Estimate parameters in a probit latent variable model via a composite likelihood decomposition.

complik(
  x,
  data,
  k = 2,
  type = c("all", "nearest"),
  pairlist,
  messages = 0,
  estimator = "normal",
  quick = FALSE,
  ...
)

Arguments

x

lvm-object

data

data.frame

k

Size of composite groups

type

Determines number of groups. With type="nearest" (default) only neighboring items will be grouped, e.g. for k=2 (y1,y2),(y2,y3),... With type="all" all combinations of size k are included

pairlist

A list of indices specifying the composite groups. Optional argument which overrides k and type but gives complete flexibility in the specification of the composite likelihood

messages

Control amount of messages printed

estimator

Model (pseudo-likelihood) to use for the pairs/groups

quick

If TRUE the parameter estimates are calculated but all additional information such as standard errors are skipped

...

Additional arguments parsed on to lower-level functions

Value

An object of class estimate.complik inheriting methods from lvm

See also

estimate

Author

Klaus K. Holst

Examples

m <- lvm(c(y1,y2,y3)~b*x+1*u[0],latent=~u) ordinal(m,K=2) <- ~y1+y2+y3 d <- sim(m,50,seed=1) if (requireNamespace("mets", quietly=TRUE)) { e1 <- complik(m,d,control=list(trace=1),type="all") }
#> 0: 194.34186: 0.604480 0.584480 0.484480 0.00000 0.900000 #> 1: 163.15053: 0.540600 0.484448 0.209475 0.939309 1.06729 #> 2: 160.91018: 0.418339 0.334648 -0.188107 1.05711 1.14818 #> 3: 160.77829: 0.376957 0.300830 -0.155237 1.00706 1.18949 #> 4: 160.76097: 0.369541 0.291889 -0.159621 1.03751 1.20710 #> 5: 160.74923: 0.370653 0.293179 -0.167035 1.02015 1.23923 #> 6: 160.73532: 0.376191 0.296936 -0.168307 1.04656 1.26468 #> 7: 160.72558: 0.379780 0.299688 -0.168631 1.04345 1.30158 #> 8: 160.72091: 0.369539 0.303131 -0.165350 1.05346 1.33569 #> 9: 160.71897: 0.382701 0.295562 -0.170712 1.05926 1.33925 #> 10: 160.71733: 0.379230 0.296353 -0.167731 1.05972 1.35609 #> 11: 160.71701: 0.379746 0.299743 -0.170560 1.06152 1.35778 #> 12: 160.71683: 0.380300 0.298684 -0.169147 1.06113 1.36250 #> 13: 160.71663: 0.380886 0.299718 -0.170369 1.06381 1.36647 #> 14: 160.71650: 0.380928 0.300399 -0.170596 1.06425 1.37148 #> 15: 160.71645: 0.381806 0.299785 -0.170466 1.06543 1.37630 #> 16: 160.71643: 0.381561 0.300967 -0.170583 1.06589 1.37944 #> 17: 160.71643: 0.381542 0.300509 -0.170678 1.06575 1.37858 #> 18: 160.71643: 0.381620 0.300565 -0.170579 1.06575 1.37852 #> 19: 160.71643: 0.381587 0.300550 -0.170619 1.06575 1.37856 #> 20: 160.71643: 0.381587 0.300550 -0.170619 1.06575 1.37856