lava (version 1.6.2)

twostageCV: 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, mc.cores = 1, k = 1:4,
  nknots = 1:9, fix = TRUE, std.err = TRUE, nfolds = 5, rep = 1, ...)

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

mc.cores

number of cores to use for parallel computations

k

number of mixture components

nknots

number of knots

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

...

additional arguments to lower level functions

Examples

# NOT RUN {
 ## Reduce Ex.Timings
m1 <- lvm( x1+x2+x3 ~ u1, latent= ~u1)
m2 <- lvm( y1+y2+y3 ~ u2, latent= ~u2)
m <- functional(merge(m1,m2), u2~u1, f=function(x) sin(x)+x)
n <- 200
distribution(m, ~u1) <- uniform.lvm(-6,6)
d <- sim(m,n=200,seed=1) 
val <- twostageCV(m1,m2,data=d, std.err=FALSE,  nknots=2:5, K=1:3, mc.cores=1, nfolds=5)
# }