Description Usage Arguments Details Value See Also Examples

View source: R/estimatr_lm_lin.R

This function is a wrapper for `lm_robust`

that
is useful for estimating treatment effects with pre-treatment covariate
data. This implements the method described by Lin (2013).

1 2 |

`formula` |
an object of class formula, as in |

`covariates` |
a right-sided formula with pre-treatment covariates on
the right hand side, such as |

`data` |
A |

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

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

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

`se_type` |
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" are permissible. |

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

`alpha` |
The significance level, 0.05 by default. |

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

`try_cholesky` |
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 is simply a wrapper for `lm_robust`

and implements
the Lin estimator (see the reference below). This method
pre-processes the data by taking the covariates specified in the
``covariates``

argument, centering them by subtracting from each covariate
its mean, and interacting them with the treatment. If the treatment has
multiple values, a series of dummies for each value is created and each of
those is interacted with the demeaned covariates. More details can be found
in the
Getting Started vignette
and the
mathematical notes.

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.

Users who want to print the results in TeX of HTML can use the
`extract`

function and the texreg package.

An object of class `"lm_robust"`

is a list containing at least the
following components:

`coefficients` |
the estimated coefficients |

`std.error` |
the estimated standard errors |

`df` |
the estimated degrees of freedom |

`p.value` |
the p-values from a two-sided t-test using |

`ci.lower` |
the lower bound of the |

`ci.upper` |
the upper bound of the |

`term` |
a character vector of coefficient names |

`alpha` |
the significance level specified by the user |

`se_type` |
the standard error type specified by the user |

`res_var` |
the residual variance |

`N` |
the number of observations used |

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

`rank` |
the rank of the fitted model |

`vcov` |
the fitted variance covariance matrix |

`r.squared` |
The
where |

`adj.r.squared` |
The |

`weighted` |
whether or not weights were applied |

`call` |
the original function call |

We also return `terms`

and `contrasts`

, used by `predict`

,
and `scaled_center`

the means of each of the covariates used for centering them

#' @references Freedman, David A. 2008. "On Regression Adjustments in Experiments with Several Treatments." The Annals of Applied Statistics. JSTOR, 176-96. https://doi.org/10.1214/07-AOAS143.

Lin, Winston. 2013. "Agnostic Notes on Regression Adjustments to Experimental Data: Reexamining Freedman's Critique." The Annals of Applied Statistics 7 (1). Institute of Mathematical Statistics: 295-318. https://doi.org/10.1214/12-AOAS583.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | ```
library(fabricatr)
library(randomizr)
dat <- fabricate(
N = 40,
x = rnorm(N, mean = 2.3),
x2 = rpois(N, lambda = 2),
x3 = runif(N),
y0 = rnorm(N) + x,
y1 = rnorm(N) + x + 0.35
)
dat$z <- complete_ra(N = nrow(dat))
dat$y <- ifelse(dat$z == 1, dat$y1, dat$y0)
# Same specification as lm_robust() with one additional argument
lmlin_out <- lm_lin(y ~ z, covariates = ~ x, data = dat)
tidy(lmlin_out)
# Works with multiple pre-treatment covariates
lm_lin(y ~ z, covariates = ~ x + x2, data = dat)
# Also centers data AFTER evaluating any functions in formula
lmlin_out2 <- lm_lin(y ~ z, covariates = ~ x + log(x3), data = dat)
lmlin_out2$scaled_center["log(x3)"]
mean(log(dat$x3))
# Works easily with clusters
dat$clusterID <- rep(1:20, each = 2)
dat$z_clust <- cluster_ra(clusters = dat$clusterID)
lm_lin(y ~ z_clust, covariates = ~ x, data = dat, clusters = clusterID)
# Works with multi-valued treatments
dat$z_multi <- sample(1:3, size = nrow(dat), replace = TRUE)
lm_lin(y ~ z_multi, covariates = ~ x, data = dat)
# Stratified estimator with blocks
dat$blockID <- rep(1:5, each = 8)
dat$z_block <- block_ra(blocks = dat$blockID)
lm_lin(y ~ z_block, ~ factor(blockID), data = dat)
## Not run:
# Can also use 'margins' package if you have it installed to get
# marginal effects
library(margins)
lmlout <- lm_lin(y ~ z_block, ~ x, data = dat)
summary(margins(lmlout))
# Can output results using 'texreg'
library(texreg)
texregobj <- extract(lmlout)
## End(Not run)
``` |

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