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.

### See Also

`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]