epi.prcc {epiR} | R Documentation |
Compute partial rank correlation coefficients.
epi.prcc(dat)
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. |
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.
Blower S, Dowlatabladi H (1994). Sensitivity ans 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 <- as.data.frame(rnorm(n = 10, mean = 120, sd = 10)) x2 <- as.data.frame(rnorm(n = 10, mean = 80, sd = 5)) x3 <- as.data.frame(rnorm(n = 10, mean = 40, sd = 20)) y <- 2 + (0.5 * x1) + (0.7 * x2) + (0.2 * x3) dat <- as.data.frame(cbind(x1, x2, x3, y)) names(dat) <- c("X1", "X2", "X3", "Y") epi.prcc(dat)