print.mitml.testConstraints <- function(x, digits = 3, sci.limit = 5, ...){
# print method for MI estimates
cll <- x$call
test <- x$test
constraints <- x$constraints
method <- x$method
m <- x$m
adj.df <- x$adj.df
df.com <- x$df.com
# print header
cat("\nCall:\n", paste(deparse(cll)), sep = "\n")
cat("\nHypothesis test calculated from", m, "imputed data sets. The following\nconstraints were specified:\n\n")
# print constrained estimates
est <- cbind(x$Qbar, sqrt(diag(x$T)))
colnames(est) <- c("Estimate", "Std. Error")
rownames(est) <- paste0(constraints, ":")
out <- .formatTable(est, digits = digits, sci.limit = sci.limit)
for(i in seq_len(nrow(out))) cat(" ", out[i,], "\n")
# print method
cat("\nCombination method:", method, "\n\n")
# print test results
test.digits <- c(digits, 0, rep(digits, ncol(test)-2))
out <- .formatTable(test, digits = test.digits, sci.limit = sci.limit)
for(i in seq_len(nrow(out))) cat(" ", out[i,], "\n")
# print footer
if(method == "D1"){
cat("\n")
if(adj.df){
cat(c("Hypothesis test adjusted for small samples with",
paste0("df=[", paste(df.com, collapse = ","), "]\ncomplete-data degrees of freedom.")))
}else{
cat("Unadjusted hypothesis test as appropriate in larger samples.")
}
cat("\n")
}
cat("\n")
invisible()
}
summary.mitml.testConstraints <- function(object, ...){
# summary method for objects of class mitml.testConstraints
print.mitml.testConstraints(object, ...)
}
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