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, ... )
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 level functions |
## 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
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