sitcor {SIT}

Conduct the sliced independence test.

Description

This function computes the sit coefficient between two vectors x and y, possibly all paired coefficients for a matrix.

Usage

sitcor(
  x,
  y = NULL,
  c = 2,
  pvalue = FALSE,
  ties = FALSE,
  method = "asymptotic",
  nperm = 199,
  factor = FALSE
)

Arguments

x

Vector of numeric values in the first coordinate.

y

Vector of numeric values in the second coordinate.

c

The number of observations in each slice.

pvalue

Whether or not to return the p-value of rejecting independence, if TRUE the function also returns the standard deviation of sit.

ties

Do we need to handle ties? If ties=TRUE the algorithm assumes that the data has ties and employs the more elaborated theory for calculating s.d. and P-value. Otherwise, it uses the simpler theory. There is no harm in putting ties = TRUE even if there are no ties.

method

If method = "asymptotic" the function returns P-values computed by the asymptotic theory (not available in the presence of ties). If method = "permutation", a permutation test with nperm permutations is employed to estimate the P-value. Usually, there is no need for the permutation test. The asymptotic theory is good enough.

nperm

In the case of a permutation test, nperm is the number of permutations to do.

factor

Whether to transform integers into factors, the default is to leave them alone.

Value

In the case pvalue=FALSE, function returns the value of the sit coefficient, if the input is a matrix, a matrix of coefficients is returned. In the case pvalue=TRUE is chosen, the function returns a list:

sitcor

The value of the sit coefficient.

sd

The standard deviation.

pval

The test p-value.

Author(s)

Yilin Zhang, Canyi Chen & Liping Zhu

References

Zhang Y., Chen C., & Zhu L. (2022). Sliced Independence Test. Statistica Sinica. https://doi.org/10.5705/ss.202021.0203.

Examples

##---- Should be DIRECTLY executable !! ----
library("psychTools")
data(peas)
# Visualize       the peas data
library(ggplot2)
ggplot(peas,aes(parent,child)) +
geom_count() + scale_radius(range=c(0,5)) +
       xlim(c(13.5,24))+ylim(c(13.5,24))+       coord_fixed() +
       theme(legend.position="bottom")
# Compute one of the coefficients
sitcor(peas$parent,peas$child, c = 4, pvalue=TRUE)
sitcor(peas$child,peas$parent, c = 4)
# Compute all the coefficients
sitcor(peas, c = 4)