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Model searching

Source: R/modelsearch.R
modelsearch.Rd

Performs Wald or score tests

Usage

modelsearch(x, k = 1, dir = "forward", type = "all", ...)

Arguments

x

lvmfit-object

k

Number of parameters to test simultaneously. For equivalence the number of additional associations to be added instead of rel.

dir

Direction to do model search. "forward" := add associations/arrows to model/graph (score tests), "backward" := remove associations/arrows from model/graph (wald test)

type

If equal to 'correlation' only consider score tests for covariance parameters. If equal to 'regression' go through direct effects only (default 'all' is to do both)

...

Additional arguments to be passed to the low level functions

Value

Matrix of test-statistics and p-values

See also

compare, equivalence

Author

Klaus K. Holst

Examples


m <- lvm();
regression(m) <- c(y1,y2,y3) ~ eta; latent(m) <- ~eta
regression(m) <- eta ~ x
m0 <- m; regression(m0) <- y2 ~ x
dd <- sim(m0,100)[,manifest(m0)]
e <- estimate(m,dd);
modelsearch(e,messages=0)
#>  Score: S P(S>s) Index  holm BH    
#>  0.003352 0.9538 y1~~y2 1    0.9538
#>  0.003352 0.9538 y1~y2  1    0.9538
#>  0.003352 0.9538 y2~y1  1    0.9538
#>  0.003352 0.9538 y3~~x  1    0.9538
#>  0.003352 0.9538 y3~x   1    0.9538
#>  0.003352 0.9538 x~y3   1    0.9538
#>  0.221    0.6383 y1~~x  1    0.9538
#>  0.221    0.6383 y1~x   1    0.9538
#>  0.221    0.6383 x~y1   1    0.9538
#>  0.221    0.6383 y2~~y3 1    0.9538
#>  0.221    0.6383 y2~y3  1    0.9538
#>  0.221    0.6383 y3~y2  1    0.9538
#>  0.3867   0.534  y1~~y3 1    0.9538
#>  0.3867   0.534  y1~y3  1    0.9538
#>  0.3867   0.534  y3~y1  1    0.9538
#>  0.3867   0.534  y2~~x  1    0.9538
#>  0.3867   0.534  y2~x   1    0.9538
#>  0.3867   0.534  x~y2   1    0.9538
modelsearch(e,messages=0,type="cor")
#>  Score: S P(S>s) Index  holm BH    
#>  0.003352 0.9538 y1~~y2 1    0.9538
#>  0.003352 0.9538 y3~~x  1    0.9538
#>  0.221    0.6383 y1~~x  1    0.9538
#>  0.221    0.6383 y2~~y3 1    0.9538
#>  0.3867   0.534  y1~~y3 1    0.9538
#>  0.3867   0.534  y2~~x  1    0.9538