pcc: Partial Correlation Coefficients

Description

pcc computes the Partial Correlation Coefficients (PCC), or Partial Rank Correlation Coefficients (PRCC), which are sensitivity indices based on linear (resp. monotonic) assumptions, in the case of (linearly) correlated factors.

Usage

pcc(X, y, rank = FALSE, nboot = 0, conf = 0.95)
## S3 method for class 'pcc':
print(x, \dots)
## S3 method for class 'pcc':
plot(x, ylim = c(-1,1), ...)

Arguments

a data frame (or object coercible by as.data.frame) containing the design of experiments (model input variables).

a vector containing the responses corresponding to the design of experiments (model output variables).

rank

logical. If TRUE, the analysis is done on the ranks.

nboot

the number of bootstrap replicates.

conf

the confidence level of the bootstrap confidence intervals.

the object returned by pcc.

ylim

the y-coordinate limits of the plot.

...

arguments to be passed to methods, such as graphical parameters (see par).

Value

pcc returns a list of class "pcc", containing the following components:
callthe matched call.
PCCa data frame containing the estimations of the PCC indices, bias and confidence intervals (if rank = TRUE).
PRCCa data frame containing the estimations of the PRCC indices, bias and confidence intervals (if rank = TRUE).

References

A. Saltelli, K. Chan and E. M. Scott eds, 2000, Sensitivity Analysis, Wiley.

Examples

Run this code

# a 100-sample with X1 ~ U(0.5, 1.5)
#                   X2 ~ U(1.5, 4.5)
#                   X3 ~ U(4.5, 13.5)
library(boot)
n <- 100
X <- data.frame(X1 = runif(n, 0.5, 1.5),
                X2 = runif(n, 1.5, 4.5),
                X3 = runif(n, 4.5, 13.5))

# linear model : Y = X1 + X2 + X3
y <- with(X, X1 + X2 + X3)

# sensitivity analysis
x <- pcc(X, y, nboot = 100)
print(x)
#plot(x) # TODO: find another example...

Run the code above in your browser using DataLab

Last chance! 50% off unlimited learning