Tidy report of regression models (most model types are supported). This function uses:
Usage
model_summary(
model.list,
std = FALSE,
digits = 3,
file = NULL,
check = TRUE,
zero = ifelse(std, FALSE, TRUE),
modify.se = NULL,
modify.head = NULL,
line = TRUE,
bold = 0,
...
)Arguments
- model.list
A single model or a list of (various types of) models. Most types of regression models are supported!
- std
Standardized coefficients? Defaults to
FALSE. Only applicable to linear models and linear mixed models. Not applicable to generalized linear (mixed) models.- digits
Number of decimal places of output. Defaults to
3.- file
File name of MS Word (
.doc).- check
If there is only one model in
model.list, it checks for multicollinearity usingperformance::check_collinearity(). You may turn it off by settingcheck=FALSE.- zero
Display "0" before "."? Defaults to
TRUE.- modify.se
Replace standard errors. Useful if you need to replace raw SEs with robust SEs. New SEs should be provided as a list of numeric vectors. See usage in
texreg::screenreg().- modify.head
Replace model names.
- line
Lines look like true line (
TRUE) or=== --- ===(FALSE). Only relevant to R Console output.- bold
The p-value threshold below which the coefficients will be formatted in bold.
- ...
Other arguments passed to
texreg::screenreg()ortexreg::htmlreg().
Examples
#### Example 1: Linear Model ####
lm1 = lm(Temp ~ Month + Day, data=airquality)
lm2 = lm(Temp ~ Month + Day + Wind + Solar.R, data=airquality)
model_summary(lm1)
#>
#> Model Summary
#>
#> ────────────────────────
#> (1) Temp
#> ────────────────────────
#> (Intercept) 60.406 ***
#> (3.718)
#> Month 2.806 ***
#> (0.490)
#> Day -0.136
#> (0.078)
#> ────────────────────────
#> R^2 0.193
#> Adj. R^2 0.183
#> Num. obs. 153
#> ────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#>
#> # Check for Multicollinearity
#>
#> Low Correlation
#>
#> Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
#> Month 1.00 [1.00, Inf] 1.00 1.00 [0.00, 1.00]
#> Day 1.00 [1.00, Inf] 1.00 1.00 [0.00, 1.00]
#>
model_summary(lm2)
#>
#> Model Summary
#>
#> ────────────────────────
#> (1) Temp
#> ────────────────────────
#> (Intercept) 68.770 ***
#> (4.391)
#> Month 2.225 ***
#> (0.441)
#> Day -0.084
#> (0.070)
#> Wind -1.003 ***
#> (0.176)
#> Solar.R 0.027 ***
#> (0.007)
#> ────────────────────────
#> R^2 0.387
#> Adj. R^2 0.369
#> Num. obs. 146
#> ────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#>
#> # Check for Multicollinearity
#>
#> Low Correlation
#>
#> Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
#> Month 1.03 [1.00, 4.91] 1.02 0.97 [0.20, 1.00]
#> Day 1.02 [1.00, 22.58] 1.01 0.98 [0.04, 1.00]
#> Wind 1.03 [1.00, 6.15] 1.02 0.97 [0.16, 1.00]
#> Solar.R 1.03 [1.00, 5.32] 1.02 0.97 [0.19, 1.00]
#>
model_summary(list(lm1, lm2))
#>
#> Model Summary
#>
#> ─────────────────────────────────────
#> (1) Temp (2) Temp
#> ─────────────────────────────────────
#> (Intercept) 60.406 *** 68.770 ***
#> (3.718) (4.391)
#> Month 2.806 *** 2.225 ***
#> (0.490) (0.441)
#> Day -0.136 -0.084
#> (0.078) (0.070)
#> Wind -1.003 ***
#> (0.176)
#> Solar.R 0.027 ***
#> (0.007)
#> ─────────────────────────────────────
#> R^2 0.193 0.387
#> Adj. R^2 0.183 0.369
#> Num. obs. 153 146
#> ─────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#>
model_summary(list(lm1, lm2), std=TRUE, digits=2)
#>
#> Model Summary
#>
#> ─────────────────────────────────
#> (1) Temp (2) Temp
#> ─────────────────────────────────
#> Month .42 *** .34 ***
#> (.07) (.07)
#> Day -.13 -.08
#> (.07) (.07)
#> Wind -.38 ***
#> (.07)
#> Solar.R .27 ***
#> (.07)
#> ─────────────────────────────────
#> R^2 .19 .39
#> Adj. R^2 .18 .37
#> Num. obs. 153 146
