epiR (version 1.0-15)

epi.prcc: Partial rank correlation coefficients

Description

Compute partial rank correlation coefficients.

Usage

epi.prcc(dat, sided.test = 2)

Arguments

dat

a data frame comprised of K + 1 columns and N rows, where K represents the number of model parameters being evaluated and N represents the number of replications of the model. The last column of the data frame (i.e. column K + 1) provides the model output.

sided.test

use a one- or two-sided test? Use a two-sided test if you wish to evaluate whether or not the partial rank correlation coefficient is greater than or less than zero. Use a one-sided test to evaluate whether or not the partial rank correlation coefficient is greater than zero.

Value

A data frame with three elements: gamma the partial rank corellation coefficient between each input parameter and the outcome, test.statistic the test statistic used to determine the significance of non-zero values of gamma, and p.value the associated P-value.

Details

If the number of parameters K is greater than the number of model replications N an error will be returned.

References

Blower S, Dowlatabladi H (1994). Sensitivity and uncertainty analysis of complex models of disease transmission: an HIV model, as an example. International Statistical Review 62: 229 - 243.

Sanchez M, Blower S (1997) Uncertainty and sensitivity analysis of the basic reproductive rate. American Journal of Epidemiology 145: 1127 - 1137.

Examples

# NOT RUN {
## Create a matrix of simulation results:
x1 <- data.frame(rnorm(n = 10, mean = 120, sd = 10))
x2 <- data.frame(rnorm(n = 10, mean = 80, sd = 5))
x3 <- data.frame(rnorm(n = 10, mean = 40, sd = 20))
y <- 2 + (0.5 * x1) + (0.7 * x2) + (0.2 * x3)

dat <- data.frame(cbind(X1 = x1, X2 = x2, X3 = x3, Y = y))
epi.prcc(dat, sided.test = 2)

# }