convergence.stats: Computes statistics to assess convergence of the nominal...
In pleLMA: Pseudo-Likelihood Estimation of Log-Multiplicative Association Models

Description Usage Arguments Value Examples

For the nominal model, convergence statistics are computed for each item, as well as the algorithm as a whole. The main argument is the history or log from fitting item regressions. The convergence statistics are the differences between current values of the log likelihoods and item parameter estimates and those from the previous iteration. The maximum over item of the differences of the log likelihood values is used to determine convergence of the pseudo-likelihood algorithm. This function is used internally, but it can also be used after fitting a model to examine how many iterations are required for parameter estimates to get close to the final values and whether any item parameters are still changing.

1	convergence.stats(item.log, nitems, nless, LambdaName, NuName)

`item.log`	Iteration history of items' log likelihoods and parameter estimates
`nitems`	Number of items
`nless`	Number of unique marginal terms (i.e., lambdas) and unique category scale values(i.e., nus)
`LambdaName`	Names of lambdas in item regressions
`NuName`	Names of nu in item regressions

diff.last Differences between item loglikes & parameters on last two iterations

criterion.loglike Maximum over items of the absolute value of LogLike differences

criterion.items Sum of item differences of item parameters

 # 9 items from dass data for 250 cases
 data(dass)
 inData <- dass[1:250,c("d1", "d2", "d3", "a1","a2","a3","s1","s2","s3")]

#---   input for uni-dimensional
 inTraitAdj  <- matrix(1, nrow=1, ncol=1)
 inItemTraitAdj <- matrix(1, nrow=9, ncol=1)

 #--- Uni-dimensional Nominal Model
 n1 <- ple.lma(inData, model.type="nominal", inItemTraitAdj,inTraitAdj, tol=1e-02)

# Since this function in internal to fit.nominal, need to also run
 s <- set.up(inData, model.type='nominal', inTraitAdj, inItemTraitAdj)

 convergence.stats(n1$item.log, n1$nitems, n1$nless, s$LambdaName, s$NuName)

#--- Multidimensional models
#--- re-define inTraitAdj and inItemTraitAdj for 3 dimensional models
  inTraitAdj  <- matrix(1, nrow=3, ncol=3)

  dpress <- matrix(c(1,0,0), nrow=3, ncol=3, byrow=TRUE)
  anxiety <- matrix(c(0,1,0), nrow=3, ncol=3, byrow=TRUE)
  stress <- matrix(c(0,0,1), nrow=3, ncol=3, byrow=TRUE)
  das <- list(dpress, anxiety, stress)
  inItemTraitAdj <- rbind(das[[1]], das[[2]], das[[3]])

#--- 3 dimensional nominal
  n3 <- ple.lma(inData, model.type="nominal", inItemTraitAdj, inTraitAdj, tol=1e-03)
  s <- set.up(inData, model.type='nominal', inTraitAdj, inItemTraitAdj)
  convergence.stats(n3$item.log, n3$nitems, n3$nless, s$LambdaName, s$NuName)