After evaluating a data stack with blend_predictions(), some number of candidates will have nonzero stacking coefficients. Such candidates are referred to as "members." Since members' predictions will ultimately inform the model stack's predictions, members should be trained on the full training set using fit_members().

fit_members(model_stack, ...)

Arguments

model_stack

A model_stack object outputted by blend_predictions().

...

Additional arguments. Currently ignored.

Value

A model_stack object with a subclass linear_stack---this fitted model contains the necessary components to predict on new data.

Details

To fit members in parallel, please register a parallel backend function. See the documentation of foreach::foreach() for examples.

Example Data

This package provides some resampling objects and datasets for use in examples and vignettes derived from a study on 1212 red-eyed tree frog embryos!

Red-eyed tree frog (RETF) embryos can hatch earlier than their normal 7ish days if they detect potential predator threat. Researchers wanted to determine how, and when, these tree frog embryos were able to detect stimulus from their environment. To do so, they subjected the embryos at varying developmental stages to "predator stimulus" by jiggling the embryos with a blunt probe. Beforehand, though some of the embryos were treated with gentamicin, a compound that knocks out their lateral line (a sensory organ.) Researcher Julie Jung and her crew found that these factors inform whether an embryo hatches prematurely or not!

Note that the data included with the stacks package is not necessarily a representative or unbiased subset of the complete dataset, and is only for demonstrative purposes.

reg_folds and class_folds are rset cross-fold validation objects from rsample, splitting the training data into for the regression and classification model objects, respectively. tree_frogs_reg_test and tree_frogs_class_test are the analogous testing sets.

reg_res_lr, reg_res_svm, and reg_res_sp contain regression tuning results for a linear regression, support vector machine, and spline model, respectively, fitting latency (i.e. how long the embryos took to hatch in response to the jiggle) in the tree_frogs data, using most all of the other variables as predictors. Note that the data underlying these models is filtered to include data only from embryos that hatched in response to the stimulus.

class_res_rf and class_res_nn contain multiclass classification tuning results for a random forest and neural network classification model, respectively, fitting reflex (a measure of ear function) in the data using most all of the other variables as predictors.

log_res_rf and log_res_nn, contain binary classification tuning results for a random forest and neural network classification model, respectively, fitting hatched (whether or not the embryos hatched in response to the stimulus) using most all of the other variables as predictors.

See ?example_data to learn more about these objects, as well as browse the source code that generated them.

See also

Other core verbs: add_candidates(), blend_predictions(), stacks()

Examples

# \donttest{
# see the "Example Data" section above for
# clarification on the objects used in these examples!

# put together a data stack
reg_st <- 
  stacks() %>%
  add_candidates(reg_res_lr) %>%
  add_candidates(reg_res_svm) %>%
  add_candidates(reg_res_sp)
  
reg_st
#> # A data stack with 3 model definitions and 15 candidate members:
#> #   reg_res_lr: 1 model configuration
#> #   reg_res_svm: 5 model configurations
#> #   reg_res_sp: 9 model configurations
#> # Outcome: latency (numeric)

# evaluate the data stack and fit the member models
reg_st %>%
  blend_predictions() %>%
  fit_members()
#> ── A stacked ensemble model ─────────────────────────────────────
#> 
#> Out of 15 possible candidate members, the ensemble retained 4.
#> Penalty: 0.1.
#> Mixture: 1.
#> 
#> The 4 highest weighted members are:
#> # A tibble: 4 x 3
#>   member          type       weight
#>   <chr>           <chr>       <dbl>
#> 1 reg_res_svm_1_5 svm_rbf     2.64 
#> 2 reg_res_svm_1_3 svm_rbf     0.675
#> 3 reg_res_svm_1_1 svm_rbf     0.302
#> 4 reg_res_sp_2_1  linear_reg  0.236
  
reg_st
#> # A data stack with 3 model definitions and 15 candidate members:
#> #   reg_res_lr: 1 model configuration
#> #   reg_res_svm: 5 model configurations
#> #   reg_res_sp: 9 model configurations
#> # Outcome: latency (numeric)
  
# do the same with multinomial classification models
class_st <-
  stacks() %>%
  add_candidates(class_res_nn) %>%
  add_candidates(class_res_rf) %>%
  blend_predictions() %>%
  fit_members()
#> ! Bootstrap05: preprocessor 1/1, model 1/1: from glmnet Fortran code (error code -89); ...
#> ! Bootstrap18: preprocessor 1/1, model 1/1: from glmnet Fortran code (error code -63); ...
#> ! Bootstrap23: preprocessor 1/1, model 1/1: from glmnet Fortran code (error code -58); ...
#> ! Bootstrap24: preprocessor 1/1, model 1/1: from glmnet Fortran code (error code -79); ...
#> Error in {    asNamespace("stacks")$fit_member(name = mem, wflows = model_stack[["model_defs"]],         members_map = members_map, train_dat = dat)} task 2 failed - "This engine requires some package installs: 'ranger'"
  
class_st
#> Error in eval(expr, envir, enclos) object 'class_st' not found
  
# ...or binomial classification models
log_st <-
  stacks() %>%
  add_candidates(log_res_nn) %>%
  add_candidates(log_res_rf) %>%
  blend_predictions() %>%
  fit_members()
#> Error in {    asNamespace("stacks")$fit_member(name = mem, wflows = model_stack[["model_defs"]],         members_map = members_map, train_dat = dat)} task 2 failed - "This engine requires some package installs: 'ranger'"
  
log_st
#> Error in eval(expr, envir, enclos) object 'log_st' not found
# }