predictor_assessment: Model quality assessment

Description Usage Arguments Details Value Note See Also Examples

Description

Provide several metrics to assess the quality of the predictions of a model (see note) against observations.

Usage

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R2(sim, obs, na.action = stats::na.omit)

RMSE(sim, obs, na.rm = T)

nRMSE(sim, obs, na.rm = T)

MAE(sim, obs, na.rm = T)

ABS(sim, obs, na.rm = T)

MSE(sim, obs, na.rm = T)

EF(sim, obs, na.rm = T)

NSE(sim, obs, na.rm = T)

Bias(sim, obs, na.rm = T)

MAPE(sim, obs, na.rm = T)

FVU(sim, obs, na.rm = T)

RME(sim, obs, na.rm = T)

Arguments

sim

Simulated values

obs

Observed values

na.action

A function which indicates what should happen when the data contain NAs.

na.rm

Boolean. Remove NA values if TRUE (default)

Details

The statistics for model quality can differ between sources. Here is a short description of each statistic and its equation (see html version for LATEX):

Value

A statistic depending on the function used.

Note

SS_res is the residual sum of squares and SS_tot the total sum of squares. They are computed as:

SS_res= sum((obs-sim)^2)

SS_tot= sum((obs-mean(obs))^2

Also, it should be noted that y_i refers to the observed values and \hat{y_i} to the predicted values, and \bar{y} to the mean value of observations.

See Also

This function was inspired from the evaluate() function from the SticsEvalR package. This function is used by stics_eval

Examples

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library(sticRs)
sim= rnorm(n = 5,mean = 1,sd = 1)
obs= rnorm(n = 5,mean = 1,sd = 1)
RMSE(sim,obs)

VEZY/sticRs documentation built on Oct. 1, 2018, 1:06 p.m.