pcc {sensitivity} | R Documentation |
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.
pcc(X, y, rank = FALSE, nboot = 0, conf = 0.95) ## S3 method for class 'pcc': print(x, ...) ## S3 method for class 'pcc': plot(x, ylim = c(-1,1), ...)
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 pcc . |
ylim |
the y-coordinate limits of the plot. |
... |
arguments to be passed to methods, such as graphical
parameters (see par ). |
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 ). |
PRCC |
a data frame containing the estimations of the PRCC
indices, bias and confidence intervals (if rank = TRUE ). |
A. Saltelli, K. Chan and E. M. Scott eds, 2000, Sensitivity Analysis, Wiley.
# 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 <- pcc(X, y, nboot = 100) print(x) #plot(x) # TODO: find another example...