| get_GOF | R Documentation | 
Goodness-of-fitting (GOF) of fine curve fitting results.
get_GOF(x, ...)
## S3 method for class 'list'
get_GOF(x, ...)
## S3 method for class 'fFITs'
get_GOF(x, ...)
## S3 method for class 'fFIT'
get_GOF(x, data, ...)
| x | 
 | 
| ... | ignored. | 
| data | A data.frame with the columns of  | 
meth: The name of fine curve fitting method
RMSE: Root Mean Square Error
NSE : Nash-Sutcliffe model efficiency coefficient
R   : Pearson-Correlation
R2  : determined coefficient
pvalue: pvalue of R
n   : The number of observations
 https://en.wikipedia.org/wiki/Nash-Sutcliffe_model_efficiency_coefficient 
https://en.wikipedia.org/wiki/Pearson_correlation_coefficient
curvefit()
library(phenofit)
# simulate vegetation time-series
FUN = doubleLog.Beck
par  = c( mn  = 0.1, mx  = 0.7, sos = 50, rsp = 0.1, eos = 250, rau = 0.1)
t    <- seq(1, 365, 8)
tout <- seq(1, 365, 1)
y <- FUN(par, t)
methods <- c("AG", "Beck", "Elmore", "Gu", "Zhang") # "Klos" too slow
fit <- curvefit(y, t, tout, methods) # `fFITs` (fine-fitting) object 
fits <- list(`2001` = fit, `2002` = fit) # multiple years
l_param   <- get_param(fits)
d_GOF     <- get_GOF(fits)
d_fitting <- get_fitting(fits)
l_pheno   <- get_pheno(fits, "AG", IsPlot=TRUE)
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