linsa {sensitivity} | R Documentation |
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).
linsa(model = NULL, x, pcc = TRUE, rank = FALSE, nboot = 0, conf = 0.95, ...) ## S3 method for class 'linsa': compute(sa, y = NULL)
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. |
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.
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. |
Saltelli, A., Chan, K. and Scott, E. M., 2000, Sensitivity analysis, Wiley.
# 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)