r {HYPEtools}

Pearson product-moment correlation coefficient r

Description

Pearson product-moment correlation coefficient calculation, a specific case of function cor.

Usage

r(sim, obs, ...)

## S3 method for class 'HypeSingleVar'
r(sim, obs, progbar = TRUE, ...)

Arguments

sim

HypeSingleVar array with simulated variable (one or several iterations).

obs

HypeSingleVar array with observed variable, (one iteration). If several iterations are present in the array, only the first will be used.

...

Ignored.

progbar

Logical, if TRUE progress bars will be printed for main computational steps.

Details

This function wraps a call to cor(x = obs, y = sim, use = "na.or.complete", method = "pearson").

Method r.HypeSingleVar calculates Pearson's r for imported HYPE outputs with single variables for several catchments, i.e. time and map files, optionally multiple model runs combined, typically results from calibration runs.

Value

r.HypeSingleVar returns a 2-dimensional array of Pearson correlation coefficients for all SUBIDs and model iterations provided in argument sim, with values in the same order as the second and third dimension in sim, i.e. [subid, iteration].

See Also

cor, on which the function is based. ReadWsOutput for importing HYPE calibration results.

Examples

# Create dummy data, discharge observations with added white noise as model simulations
te1 <- ReadObs(filename = system.file("demo_model", "Qobs.txt", package = "HYPEtools"))
te1 <- HypeSingleVar(x = array(data = unlist(te1[, -1]) + runif(n = nrow(te1), 
                                                                min = -.5, max = .5), 
                               dim = c(nrow(te1), ncol(te1) - 1, 1), 
                               dimnames = list(rownames(te1), colnames(te1)[-1])), 
                     datetime = te1$DATE, subid = obsid(te1), hype.var = "cout")
te2 <- ReadObs(filename = system.file("demo_model", "Qobs.txt", package = "HYPEtools"))
te2 <- HypeSingleVar(x = array(data = unlist(te2[, -1]), 
                               dim = c(nrow(te2), ncol(te2) - 1, 1), 
                               dimnames = list(rownames(te2), colnames(te2)[-1])), 
                     datetime = te2$DATE, subid = obsid(te2), hype.var = "rout")
# Pearson correlation
r(sim = te1, obs = te2, progbar = FALSE)