add_settings_df_rows: Add rows to a settings_df
In metasnf: Meta Clustering with Similarity Network Fusion

add_settings_df_rows

R Documentation

Add rows to a settings_df

Description

Add rows to a settings_df

Usage

add_settings_df_rows(
  sdf,
  n_solutions = 0,
  min_removed_inputs = 0,
  max_removed_inputs = sum(startsWith(colnames(sdf), "inc_")) - 1,
  dropout_dist = "exponential",
  min_alpha = NULL,
  max_alpha = NULL,
  min_k = NULL,
  max_k = NULL,
  min_t = NULL,
  max_t = NULL,
  alpha_values = NULL,
  k_values = NULL,
  t_values = NULL,
  possible_snf_schemes = c(1, 2, 3),
  clustering_algorithms = NULL,
  continuous_distances = NULL,
  discrete_distances = NULL,
  ordinal_distances = NULL,
  categorical_distances = NULL,
  mixed_distances = NULL,
  dfl = NULL,
  snf_input_weights = NULL,
  snf_domain_weights = NULL,
  retry_limit = 10,
  allow_duplicates = FALSE
)

Arguments

`sdf`	The existing settings data frame
`n_solutions`	Number of rows to generate for the settings data frame.
`min_removed_inputs`	The smallest number of input data frames that may be randomly removed. By default, 0.
`max_removed_inputs`	The largest number of input data frames that may be randomly removed. By default, this is 1 less than all the provided input data frames in the data list.
`dropout_dist`	Parameter controlling how the random removal of input data frames should occur. Can be "none" (no input data frames are randomly removed), "uniform" (uniformly sample between min_removed_inputs and max_removed_inputs to determine number of input data frames to remove), or "exponential" (pick number of input data frames to remove by sampling from min_removed_inputs to max_removed_inputs with an exponential distribution; the default).
`min_alpha`	The minimum value that the alpha hyperparameter can have. Random assigned value of alpha for each row will be obtained by uniformly sampling numbers between `min_alpha` and `max_alpha` at intervals of 0.1. Cannot be used in conjunction with the `alpha_values` parameter.
`max_alpha`	The maximum value that the alpha hyperparameter can have. See `min_alpha` parameter. Cannot be used in conjunction with the `alpha_values` parameter.
`min_k`	The minimum value that the k hyperparameter can have. Random assigned value of k for each row will be obtained by uniformly sampling numbers between `min_k` and `max_k` at intervals of 1. Cannot be used in conjunction with the `k_values` parameter.
`max_k`	The maximum value that the k hyperparameter can have. See `min_k` parameter. Cannot be used in conjunction with the `k_values` parameter.
`min_t`	The minimum value that the t hyperparameter can have. Random assigned value of t for each row will be obtained by uniformly sampling numbers between `min_t` and `max_t` at intervals of 1. Cannot be used in conjunction with the `t_values` parameter.
`max_t`	The maximum value that the t hyperparameter can have. See `min_t` parameter. Cannot be used in conjunction with the `t_values` parameter.
`alpha_values`	A number or numeric vector of a set of possible values that alpha can take on. Value will be obtained by uniformly sampling the vector. Cannot be used in conjunction with the `min_alpha` or `max_alpha` parameters.
`k_values`	A number or numeric vector of a set of possible values that k can take on. Value will be obtained by uniformly sampling the vector. Cannot be used in conjunction with the `min_k` or `max_k` parameters.
`t_values`	A number or numeric vector of a set of possible values that t can take on. Value will be obtained by uniformly sampling the vector. Cannot be used in conjunction with the `min_t` or `max_t` parameters.
`possible_snf_schemes`	A vector containing the possible snf_schemes to uniformly randomly select from. By default, the vector contains all 3 possible schemes: c(1, 2, 3). 1 corresponds to the "individual" scheme, 2 corresponds to the "domain" scheme, and 3 corresponds to the "twostep" scheme.
`clustering_algorithms`	A list of clustering algorithms to uniformly randomly pick from when clustering. When not specified, randomly select between spectral clustering using the eigen-gap heuristic and spectral clustering using the rotation cost heuristic. See ?clust_fns_list for more details on running custom clustering algorithms.
`continuous_distances`	A vector of continuous distance metrics to use when a custom dist_fns_list is provided.
`discrete_distances`	A vector of categorical distance metrics to use when a custom dist_fns_list is provided.
`ordinal_distances`	A vector of categorical distance metrics to use when a custom dist_fns_list is provided.
`categorical_distances`	A vector of categorical distance metrics to use when a custom dist_fns_list is provided.
`mixed_distances`	A vector of mixed distance metrics to use when a custom dist_fns_list is provided.
`dfl`	List containing distance metrics to vary over. See ?generate_dist_fns_list.
`snf_input_weights`	Nested list containing weights for when SNF is used to merge individual input measures (see ?generate_snf_weights)
`snf_domain_weights`	Nested list containing weights for when SNF is used to merge domains (see ?generate_snf_weights)
`retry_limit`	The maximum number of attempts to generate a novel row. This function does not return matrices with identical rows. As the range of requested possible settings tightens and the number of requested rows increases, the risk of randomly generating a row that already exists increases. If a new random row has matched an existing row `retry_limit` number of times, the function will terminate.
`allow_duplicates`	If TRUE, enables creation of a settings data frame with duplicate non-feature weighting related hyperparameters. This function should only be used when paired with a custom weights matrix that has non-duplicate rows.