twostageCV function

Cross-validated two-stage estimator

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

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
}