Title: Module to Compute Influence and Leverage Statistics for Regression Models with Clustered Errors
Version: 0.7.2
Description: Module to compute cluster specific information for regression models with clustered errors, including leverage and influence statistics. Models of type 'lm' and 'fixest'(from the 'stats' and 'fixest' packages) are supported. 'summclust' implements similar features as the user-written 'summclust.ado' Stata module (MacKinnon, Nielsen & Webb, 2022; <doi:10.48550/arXiv.2205.03288>).
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.2.3
Imports: utils, dreamerr, MASS, collapse, generics, cli, rlang
Suggests: ggplot2, latex2exp, fabricatr, fixest, haven, sandwich, lmtest, testthat (≥ 3.0.0), knitr, rmarkdown, covr, httr
Config/testthat/edition: 3
URL: https://s3alfisc.github.io/summclust/
BugReports: https://github.com/s3alfisc/summclust/issues
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2023-08-08 18:01:43 UTC; alexa
Author: Alexander Fischer [aut, cre]
Maintainer: Alexander Fischer <alexander-fischer1801@t-online.de>
Repository: CRAN
Date/Publication: 2023-08-10 06:20:02 UTC

Plotting method for objects of type summclust

Description

Plots residual leverage, partial leverage and the leave-one-cluster-out regression coefficients

Usage

## S3 method for class 'summclust'
plot(x, ...)

Arguments

x

An object of type summclust

...

other optional function arguments

Details

Note that the function requires ggplot2 to be installed.

Value

A list containing

residual_leverage

A ggplot of the residual leverages

coef_leverage

A ggplot of the coefficient leverages

coef_beta

A ggplot of the leave-one-out cluster jackknife regression coefficients

References

MacKinnon, James G., Morten Ørregaard Nielsen, and Matthew D. Webb. "Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust." arXiv preprint arXiv:2205.03288 (2022).

Examples


library(summclust)
data(mtcars)
mtcars

fit <- lm(mpg ~ cyl + disp + hp, data = mtcars)
summ <- summclust(fit, params = ~cyl + disp, cluster = ~carb)
summary(summ)
tidy(summ)
plot(summ)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

generics

tidy

A summary() method for objects of type summclust

Description

A summary() method for objects of type summclust

Usage

## S3 method for class 'summclust'
summary(object, ...)

Arguments

object

An object of type summclust

...

misc arguments

Value

A printed summary with pvalues, t-statistics, confidence intervals, and leverage statistics

References

MacKinnon, James G., Morten Ørregaard Nielsen, and Matthew D. Webb. "Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust." arXiv preprint arXiv:2205.03288 (2022).

Examples


library(summclust)
data(mtcars)
mtcars

fit <- lm(mpg ~ cyl + disp + hp, data = mtcars)
summ <- summclust(fit, params = ~cyl + disp, cluster = ~carb)
summary(summ)
tidy(summ)
plot(summ)

Compute Influence and Leverage Metrics

Description

Compute influence and leverage metrics for clustered inference based on the Cluster Jackknife described in MacKinnon, Nielsen & Webb (2022).

Usage

summclust(obj, ...)

Arguments

obj

An object of class lm or fixest

...

Other arguments

Value

An object of type summclust, including a CRV3 variance-covariance estimate as described in MacKinnon, Nielsen & Webb (2022)

References

MacKinnon, James G., Morten Ørregaard Nielsen, and Matthew D. Webb. "Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust." arXiv preprint arXiv:2205.03288 (2022).

See Also

summclust.lm, summclust.fixest

Examples


library(summclust)
data(mtcars)
mtcars

fit <- lm(mpg ~ cyl + disp + hp, data = mtcars)
summ <- summclust(fit, params = ~cyl + disp, cluster = ~carb)
summary(summ)
tidy(summ)
plot(summ)

Compute Influence and Leverage Metrics for objects of type fixest

Description

Compute influence and leverage metrics for clustered inference based on the Cluster Jackknife as described in MacKinnon, Nielsen & Webb (2022) for objects of type fixest.

