library(psych)
library(ltm)
## Loading required package: MASS
## Loading required package: msm
## Loading required package: polycor
##
## Attaching package: 'polycor'
## The following object is masked from 'package:psych':
##
## polyserial
##
## Attaching package: 'ltm'
## The following object is masked from 'package:psych':
##
## factor.scores
library(knitr)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following object is masked from 'package:MASS':
##
## select
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
data<-read.csv("/home/heru/Desktop/Mybfi.csv", header=TRUE, sep=",")
bfidata<-data[2:6]
bfidata2<-na.omit(bfidata)
#Global item
fit1 <- grm(bfidata2, constrained = TRUE)
fit2 <- grm(bfidata2, constrained = FALSE)
#Uji model unconstrained vs constrained)
anova(fit1,fit2)
##
## Likelihood Ratio Table
## AIC BIC log.Lik LRT df p.value
## fit1 40608.97 40768.39 -20277.49
## fit2 38783.65 38966.69 -19360.83 1833.32 4 <0.001
thetagrm<-factor.scores.grm(fit2, resp.patterns = bfidata2)
bfidata2$score <- rowSums(bfidata2[,1:5])
theta2<-thetagrm$score.dat$z1
mydf1 <- data.frame(bfidata2, theta = theta2)
options(digits = 3)
knitr::kable(subset(mydf1,bfidata2$score == 25), caption = 'There are several different patterns all with a score of 25')
There are several different patterns all with a score of 25
| 2354 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2355 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2356 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2357 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2358 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2359 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2360 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2361 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2362 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2363 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2364 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2365 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2366 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2367 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2368 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2369 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2370 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2371 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2372 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2373 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2374 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2375 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2376 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2377 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2378 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2379 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2393 |
1 |
6 |
6 |
6 |
6 |
25 |
-1.583 |
| 2394 |
5 |
6 |
5 |
4 |
5 |
25 |
-0.215 |
| 2395 |
1 |
6 |
6 |
6 |
6 |
25 |
-1.583 |
| 2396 |
6 |
1 |
6 |
6 |
6 |
25 |
-0.687 |
| 2397 |
1 |
6 |
6 |
6 |
6 |
25 |
-1.583 |
| 2398 |
1 |
6 |
6 |
6 |
6 |
25 |
-1.583 |
| 2399 |
5 |
5 |
5 |
5 |
5 |
25 |
-0.040 |
| 2400 |
3 |
6 |
5 |
6 |
5 |
25 |
-0.495 |
| 2401 |
1 |
6 |
6 |
6 |
6 |
25 |
-1.583 |
| 2402 |
1 |
6 |
6 |
6 |
6 |
25 |
-1.583 |
| 2403 |
4 |
5 |
5 |
6 |
5 |
25 |
-0.188 |