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#                       L10 - Computerized adaptive testing                        #
#                                    NMST570                                       #
#                             Last update: 06/01/2020                              #
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# Slides available at http://www.cs.cas.cz/martinkova/NMST570.html
# HW assignment available at http://www.cs.cas.cz/hladka/documents/NMST570_HW10.pdf

####################################################################################
# Loading packages #################################################################
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library(mirtCAT)
options(stringsAsFactors = FALSE)

####################################################################################
# Create simple non-adaptive interface #############################################
####################################################################################

# Questions
questions <- c("Building CATs with mirtCAT is difficult.",
               "Building tests with mirtCAT requires a lot of coding.",
               "I would use mirtCAT in my research.")

# Potential options for each item
options <- matrix(c("Strongly Disagree", "Disagree", "Neutral", "Agree", "Strongly Agree"),
                  nrow = 3, ncol = 5, byrow = TRUE)


df <- data.frame(Question = questions, Option = options, Type = "radio")
df

# Run the mirtCAT web interface and store results
?mirtCAT
result1 <- mirtCAT(df = df)

print(result1)
summary(result1)

#---------------------------------------------
# Ex. 1.1
#---------------------------------------------

# Create your own non-adaptive interface with at least 3 questions
# You can use different types of responses 
# (see argument df and Type in help of mirtCAT)


####################################################################################
# Adding the demographics page #####################################################
####################################################################################

# Include a demographics page to collect occupation and gender variables
demographics <- list(textInput(inputId = 'occupation',
                               label = 'What is your occupation?',
                               value = ''),
                     selectInput(inputId = 'gender',
                                 label = 'Please select your gender.',
                                 choices = c('', 'Male', 'Female', 'Other'),
                                 selected = ''))

# Run the mirtCAT web interface and store results
result2 <- mirtCAT(df, 
                   shinyGUI = list(demographics = demographics,
                                   demographics_inputIDs = c('occupation', 'gender')))
summary(result2)

#---------------------------------------------
# Ex. 1.2
#---------------------------------------------

# Add some demographics into your own interface from Ex. 1
# Change title and authors via argument shinyGUI argument:
# e.g. shinyGUI(title = "My title",
#               authors = "Me")
?mirtCAT

####################################################################################
# Unidimensional CAT ###############################################################
####################################################################################

# Define IRT item parameters (not IRT parameterization!)
set.seed(1234)
### Number of items
nitems <- 100
### Item names
itemnames <- paste0('Item.', 1:nitems)
### Discrimination
a <- matrix(round(rlnorm(nitems, .2, .3), 2))
### Location parameter ("difficulty")
d <- matrix(round(rnorm(nitems), 2))
### Parameters, pseudo-guessing 0.2 for all items
(pars <- data.frame(a1 = a, d = d, g = 0.2))
### Create 3PL model - mirt object
mod <- generate.mirt_object(pars, '3PL')

# Define questions and possible answers
# Math questions for addition
questions <- answers <- character(nitems)
choices <- matrix('a', nitems, 5)
spacing <- floor(d - min(d)) + 1 # easier items have more variation

for(i in 1:nitems){
  # generate number from 1 to 100
  n1 <- sample(1:100, 1)
  # generate number from 101 to 200
  n2 <- sample(101:200, 1)
  # sum generated numbers 
  ans <- n1 + n2
  # create question
  questions[i] <- paste0(n1, ' + ', n2, ' = ?')
  answers[i] <- as.character(ans)
  # create distractors
  ch <- ans + sample(c(-5:-1, 1:5) * spacing[i, ], 5)
  ch[sample(1:5, 1)] <- ans
  choices[i, ] <- as.character(ch)
}


df <- data.frame(Questions = questions, 
                 Answer = answers, 
                 Option = choices, 
                 Type = 'radio')
head(df)

# Run the mirtCAT web interface and store results
my_result <- mirtCAT(df, mod, 
                     method = 'EAP', 
                     criteria = 'MI', 
                     start_item = 1,
                     design = list(min_SEM = 0.6, min_items = 10))
print(my_result)
plot(my_result)
summary(my_result)

#---------------------------------------------
# Ex. 1.3
#---------------------------------------------

# Run the previous CAT and answer following questions:
# 1. How many items did you answer until the stopping criteria were met?
# 2. Which stopping criterion was met?
# 3. What is your estimated ability theta? What is its standard error?

