CESER: An R Package to Compute Cluster Estimated Standard Errors Cover

CESER: An R Package to Compute Cluster Estimated Standard Errors

Journal of Open Research Software

Volume 9 (2021): Issue 1

By: Diogo Ferrari

Open Access

|Nov 2021

Figures & Tables

Table 1

Comparing raw standard errors, CRSE, and CESE.

COVARIATE	STD. ERRORS
COVARIATE	ESTIMATE	RAW	CRSE_HC1	CRSE_HC3	CESE
(Intercept)	2.7043	0.5848	0.4886	0.6135	1.2641
enpc	0.3040	0.1889	0.2517	0.3050	0.3542
fapres	–0.6118	0.1654	0.2038	0.2727	0.3784
enpcfapres	0.2078	0.0604	0.0826	0.1039	0.1127
proximity	–0.0224	0.2786	0.2544	0.3208	0.3738
eneg	–0.0657	0.1479	0.1415	0.1659	0.1986
logmag	–0.1815	0.2463	0.4387	0.5229	0.4737
logmag_eneg	0.3605	0.1334	0.2883	0.3473	0.2463
COVARIATE	CONFIDENCE INTERVALS
COVARIATE	ESTIMATE	RAW	CRSE_HC1	CRSE_HC3	CESE
(Intercept)	2.7043	(1.558, 3.85)	(1.747, 3.662)	(1.502, 3.907)	(0.227, 5.182)
enpc	0.3040	(–0.066, 0.674)	(–0.189, 0.797)	(–0.294, 0.902)	(–0.39, 0.998)
fapres	–0.6118	(–0.936, –0.288)	(–1.011, –0.212)	(–1.146, –0.077)	(–1.354, 0.13)
enpcfapres	0.2078	(0.089, 0.326)	(0.046, 0.37)	(0.004, 0.411)	(–0.013, 0.429)
proximity	–0.0224	(–0.568, 0.524)	(–0.521, 0.476)	(–0.651, 0.606)	(–0.755, 0.71)
eneg	–0.0657	(–0.356, 0.224)	(–0.343, 0.212)	(–0.391, 0.259)	(–0.455, 0.324)
logmag	–0.1815	(–0.664, 0.301)	(–1.041, 0.678)	(–1.206, 0.843)	(–1.11, 0.747)
logmag_eneg	0.3605	(0.099, 0.622)	(–0.205, 0.926)	(–0.32, 1.041)	(–0.122, 0.843)

	`(Intercept)`	`enpc`	`fapres`	`enpcfapres`	`proximity`
`(Intercept)`	`0.34193`	`–0.080109`	`–0.06498717`	`0.0227605`	`–0.0416369`
`enpc`	`–0.08011`	`0.035697`	`0.02401318`	`–0.0102825`	`0.0059204`
`fapres`	`–0.06499`	`0.024013`	`0.02734250`	`–0.0090018`	`–0.0004345`
`enpcfapres`	`0.02276`	`–0.010283`	`–0.00900179`	`0.0036430`	`–0.0014388`
`proximity`	`–0.04164`	`0.005920`	`–0.00043452`	`–0.0014388`	`0.0776196`
`eneg`	`–0.03580`	`–0.001477`	`–0.00251785`	`0.0007025`	`–0.0039084`
`logmag`	`–0.05448`	`–0.006981`	`0.00017420`	`0.0021400`	`–0.0023836`
`logmag_eneg`	`0.02532`	`0.001833`	`–0.00007513`	`–0.0007721`	`–0.0009086`
	`eneg`	`logmag`	`logmag_eneg`
`(Intercept)`	`–0.0358050`	`–0.0544826`	`0.02532042`
`enpc`	`–0.0014768`	`–0.0069809`	`0.00183259`
`fapres`	`–0.0025179`	`0.0001742`	`–0.00007513`
`enpcfapres`	`0.0007025`	`0.0021400`	`–0.00077214`
`proximity`	`–0.0039084`	`–0.0023836`	`–0.00090860`
`eneg`	`0.0218856`	`0.0222887`	`–0.01190289`
`logmag`	`0.0222887`	`0.0606796`	`–0.02995518`
`logmag_eneg`	`–0.0119029`	`–0.0299552`	`0.01778317`

	`(Intercept)`	`enpc`	`fapres`	`enpcfapres`	`proximity`
`(Intercept)`	`0.376409`	`–0.0929549`	`–0.06620`	`0.022499`	`–0.0315432`
`enpc`	`–0.092955`	`0.0930327`	`0.05081`	`–0.026847`	`0.0000196`
`fapres`	`–0.066198`	`0.0508080`	`0.07437`	`–0.024184`	`–0.0177849`
`enpcfapres`	`0.022499`	`–0.0268474`	`–0.02418`	`0.010785`	`0.0020836`
`proximity`	`–0.031543`	`0.0000196`	`–0.01778`	`0.002084`	`0.1029317`
`eneg`	`0.001905`	`–0.0165885`	`–0.02183`	`0.007097`	`–0.0200007`
`logmag`	`–0.030573`	`–0.0642203`	`–0.04945`	`0.022924`	`–0.0285040`
`logmag_eneg`	`–0.002075`	`0.0124010`	`0.02094`	`–0.007229`	`0.0317879`
	`eneg`	`logmag`	`logmag_eneg`
`(Intercept)`	`0.001905`	`–0.03057`	`–0.002075`
`enpc`	`–0.016589`	`–0.06422`	`0.012401`
`fapres`	`–0.021832`	`–0.04945`	`0.020940`
`enpcfapres`	`0.007097`	`0.02292`	`–0.007229`
`proximity`	`–0.020001`	`–0.02850`	`0.031788`
`eneg`	`0.027519`	`0.06041`	`–0.039241`
`logmag`	`0.060413`	`0.27344`	`–0.158061`
`logmag_eneg`	`–0.039241`	`–0.15806`	`0.120629`

