test_goodness_of_fit {gauseR}

Test goodness of fit

Description

Tests goodness of fit for predictions vs. observations. This statistic can be though of in the same way as a classic "R2", except that it measures scatter around the 1-1 line, rather than around a fitted regresson line of observed vs. predicted values. Value close to 1 indicate a that predictions match observations closely. Values at or below zero indicate that predictions do not match observations any better than the grand mean taken across all observations.

Usage

test_goodness_of_fit(observed, predicted, bycolumn = FALSE, droptimecol = TRUE)

Arguments

observed

A vector or matrix of observed values.

predicted

A vector or matrix of predicted values.

bycolumn

If TRUE, then separate values are calculated for each column in observed and predicted.

droptimecol

If TRUE, will automatically remove the column labeled "time" in the predicted variable. This is useful for dealing with the default output of the gause_wrapper function. Defaults to FALSE.

Examples

#load competition data
data("gause_1934_science_f02_03")

#subset out data from species grown in mixture
mixturedat<-gause_1934_science_f02_03[gause_1934_science_f02_03$Treatment=="Mixture",]

#extract time and species data
time<-mixturedat$Day
species<-data.frame(mixturedat$Volume_Species1, mixturedat$Volume_Species2)
colnames(species)<-c("P_caudatum", "P_aurelia")

#run wrapper
#note - keeptimes=TRUE is needed, so that predicted time steps match
#observed time steps
gause_out<-gause_wrapper(time=time, species=species, keeptimes = TRUE)

#calculate goodness of fit
test_goodness_of_fit(observed=species, predicted=gause_out)

# > 0.9 for both time series - these are good fits!