Compute quantile regressions via quantile spacings

Compute quantile regressions via quantile spacings

qs(
  formula,
  data = NULL,
  quantiles = c(0.9, 0.75, 0.5, 0.25, 0.1),
  baseline_quantile = 0.5,
  cluster_formula = NULL,
  weights = NULL,
  algorithm = "sfn",
  control = qs_control(),
  std_err_control = se_control(),
  parallel = TRUE,
  calc_se = TRUE,
  seed = NULL,
  ...
)

Arguments

formula

an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.

data

an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula)

quantiles

vector of quantiles to be estimated

baseline_quantile

baseline quantile to measure spacings from (defaults to 0.5)

cluster_formula

formula (e.g. ~X1 + X2) giving the clustering formula

weights

optional vector of weights for weighted quantile regression

algorithm

What algorithm to use for fitting underlying regressions. Either one of "sfn", "br", "lasso", "post_lasso", or a function name which estimates quantiles. See details.

control

control parameters to pass to the control arguments of quantreg_spacing, the lower-level function called by qs. This is set via the function qs_control, which returns a named list, with elements including:

  • trunc: whether to truncate residual values below the argument "small"

  • small: level of "small" values to guarentee numerical stability. If not specified, set dynamically based on the standard deviation of the outcome variable.

  • lambda: For penalized regression, you can specify a level of lambda which will weight the penalty. If not set, will be determined based on 10-fold cross-validation.

  • output_quantiles: whether to save fitted quantiles as part of the function output

  • calc_avg_me: whether to return average marginal effects as part of the fitted object

  • lambda: the penalization factor to be passed to penalized regression algorithms

std_err_control

control parameters to pass to the control arguments of quantreg_spacing, the lower-level function called by standard_errors. Possible arguments include:

  • se_method: Method to use for standard errors, either "weighted_bootstrap", "subsample", "bootstrap" or "custom" along with a specified subsampling method and subsample percent. If specifying "custom", must also specify subsampling_percent and draw_weights. If you specify "subsample", subsampling percent defaults to 0.2, but can be changed. See details for details.

  • num_bs: Number of bootstrap iterations to use, defaults to 100.

  • subsample_percent: A number between 0 and one, specifying the percent of the data to subsample for standard error calculations

  • draw_weights: Whether to use random exponential weights for bootstrap, either TRUE or FALSE

  • sampling_method One of "leaveRows", "subsampleRows", or "bootstrapRows". leaveRows doesn't resample rows at all. subsampleRows samples without replacement given some percentage of the data (specified via subsample_percent), and bootstrapRows samples with replacement.`

parallel

whether to run bootstrap in parallel

calc_se

boolean, whether or not to calculate standard errors

seed

what seed to use for replicable RNG

...

additional arguments, ignored for now

Details

The qs function is a higher-level interface to fitting quantile spacings model, handling both the quantile spacings regression, allowing the user to specify a number of possible algorithms and methods for standard errors. It also supports