twostageCV {lava} | R Documentation |
Cross-validated two-stage estimator for non-linear SEM
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, ...)
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 |
## 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) ## End(Not run)