fit_net_functions: Fits a conditional logistic regression/SSF/iSSF with...

In NINAnor/oneimpact: Tools for the assessment of the cumulative impacts of anthropogenic features in ecological studies

Fits a conditional logistic regression/SSF/iSSF with penalized regression using glmnet in a train-validate-test setup

Description

By default, fit_net_clogit() does not standardize predictor variables. If you want numeric variables to be standardized, you can either use ⁠[bag_fit_net_clogit()]⁠ with parameter standardize = TRUE or provide an already standardized data set as input.

By default, fit_net_logit() does not standardize predictor variables. If you want numeric variables to be standardized, you can either use ⁠[bag_fit_net_logit()]⁠ with parameter standardize = TRUE or provide an already standardized data set as input.

Usage

fit_net_clogit(
  f,
  data,
  samples,
  i = 1,
  kernel_vars = c("step_length", "ta"),
  metric = c("coxnet.deviance", "Cindex", "conditionalAUC", "conditionalSomersD")[1],
  metrics_evaluate = c("coxnet.deviance", "Cindex", "conditionalAUC"),
  method = c("Lasso", "Ridge", "AdaptiveLasso", "DistanceDecay-AdaptiveLasso",
    "DD-AdaptiveLasso", "OneZOI-AdaptiveLasso", "OZ-AdaptiveLasso",
    "Grouped-AdaptiveLasso", "G-AdaptiveLasso", "HypothesisDriven-AdaptiveLasso",
    "HD-AdaptiveLasso", "ElasticNet")[1],
  alpha = NULL,
  penalty.factor = NULL,
  gamma = 1,
  standardize = c("internal", "external", FALSE)[1],
  predictor_table = NULL,
  function_lasso_decay = c(log, function(x) x/1000)[[1]],
  value_lasso_decay = 1,
  function_hypothesis = c(exp)[[1]],
  expected_sign_hypothesis = -1,
  factor_grouped_lasso = 1,
  replace_missing_NA = TRUE,
  na.action = "na.pass",
  out_dir_file = NULL,
  verbose = FALSE,
  ...
)

fit_net_ssf(
  f,
  data,
  samples,
  i = 1,
  kernel_vars = c("step_length", "ta"),
  metric = c("coxnet.deviance", "Cindex", "conditionalAUC", "conditionalSomersD")[1],
  metrics_evaluate = c("coxnet.deviance", "Cindex", "conditionalAUC"),
  method = c("Lasso", "Ridge", "AdaptiveLasso", "DistanceDecay-AdaptiveLasso",
    "DD-AdaptiveLasso", "OneZOI-AdaptiveLasso", "OZ-AdaptiveLasso",
    "Grouped-AdaptiveLasso", "G-AdaptiveLasso", "HypothesisDriven-AdaptiveLasso",
    "HD-AdaptiveLasso", "ElasticNet")[1],
  alpha = NULL,
  penalty.factor = NULL,
  gamma = 1,
  standardize = c("internal", "external", FALSE)[1],
  predictor_table = NULL,
  function_lasso_decay = c(log, function(x) x/1000)[[1]],
  value_lasso_decay = 1,
  function_hypothesis = c(exp)[[1]],
  expected_sign_hypothesis = -1,
  factor_grouped_lasso = 1,
  replace_missing_NA = TRUE,
  na.action = "na.pass",
  out_dir_file = NULL,
  verbose = FALSE,
  ...
)

fit_net_issf(
  f,
  data,
  samples,
  i = 1,
  kernel_vars = c("step_length", "ta"),
  metric = c("coxnet.deviance", "Cindex", "conditionalAUC", "conditionalSomersD")[1],
  metrics_evaluate = c("coxnet.deviance", "Cindex", "conditionalAUC"),
  method = c("Lasso", "Ridge", "AdaptiveLasso", "DistanceDecay-AdaptiveLasso",
    "DD-AdaptiveLasso", "OneZOI-AdaptiveLasso", "OZ-AdaptiveLasso",
    "Grouped-AdaptiveLasso", "G-AdaptiveLasso", "HypothesisDriven-AdaptiveLasso",
    "HD-AdaptiveLasso", "ElasticNet")[1],
  alpha = NULL,
  penalty.factor = NULL,
  gamma = 1,
  standardize = c("internal", "external", FALSE)[1],
  predictor_table = NULL,
  function_lasso_decay = c(log, function(x) x/1000)[[1]],
  value_lasso_decay = 1,
  function_hypothesis = c(exp)[[1]],
  expected_sign_hypothesis = -1,
  factor_grouped_lasso = 1,
  replace_missing_NA = TRUE,
  na.action = "na.pass",
  out_dir_file = NULL,
  verbose = FALSE,
  ...
)

