sensitivity (version 1.22.2)

pcc: Partial Correlation Coefficients

Description

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

Usage

pcc(X, y, rank = FALSE, semi = FALSE, logistic = FALSE, nboot = 0, conf = 0.95)
# S3 method for pcc
print(x, …)
# S3 method for pcc
plot(x, ylim = c(-1,1), …)
# S3 method for pcc
ggplot(x, ylim = c(-1,1), …)

Arguments

X

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

y

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.

semi

logical. If TRUE, semi-PCC are computed.

logistic

logical. If TRUE, the analysis is done via a logistic regression (binomial GLM).

nboot

the number of bootstrap replicates.

conf

the confidence level of the bootstrap confidence intervals.

x

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:

call

the matched call.

PCC

a data frame containing the estimations of the PCC indices, bias and confidence intervals (if rank = TRUE and semi = FALSE).

PRCC

a data frame containing the estimations of the PRCC indices, bias and confidence intervals (if rank = TRUE and semi = FALSE).

SPCC

a data frame containing the estimations of the PCC indices, bias and confidence intervals (if rank = TRUE and semi = TRUE).

SPRCC

a data frame containing the estimations of the PRCC indices, bias and confidence intervals (if rank = TRUE and semi = TRUE).

Details

Logistic regression model (logistic = TRUE) and rank-based indices (rank = TRUE) are incompatible.

References

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

J.W. Johnson and J.M. LeBreton, History and use of relative importance indices in organizational research, Organizational Research Methods, 7:238-257, 2004.

See Also

src

Examples

# NOT RUN {
# 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 = X1^2 + X2 + X3
y <- with(X, X1^2 + X2 + X3)

# sensitivity analysis
x <- pcc(X, y, nboot = 100)
print(x)
plot(x)

library(ggplot2)
ggplot(x)

x <- pcc(X, y, semi = TRUE, nboot = 100)
print(x)
plot(x)
# }