# R/unitest.R In np: Nonparametric Kernel Smoothing Methods for Mixed Data Types

#### Defines functions summary.unitestprint.unitestunitest

```unitest <- function(Srho,
Srho.bootstrap,
P,
bootstrap,
boot.num,
bw.x,
bw.y) {

tuni = list(Srho = Srho,
Srho.bootstrap = Srho.bootstrap,
P = P,
bootstrap = bootstrap,
boot.num = boot.num,
bw.x = bw.x,
bw.y = bw.y)

if(bootstrap) {

reject <- ' '

if (P < 0.1)
reject <- '.'

if (P < 0.05)
reject <- '*'

if (P < 0.01)
reject <- '**'

if (P < 0.001)
reject <- '***'

tuni\$reject <- reject
tuni\$rejectNum <- switch(reject,
' ' = 100,
'.' = 10,
'*' = 5,
'**' = 1,
'***' = 0.1)

}

class(tuni) <- "unitest"

tuni

}

print.unitest <- function(x, ...){
if(x\$bootstrap) {
cat("\nConsistent Univariate Entropy Density Equality Test",
paste("\n", format(x\$boot.num), " Bootstrap Replications",sep=""),
"\n\nTest Statistic ", sQuote("Srho"), ": ",
format(x\$Srho), "\tP Value: ", format.pval(x\$P)," ", x\$reject,
"\n---\nSignif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1",
ifelse(x\$reject == ' ', "\nFail to reject the null of equality at the 10% level",
paste("\nNull of equality is rejected at the ", x\$rejectNum, "% level", sep="")),
"\n\n", sep="")
} else {
cat("\nConsistent Univariate Density Difference Metric Entropy",
"\n\nMetric Entropy ", sQuote("Srho"),": ",
format(x\$Srho),"\n\n", sep="")
}
}

summary.unitest <- function(object, ...){
print(object)
}
```

## Try the np package in your browser

Any scripts or data that you put into this service are public.

np documentation built on March 31, 2023, 9:41 p.m.