rPearson {hydroGOF} | R Documentation |
Correlation of sim
and obs
if these are vectors, with treatment of missing values. If sim
and obs
are matrices then the covariances (or correlations) between the columns of sim
and the columns of obs
are computed. It is a wrapper to the cor
function.
rPearson(sim, obs, ...) ## Default S3 method: rPearson(sim, obs, ...) ## S3 method for class 'matrix' rPearson(sim, obs, na.rm=TRUE, ...) ## S3 method for class 'data.frame' rPearson(sim, obs, na.rm=TRUE, ...) ## S3 method for class 'zoo' rPearson(sim, obs, na.rm=TRUE, ...)
sim |
numeric, zoo, matrix or data.frame with simulated values |
obs |
numeric, zoo, matrix or data.frame with observed values |
na.rm |
a logical value indicating whether 'NA' should be stripped before the computation proceeds. |
... |
further arguments passed to or from other methods. |
It is a wrapper to the cor
function.
Mean squared error between sim
and obs
.
If sim
and obs
are matrixes, the returned value is a vector, with the mean squared error between each column of sim
and obs
.
obs
and sim
has to have the same length/dimension
The missing values in obs
and sim
are removed before the computation proceeds, and only those positions with non-missing values in obs
and sim
are considered in the computation
Mauricio Zambrano Bigiarini <mzb.devel@gmail.com>
obs <- 1:10 sim <- 1:10 rPearson(sim, obs) obs <- 1:10 sim <- 2:11 rPearson(sim, obs) ################## # Loading daily streamflows of the Ega River (Spain), from 1961 to 1970 data(EgaEnEstellaQts) obs <- EgaEnEstellaQts # Generating a simulated daily time series, initially equal to the observed series sim <- obs # Computing the linear correlation for the "best" case rPearson(sim=sim, obs=obs) # Randomly changing the first 2000 elements of 'sim', by using a normal distribution # with mean 10 and standard deviation equal to 1 (default of 'rnorm'). sim[1:2000] <- obs[1:2000] + rnorm(2000, mean=10) # Computing the new correlation value rPearson(sim=sim, obs=obs)