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