SecondaryComparison {biogrowth}  R Documentation 
SecondaryComparison class
Description
The SecondaryComparison
class contains several functions for model comparison
and model selection of growth models. It should not be instanced directly. Instead,
it should be constructed using compare_secondary_fits()
.
It includes two type of tools for model selection and comparison: statistical indexes and visual analyses. Please check the sections below for details.
Note that all these tools use the names defined in compare_secondary_fits()
, so
we recommend passing a named list to that function.
Usage
## S3 method for class 'SecondaryComparison'
coef(object, ...)
## S3 method for class 'SecondaryComparison'
summary(object, ...)
## S3 method for class 'SecondaryComparison'
print(x, ...)
## S3 method for class 'SecondaryComparison'
plot(x, y, ..., type = 1, add_trend = TRUE)
Arguments
object 
an instance of SecondaryComparison 
... 
ignored 
x 
an instance of SecondaryComparison 
y 
ignored 
type 
if type==1, the plot compares the model predictions. If type ==2, the plot compares the parameter estimates. 
add_trend 
should a trend line of the residuals be added for type==3? 
Methods (by generic)

coef(SecondaryComparison)
: table of parameter estimates 
summary(SecondaryComparison)
: summary table for the comparison 
print(SecondaryComparison)
: print of the model comparison 
plot(SecondaryComparison)
: illustrations comparing the fitted models
Statistical indexes
SecondaryComparison
implements two S3 methods to obtain numerical values to facilitate
model comparison and selection.
the
coef
method returns a tibble with the values of the parameter estimates and their corresponding standard errors for each model.the
summary
returns a tibble with the AIC, number of degrees of freedom, mean error and root mean squared error for each model.
Visual analyses
The S3 plot method can generate three types of plots:
when
type = 1
, the plot compares the observations against the model predictions for each model. The plot includes a linear model fitted to the residuals. In the case of a perfect fit, the line would have slope=1 and intercept=0 (shown as a black, dashed line).when
type = 2
, the plot compares the parameter estimates using error bars, where the limits of the error bars are the expected value +/ one standard error. In case one model does not has some model parameter (i.e. either because it is not defined or because it was fixed), the parameter is not included in the plot.