R/mleEst.R

In swnydick/catIrt: Simulate IRT-Based Computerized Adaptive Tests

mleEst <-
function(resp,                         # The vector of responses
         params,                       # The item parameters
         range = c(-6, 6),             # The integer to maximize over
         mod = c("brm", "grm"),        # The model
         ...)
{

# First turn params into a matrix:
  params <- rbind(params)
   
# And turn response into a matrix:
  resp <- {if( dim(params)[1] > 1) rbind(resp)  # ... --> turn it into a multi-column matrix,
           else                    cbind(resp)} # ... --> or a 1-column matrix
  
#~~~~~~~~~~~~~~~~~#
# Argument Checks #
#~~~~~~~~~~~~~~~~~#

# Make sure that the arguments are OK:

## 1 ## (Make sure that params and resp are ALL numeric)
  if(mode(params) != "numeric")
    stop("params need to be numeric")
    
  if(!is.null(resp) & mode(resp) != "numeric")
    stop("resp needs to be numeric")

## 2 ## (Make sure that the dimensions of params and response are equal)
  if(!is.null(resp) & (dim(resp)[2] != dim(params)[1]))
    stop("number of params does not match the length of resp")


#~~~~~~~~~~~~~~~~~~~~~~~~#
# Maximum Likelihood Est #
#~~~~~~~~~~~~~~~~~~~~~~~~#

# Indicate the lower/upper boundary of the search:
  if(is.null(range))
    range <- c(-6, 6)

  l <- range[1]; u <- range[2]
  
  est <- NULL # a vector for estimates
  
# Then, maximize the loglikelihood function over that interval for each person:
  likFun <- get(paste0("logLik.", mod))
  for(i in 1:dim(resp)[1]){
    est[i] <- optimize(likFun, lower = l, upper = u, maximum = TRUE,
                       u = resp[i, ], params = params,
                       type = "MLE")$max
  } # END for i LOOP
  
# And pull out the information as well as the SEM:
  info <- get(paste("FI.", mod, sep = ""))(params = params,
                                           theta  = est,
                                           type   = "observed",
                                           resp   = resp)
  
  list(theta = est, info = info$test, sem = info$sem)
  
} # END mleEst FUNCTION