qs.Rd
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, ... )
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
|
std_err_control | control parameters to pass to the control arguments of
|
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 |
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