Closed testing procedure

closed.testing(
  object,
  idx = seq_along(coef(object)),
  null = rep(0, length(idx)),
  ...
)

Arguments

object: estimate object
idx: Index of parameters to adjust for multiple testing
null: Null hypothesis value
...: Additional arguments

Examples

m <- lvm()
regression(m, c(y1,y2,y3,y4,y5,y6,y7)~x) <- c(0,0.25,0,0.25,0.25,0,0)
regression(m, to=endogenous(m), from="u") <- 1
variance(m,endogenous(m)) <- 1
set.seed(2)
d <- sim(m,200)
l1 <- lm(y1~x,d)
l2 <- lm(y2~x,d)
l3 <- lm(y3~x,d)
l4 <- lm(y4~x,d)
l5 <- lm(y5~x,d)
l6 <- lm(y6~x,d)
l7 <- lm(y7~x,d)

(a <- merge(l1,l2,l3,l4,l5,l6,l7,subset=2))
#>     Estimate Std.Err      2.5%  97.5%   P-value
#> x   -0.02201 0.09932 -0.216676 0.1727 8.246e-01
#> ___                                            
#> x.1  0.37231 0.11565  0.145637 0.5990 1.285e-03
#> ___                                            
#> x.2  0.11982 0.11103 -0.097795 0.3374 2.805e-01
#> ___                                            
#> x.3  0.42234 0.09264  0.240763 0.6039 5.143e-06
#> ___                                            
#> x.4  0.29344 0.12136  0.055578 0.5313 1.561e-02
#> ___                                            
#> x.5  0.20565 0.10618 -0.002458 0.4138 5.277e-02
#> ___                                            
#> x.6  0.05240 0.11817 -0.179216 0.2840 6.575e-01
if (requireNamespace("mets",quietly=TRUE)) {
   p.correct(a)
}
#> [1] 0.009453147
as.vector(closed.testing(a))
#> [1] 0.8246281477 0.0207936759 0.6158722827 0.0001020005 0.0821962107
#> [6] 0.1907676602 0.7835113360