Usage

## S3 method for class 'fixest'
summclust(
  obj,
  cluster,
  params,
  absorb_cluster_fixef = TRUE,
  type = "CRV3",
  ...
)

Arguments

obj

An object of type fixest

cluster

A clustering vector

params

A character vector of variables for which leverage statistics should be computed. If NULL, leverage statistics will be computed for all k model covariates

absorb_cluster_fixef

TRUE by default. Should the cluster fixed effects be projected out? This increases numerical stability and decreases computational costs

type

"CRV3" or "CRV3J" following MacKinnon, Nielsen & Webb

...

other function arguments passed to 'vcov'

Value

An object of type summclust, including a CRV3 variance-covariance estimate as described in MacKinnon, Nielsen & Webb (2022)

coef_estimates

The coefficient estimates of the linear model.

vcov

A CRV3 or CRV3J variance-covariance matrix estimate as described in MacKinnon, Nielsen & Webb (2022)

leverage_g

A vector of leverages.

leverage_avg

The cluster leverage.

partial_leverage

The partial leverages.

coef_var_leverage_avg

Coefficient of Variation for the leverage statistic

coef_var_leverage_g

Coefficient of Variation for the Partial Leverage Statistics

coef_var_N_G

Coefficient of Variation for the Cluster Sizes.

beta_jack

The jackknifed' leave-on-cluster-out regression coefficients.

params

The input parameter vector 'params'.

N_G

The number of clusters-

call

The summclust() function call.

cluster

The names of the clusters.

References

MacKinnon, James G., Morten Ørregaard Nielsen, and Matthew D. Webb. "Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust." arXiv preprint arXiv:2205.03288 (2022).

Examples


library(summclust)
data(mtcars)
mtcars

fit <- lm(mpg ~ cyl + disp + hp, data = mtcars)
summ <- summclust(fit, params = ~cyl + disp, cluster = ~carb)
summary(summ)
tidy(summ)
plot(summ)

Compute Influence and Leverage Metrics for objects of type lm

Description

Compute influence and leverage metrics for clustered inference based on the Cluster Jackknife as described in MacKinnon, Nielsen & Webb (2022) for objects of type lm.

Usage

## S3 method for class 'lm'
summclust(obj, cluster, params, type = "CRV3", ...)

Arguments

obj

An object of type lm

cluster

A clustering vector

params

A character vector of variables for which leverage statistics should be computed.

type

"CRV3" or "CRV3J" following MacKinnon, Nielsen & Webb. CRV3 by default

...

other function arguments passed to 'vcov'

Value

An object of type summclust, including a CRV3 variance-covariance estimate as described in MacKinnon, Nielsen & Webb (2022)

coef_estimates

The coefficient estimates of the linear model.

vcov

A CRV3 or CRV3J variance-covariance matrix estimate as described in MacKinnon, Nielsen & Webb (2022)

leverage_g

A vector of leverages.

leverage_avg

The cluster leverage.

partial_leverage

The partial leverages.

beta_jack

The jackknifed' leave-on-cluster-out regression coefficients.

params

The input parameter vector 'params'.

N_G

The number of clusters-

call

The summclust() function call.

cluster

The names of the clusters.

References

MacKinnon, James G., Morten Ørregaard Nielsen, and Matthew D. Webb. "Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust." arXiv preprint arXiv:2205.03288 (2022).

Examples


library(summclust)
data(mtcars)
mtcars

fit <- lm(mpg ~ cyl + disp + hp, data = mtcars)
summ <- summclust(fit, params = ~cyl + disp, cluster = ~carb)
summary(summ)
tidy(summ)
plot(summ)

S3 method to summarize objects of class boottest into tidy data.frame

Description

Obtain results from a summclust object in a tidy data frame.

Usage

## S3 method for class 'summclust'
tidy(x, ...)

Arguments

x

An object of class 'summclust'

...

