4.7 R-RUM Estimation

To estimate the R-RUM, call GDINA function again and specify the data and Q-matrix as the first two arguments.

Code

#Fit the data using R-RUM
fit.rrum <- GDINA::GDINA(dat = data1, Q = Q1, model = "RRUM", verbose = 0)

To print some general model estimation information, type fit.rrum in Rstudio console:

Code

fit.rrum

## Call:
## GDINA(dat = data1, Q = Q1, model = "RRUM", verbose = 0)
## 
## GDINA version 2.9.3 (2022-08-13) 
## ===============================================
## Data
## -----------------------------------------------
## # of individuals    groups    items         
##              837         1       15
## ===============================================
## Model
## -----------------------------------------------
## Fitted model(s)       = RRUM 
## Attribute structure   = saturated 
## Attribute level       = Dichotomous 
## ===============================================
## Estimation
## -----------------------------------------------
## Number of iterations  = 49 
## 
## For the final iteration:
##   Max abs change in item success prob. = 0.0001 
##   Max abs change in mixing proportions = 0.0000 
##   Change in -2 log-likelihood          = 0.0004 
##   Converged?                           = TRUE 
## 
## Time used             = 0.4454 secs

To extract item parameters, we can use coef function, as in

Code

coef(fit.rrum)

## $`Item 1`
## P(0) P(1) 
## 0.93 0.98 
## 
## $`Item 2`
## P(0) P(1) 
## 0.27 0.83 
## 
## $`Item 3`
## P(0) P(1) 
## 0.54 0.80 
## 
## $`Item 4`
## P(0) P(1) 
## 0.48 0.88 
## 
## $`Item 5`
## P(00) P(10) P(01) P(11) 
##  0.33  0.65  0.46  0.92 
## 
## $`Item 6`
## P(00) P(10) P(01) P(11) 
##  0.11  0.47  0.17  0.73 
## 
## $`Item 7`
## P(00) P(10) P(01) P(11) 
##  0.15  0.33  0.36  0.80 
## 
## $`Item 8`
## P(00) P(10) P(01) P(11) 
##  0.20  0.64  0.25  0.80 
## 
## $`Item 9`
## P(00) P(10) P(01) P(11) 
##  0.17  0.40  0.29  0.68 
## 
## $`Item 10`
## P(00) P(10) P(01) P(11) 
##  0.37  0.73  0.37  0.72 
## 
## $`Item 11`
## P(00) P(10) P(01) P(11) 
##  0.50  0.64  0.71  0.90 
## 
## $`Item 12`
## P(00) P(10) P(01) P(11) 
##  0.19  0.36  0.36  0.70 
## 
## $`Item 13`
## P(00) P(10) P(01) P(11) 
##  0.14  0.24  0.34  0.59 
## 
## $`Item 14`
## P(000) P(100) P(010) P(001) P(110) P(101) P(011) P(111) 
##   0.14   0.52   0.17   0.14   0.64   0.51   0.17   0.63 
## 
## $`Item 15`
## P(000) P(100) P(010) P(001) P(110) P(101) P(011) P(111) 
##   0.15   0.27   0.21   0.22   0.39   0.42   0.32   0.59

To obtain delta parameters, specify what = “delta”:

Code

coef(fit.rrum, what = "delta")

## $`Item 1`
##     d0     d1 
## -0.070  0.049 
## 
## $`Item 2`
##   d0   d1 
## -1.3  1.1 
## 
## $`Item 3`
##    d0    d1 
## -0.62  0.39 
## 
## $`Item 4`
##    d0    d1 
## -0.73  0.60 
## 
## $`Item 5`
##    d0    d1    d2 
## -1.12  0.69  0.34 
## 
## $`Item 6`
##    d0    d1    d2 
## -2.22  1.46  0.44 
## 
## $`Item 7`
##    d0    d1    d2 
## -1.93  0.81  0.90 
## 
## $`Item 8`
##    d0    d1    d2 
## -1.62  1.17  0.24 
## 
## $`Item 9`
##    d0    d1    d2 
## -1.77  0.84  0.54 
## 
## $`Item 10`
##     d0     d1     d2 
## -0.983  0.670 -0.017 
## 
## $`Item 11`
##    d0    d1    d2 
## -0.69  0.24  0.35 
## 
## $`Item 12`
##    d0    d1    d2 
## -1.68  0.66  0.65 
## 
## $`Item 13`
##    d0    d1    d2 
## -1.99  0.54  0.91 
## 
## $`Item 14`
##     d0     d1     d2     d3 
## -1.956  1.308  0.205 -0.022 
## 
## $`Item 15`
##    d0    d1    d2    d3 
## -1.93  0.62  0.35  0.43

We can also obtain the RRUM original parameters, using the following code:

Code

coef(fit.rrum, what = "rrum")

## $`Item 1`
##  pi*   r1 
## 0.98 0.95 
## 
## $`Item 2`
##  pi*   r1 
## 0.83 0.32 
## 
## $`Item 3`
##  pi*   r1 
## 0.80 0.68 
## 
## $`Item 4`
##  pi*   r1 
## 0.88 0.55 
## 
## $`Item 5`
##  pi*   r1   r2 
## 0.92 0.50 0.71 
## 
## $`Item 6`
##  pi*   r1   r2 
## 0.73 0.23 0.64 
## 
## $`Item 7`
##  pi*   r1   r2 
## 0.80 0.44 0.41 
## 
## $`Item 8`
##  pi*   r1   r2 
## 0.80 0.31 0.79 
## 
## $`Item 9`
##  pi*   r1   r2 
## 0.68 0.43 0.58 
## 
## $`Item 10`
##  pi*   r1   r2 
## 0.72 0.51 1.02 
## 
## $`Item 11`
##  pi*   r1   r2 
## 0.90 0.79 0.71 
## 
## $`Item 12`
##  pi*   r1   r2 
## 0.70 0.51 0.52 
## 
## $`Item 13`
##  pi*   r1   r2 
## 0.59 0.58 0.40 
## 
## $`Item 14`
##  pi*   r1   r2   r3 
## 0.63 0.27 0.81 1.02 
## 
## $`Item 15`
##  pi*   r1   r2   r3 
## 0.59 0.54 0.70 0.65