Studentized bootstrap variance estimate for scoreFn(yValues,modelValues).

resampleScoreModel(
  modelValues,
  yValues,
  scoreFn,
  ...,
  na.rm = FALSE,
  returnScores = FALSE,
  nRep = 100,
  parallelCluster = NULL
)

Arguments

modelValues	numeric array of predictions (model to test).
yValues	numeric/logical array of outcomes, dependent, or truth values
scoreFn	function with signature scoreFn(modelValues,yValues) returning scalar numeric score.
...	not used, forces later arguments to be bound by name
na.rm	logical, if TRUE remove NA values
returnScores	logical if TRUE return detailed resampledScores
nRep	integer number of repititions to perform
parallelCluster	optional snow-style parallel cluster.

Value

summaries

Examples


set.seed(25325)
y <- 1:5
m1 <- c(1,1,2,2,2)
cor.test(m1,y,alternative='greater')
#> 
#> 	Pearson's product-moment correlation
#> 
#> data:  m1 and y
#> t = 3, df = 3, p-value = 0.02883
#> alternative hypothesis: true correlation is greater than 0
#> 95 percent confidence interval:
#>  0.1526678 1.0000000
#> sample estimates:
#>       cor 
#> 0.8660254 
#> 
f <- function(modelValues,yValues) {
 if((sd(modelValues)<=0)||(sd(yValues)<=0)) {
   return(0)
 }
 cor(modelValues,yValues)
}
s <- sigr::resampleScoreModel(m1,y,f)
print(s)
#> $fnName
#> [1] "resampleScoreModel"
#> 
#> $observedScore
#> [1] 0.8660254
#> 
#> $bias
#> [1] -0.06201873
#> 
#> $sd
#> [1] 0.2768382
#> 
#> $nNA
#> [1] 0
#> 
#> $n
#> [1] 5
#> 
z <- (s$observedScore-0)/s$sd # should check size of z relative to bias!
pValue <- pt(z,df=length(y)-2,lower.tail=FALSE)
pValue
#> [1] 0.0260677