fit_net_logit(
  f,
  data,
  samples,
  i = 1,
  metric = c("AUC")[1],
  metrics_evaluate = c("AUC"),
  method = c("Lasso", "Ridge", "AdaptiveLasso", "DistanceDecay-AdaptiveLasso",
    "DD-AdaptiveLasso", "OneZOI-AdaptiveLasso", "OZ-AdaptiveLasso",
    "Grouped-AdaptiveLasso", "G-AdaptiveLasso", "HypothesisDriven-AdaptiveLasso",
    "HD-AdaptiveLasso", "ElasticNet")[1],
  alpha = NULL,
  penalty.factor = NULL,
  gamma = 1,
  standardize = c("internal", "external", FALSE)[1],
  predictor_table = NULL,
  function_lasso_decay = c(log, function(x) x/1000)[[1]],
  value_lasso_decay = 1,
  function_hypothesis = c(exp)[[1]],
  expected_sign_hypothesis = -1,
  factor_grouped_lasso = 1,
  replace_missing_NA = TRUE,
  na.action = "na.pass",
  out_dir_file = NULL,
  verbose = FALSE,
  ...
)

fit_net_rsf(
  f,
  data,
  samples,
  i = 1,
  metric = c("AUC")[1],
  metrics_evaluate = c("AUC"),
  method = c("Lasso", "Ridge", "AdaptiveLasso", "DistanceDecay-AdaptiveLasso",
    "DD-AdaptiveLasso", "OneZOI-AdaptiveLasso", "OZ-AdaptiveLasso",
    "Grouped-AdaptiveLasso", "G-AdaptiveLasso", "HypothesisDriven-AdaptiveLasso",
    "HD-AdaptiveLasso", "ElasticNet")[1],
  alpha = NULL,
  penalty.factor = NULL,
  gamma = 1,
  standardize = c("internal", "external", FALSE)[1],
  predictor_table = NULL,
  function_lasso_decay = c(log, function(x) x/1000)[[1]],
  value_lasso_decay = 1,
  function_hypothesis = c(exp)[[1]],
  expected_sign_hypothesis = -1,
  factor_grouped_lasso = 1,
  replace_missing_NA = TRUE,
  na.action = "na.pass",
  out_dir_file = NULL,
  verbose = FALSE,
  ...
)

grouped_func(coefs, phi_group = 0)

Arguments

f	⁠[formula]⁠ Formula of the model to be fitted, with all possible candidate terms.
data	⁠[data.frame,tibble]⁠ Complete data set to be analyzed.
samples	⁠[list]⁠ List of samples with at least three elements: train, test, and validate. Each elements might have several elements, each representing the lines of data to be sampled for each resample. Typically, this is computed by the function create_resamples().
kernel_vars	⁠[vector,character=c("step_length", "ta")]⁠ Vector of strings with the names of the variables related to the movement kernel, included in the model (for instance, "step_length" and "turning_angle")
metric	⁠[function,character]{AUC, conditionalBoyce, conditionalSomersD, conditionalAUC}⁠ Function representing the metric to evaluate goodness-of-fit. One of AUC (Default), conditionalBoyce, conditionalSomersD, and conditionalAUC. A user-defined function might be provided, with a condition that it must be maximized to find the best fit model. It can also be a character, in case it should be one of the following: c("AUC", "conditionalAUC", "conditionalBoyce", "conditionalSomersD").
method	⁠[character="Lasso"]⁠ The penalized regression method used for fitting each model. Default is method = "Lasso", but it could be method = "Ridge" or different flavors of "AdaptiveLasso" (see details below).
gamma	⁠[numeric(1)=1]{(0.5, 1, 2)}⁠ Gamma is the exponent for defining the vector of penalty weights when ⁠method = "AdaptiveLasso⁠. This means that the penalties are defined as penalty.factor = 1/(coef_ridge^gamma), where coef_ridge are the coefficients of a Ridge regression. Default is gamma = 1, but values of 0.5 or 2 could also be tried, as suggested by the authors (Zou et al 2006).
standardize	⁠[logical(1)=TRUE]⁠ Logical flag for predictor variable standardization, prior to fitting the model sequence. The coefficients are always returned on the original scale. Default is standardize=TRUE. If variables are in the same units already, you might not wish to standardize them.
replace_missing_NA	⁠[logical(1)=TRUE]⁠ If TRUE (default), any variables missing from the data (i.e. with variance zero) are removed from the formula for the model fitting procedure, and a NA is set as its coefficient in the output. If FALSE, the function raises an error if there are variables with variance zero in the formula.
out_dir_file	⁠[character(1)=NULL]⁠ String with the prefix of the file name (and the folder) where the result of each model will be saved. E.g. if out_dir_file = "output/test_", the models will be saved as RDS files names "test_i1.rds", "test_i2.rds", etc, within the folder "output".
...	Options for net_logit() and glmnet::glmnet().
phi_group	Additional penalty constant for the group-based penalties. A value in the interval 0, Inf where 0 is no additional penalty and higher values correspond to higher penalties.

References

Zou, H., 2006. The Adaptive Lasso and Its Oracle Properties. Journal of the American Statistical Association 101, 1418–1429. https://doi.org/10.1198/016214506000000735

Zou, H., 2006. The Adaptive Lasso and Its Oracle Properties. Journal of the American Statistical Association 101, 1418–1429. https://doi.org/10.1198/016214506000000735

NINAnor/oneimpact documentation built on June 14, 2025, 12:27 a.m.