lm_robust: Ordinary Least Squares with Robust Standard Errors

View source: R/estimatr_lm_robust.R

lm_robustR Documentation

Ordinary Least Squares with Robust Standard Errors


This formula fits a linear model, provides a variety of options for robust standard errors, and conducts coefficient tests


  se_type = NULL,
  ci = TRUE,
  alpha = 0.05,
  return_vcov = TRUE,
  try_cholesky = FALSE



an object of class formula, as in lm


A data.frame


the bare (unquoted) names of the weights variable in the supplied data.


An optional bare (unquoted) expression specifying a subset of observations to be used.


An optional bare (unquoted) name of the variable that corresponds to the clusters in the data.


An optional right-sided formula containing the fixed effects that will be projected out of the data, such as ~ blockID. Do not pass multiple-fixed effects with intersecting groups. Speed gains are greatest for variables with large numbers of groups and when using "HC1" or "stata" standard errors. See 'Details'.


The sort of standard error sought. If clusters is not specified the options are "HC0", "HC1" (or "stata", the equivalent), "HC2" (default), "HC3", or "classical". If clusters is specified the options are "CR0", "CR2" (default), or "stata". Can also specify "none", which may speed up estimation of the coefficients.


logical. Whether to compute and return p-values and confidence intervals, TRUE by default.


The significance level, 0.05 by default.


logical. Whether to return the variance-covariance matrix for later usage, TRUE by default.


logical. Whether to try using a Cholesky decomposition to solve least squares instead of a QR decomposition, FALSE by default. Using a Cholesky decomposition may result in speed gains, but should only be used if users are sure their model is full-rank (i.e., there is no perfect multi-collinearity)


This function performs linear regression and provides a variety of standard errors. It takes a formula and data much in the same was as lm does, and all auxiliary variables, such as clusters and weights, can be passed either as quoted names of columns, as bare column names, or as a self-contained vector. Examples of usage can be seen below and in the Getting Started vignette.

The mathematical notes in this vignette specify the exact estimators used by this function. The default variance estimators have been chosen largely in accordance with the procedures in this manual. The default for the case without clusters is the HC2 estimator and the default with clusters is the analogous CR2 estimator. Users can easily replicate Stata standard errors in the clustered or non-clustered case by setting `se_type` = "stata".

The function estimates the coefficients and standard errors in C++, using the RcppEigen package. By default, we estimate the coefficients using Column-Pivoting QR decomposition from the Eigen C++ library, although users could get faster solutions by setting `try_cholesky` = TRUE to use a Cholesky decomposition instead. This will likely result in quicker solutions, but the algorithm does not reliably detect when there are linear dependencies in the model and may fail silently if they exist.

If `fixed_effects` are specified, both the outcome and design matrix are centered using the method of alternating projections (Halperin 1962; Gaure 2013). Specifying fixed effects in this way will result in large speed gains with standard error estimators that do not need to invert the matrix of fixed effects. This means using "classical", "HC0", "HC1", "CR0", or "stata" standard errors will be faster than other standard error estimators. Be wary when specifying fixed effects that may result in perfect fits for some observations or if there are intersecting groups across multiple fixed effect variables (e.g. if you specify both "year" and "country" fixed effects with an unbalanced panel where one year you only have data for one country).

As with `lm()`, multivariate regression (multiple outcomes) will only admit observations into the estimation that have no missingness on any outcome.


An object of class "lm_robust".

The post-estimation commands functions summary and tidy return results in a data.frame. To get useful data out of the return, you can use these data frames, you can use the resulting list directly, or you can use the generic accessor functions coef, vcov, confint, and predict. Marginal effects and uncertainty about them can be gotten by passing this object to margins from the margins, or to emmeans in the emmeans package.

Users who want to print the results in TeX of HTML can use the extract function and the texreg package.

If users specify a multivariate linear regression model (multiple outcomes), then some of the below components will be of higher dimension to accommodate the additional models.

An object of class "lm_robust" is a list containing at least the following components:


the estimated coefficients


the estimated standard errors


the t-statistic


the estimated degrees of freedom


the p-values from a two-sided t-test using coefficients, std.error, and df


the lower bound of the 1 - alpha percent confidence interval


the upper bound of the 1 - alpha percent confidence interval


a character vector of coefficient names


the significance level specified by the user


the standard error type specified by the user


the residual variance


the number of observations used


the number of columns in the design matrix (includes linearly dependent columns!)


the rank of the fitted model


the fitted variance covariance matrix


The R^2,

R^2 = 1 - Sum(e[i]^2) / Sum((y[i] - y^*)^2),

where y^* is the mean of y[i] if there is an intercept and zero otherwise, and e[i] is the ith residual.


