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, ...)

model_stack | A |
---|---|

... | Additional arguments. Currently ignored. |

A `model_stack`

object with a subclass `linear_stack`

---this fitted
model contains the necessary components to predict on new data.

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

for examples.

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.

Other core verbs:
`add_candidates()`

,
`blend_predictions()`

,
`stacks()`

# \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 tibble: 5 x 3 #> member type weight #> <chr> <chr> <dbl> #> 1 reg_res_svm_1_1 svm_rbf 0.442 #> 2 reg_res_svm_1_3 svm_rbf 0.265 #> 3 reg_res_sp_4_1 linear_reg 0.261 #> 4 reg_res_sp_9_1 linear_reg 0.0860 #> 5 reg_res_sp_2_1 linear_reg 0.0480reg_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 -59); ...#> ! Bootstrap19: preprocessor 1/1, model 1/1: from glmnet Fortran code (error code -86); ...#> ! Bootstrap22: preprocessor 1/1, model 1/1: from glmnet Fortran code (error code -61); ...class_st#>#> #>#> #>#>#> #>#> # A tibble: 10 x 4 #> member type weight class #> <chr> <chr> <dbl> <chr> #> 1 .pred_full_class_res_nn_1_1 mlp 28.8 full #> 2 .pred_mid_class_res_rf_1_01 rand_forest 10.9 mid #> 3 .pred_mid_class_res_nn_1_1 mlp 7.82 mid #> 4 .pred_mid_class_res_rf_1_04 rand_forest 5.76 low #> 5 .pred_mid_class_res_rf_1_08 rand_forest 5.53 low #> 6 .pred_mid_class_res_rf_1_07 rand_forest 4.48 low #> 7 .pred_mid_class_res_rf_1_05 rand_forest 1.80 mid #> 8 .pred_mid_class_res_rf_1_10 rand_forest 1.36 mid #> 9 .pred_mid_class_res_rf_1_02 rand_forest 0.552 low #> 10 .pred_full_class_res_rf_1_04 rand_forest 0.284 mid# ...or binomial classification models log_st <- stacks() %>% add_candidates(log_res_nn) %>% add_candidates(log_res_rf) %>% blend_predictions() %>% fit_members() log_st#>#> #>#> #>#> #>#> # A tibble: 4 x 3 #> member type weight #> <chr> <chr> <dbl> #> 1 .pred_yes_log_res_nn_1_1 mlp 6.09 #> 2 .pred_yes_log_res_rf_1_09 rand_forest 1.87 #> 3 .pred_yes_log_res_rf_1_05 rand_forest 1.45 #> 4 .pred_yes_log_res_rf_1_06 rand_forest 0.842# }