summary.CDM.RdSummary method for CDM objects
# S3 method for class 'CDM'
summary(object, ...)An object of class "CDM" returned by the CDM function.
Additional arguments.
A list containing summary statistics of the CDM.
set.seed(123)
library(Qval)
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$dat, Q, model = "GDINA",
method = "EM", maxitr = 2000, verbose = 1)
#>
Iter = 1 Max. abs. change = 0.57951 Deviance = 10238.69
Iter = 2 Max. abs. change = 0.09509 Deviance = 8501.84
Iter = 3 Max. abs. change = 0.03995 Deviance = 8478.24
Iter = 4 Max. abs. change = 0.01792 Deviance = 8477.13
Iter = 5 Max. abs. change = 0.02604 Deviance = 8477.02
Iter = 6 Max. abs. change = 0.01142 Deviance = 8476.97
Iter = 7 Max. abs. change = 0.01740 Deviance = 8476.96
Iter = 8 Max. abs. change = 0.00686 Deviance = 8476.95
Iter = 9 Max. abs. change = 0.00466 Deviance = 8476.94
Iter = 10 Max. abs. change = 0.00253 Deviance = 8476.94
Iter = 11 Max. abs. change = 0.00030 Deviance = 8476.94
Iter = 12 Max. abs. change = 0.00014 Deviance = 8476.94
Iter = 13 Max. abs. change = 0.00016 Deviance = 8476.94
Iter = 14 Max. abs. change = 0.00760 Deviance = 8476.94
Iter = 15 Max. abs. change = 0.00246 Deviance = 8476.94
Iter = 16 Max. abs. change = 0.00502 Deviance = 8476.94
Iter = 17 Max. abs. change = 0.00017 Deviance = 8476.94
Iter = 18 Max. abs. change = 0.00109 Deviance = 8476.94
Iter = 19 Max. abs. change = 0.00004 Deviance = 8476.94
summary(CDM.obj)
#> Call:
#> CDM(Y = data.obj$dat, Q = Q, model = "GDINA",
#> method = "EM", maxitr = 2000,
#> verbose = 1)
#>
#> ==============================================
#> 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