rPearson: Mean Squared Error

Description

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.

Usage

rPearson(sim, obs, ...)
# S3 method for default
rPearson(sim, obs, ...)
# S3 method for matrix
rPearson(sim, obs, na.rm=TRUE, ...)
# S3 method for data.frame
rPearson(sim, obs, na.rm=TRUE, ...)
# S3 method for zoo
rPearson(sim, obs, na.rm=TRUE, ...)

Arguments

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. When an 'NA' value is found at the i-th position in obs OR sim, the i-th value of obs AND sim are removed before the computation.

…

further arguments passed to or from other methods.

Value

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.

Details

It is a wrapper to the cor function.

Examples

Run this code

# NOT RUN {
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)
# }