epi.prcc {epiR} | R Documentation |
Compute partial rank correlation coefficients.
epi.prcc(dat, sided.test = 2)
dat |
a data frame comprised of |
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. |
If the number of parameters K
is greater than the number of model replications N
an error will be returned.
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.
Jonathon Marshall, J.C.Marshall@massey.ac.nz.
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.
## 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)