####################################################################################
# Offline example ##################################################################
####################################################################################

# Using previous assingment and framework, generate random response pattern
set.seed(123)

pattern <- generate_pattern(mod, Theta = 1)

print(pattern)

result4 <- mirtCAT(mo = mod, 
                   local_pattern = pattern, 
                   start_item = 1,
                   criteria = "MEPV",
                   design = list(min_SEM = 0.6, min_items = 10))

print(result4)
summary(result4)
plot(result4)

#---------------------------------------------
# Ex. 1.4
#---------------------------------------------

# Run the previous CAT and answer following questions:
# 1. How many items were assigned? Which items were assigned?
# 2. Provide estimate of ability theta and its standard error.

####################################################################################
# Multidimensional example #########################################################
####################################################################################

# This example demonstrates how a multidimensional IRT test can be scored and 
# modified for offline and on-line use. As well, it compares the effect of scoring 
# the entire test given all possible responses to the use of a CAT scheme. The final 
# section demonstrates how various elements in the GUI can be modified for more 
# customized presentations.

# The following code-block defines 100 items for a two factor math test measuring 
# addition and multiplication.

# define population IRT parameters
set.seed(1234)
nitems <- 120
itemnames <- paste0("Item.", 1:nitems)
a <- matrix(c(rlnorm(nitems/2, 0.2, 0.3), rnorm(nitems/4, 0, 0.3), numeric(nitems/2),
              rnorm(nitems/4, 0, 0.3), rlnorm(nitems/2, 0.2, 0.3)), nitems)
d <- matrix(rnorm(nitems))
pars <- data.frame(a, d)
colnames(pars) <- c("a1", "a2", "d")

# addition and multiplication are positively correlated
trait_cov <- matrix(c(1, 0.5, 0.5, 1), 2, 2)

# create mirt_object
mod <- generate.mirt_object(pars, itemtype = "2PL", latent_covariance = trait_cov)

# math items definitions addition for one factor and multiplication for the other
questions <- answers <- character(nitems)
options <- matrix("", nitems, 5)
spacing <- floor(d - min(d)) + 1  # easier items have more variation

for (i in 1:nitems) {
  if (i < 31) {
    # addition
    n1 <- sample(1:100, 1)
    n2 <- sample(101:200, 1)
    ans <- n1 + n2
    questions[i] <- paste0(n1, " + ", n2, " = ?")
  } else if (i < 61) {
    # addition and multiplication
    n1 <- sample(1:50, 1)
    n2 <- sample(51:100, 1)
    m1 <- sample(1:10, 1)
    m2 <- sample(1:10, 1)
    ans <- n1 + n2 + m1 * m2
    questions[i] <- paste0(n1, " + ", n2, " + ", m1, " * ", m2, " = ?")
  } else if (i < 91) {
    # multiplication and addition
    n1 <- sample(1:10, 1)
    n2 <- sample(1:10, 1)
    m1 <- sample(1:25, 1)
    m2 <- sample(1:25, 1)
    ans <- n1 + n2 + m1 * m2
    questions[i] <- paste0(m1, " * ", m2, " + ", n1, " + ", n2, " = ?")
  } else {
    # multiplication
    m1 <- sample(1:50, 1)
    m2 <- sample(1:50, 1)
    ans <- n1 + n2 + m1 * m2
    questions[i] <- paste0(m1, " * ", m2, " = ?")
  }
  answers[i] <- as.character(ans)
  ch <- ans + sample(c(-5:-1, 1:5) * spacing[i, ], 5)
  ch[sample(1:5, 1)] <- ans
  options[i, ] <- as.character(ch)
}

# load list of items and their answers
df <- data.frame(Question = questions, 
                 Option = options, 
                 Answer = answers,
                 Type = "radio")
head(df)
tail(df)

set.seed(1234)

# theta is now two-dimensional (addition and multiplication)
pat <- generate_pattern(mo = mod, Theta = c(1, 0.5), df = df)
head(pat)

#---------------------------------------------
# Set min_items to number of items in bank - fixed linear testing
result5a <- mirtCAT(df = df, mo = mod, 
                  local_pattern = pat, 
                  design = list(min_items = 120))
print(result5a)
summary(result5a)
plot(result5a, scales = list(x = list(at = NULL)))

#---------------------------------------------
# MCAT using D-rule
result5b <- mirtCAT(df = df, mo = mod, 
                   criteria = "Drule", 
                   local_pattern = pat,
                   start_item = "Drule", 
                   design = list(min_SEM = 0.3))
print(result5b)
summary(result5b)
plot(result5b, scales = list(x = list(at = NULL)))

#---------------------------------------------
# Ex. 1.5
#---------------------------------------------

# Run the previous MCAT and answer following questions:
# 1. How many items were assigned using fixed linear testing and MCAT?
# 2. Provide estimates of ability theta and their standard error using fixed linear testing and MCAT.