twostageCV: Cross-validated two-stage estimator

In lava: Latent Variable Models

Cross-validated two-stage estimator

Description

Cross-validated two-stage estimator for non-linear SEM

Usage

twostageCV(
  model1,
  model2,
  data,
  control1 = list(trace = 0),
  control2 = list(trace = 0),
  knots.boundary,
  nmix = 1:4,
  df = 1:9,
  fix = TRUE,
  std.err = TRUE,
  nfolds = 5,
  rep = 1,
  messages = 0,
  ...
)

Arguments

model1	model 1 (exposure measurement error model)
model2	model 2
data	data.frame
control1	optimization parameters for model 1
control2	optimization parameters for model 1
knots.boundary	boundary points for natural cubic spline basis
nmix	number of mixture components
df	spline degrees of freedom
fix	automatically fix parameters for identification (TRUE)
std.err	calculation of standard errors (TRUE)
nfolds	Number of folds (cross-validation)
rep	Number of repeats of cross-validation
messages	print information (>0)
...	additional arguments to lower

Examples

 ## Reduce Ex.Timings##'
m1 <- lvm( x1+x2+x3 ~ u, latent= ~u)
m2 <- lvm( y ~ 1 )
m <- functional(merge(m1,m2), y ~ u, value=function(x) sin(x)+x)
distribution(m, ~u1) <- uniform.lvm(-6,6)
d <- sim(m,n=500,seed=1)
nonlinear(m2) <- y~u1
if (requireNamespace('mets', quietly=TRUE)) {
  set.seed(1)
  val <- twostageCV(m1, m2, data=d, std.err=FALSE, df=2:6, nmix=1:2,
                  nfolds=2)
  val
}

lava documentation built on May 29, 2024, 3:10 a.m.