Other arguments

Value

A data.frame containing coefficient estimates, t-statistics, standard errors, p-value, and confidence intervals based on CRV3 variance-covariance matrix and t(G-1) distribution

References

MacKinnon, James G., Morten Ørregaard Nielsen, and Matthew D. Webb. "Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust." arXiv preprint arXiv:2205.03288 (2022).

Examples


library(summclust)
data(mtcars)
mtcars

fit <- lm(mpg ~ cyl + disp + hp, data = mtcars)
summ <- summclust(fit, params = ~cyl + disp, cluster = ~carb)
summary(summ)
tidy(summ)
plot(summ)

Compute CRV3 covariance matrices via a cluster jackknife as described in MacKinnon, Nielsen & Webb (2022)

Description

Compute CRV3 covariance matrices via a cluster jackknife as described in MacKinnon, Nielsen & Webb (2022)

Usage

vcov_CR3J(obj, ...)

Arguments

obj

An object of class lm or fixest computed?

...

misc function argument

Value

An object of type 'vcov_CR3J'

References

MacKinnon, James G., Morten Ørregaard Nielsen, and Matthew D. Webb. "Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust." arXiv preprint arXiv:2205.03288 (2022).

See Also

vcov_CR3J.lm, vcov_CR3J.fixest

Examples


library(summclust)
data(mtcars)
mtcars

fit <- lm(mpg ~ cyl + disp + hp, data = mtcars)
summ <- vcov_CR3J(fit, cluster = ~carb)

Compute CRV3 covariance matrices via a cluster jackknife as described in MacKinnon, Nielsen & Webb (2022) for objects of type fixest

Description

Compute CRV3 covariance matrices via a cluster jackknife as described in MacKinnon, Nielsen & Webb (2022) for objects of type fixest

Usage

## S3 method for class 'fixest'
vcov_CR3J(
  obj,
  cluster,
  type = "CRV3",
  return_all = FALSE,
  absorb_cluster_fixef = TRUE,
  ...
)

Arguments

obj

An object of type fixest

cluster

A clustering vector

type

"CRV3" or "CRV3J" following MacKinnon, Nielsen & Webb. CRV3 by default

return_all

Logical scalar, FALSE by default. Should only the vcov be returned (FALSE) or additional results (TRUE)

absorb_cluster_fixef

TRUE by default. Should the cluster fixed effects be projected out? This increases numerical stability.

...

other function arguments passed to 'vcov'

Value

An object of class vcov_CR3J

References

MacKinnon, James G., Morten Ørregaard Nielsen, and Matthew D. Webb. "Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust." arXiv preprint arXiv:2205.03288 (2022).

Examples


library(summclust)
library(fixest)
data(mtcars)
mtcars

fit <- feols(mpg ~ cyl + disp + hp, data = mtcars)
summ <- vcov_CR3J(fit, cluster = ~carb)

Compute CRV3 covariance matrices via a cluster jackknife as described in MacKinnon, Nielsen & Webb (2022) for objects of type lm

Description

Compute CRV3 covariance matrices via a cluster jackknife as described in MacKinnon, Nielsen & Webb (2022) for objects of type lm

Usage

## S3 method for class 'lm'
vcov_CR3J(obj, cluster, type = "CRV3", return_all = FALSE, ...)

Arguments

obj

An object of type lm

cluster

A clustering vector

type

"CRV3" or "CRV3J" following MacKinnon, Nielsen & Webb. CRV3 by default

return_all

Logical scalar, FALSE by default. Should only the vcov be returned (FALSE) or additional results (TRUE)

...

other function arguments passed to 'vcov'

Value

An object of class vcov_CR3J

References

MacKinnon, James G., Morten Ørregaard Nielsen, and Matthew D. Webb. "Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust." arXiv preprint arXiv:2205.03288 (2022).

Examples


library(summclust)
data(mtcars)
mtcars

fit <- lm(mpg ~ cyl + disp + hp, data = mtcars)
summ <- vcov_CR3J(fit, cluster = ~carb)
library(summclust)
data(mtcars)
mtcars

fit <- lm(mpg ~ cyl + disp + hp, data = mtcars)
summ <- vcov_CR3J(fit, cluster = ~carb)