src: Standardized Regression Coefficients

In sensitivity: Global Sensitivity Analysis of Model Outputs and Importance Measures

Standardized Regression Coefficients

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, logistic = FALSE, nboot = 0, conf = 0.95)
## S3 method for class 'src'
print(x, ...)
## S3 method for class 'src'
plot(x, ylim = c(-1,1), ...)
## S3 method for class 'src'
ggplot(data,  mapping = aes(), ylim = c(-1, 1), ..., environment
                 = parent.frame())

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.
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 src.
data	the object returned by src.
ylim	the y-coordinate limits of the plot.
mapping	Default list of aesthetic mappings to use for plot. If not specified, must be supplied in each layer added to the plot.
environment	[Deprecated] Used prior to tidy evaluation.
...	arguments to be passed to methods, such as graphical parameters (see par).

Details

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

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).

Author(s)

Gilles Pujol and Bertrand Iooss

References

L. Clouvel, B. Iooss, V. Chabridon, M. Il Idrissi and F. Robin, 2023, An overview of variance-based importance measures in the linear regression context: comparative analyses and numerical tests, Preprint. https://hal.science/hal-04102053

B. Iooss, V. Chabridon and V. Thouvenot, Variance-based importance measures for machine learning model interpretability, Congres lambda-mu23, Saclay, France, 10-13 octobre 2022 https://hal.science/hal-03741384

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

See Also

pcc, lmg, pmvd

Examples

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

library(ggplot2)
ggplot(x)

sensitivity documentation built on Sept. 11, 2024, 9:09 p.m.