uLRT {lazy.irt} | R Documentation |

Marginal likelihood will be maximized by the EM algorithm using ordinal_reg funcion and smn function in the M-step.

uLRT(Uc, U = NULL, groupvar = NULL, idvar = NULL, ncat = NULL, type = NULL, itemname = NULL, nclass = 5, V = NULL, fixeditems = NULL, baseform = 1, monotone = 0, rho = NULL, estrho = 0, alpha = rep(1, nclass), vmin = 1e-04, vmax = 1 - vmin, maxiter = 200, eps = 1e-07, epsd = 1e-06, maxiter2 = 1, eps2 = 0, nstrict = 9, maxiter22 = 5, minp1 = 0.1, maxabsparam = 20, SQUAREM = 3, nSQUAREM = 1, minalpha = -999, maxalpha = -1, always = 1, reset1 = 0, reset2 = 1, print = 2, plot = 0, smallP = 0, debug = 0)

`Uc` |
n x nitems+1 or +2 compressed item resopnse data frame in BILOG-MG's expanded format. (idvar and groupvar) |

`U` |
n x sum(ncat)+1 or +2 uncompressed irem response data frame |

`groupvar` |
name of the grouping varible contained as a column of Uc or NULL. |

`idvar` |
name of the id varible contained as a column of Uc or NULL |

`ncat` |
nitems x 1 # of categories for each item or fNULL. |

`type` |
nitems x 1 vector of item types consisting of:
"Bn" | "G" | "PN" | "P" |

`itemname` |
nitems x 1 vector of item names or NULL. |

`nclass` |
# of classes |

`V` |
npoitns x sum(ncat) initial value for item param |

`fixeditems` |
list of items whose item parameters are to be fixed |

`baseform` |
base form number whose theta distribution is
fixed at the values given in msn[baseform,] |

`monotone` |
= 1 to enforce monotonicity |

`rho` |
initial value of probability vector of each latent class. |

`estrho` |
= 1 to estimate multigroup theta means |

`alpha` |
= vector of Dirichle parameters of length nclass |

`vmin` |
minimum value of V |

`vmax` |
maximum value of V |

`maxiter` |
max # of iterations |

`eps` |
eps for the relative improvement of lmlh |

`epsd` |
eps for the max. abs. diff. of msn |

`maxiter2` |
max # of iterations for ordinal_reg and smn when llll <= nstrict. |

`eps2` |
eps for the relative improvement of llh in ordinal_reg and smn. |

`nstrict` |
maxiter2 will be reduced to maxiter22 after nstrict iterations. |

`maxiter22` |
max # of iterations for ordinal_reg and smn when llll > nstrict. |

`minp1` |
Minimum value of p1 parameter for type != "N" items. |

`maxabsparam` |
Maximum absolute value of parameters. |

`SQUAREM` |
= 3 : See the help of iSQUAREM in lazy.accel package. |

`nSQUAREM` |
when to star iSQUAREM |

`minalpha` |
= -999 : See the help of iSQUAREM in lazy.accel package. |

`maxalpha` |
= -1 : See the help of iSQUAREM in lazy.accel package. |

`always` |
= 0 : See the help of iSQUAREM in lazy.accel package. |

`reset1` |
= 1 : See the help of iSQUAREM in lazy.accel package. |

`reset2` |
= 2 : See the help of iSQUAREM in lazy.accel package. |

`print` |
= 1 to print the result |

`plot` |
= 1 to plot the estimated theta distributions |

`smallP` |
= Minimum value of P |

`debug` |
= 1 to print intermediate result |

*** Currently, polytomous items are not allowed. ******

*** Currently, multi-group analysis is not available.

Uc is the compressed data if it is n x nitems.

U is the uncompressed data if it is n x sum(ncat).

When U, instead of Uc, is given:

Item names come from itemname or paste("Q",1:nitems,sep="").

ncat must be given.

When Uc is given:

Item name comes from colnames(Uc) or itemname.

ncat can be calculated from Uc.

Note on the iSQUAREM:

Try always=1 with maxalpha=-1 or less first.

Changing to reset1=1 and reset2=2 or increasing nSQUAREM may help.

If it seems not working, use always=0 with maxalpha=1 or less.

Changing to reset1=1 and reset2=2 may help.

If all of the above fail, be patient and use SQUAREM=0.

A list of:
V Estimated Item Parameters in a data frame

rho Class distribution

converged = 1 if converged, = 0 otherwise.

lmlh Log Marginal LIkelihood maximized

iter_hist A list of iteration history

H Posterior distribution of theta given U.

id Id variable

Varadhan, R. and Roland, C.(2007) Simple and Globally Convergent Methods
for Accelerating the Convergence of Any EM Algorithm.
Scandinavian Journal of Statistics, Vol. 35: 335-353.

Shojima, K. (2008a) Neural test theory.
In K. Shigemasu, A. Okada, T. Imaizumi, & T. Hoshino (Eds.)
New trends in psychometrics. Universal Academy Press, Inc

# #### In the following examples, maxiter is set to 20 which is #### not large enough to obtain convergence. #### # # # set.seed(1701) param=paramB1[c(1:3,7:9,13:15),] thmin=-2; thmax=2; npoint=5 N=1000 # discrete theta # theta0=seq(thmin,thmax,length=npoint) theta0=c(-2, -1, 0, 2, 3) theta=unlist(lapply( theta0, rep, round(N/npoint) )) res2 <- gendataIRT( 1, paramB1, theta=theta, compress=1 ) Uc=as.data.frame(res2$U) ncat=res2$ncat type=res2$type nclass=5 res1 <- uLRT( Uc, nclass=nclass, estrho=1, monotone=1, alpha=20 , maxiter=20, plot=1, print=1 ) # normal theta Uc2 <- gendataIRT( 1, paramB1, npoints=N, thdist="rnorm", compress=1 )$U Uc2 <- as.data.frame(Uc2) nclass=5 res1 <- uLRT( Uc2, nclass=nclass, estrho=1, monotone=1 , maxiter=20, plot=1, print=1 )

[Package *lazy.irt* version 0.1.3 Index]