rPearson {hydroGOF} R Documentation

## 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, ...)

## 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, ...)
```

### 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.

### Details

It is a wrapper to the `cor` function.

### 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`.

### Note

`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

### Author(s)

Mauricio Zambrano Bigiarini <mzb.devel@gmail.com>

`cor`

### Examples

```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)
```

[Package hydroGOF version 0.3-10 Index]