Performs Likelihood-ratio, Wald and score tests

compare(object, ...)

Arguments

object

lvmfit-object

...

Additional arguments to low-level functions

Value

Matrix of test-statistics and p-values

See also

Author

Klaus K. Holst

Examples

m <- lvm(); regression(m) <- c(y1,y2,y3) ~ eta; latent(m) <- ~eta regression(m) <- eta ~ x m2 <- regression(m, c(y3,eta) ~ x) set.seed(1) d <- sim(m,1000) e <- estimate(m,d) e2 <- estimate(m2,d) compare(e)
#> #> - Likelihood ratio test - #> #> data: #> chisq = 2.7373, df = 2, p-value = 0.2544 #> sample estimates: #> log likelihood (model) log likelihood (saturated model) #> -5045.863 -5044.494 #>
compare(e,e2) ## LRT, H0: y3<-x=0
#> #> - Likelihood ratio test - #> #> data: #> chisq = 1.6297, df = 1, p-value = 0.2017 #> sample estimates: #> log likelihood (model 1) log likelihood (model 2) #> -5045.863 -5045.048 #>
compare(e,scoretest=y3~x) ## Score-test, H0: y3~x=0
#> #> - Score test - #> #> data: y3 ~ x #> chisq = 1.6059, df = 1, p-value = 0.2051 #>
compare(e2,par=c("y3~x")) ## Wald-test, H0: y3~x=0
#> #> - Wald test - #> #> Null Hypothesis: #> [y3~x] = 0 #> #> data: #> chisq = 1.5752, df = 1, p-value = 0.2095 #> sample estimates: #> Estimate Std.Err 2.5% 97.5% #> [y3~x] -0.08157255 0.06499477 -0.20896 0.04581487 #>
B <- diag(2); colnames(B) <- c("y2~eta","y3~eta") compare(e2,contrast=B,null=c(1,1))
#> #> - Wald test - #> #> Null Hypothesis: #> [y2~eta] = 1 #> [y3~eta] = 1 #> #> data: #> chisq = 0.40264, df = 2, p-value = 0.8177 #> sample estimates: #> Estimate Std.Err 2.5% 97.5% #> [y2~eta] 1.019845 0.03770718 0.9459398 1.093749 #> [y3~eta] 1.028685 0.05598807 0.9189509 1.138420 #>
B <- rep(0,length(coef(e2))); B[1:3] <- 1 compare(e2,contrast=B)
#> #> - Wald test - #> #> Null Hypothesis: #> [y2] + [y3] + [eta] = 0 #> #> data: #> chisq = 0.15653, df = 1, p-value = 0.6924 #> sample estimates: #> Estimate Std.Err 2.5% 97.5% #> [y2] + [y3] + [eta] 0.02605068 0.06584406 -0.1030013 0.1551027 #>
compare(e,scoretest=list(y3~x,y2~x))
#> #> - Score test - #> #> data: y3 ~ xy2 ~ x #> chisq = 2.7607, df = 2, p-value = 0.2515 #>