Select best-fit linear models across multiple species

Wrapper function that runs ss_modelselect() across multiple species in a dataframe. A single best-fit equation is selected per species, based on the lowest AICc value. All allometric equations considered (and ranked) can be found in ?eqns_info and data(eqns_info).

ss_modelselect_multi(
  data,
  species = "species",
  response = "height",
  predictor = "diameter"
)

Arguments

data

Dataframe that contains the variables of interest. Each row is a measurement for an individual tree of a particular species.

species

Column name of the species variable. Defaults to species.

response

Column name of the response variable. Defaults to height.

predictor

Column name of the predictor variable. Defaults to diameter.

Value

A list of 3 elements:

ss_models_rank

List of tables showing each species' candidate models ranked by AICc value.

ss_models

List of each species' best-fit model object.

ss_models_info

Table showing each species' best-fit model information.

ss_models_info

A dataframe with the following variables:

species

Name of tree species.

modelcode

Model code for the best-fit equation.

a, b, c, d, e

Parameter estimates.

response_geom_mean

Geometric mean of the response variable used in calculation of AICc (only for transformed models).

correctn_factor

Bias correction factor to use on model predictions (only for transformed models).

predictor_min, predictor_max

Range of the predictor variable within the data used to generate the model.

response_min, response_max

Range of the response variable within the data used to generate the model.

residual_SE

Residual standard error of the model.

mean_SE

Mean standard error of the model.

adj_R2

Adjusted \(R^2\) of the model.

n

Sample size (no. of trees used to fit model).

See also

ss_modelselect() to select a best-fit model for one species.

ss_modelfit() to fit a pre-selected model for one species.

ss_modelfit_multi() to fit pre-selected models across multiple species.

Other single-species model functions: ss_modelfit_multi(), ss_modelfit(), ss_modelselect(), ss_predict(), ss_simulate()

Examples

data(urbantrees)
results <- ss_modelselect_multi(urbantrees, species = 'species',
                                response = 'height', predictor = 'diameter')

head(results$ss_models_rank[[1]]) # Highly-ranked models for 1st species in list
#>   df     AICc    model
#> 1  3 591.9422   lin_w1
#> 2  4 593.0074  quad_w1
#> 3  5 593.4045   cub_w1
#> 4  6 594.0030 quart_w1
#> 5  3 594.0139   lin_w2
#> 6  5 595.5983   cub_w2

results$ss_models[[1]] # model object for 1st species in list
#> 
#> Call:
#> lm(formula = y ~ x)
#> 
#> Coefficients:
#> (Intercept)            x  
#>       6.717        9.464  
#> 

results$ss_models_info # summary of best-fit models
#>                    species modelcode         a           b          c        d
#> 1            Albizia saman    lin_w1  6.717431   9.4640964         NA       NA
#> 2            Hopea odorata  quart_w4 -1.149669 149.3178483 -1522.6122 7908.410
#> 3     Syzygium myrtifolium  quart_w2 -4.614100 158.5128488  -748.2492 1638.676
#> 4       Terminalia mantaly   expo_w1  1.241983   3.8600410         NA       NA
#> 5 Xanthostemon chrysanthus loglog_w1  2.947907   0.5916555         NA       NA
#>            e response_geom_mean correctn_factor predictor_min predictor_max
#> 1         NA          13.566083        1.000000    0.31194369     1.5278875
#> 2 -14225.442           5.390829        1.000000    0.03183099     0.2928451
#> 3  -1321.221           7.735643        1.000000    0.04138029     0.5665916
#> 4         NA           7.596013        1.000533    0.03501409     0.5602254
#> 5         NA           5.041605        1.000883    0.02864789     0.3533240
#>   response_min response_max residual_SE mean_SE adj_R2   n
#> 1            8           20      2.2050  4.7889 0.4276 133
#> 2            2           15     11.9430  2.6339 0.5460 483
#> 3            1           18      2.1674  2.2536 0.6662 353
#> 4            3           18      0.2493  0.0615 0.6702 197
#> 5            2           13      0.2123  0.0449 0.6137 418