summary.RdGenerate concise summary statistics for objects created by the Qval package. The output is a named list tailored to the class of the input:
CDMcontains the original call, dataset dimensions, model fit object, and attribute-pattern distribution.
validationcontains the original call, suggested Q-matrix, and original Q-matrix.
sim.datacontains the original call, dataset dimensions, and attribute-pattern distribution.
# S3 method for class 'CDM'
summary(object, ...)
# S3 method for class 'validation'
summary(object, ...)
# S3 method for class 'sim.data'
summary(object, ...)An object of class CDM, validation, or sim.data.
Currently unused. Additional arguments are ignored.
A named list with class summary.<class> containing the components above.
A string capturing the original function invocation.
A numeric vector c(N, I, K) giving the number of examinees (\(N\)),
the number of items (\(I\)), and the number of attributes (\(K\)).
(CDM only) The fitted model object returned by CDM.
(CDM and sim.data) A data.frame of frequencies (freq) and proportions
(prop) of each attribute pattern.
(validation only) Suggested Q-matrix from validation.
(validation only) Original Q-matrix provided to sim.data.
summary(CDM): Summary method for CDM objects
summary(validation): Summary method for validation objects
summary(sim.data): Summary method for sim.data objects
set.seed(123)
library(Qval)
# \donttest{
################################################################
# Example 1: summary a CDM object #
################################################################
Q <- sim.Q(3, 20)
IQ <- list(P0 = runif(20, 0, 0.2), P1 = runif(20, 0.8, 1))
data.obj <- sim.data(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$dat, Q, model = "GDINA", method = "EM")
#>
Iter = 1 Max. abs. change = 0.57951 Deviance = 10238.69
Iter = 2 Max. abs. change = 0.09509 Deviance = 8501.84
Iter = 3 Max. abs. change = 0.03995 Deviance = 8478.24
Iter = 4 Max. abs. change = 0.01792 Deviance = 8477.13
Iter = 5 Max. abs. change = 0.02604 Deviance = 8477.02
Iter = 6 Max. abs. change = 0.01142 Deviance = 8476.97
Iter = 7 Max. abs. change = 0.01740 Deviance = 8476.96
Iter = 8 Max. abs. change = 0.00686 Deviance = 8476.95
Iter = 9 Max. abs. change = 0.00466 Deviance = 8476.94
Iter = 10 Max. abs. change = 0.00253 Deviance = 8476.94
Iter = 11 Max. abs. change = 0.00030 Deviance = 8476.94
Iter = 12 Max. abs. change = 0.00014 Deviance = 8476.94
Iter = 13 Max. abs. change = 0.00016 Deviance = 8476.94
Iter = 14 Max. abs. change = 0.00760 Deviance = 8476.94
Iter = 15 Max. abs. change = 0.00246 Deviance = 8476.94
Iter = 16 Max. abs. change = 0.00502 Deviance = 8476.94
Iter = 17 Max. abs. change = 0.00017 Deviance = 8476.94
Iter = 18 Max. abs. change = 0.00109 Deviance = 8476.94
Iter = 19 Max. abs. change = 0.00004 Deviance = 8476.94
summary(CDM.obj)
#> ==============================================
#> Number of items = 20
#> Number of attributes = 3
#> Number of individuals = 500
#>
#> Model Fit:
#> Deviance npar AIC BIC
#> 8476.939 87.000 8650.939 9017.610
#>
#> Distribution of Alpha Patterns:
#> 000 001 010 011 100 101 110 111
#> freq 97 20 120 145 4 13 32 69
#> prop 0.194 0.04 0.24 0.29 0.008 0.026 0.064 0.138
################################################################
# Example 2: summary a validation object #
################################################################
MQ <- sim.MQ(Q, 0.1)
#> rate of mis-specifications = 0.1
#> rate of over-specifications = 0.07
#> rate of under-specifications = 0.03
CDM.obj2 <- CDM(data.obj$dat, MQ)
#>
Iter = 1 Max. abs. change = 0.55372 Deviance = 11376.15
Iter = 2 Max. abs. change = 0.20531 Deviance = 8796.31
Iter = 3 Max. abs. change = 0.09493 Deviance = 8725.28
Iter = 4 Max. abs. change = 0.02365 Deviance = 8719.03
Iter = 5 Max. abs. change = 0.01674 Deviance = 8718.11
Iter = 6 Max. abs. change = 0.01408 Deviance = 8717.82
Iter = 7 Max. abs. change = 0.01043 Deviance = 8717.68
Iter = 8 Max. abs. change = 0.00706 Deviance = 8717.62
Iter = 9 Max. abs. change = 0.00454 Deviance = 8717.58
Iter = 10 Max. abs. change = 0.00201 Deviance = 8717.57
Iter = 11 Max. abs. change = 0.00717 Deviance = 8717.56
Iter = 12 Max. abs. change = 0.00277 Deviance = 8717.56
Iter = 13 Max. abs. change = 0.00088 Deviance = 8717.56
Iter = 14 Max. abs. change = 0.00025 Deviance = 8717.55
Iter = 15 Max. abs. change = 0.00016 Deviance = 8717.55
Iter = 16 Max. abs. change = 0.00035 Deviance = 8717.55
Iter = 17 Max. abs. change = 0.00021 Deviance = 8717.55
Iter = 18 Max. abs. change = 0.00318 Deviance = 8717.55
Iter = 19 Max. abs. change = 0.00007 Deviance = 8717.55
val.obj <- validation(data.obj$dat, MQ, CDM.obj2, method = "GDI")
#> GDI method with PAA in test level iteration ...
#> Iter = 1/ 1, 9 items have changed, ΔPVAF=1.04934
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.
################################################################
# Example 3: summary a sim.data object #
################################################################
data.obj2 <- sim.data(Q = sim.Q(3, 10), N = 1000)
#> distribute = uniform
#> model = GDINA
#> number of attributes: 3
#> number of items: 10
#> num of examinees: 1000
#> average of P0 = 0.134
#> average of P1 = 0.865
summary(data.obj2)
#> ==============================================
#> 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 134 106 119 117 127 139 130 128
#> prop 0.134 0.106 0.119 0.117 0.127 0.139 0.13 0.128
# }