Ratio of Standard Deviations

Share:

Description

Ratio of standard deviations between sim and obs, with treatment of missing values.

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
rSD(sim, obs, ...)

## Default S3 method:
rSD(sim, obs, na.rm=TRUE, ...)

## S3 method for class 'data.frame'
rSD(sim, obs, na.rm=TRUE, ...)

## S3 method for class 'matrix'
rSD(sim, obs, na.rm=TRUE, ...)

## S3 method for class 'zoo'
rSD(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

Ratio of standard deviations between sim and obs.

If sim and obs are matrixes, the returned value is a vector, with the ratio of standard deviations 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>

See Also

sd, rsr, gof, ggof

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
sim <- 1:10
obs <- 1:10
rSD(sim, obs)

sim <- 2:11
obs <- 1:10
rSD(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 'rSD' for the "best" (unattainable) case
rSD(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 'rSD'
rSD(sim=sim, obs=obs)