sensitivity (version 1.2)

linsa: Linear Sensitivity Analysis

Description

linsa computes the standardized regression coefficients (SRC) and the partial correlation coefficients (PCC). Analysis can be done on the ranks; then the indices are the standardized rank regression coefficients (SRRC) and the partial rank correlation coefficients (PRCC).

Usage

linsa(model = NULL, x, pcc = TRUE, rank = FALSE,
      nboot = 0, conf = 0.95, ...)
## S3 method for class 'linsa':
compute(sa, y = NULL)

Arguments

model

the model.

x

the input sample.

pcc

logical. If TRUE, the P(R)CCs are computed.

rank

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

nboot

the number of bootstrap replicates.

conf

the confidence level for bootstrap confidence intervals.

sa

the sensitivity analysis object.

y

the response.

...

any other arguments for model which are passed unchanged each time it is called.

Value

• linsa returns an object of class "linsa". An object of class "linsa" is a list containing the following components:
• model
  the model.
• x
  the input sample.
• rank
  logical. If TRUE, the analysis was done on the ranks.
• nboot
  the number of bootstrap replicates.
• conf
  the confidence level for bootstrap confidence intervals.
• y
  the response.
• src
  the estimations of the SRC indices (or SRRC if rank analysis is requested).
• pcc
  if requested, the estimations of the PCC indices (or PRCC if rank analysis is requested).
• call
  the matched call.

Details

model is a function or a predictor (a class with a predict method) computing the response y based on the sample given by x. If no model is specified, the indices will be computed when one gives the response.

References

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

See Also

sensitivity compute

Examples

# linear model : Y = X1 + X2 + X3

model1 <- function(x) x[, 1] + x[, 2] + x[, 3]

# a 500-sample with X1 ~ U(0.5, 1.5)
#                   X2 ~ U(1.5, 4.5)
#                   X3 ~ U(4.5, 13.5)

n <- 500
x <- data.frame(X1 = runif(n, 0.5, 1.5),
                X2 = runif(n, 1.5, 4.5),
                X3 = runif(n, 4.5, 13.5))

# sensitivity analysis

sa <- linsa(model = model1, x = x, nboot = 100)
print(sa)
par(mfrow = c(1,2))
plot(sa, ask = FALSE)