The R^2 but penalized for having more parameters, rank


a vector with the value of the F-statistic with the numerator and denominator degrees of freedom


whether or not weights were applied


the original function call


the matrix of predicted means

We also return terms and contrasts, used by predict. If fixed_effects are specified, then we return proj_fstatistic, proj_r.squared, and proj_adj.r.squared, which are model fit statistics that are computed on the projected model (after demeaning the fixed effects).


Abadie, Alberto, Susan Athey, Guido W Imbens, and Jeffrey Wooldridge. 2017. "A Class of Unbiased Estimators of the Average Treatment Effect in Randomized Experiments." arXiv Pre-Print. https://arxiv.org/abs/1710.02926v2.

Bell, Robert M, and Daniel F McCaffrey. 2002. "Bias Reduction in Standard Errors for Linear Regression with Multi-Stage Samples." Survey Methodology 28 (2): 169-82.

Gaure, Simon. 2013. "OLS with multiple high dimensional category variables." Computational Statistics & Data Analysis 66: 8-1. doi: 10.1016/j.csda.2013.03.024

Halperin, I. 1962. "The product of projection operators." Acta Scientiarum Mathematicarum (Szeged) 23(1-2): 96-99.

MacKinnon, James, and Halbert White. 1985. "Some Heteroskedasticity-Consistent Covariance Matrix Estimators with Improved Finite Sample Properties." Journal of Econometrics 29 (3): 305-25. doi: 10.1016/0304-4076(85)90158-7.

Pustejovsky, James E, and Elizabeth Tipton. 2016. "Small Sample Methods for Cluster-Robust Variance Estimation and Hypothesis Testing in Fixed Effects Models." Journal of Business & Economic Statistics. Taylor & Francis. doi: 10.1080/07350015.2016.1247004.

Samii, Cyrus, and Peter M Aronow. 2012. "On Equivalencies Between Design-Based and Regression-Based Variance Estimators for Randomized Experiments." Statistics and Probability Letters 82 (2). doi: 10.1016/j.spl.2011.10.024.


dat <- fabricate(
  N = 40,
  y = rpois(N, lambda = 4),
  x = rnorm(N),
  z = rbinom(N, 1, prob = 0.4)

# Default variance estimator is HC2 robust standard errors
lmro <- lm_robust(y ~ x + z, data = dat)

# Can tidy() the data in to a data.frame
# Can use summary() to get more statistics
# Can also get coefficients three ways
# Can also get confidence intervals from object or with new 1 - `alpha`
confint(lmro, level = 0.8)

# Can recover classical standard errors
lmclassic <- lm_robust(y ~ x + z, data = dat, se_type = "classical")

# Can easily match Stata's robust standard errors
lmstata <- lm_robust(y ~ x + z, data = dat, se_type = "stata")

# Easy to specify clusters for cluster-robust inference
dat$clusterID <- sample(1:10, size = 40, replace = TRUE)

lmclust <- lm_robust(y ~ x + z, data = dat, clusters = clusterID)

# Can also match Stata's clustered standard errors
  y ~ x + z,
  data = dat,
  clusters = clusterID,
  se_type = "stata"

# Works just as LM does with functions in the formula
dat$blockID <- rep(c("A", "B", "C", "D"), each = 10)

lm_robust(y ~ x + z + factor(blockID), data = dat)

# Weights are also easily specified
dat$w <- runif(40)

  y ~ x + z,
  data = dat,
  weights = w,
  clusters = clusterID

# Subsetting works just as in `lm()`
lm_robust(y ~ x, data = dat, subset = z == 1)

# One can also choose to set the significance level for different CIs
lm_robust(y ~ x + z, data = dat, alpha = 0.1)

# We can also specify fixed effects
# Speed gains with fixed effects are greatest with "stata" or "HC1" std.errors
tidy(lm_robust(y ~ z, data = dat, fixed_effects = ~ blockID, se_type = "HC1"))

## Not run: 
  # Can also use 'margins' or 'emmeans' package if you have them installed
  # to get marginal effects
  lmrout <- lm_robust(y ~ x + z, data = dat)

  # Can output results using 'texreg'

  # Using emmeans to obtain covariate-adjusted means
  fiber.rlm <- lm_robust(strength ~ diameter + machine, data = fiber)
  emmeans(fiber.rlm, "machine")

## End(Not run)

estimatr documentation built on July 4, 2022, 5:07 p.m.

Related to lm_robust in estimatr...