NOTE: model_summary is preferred.
Arguments
- model
A model fitted with
lmorglmfunction.- robust
[Only for
lmandglm]FALSE(default),TRUE(then the default is"HC1"),"HC0","HC1","HC2","HC3","HC4","HC4m", or"HC5". It will add a table with heteroskedasticity-robust standard errors (aka. Huber-White standard errors). For details, see?sandwich::vcovHCand?jtools::summ.lm.***
"HC1"is the default of Stata, whereas"HC3"is the default suggested by thesandwichpackage.- cluster
[Only for
lmandglm] Cluster-robust standard errors are computed if cluster is set to the name of the input data's cluster variable or is a vector of clusters.- digits
Number of decimal places of output. Defaults to
3.- ...
Other arguments. You may re-define
formula,data, orfamily.
Examples
## Example 1: OLS regression
lm = lm(Temp ~ Month + Day + Wind + Solar.R, data=airquality)
GLM_summary(lm)
#>
#> General Linear Model (OLS Regression)
#>
#> Model Fit:
#> F(4, 141) = 22.22, p = 3e-14 ***
#> R² = 0.38659 (Adjusted R² = 0.36919)
#>
#> Unstandardized Coefficients:
#> Outcome Variable: Temp
#> N = 146 (7 missing cases deleted)
#> ───────────────────────────────────────────────────────────────────
#> b S.E. t p [95% CI of b] VIF
#> ───────────────────────────────────────────────────────────────────
#> (Intercept) 68.770 (4.391) 15.662 <.001 *** [60.089, 77.450]
#> Month 2.225 (0.441) 5.047 <.001 *** [ 1.353, 3.096] 1.035
#> Day -0.084 (0.070) -1.194 .234 [-0.222, 0.055] 1.024
#> Wind -1.003 (0.176) -5.695 <.001 *** [-1.352, -0.655] 1.032
#> Solar.R 0.027 (0.007) 3.991 <.001 *** [ 0.014, 0.041] 1.034
#> ───────────────────────────────────────────────────────────────────
#>
#> Standardized Coefficients (β):
#> Outcome Variable: Temp
#> N = 146 (7 missing cases deleted)
#> ────────────────────────────────────────────────────────────────────────────
#> β S.E. t p [95% CI of β] r(partial) r(part)
#> ────────────────────────────────────────────────────────────────────────────
#> Month 0.339 (0.067) 5.047 <.001 *** [ 0.206, 0.471] 0.391 0.333
#> Day -0.080 (0.067) -1.194 .234 [-0.212, 0.052] -0.100 -0.079
#> Wind -0.382 (0.067) -5.695 <.001 *** [-0.514, -0.249] -0.432 -0.376
#> Solar.R 0.268 (0.067) 3.991 <.001 *** [ 0.135, 0.400] 0.319 0.263
#> ────────────────────────────────────────────────────────────────────────────
#>
GLM_summary(lm, robust="HC1")
#>
#> General Linear Model (OLS Regression)
#>
#> Model Fit:
#> F(4, 141) = 22.22, p = 3e-14 ***
#> R² = 0.38659 (Adjusted R² = 0.36919)
#>
#> Unstandardized Coefficients:
#> Outcome Variable: Temp
#> N = 146 (7 missing cases deleted)
#> ───────────────────────────────────────────────────────────────────
#> b S.E. t p [95% CI of b] VIF
#> ───────────────────────────────────────────────────────────────────
#> (Intercept) 68.770 (4.391) 15.662 <.001 *** [60.089, 77.450]
#> Month 2.225 (0.441) 5.047 <.001 *** [ 1.353, 3.096] 1.035
#> Day -0.084 (0.070) -1.194 .234 [-0.222, 0.055] 1.024
#> Wind -1.003 (0.176) -5.695 <.001 *** [-1.352, -0.655] 1.032
#> Solar.R 0.027 (0.007) 3.991 <.001 *** [ 0.014, 0.041] 1.034
#> ───────────────────────────────────────────────────────────────────
#>
#> Heteroskedasticity-Robust Standard Errors:
#> ─────────────────────────────────────────────────────────────
#> b S.E. t p [95% CI of b]
#> ─────────────────────────────────────────────────────────────
#> (Intercept) 68.770 (4.550) 15.113 <.001 *** [59.774, 77.766]
#> Month 2.225 (0.488) 4.557 <.001 *** [ 1.260, 3.190]
#> Day -0.084 (0.068) -1.231 .220 [-0.218, 0.051]
#> Wind -1.003 (0.152) -6.604 <.001 *** [-1.304, -0.703]
#> Solar.R 0.027 (0.007) 4.089 <.001 *** [ 0.014, 0.041]
#> ─────────────────────────────────────────────────────────────
#> Robust S.E.: type = HC1.
