extracat (version 1.7-6)

qBCI: Quantile BCI

Description

Bins numeric variables according to their quantiles and computes the Bertin Classification Index BCI. The data.frame method computes the multivariate qBCI and not the pairwise values (c.f. cmat).

Usage

qBCI(x, ...)
# S3 method for default
qBCI(x, y, p = NULL, k = 5, iter=20, …)
# S3 method for data.frame
qBCI(x,p = NULL, k = 5, sort = TRUE, iter=20, …)

Arguments

x

A numeric vector (in this case y needs to be specified) or a data.frame with numeric or factor variables.

y

A numeric vector.

p

A percentage to use for the quantiles sequence. See details.

k

A minimum expected number of observations in each cell after the binning.

sort

Whether or not to compute the BCI for the optimized tables or not. If not, kendalls is usually a better alternative.

iter

An optile parameter.

…

dots

Value

A value between 0 and 1.

Details

The breakpoints for the binning are the data quantiles according to equidistant probabilities seq(0,1,p) where p is minimal under the condition that each cell has an expected number of observations of at least k.

See Also

BCI,kendalls, wdcor, cmat

Examples

# NOT RUN {
qBCI(rnorm(100),runif(100))


# non-functional relationship:
x1 <- runif(500,0,10)
x2 <- runif(500,0,10)
y1 <- x1+rnorm(500,sd=1) 
y2 <- 10-x2+rnorm(500,sd=1) 

x <- c(x1,x2)
y <- c(y1,y2)

plot(x,y, pch = 19)

wdcor(x,y)
1 - qBCI(x,y)


y1 <- x1+rnorm(500,sd=0.1) 
y2 <- 10-x2+rnorm(500,sd=0.1) 

x <- c(x1,x2)
y <- c(y1,y2)

plot(x,y, pch = 19)

wdcor(x,y)
1 - qBCI(x,y)

# or a quadratic curve:
test <- sapply(seq(0,4,0.2),function(s){
x <- runif(200,-1,1)
y <- 5+12*x^2+rnorm(200,sd=s)
return(c(cor(x,y),
wdcor(x,y),
1 - qBCI(x,y)))
})


plot(test[3,],type="l", ylim=c(-0.2,1))
lines(test[1,], col = 2, lwd = 2)

lines(test[2,], col = 3, lwd = 2)

# }
# NOT RUN {
# }