Internal method that creates linear fits

Internal method that creates linear fits

Usage

lm_robust_fit(
  y,
  X,
  weights,
  cluster,
  ci = TRUE,
  se_type,
  has_int,
  alpha = 0.05,
  return_vcov = TRUE,
  return_fit = TRUE,
  try_cholesky = FALSE,
  iv_stage = list(0),
  fe_rank = 0L,
  fe_leverage = NULL,
  femat = NULL,
  linear_hypothesis = NULL
)

Arguments

y

numeric outcome vector or matrix

X

numeric design matrix

weights

numeric weights vector

cluster

numeric cluster vector

ci

boolean, whether to return confidence intervals and p-values

se_type

character denoting which kind of SEs to return

has_int

logical, whether the model has an intercept

alpha

numeric, test size for confidence intervals

return_vcov

logical, whether to return the vcov matrix

return_fit

logical, whether to return fitted values

try_cholesky

logical. Solve by Cholesky decomposition of X'X rather than by the pivoted QR, falling back to the QR where the design is rank deficient. See lm_robust() for when the fast path is safe.

iv_stage

list of length one or two for 2SLS stages

fe_rank

integer, degrees of freedom absorbed by fixed effects

fe_leverage

numeric vector of per-observation leverage contributed by the absorbed fixed effects, or NULL. h_ii of the full design splits exactly into the demeaned-X leverage plus this term, for any number of FE factors, which is what makes HC2 and HC3 available under fixed_effects without building the dummy matrix.

femat

optional numeric matrix of fixed-effect dummies for the estimation sample. Only CR2 needs it: its adjustment is built from cluster-level blocks of the hat matrix, which do not decompose the way the diagonal does. HC2 and HC3 take fe_leverage instead. NULL unless the requested se_type requires it.

linear_hypothesis

optional hypotheses, in the form lh_robust() takes, whose CR2 Satterthwaite degrees of freedom are returned as hypothesis_df. Ignored for every other se_type, where a combination of coefficients has the same degrees of freedom as each of them.

Examples

# The fitter behind lm_robust(), exported for packages that have already
# built their own design matrix. Most users want lm_robust().
set.seed(45)
X <- cbind(`(Intercept)` = 1, x = rnorm(50))
y <- X[, "x"] + rnorm(50)

lm_robust_fit(
  y = y, X = X,
  weights = NULL, cluster = NULL,
  se_type = "HC2", has_int = TRUE
)
#>               Estimate Std. Error   t value     Pr(>|t|)   CI Lower CI Upper DF
#> (Intercept) 0.02772598  0.1691670 0.1638971 8.705003e-01 -0.3124071 0.367859 48
#> x           1.14688528  0.1601907 7.1595005 4.189198e-09  0.8248003 1.468970 48