src: Standardized Regression Coefficients

In sensitivity: Sensitivity Analysis

Description

src computes the Standardized Regression Coefficients (SRC), or the Standardized Rank Regression Coefficients (SRRC), which are sensitivity indices based on linear or monotonic assumptions in the case of independent factors.

Usage

src(X, y, rank = FALSE, nboot = 0, conf = 0.95)
## S3 method for class 'src'
print(x, ...)
## S3 method for class 'src'
plot(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.
nboot	the number of bootstrap replicates.
conf	the confidence level of the bootstrap confidence intervals.
x	the object returned by src.
ylim	the y-coordinate limits of the plot.
...	arguments to be passed to methods, such as graphical parameters (see par).

Value

src returns a list of class "src", containing the following components:

call	the matched call.
SRC	a data frame containing the estimations of the SRC indices, bias and confidence intervals (if rank = FALSE).
SRRC	a data frame containing the estimations of the SRRC indices, bias and confidence intervals (if rank = TRUE).

References

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

See Also

pcc

Examples

# a 100-sample with X1 ~ U(0.5, 1.5)
#                   X2 ~ U(1.5, 4.5)
#                   X3 ~ U(4.5, 13.5)

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 <- src(X, y, nboot = 100)
print(x)
plot(x)

sensitivity documentation built on May 2, 2019, 5:56 p.m.