.20
Iter = 5 Max. abs. change = 0.00702 Deviance = 9090.55
Iter = 6 Max. abs. change = 0.00397 Deviance = 9090.47
Iter = 7 Max. abs. change = 0.00229 Deviance = 9090.46
Iter = 8 Max. abs. change = 0.00148 Deviance = 9090.46
Iter = 9 Max. abs. change = 0.00081 Deviance = 9090.46
Iter = 10 Max. abs. change = 0.00054 Deviance = 9090.46
Iter = 11 Max. abs. change = 0.00033 Deviance = 9090.45
Iter = 12 Max. abs. change = 0.00020 Deviance = 9090.45
Iter = 13 Max. abs. change = 0.00010 Deviance = 9090.45
Iter = 14 Max. abs. change = 0.00004 Deviance = 9090.45
validation.obj <- validation(data.obj$dat, MQ, CDM.obj,
method = "GDI")
#> GDI method with PAA in test level iteration ...
#> Iter = 1/ 1, 6 items have changed, ΔPVAF=1.37410
print(validation.obj)
#> Qval version1.2.2(2025-05-02)
#> ==============================================
#>
#> Suggested Q-matrix:
#>
#> A1 A2 A3
#> item 1 0 1 0
#> item 2 1 0 1
#> item 3 1 0* 1
#> item 4 0 0 1
#> item 5 1 0 1
#> item 6 0* 0* 1*
#> item 7 0 0* 1
#> item 8 0 1 0
#> item 9 0 0 1
#> item 10 0* 1* 1
#> item 11 1 1 0*
#> item 12 0 1 1
#> item 13 1 0 0
#> item 14 0 0 1
#> item 15 1 0* 0
#> item 16 0 1 1
#> item 17 1 1 1
#> item 18 1 0 1
#> item 19 0 1 0
#> item 20 1 1 1
#> Note: * denotes a modified element.
################################################################
# Example 3: print a sim.data object #
################################################################
set.seed(123)
Q2 <- sim.Q(3, 10)
data.obj2 <- sim.data(Q = Q2, N = 1000)
#> distribute = uniform
#> model = GDINA
#> number of attributes: 3
#> number of items: 10
#> num of examinees: 1000
#> average of P0 = 0.174
#> average of P1 = 0.889
print(data.obj2)
#> Qval version1.2.2(2025-05-02)
#> ==============================================
#> Number of items = 10
#> Number of attributes = 3
#> Number of individuals = 1000
#> To extract components, use the method extract.
################################################################
# Example 4: print summary objects #
################################################################
summary.CDM.obj <- summary(CDM.obj)
print(summary.CDM.obj)
#> ==============================================
#> Number of items = 20
#> Number of attributes = 3
#> Number of individuals = 500
#>
#> Model Fit:
#> Deviance npar AIC BIC
#> 9090.455 89.000 9268.455 9643.555
#>
#> Distribution of Alpha Patterns:
#> 000 001 010 011 100 101 110 111
#> freq 96 17 124 135 4 11 33 80
#> prop 0.192 0.034 0.248 0.27 0.008 0.022 0.066 0.16
summary.val.obj <- summary(validation.obj)
print(summary.val.obj)
#> ==============================================
#>
#> Suggested Q-matrix:
#>
#> A1 A2 A3
#> item 1 0 1 0
#> item 2 1 0 1
#> item 3 1 0* 1
#> item 4 0 0 1
#> item 5 1 0 1
#> item 6 0* 0* 1*
#> item 7 0 0* 1
#> item 8 0 1 0
#> item 9 0 0 1
#> item 10 0* 1* 1
#> item 11 1 1 0*
#> item 12 0 1 1
#> item 13 1 0 0
#> item 14 0 0 1
#> item 15 1 0* 0
#> item 16 0 1 1
#> item 17 1 1 1
#> item 18 1 0 1
#> item 19 0 1 0
#> item 20 1 1 1
#> Note: * denotes a modified element.
summary.sim.obj <- summary(data.obj2)
print(summary.sim.obj)
#> ==============================================
#> Number of items = 10
#> Number of attributes = 3
#> Number of individuals = 1000
#>
#> Distribution of Alpha Patterns:
#> 000 001 010 011 100 101 110 111
#> freq 126 116 118 137 130 119 121 133
#> prop 0.126 0.116 0.118 0.137 0.13 0.119 0.121 0.133
# }