calibrate_: Internal function to perform multilevel calibration

In ebenmichael/multical: Multilevel calibration weighting for surveys

Description

Internal function to perform multilevel calibration

Usage

calibrate_(
  cells,
  sample_counts,
  target_counts,
  order = NULL,
  lambda = 1,
  lambda_max = NULL,
  n_lambda = 100,
  lambda_min_ratio = 1e-05,
  lowlim = 0,
  uplim = Inf,
  verbose = FALSE,
  ...
)

Arguments

cells	Dataframe of distinct cells
sample_counts	Vector of sample counts for each cell
target_counts	Vector of target counts for each cell
order	Integer. What order interactions to balance. Default is all orders
lambda	Numeric. Regularization hyperparamter, by default fits weights for a range of values
lambda_max	Numeric. Maximum hyperparameter to fit weights with, default is the root sum of squared differences between the (unweighted) sample and the target
n_lambda	Integer. Number of hyper-parameters to fit weights for, from lambda_max to lambda_max * lambda_min_ratio, equally spaced on the log scale. Default, 20
lambda_min_ratio	Numeric. Ratio of min to max lambda to consider.
lowlim	Lower bound on weights, default 0
uplim	Upper bound on weights, default Inf
verbose	Boolean. Show optimization information, default False
...	Additional parameters for osqp

ebenmichael/multical documentation built on Dec. 20, 2021, 3:12 a.m.