#>
#> Standardized Coefficients (β):
#> Outcome Variable: Temp
#> N = 146 (7 missing cases deleted)
#> ────────────────────────────────────────────────────────────────────────────
#> β S.E. t p [95% CI of β] r(partial) r(part)
#> ────────────────────────────────────────────────────────────────────────────
#> Month 0.339 (0.067) 5.047 <.001 *** [ 0.206, 0.471] 0.391 0.333
#> Day -0.080 (0.067) -1.194 .234 [-0.212, 0.052] -0.100 -0.079
#> Wind -0.382 (0.067) -5.695 <.001 *** [-0.514, -0.249] -0.432 -0.376
#> Solar.R 0.268 (0.067) 3.991 <.001 *** [ 0.135, 0.400] 0.319 0.263
#> ────────────────────────────────────────────────────────────────────────────
#>
# Stata's default is "HC1"
# R package <sandwich>'s default is "HC3"
## Example 2: Logistic regression
glm = glm(case ~ age + parity + education + spontaneous + induced,
data=infert, family=binomial)
GLM_summary(glm)
#>
#> Generalized Linear Model (GLM)
#>
#> Model Fit:
#> AIC = 271.798
#> BIC = 296.392
#> χ²(6) = 58.37, p = 1e-10 ***
#> ─────── Pseudo-R² ───────
#> McFadden’s R² = 0.18463 (= 1 - logLik(model)/logLik(null.model))
#> Nagelkerke’s R² = 0.29107 (= Cragg-Uhler’s R², adjusts Cox & Snell’s)
#>
#> Unstandardized Coefficients:
#> Outcome Variable: case (family: binomial; link function: logit)
#> N = 248
#> ──────────────────────────────────────────────────────────────────────────────
#> b S.E. z p [95% CI of b] OR VIF
#> ──────────────────────────────────────────────────────────────────────────────
#> (Intercept) -1.149 (1.412) -0.814 .416 [-3.917, 1.619] 0.317
#> age 0.040 (0.031) 1.269 .205 [-0.022, 0.101] 1.040 1.174
#> parity -0.828 (0.196) -4.215 <.001 *** [-1.213, -0.443] 0.437 2.480
#> education6-11yrs -1.044 (0.793) -1.318 .188 [-2.598, 0.509] 0.352 1.361
#> education12+ yrs -1.403 (0.834) -1.682 .093 . [-3.038, 0.232] 0.246 1.361
#> spontaneous 2.046 (0.310) 6.596 <.001 *** [ 1.438, 2.654] 7.736 2.191
#> induced 1.289 (0.301) 4.275 <.001 *** [ 0.698, 1.880] 3.628 2.222
#> ──────────────────────────────────────────────────────────────────────────────
#> OR = odds ratio.
#>
GLM_summary(glm, robust="HC1", cluster="stratum")
#>
#> Generalized Linear Model (GLM)
#>
#> Model Fit:
#> AIC = 271.798
#> BIC = 296.392
#> χ²(6) = 58.37, p = 1e-10 ***
#> ─────── Pseudo-R² ───────
#> McFadden’s R² = 0.18463 (= 1 - logLik(model)/logLik(null.model))
#> Nagelkerke’s R² = 0.29107 (= Cragg-Uhler’s R², adjusts Cox & Snell’s)
#>
#> Unstandardized Coefficients:
#> Outcome Variable: case (family: binomial; link function: logit)
#> N = 248
#> ──────────────────────────────────────────────────────────────────────────────
#> b S.E. z p [95% CI of b] OR VIF
#> ──────────────────────────────────────────────────────────────────────────────
#> (Intercept) -1.149 (1.412) -0.814 .416 [-3.917, 1.619] 0.317
#> age 0.040 (0.031) 1.269 .205 [-0.022, 0.101] 1.040 1.174
#> parity -0.828 (0.196) -4.215 <.001 *** [-1.213, -0.443] 0.437 2.480
#> education6-11yrs -1.044 (0.793) -1.318 .188 [-2.598, 0.509] 0.352 1.361
#> education12+ yrs -1.403 (0.834) -1.682 .093 . [-3.038, 0.232] 0.246 1.361
#> spontaneous 2.046 (0.310) 6.596 <.001 *** [ 1.438, 2.654] 7.736 2.191
#> induced 1.289 (0.301) 4.275 <.001 *** [ 0.698, 1.880] 3.628 2.222
#> ──────────────────────────────────────────────────────────────────────────────
#> OR = odds ratio.
#>
#> Cluster-Robust Standard Errors:
#> ──────────────────────────────────────────────────────────────────
#> b S.E. z p [95% CI of b]
#> ──────────────────────────────────────────────────────────────────
#> (Intercept) -1.149 (0.810) -1.418 .156 [-2.737, 0.439]
#> age 0.040 (0.017) 2.373 .018 * [ 0.007, 0.072]
#> parity -0.828 (0.194) -4.278 <.001 *** [-1.208, -0.449]
#> education6-11yrs -1.044 (0.542) -1.926 .054 . [-2.107, 0.018]
#> education12+ yrs -1.403 (0.587) -2.392 .017 * [-2.553, -0.253]
#> spontaneous 2.046 (0.351) 5.832 <.001 *** [ 1.358, 2.733]
#> induced 1.289 (0.319) 4.044 <.001 *** [ 0.664, 1.913]
#> ──────────────────────────────────────────────────────────────────
#> Robust S.E.: type = HC1; clustering variable = stratum.
#>