	`(Intercept)`	`enpc`	`fapres`	`enpcfapres`	`proximity`
`(Intercept)`	`1.59804`	`–0.3565890`	`–0.326045`	`0.0928614`	`–0.086959`
`enpc`	`–0.35659`	`0.1254735`	`0.104834`	`–0.0354704`	`–0.003333`
`fapres`	`–0.32604`	`0.1048342`	`0.143206`	`–0.0389794`	`–0.017879`
`enpcfapres`	`0.09286`	`–0.0354704`	`–0.038979`	`0.0126978`	`0.003218`
`proximity`	`–0.08696`	`–0.0033328`	`–0.017879`	`0.0032179`	`0.139695`
`eneg`	`–0.08737`	`0.0028258`	`–0.007081`	`0.0010940`	`–0.005680`
`logmag`	`–0.22422`	`0.0009845`	`0.006688`	`0.0038080`	`0.009776`
`logmag_eneg`	`0.08381`	`–0.0058250`	`–0.011500`	`0.0008569`	`0.004472`
	`eneg`	`logmag`	`logmag_eneg`
`(Intercept)`	`–0.087372`	`–0.2242235`	`0.0838093`
`enpc`	`0.002826`	`0.0009845`	`–0.0058250`
`fapres`	`–0.007081`	`0.0066880`	`–0.0115004`
`enpcfapres`	`0.001094`	`0.0038080`	`0.0008569`
`proximity`	`–0.005680`	`0.0097761`	`0.0044718`
`eneg`	`0.039433`	`0.0481561`	`–0.0231003`
`logmag`	`0.048156`	`0.2244237`	`–0.1048418`
`logmag_eneg`	`–0.023100`	`–0.1048418`	`0.0606626`

`(Intercept)`	`enpc`	`fapres`	`enpcfapres`	`proximity`
`0.58475`	`0.18894`	`0.16536`	`0.06036`	`0.27860`
`eneg`	`logmag`	`logmag_eneg`
`0.14794`	`0.24633`	`0.13335`

`(Intercept)`	`enpc`	`fapres`	`enpcfapres`	`proximity`
`0.6135`	`0.3050`	`0.2727`	`0.1039`	`0.3208`
`eneg`	`logmag`	`logmag_eneg`
`0.1659`	`0.5229`	`0.3473`

`(Intercept)`	`enpc`	`fapres`	`enpcfapres`	`proximity`
`1.2641`	`0.3542`	`0.3784`	`0.1127`	`0.3738`
`eneg`	`logmag`	`logmag_eneg`
`0.1986`	`0.4737`	`0.2463`

`Min`	`1Q`	`Median`	`3Q`	`Max`
`–3.559`	`–0.819`	`–0.361`	`0.377`	`9.039`

	`Estimate`	`Std.Error`	`t value`	`Pr (>\|t\|)`
`(Intercept)`	`2.7043`	`0.5848`	`4.62`	`0.0000056`	`***`
`enpc`	`0.3040`	`0.1889`	`1.61`	`0.10871`
`fapres`	`–0.6118`	`0.1654`	`–3.70`	`0.00026`	`***`
`enpcfapres`	`0.2078`	`0.0604`	`3.44`	`0.00066`	`***`
`proximity`	`–0.0224`	`0.2786`	`–0.08`	`0.93589`
`eneg`	`–0.0657`	`0.1479`	`–0.44`	`0.65748`
`logmag`	`–0.1815`	`0.2463`	`–0.74`	`0.46193`
`logmag_eneg`	`0.3605`	`0.1334`	`2.70`	`0.00727`	`**`
`–--`

	`Estimate`	`Std.Error`	`t value`	`Pr(>\|t\|)`
`(Intercept)`	`2.7043`	`0.6135`	`4.41`	`0.000015`	`***`
`enpc`	`0.3040`	`0.3050`	`1.00`	`0.320`
`fapres`	`–0.6118`	`0.2727`	`–2.24`	`0.026`	`*`
`enpcfapres`	`0.2078`	`0.1039`	`2.00`	`0.046`	`*`
`proximity`	`–0.0224`	`0.3208`	`–0.07`	`0.944`
`eneg`	`–0.0657`	`0.1659`	`–0.40`	`0.693`
`logmag`	`–0.1815`	`0.5229`	`–0.35`	`0.729`
`logmag_eneg`	`0.3605`	`0.3473`	`1.04`	`0.300`
`–--`

	`Estimate`	`Std.Error`	`t value`	`Pr (>\|t\|)`
`(Intercept)`	`2.7043`	`1.2641`	`2.14`	`0.033`	`*`
`enpc`	`0.3040`	`0.3542`	`0.86`	`0.391`
`fapres`	`–0.6118`	`0.3784`	`–1.62`	`0.107`
`enpcfapres`	`0.2078`	`0.1127`	`1.84`	`0.066`	`.`
`proximity`	`–0.0224`	`0.3738`	`–0.06`	`0.952`
`eneg`	`–0.0657`	`0.1986`	`–0.33`	`0.741`
`logmag`	`–0.1815`	`0.4737`	`–0.38`	`0.702`
`logmag_eneg`	`0.3605`	`0.2463`	`1.46`	`0.144`
`–--`

DOI: https://doi.org/10.5334/jors.355 | Journal eISSN: 2049-9647

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Language: English

Submitted on: Oct 19, 2020

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Accepted on: Oct 6, 2021

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Published on: Nov 30, 2021

Published by: Ubiquity Press

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

Clustered robust standard errors,

Clustered data,

Confidence Intervals,

Regression Analysis

© 2021 Diogo Ferrari, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

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