Cross-validated two-stage estimator

Source: R/twostage.R

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
}
#> ────────────────────────────────────────────────────────────────────────────────
#> Selected mixture model: 1 component
#>       AIC1
#> 1 5130.210
#> 2 5132.707
#> ────────────────────────────────────────────────────────────────────────────────
#> Selected spline model degrees of freedom: 3
#> Knots: -2.674 -0.7956 1.082 2.96 
#> 
#>      RMSE(nfolds=2, rep=1)
#> df:1              5.353550
#> df:2              5.260141
#> df:3              4.851035
#> df:4              5.329716
#> df:5              6.220957
#> df:6              5.792509
#> ────────────────────────────────────────────────────────────────────────────────
#> 
#>                     Estimate Std. Error Z-value P-value
#> Regressions:                                           
#>    y~u1_1            1.38092                           
#>    y~u1_2            0.02123                           
#>    y~u1_3           -0.08440                           
#> Intercepts:                                            
#>    y                -0.33435                           
#> Residual Variances:                                    
#>    y                 1.61964