Power Analysis for DSL Regression

Usage

power_dsl(
  labeled_size = NULL,
  dsl_out = NULL,
  model = "lm",
  formula,
  predicted_var,
  prediction = NULL,
  data,
  cluster = NULL,
  labeled = NULL,
  sample_prob = NULL,
  index = NULL,
  fixed_effect = "oneway",
  sl_method = "grf",
  feature = NULL,
  family = "gaussian",
  cross_fit = 5,
  sample_split = 10,
  seed = 1234
)

Arguments

labeled_size: A vector indicating the number of labeled documents for which the function predicts standard errors.
dsl_out: An output from function dsl. When this is supplied, the remaining arguments are overwritten by arguments specified in the output of dsl. When this is NULL, the function will use arguments specified below.
model: A regression model dsl currently supports lm (linear regression), logit (logistic regression), and felm (fixed-effects regression).
formula: A formula used in the specified regression model.
predicted_var: A vector of column names in the data that correspond to variables that need to be predicted.
prediction: A vector of column names in the data that correspond to predictions of predicted_var.
data: A data frame. The class should be data.frame.
cluster: A column name in the data that indicates the level at which cluster standard errors are calculated. Default is NULL.
labeled: (Optional) A column name in the data that indicates which observation is labeled. It should be a vector of 1 (labeled) and 0 (non-labeled). When NULL, the function assumes that observations that have NA in predicted_var are non-labeled and other observations are labeled.
sample_prob: (Optional) A column name in the data that correspond to the sampling probability for labeling a particular observation. When NULL, the function assumes random sampling with equal probabilities.
index: (Used when model = "felm") A vector of column names specifying fixed effects. When fixed_effect = oneway, it has one element. When fixed_effect = twoways, it has two elements, e.g., index = c("state", "year").
fixed_effect: (Used when model = "felm") A type of fixed effects regression you run. oneway (one-way fixed effects) or twoways (two-way fixed effects).
sl_method: A name of a supervised machine learning model used internally to predict predicted_var by fine-tuning prediction or using predictors (specified in feature) when prediction = NULL. Users can run available_method() to see available supervised machine learning methods. Default is grf (generalized random forest).
feature: A vector of column names in the data that correspond to predictors used to fit a supervised machine learning (specified in sl_method).
family: (Used when making predictions) A variable type of predicted_var. Default is gaussian.
cross_fit: The fold of cross-fitting. Default is 5.
sample_split: The number of sampling-splitting. Default is 10.
seed: Numeric seed used internally. Default is 1234.

Value

dsl returns an object of dsl class.

predicted_se: Predicted standard errors for coefficients. The first row shows the current standard errors for coefficients. The remaining rows show predicted standard errors.
labeled_size: A vector indicating the number of labeled documents for which the function predicts standard errors.
dsl_out: An output from function dsl.