model_bootstrap  R Documentation 
Execute full model bootstrapping with ALE calculation on each bootstrap run
model_bootstrap(
data,
model,
...,
model_call_string = NULL,
model_call_string_vars = character(),
parallel = parallel::detectCores(logical = FALSE)  1,
model_packages = as.character(NA),
boot_it = 100,
seed = 0,
boot_alpha = 0.05,
boot_centre = "mean",
output = c("ale", "model_stats", "model_coefs"),
ale_options = list(),
tidy_options = list(),
glance_options = list(),
compact_plots = FALSE,
silent = FALSE
)
data 
dataframe. Dataset that will be bootstrapped. 
model 
See documentation for 
... 
not used. Inserted to require explicit naming of subsequent arguments. 
model_call_string 
character string. If NULL, 
model_call_string_vars 
character. Character vector of names of variables
included in 
parallel 
See documentation for 
model_packages 
See documentation for 
boot_it 
integer from 0 to Inf. Number of bootstrap iterations.
If boot_it = 0, then the model is run as normal once on the full 
seed 
integer. Random seed. Supply this between runs to assure identical bootstrap samples are generated each time on the same data. 
boot_alpha 
numeric. The confidence level for the bootstrap confidence intervals is 1  boot_alpha. For example, the default 0.05 will give a 95% confidence interval, that is, from the 2.5% to the 97.5% percentile. 
boot_centre 
See See documentation for 
output 
character vector. Which types of bootstraps to calculate and return:

ale_options , tidy_options , glance_options 
list of named arguments.
Arguments to pass to the 
compact_plots 
See documentation for 
silent 
See documentation for 
No modelling results, with or without ALE, should be considered reliable without
being bootstrapped. For large datasets, normally the model provided to ale()
is the final deployment model that has been validated and evaluated on
training and testing on subsets; that is why ale()
is calculated on the full
dataset. However, when a dataset is too small to be subdivided into training
and test sets for a standard machine learning process, then the entire model
should be bootstrapped. That is, multiple models should be trained, one on
each bootstrap sample. The reliable results are the average results of all
the bootstrap models, however many there are. For details, see the vignette
on small datasets or the details and examples below.
model_bootstrap()
automatically carries out fullmodel bootstrapping suitable
for small datasets. Specifically, it:
Creates multiple bootstrap samples (default 100; the user can specify any number);
Creates a model on each bootstrap sample;
Calculates model overall statistics, variable coefficients, and ALE values for each model on each bootstrap sample;
Calculates the mean, median, and lower and upper confidence intervals for each of those values across all bootstrap samples.
Pvalues
The broom::tidy()
summary statistics will provide pvalues as normal, but the
situation is somewhat complicated with pvalues for ALE statistics. The challenge
is that the procedure for obtaining their pvalues is very slow: it involves
retraining the model 1000 times. Thus, it is not efficient to calculate pvalues
on every execution of model_bootstrap()
. Although the ale()
function provides
an 'auto' option for creating pvalues,
that option is disabled in model_bootstrap()
because it would be far too slow:
it would involve retraining the model 1000 times the number of bootstrap iterations.
Rather, you must first create a pvalues function object using the procedure
described in help(create_p_funs)
. If the name of your pvalues object is
p_funs
, you can then request pvalues each time you run model_bootstrap()
by passing it the argument ale_options = list(p_values = p_funs)
.
list with tibbles of the following elements (depending on values requested in
the output
argument:
model_stats: bootstrapped results from broom::glance()
model_coefs: bootstrapped results from broom::tidy()
ale: bootstrapped ALE results
data: ALE data (see ale()
for details about the format)
stats: ALE statistics. The same data is duplicated with different views that might be variously useful. The column
by_term: statistic, estimate, conf.low, median, mean, conf.high.
("term" means variable name.)
The column names are compatible with the broom
package. The confidence intervals
are based on the ale()
function defaults; they can be changed with the
ale_options
argument. The estimate is the median or the mean, depending
on the boot_centre
argument.
by_statistic: term, estimate, conf.low, median, mean, conf.high.
estimate: term, then one column per statistic Provided with the default estimate. This view does not present confidence intervals.
plots: ALE plots (see ale()
for details about the format)
boot_data: full bootstrap data (not returned by default)
other values: the boot_it
, seed
, boot_alpha
, and boot_centre
arguments that
were originally passed are returned for reference.
Okoli, Chitu. 2023. “Statistical Inference Using Machine Learning and Classical Techniques Based on Accumulated Local Effects (ALE).” arXiv. https://arxiv.org/abs/2310.09877.
# attitude dataset
attitude
## ALE for general additive models (GAM)
## GAM is tweaked to work on the small dataset.
gam_attitude < mgcv::gam(rating ~ complaints + privileges + s(learning) +
raises + s(critical) + advance,
data = attitude)
summary(gam_attitude)
# Full model bootstrapping
# Only 4 bootstrap iterations for a rapid example; default is 100
# Increase value of boot_it for more realistic results
mb_gam < model_bootstrap(
attitude,
gam_attitude,
boot_it = 4,
parallel = 2 # CRAN limit (delete this line on your own computer)
)
# If the model is not standard, supply model_call_string with
# 'data = boot_data' in the string (not as a direct argument to [model_bootstrap()])
mb_gam < model_bootstrap(
attitude,
gam_attitude,
model_call_string = 'mgcv::gam(
rating ~ complaints + privileges + s(learning) +
raises + s(critical) + advance,
data = boot_data
)',
boot_it = 4,
parallel = 2 # CRAN limit (delete this line on your own computer)
)
# Model statistics and coefficients
mb_gam$model_stats
mb_gam$model_coefs
# Plot ALE
mb_gam$ale$plots >
patchwork::wrap_plots()
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