The goal of regweight is to make it easy to diagnose a model using Aronow and Samii (2015) regression weights.

In short, these weights show which observations are most influential for
determining the observed value of a coefficient in a linear regression.
If the linear regression is aiming to estimate causal effects, this
implies that the OLS *estimand* may differ from the average treatment
effect. These linear regression weights provide, in some sense, the most
precise estimate available given a conditioning set (and a linear
model). These weights are in expectation the conditional variance of the
variable of interest (given the other covariates in the model).

For more details, see `vignette("example-usage")`

.

You can install regweight like so:

```
# From CRAN:
install.packages("regweight")
# Or the development version from GitHub:
# install.packages("devtools")
devtools::install_github("ddimmery/regweight")
```

This is a basic example which shows you how to analyze the implicit regression weights in a simple problem:

```
library(regweight)
library(estimatr)
data(penguins, package = "palmerpenguins")
model <- lm_robust(body_mass_g ~ ., penguins)
summary(model)
#>
#> Call:
#> lm_robust(formula = body_mass_g ~ ., data = penguins)
#>
#> Standard error type: HC2
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|) CI Lower CI Upper DF
#> (Intercept) 84087.94 41946.611 2.0046 4.584e-02 1564.884 166611.005 323
#> speciesChinstrap -282.54 79.288 -3.5635 4.212e-04 -438.525 -126.554 323
#> speciesGentoo 890.96 132.512 6.7236 8.048e-11 630.263 1151.653 323
#> islandDream -21.18 56.015 -0.3781 7.056e-01 -131.380 89.019 323
#> islandTorgersen -58.78 63.182 -0.9303 3.529e-01 -183.078 65.524 323
#> bill_length_mm 18.96 6.214 3.0516 2.465e-03 6.738 31.190 323
#> bill_depth_mm 60.80 18.841 3.2270 1.379e-03 23.732 97.863 323
#> flipper_length_mm 18.50 2.878 6.4283 4.632e-10 12.841 24.167 323
#> sexmale 378.98 45.265 8.3724 1.737e-15 289.926 468.028 323
#> year -42.78 20.953 -2.0420 4.197e-02 -84.006 -1.563 323
#>
#> Multiple R-squared: 0.8768 , Adjusted R-squared: 0.8734
#> F-statistic: 298.1 on 9 and 323 DF, p-value: < 2.2e-16
```

Let’s say that we want to explore the effect of `flipper_length_mm`

on
`body_mass_g`

. Which units have high implicit weight in estimating this
effect?

It’s very easy to use `regweight`

to answer this question:

```
rw_model <- calculate_weights(model, "flipper_length_mm")
hist(rw_model)
#> Warning: Removed 11 rows containing non-finite values (stat_bin).
```

We can see how the distribution of weights over islands varies:

```
plot(rw_model, penguins$island)
```

We can similarly see the implicit distribution of `bill_length_mm`

in
the nominal (unweighted) and implicit (regression weighted) sample:

```
plot(rw_model, penguins$bill_length_mm)
```

Or get a table of summary statistics:

```
summary(
rw_model,
dplyr::select(penguins, island, bill_length_mm),
output = "html"
)
```

Covariate
Value
Nominal
Implicit
Mean
Std. Dev.
Mean
Std. Dev.
Discrete variables
island
Biscoe
0.488
0.453
Dream
0.360
0.416
Torgersen
0.151
0.131
Continuous variables
bill_length_mm
43.9
5.46
43.8
5.78
**Any scripts or data that you put into this service are public.**

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.