pcc {sensitivity} R Documentation

## Partial Correlation Coefficients

### Description

`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.

### Usage

```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), ...)
```

### 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 `pcc`. `ylim` the y-coordinate limits of the plot. `...` arguments to be passed to methods, such as graphical parameters (see `par`).

### Value

`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`).

### References

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

`src`

### 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 <- pcc(X, y, nboot = 100)
print(x)
#plot(x) # TODO: find another example...
```

[Package sensitivity version 1.4-0 Index]