#> ─────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#>
model_summary(list(lm1, lm2), file="OLS Models.doc")
#> ✔ Table saved to '/home/runner/work/bruceR/bruceR/docs/reference/OLS Models.doc'
#>
unlink("OLS Models.doc") # delete file for code check
#### Example 2: Generalized Linear Model ####
glm1 = glm(case ~ age + parity,
data=infert, family=binomial)
glm2 = glm(case ~ age + parity + education + spontaneous + induced,
data=infert, family=binomial)
model_summary(list(glm1, glm2)) # "std" is not applicable to glm
#>
#> Model Summary
#>
#> ───────────────────────────────────────
#> (1) case (2) case
#> ───────────────────────────────────────
#> (Intercept) -0.754 -1.149
#> (0.836) (1.412)
#> age 0.001 0.040
#> (0.026) (0.031)
#> parity 0.015 -0.828 ***
#> (0.108) (0.196)
#> education6-11yrs -1.044
#> (0.793)
#> education12+ yrs -1.403
#> (0.834)
#> spontaneous 2.046 ***
#> (0.310)
#> induced 1.289 ***
#> (0.301)
#> ───────────────────────────────────────
#> McFadden's R^2 0.000 0.185
#> Nagelkerke's R^2 0.000 0.291
#> AIC 322.150 271.798
#> BIC 332.690 296.392
#> Num. obs. 248 248
#> ───────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#>
model_summary(list(glm1, glm2), file="GLM Models.doc")
#> ✔ Table saved to '/home/runner/work/bruceR/bruceR/docs/reference/GLM Models.doc'
#>
unlink("GLM Models.doc") # delete file for code check
#### Example 3: Linear Mixed Model ####
library(lmerTest)
hlm1 = lmer(Reaction ~ (1 | Subject), data=sleepstudy)
hlm2 = lmer(Reaction ~ Days + (1 | Subject), data=sleepstudy)
hlm3 = lmer(Reaction ~ Days + (Days | Subject), data=sleepstudy)
model_summary(list(hlm1, hlm2, hlm3))
#>
#> Model Summary
#>
#> ───────────────────────────────────────────────────────────────────────
#> (1) Reaction (2) Reaction (3) Reaction
#> ───────────────────────────────────────────────────────────────────────
#> (Intercept) 298.508 *** 251.405 *** 251.405 ***
#> (9.050) (9.747) (6.825)
#> Days 10.467 *** 10.467 ***
#> (0.804) (1.546)
#> ───────────────────────────────────────────────────────────────────────
#> Marginal R^2 0.000 0.280 0.279
#> Conditional R^2 0.395 0.704 0.799
#> AIC 1910.327 1794.465 1755.628
#> BIC 1919.905 1807.237 1774.786
#> Num. obs. 180 180 180
#> Num. groups: Subject 18 18 18
#> Var: Subject (Intercept) 1278.338 1378.179 612.100
#> Var: Residual 1958.865 960.457 654.940
#> Var: Subject Days 35.072
#> Cov: Subject (Intercept) Days 9.604
#> ───────────────────────────────────────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#>
model_summary(list(hlm1, hlm2, hlm3), std=TRUE)
#>
#> Model Summary
#>
#> ───────────────────────────────────────────────────────────────────────
#> (1) Reaction (2) Reaction (3) Reaction
#> ───────────────────────────────────────────────────────────────────────
#> Days .535 *** .535 ***
#> (.041) (.079)
#> ───────────────────────────────────────────────────────────────────────
#> Marginal R^2 .000 .280 .279
#> Conditional R^2 .395 .704 .799
#> AIC 1910.327 1794.465 1755.628
#> BIC 1919.905 1807.237 1774.786
#> Num. obs. 180 180 180
#> Num. groups: Subject 18 18 18
#> Var: Subject (Intercept) 1278.338 1378.179 612.100
#> Var: Residual 1958.865 960.457 654.940
#> Var: Subject Days 35.072
#> Cov: Subject (Intercept) Days 9.604
#> ───────────────────────────────────────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#>
model_summary(list(hlm1, hlm2, hlm3), file="HLM Models.doc")
#> ✔ Table saved to '/home/runner/work/bruceR/bruceR/docs/reference/HLM Models.doc'
#>
unlink("HLM Models.doc") # delete file for code check
#### Example 4: Generalized Linear Mixed Model ####
library(lmerTest)
data.glmm = MASS::bacteria
glmm1 = glmer(y ~ trt + week + (1 | ID), data=data.glmm, family=binomial)
glmm2 = glmer(y ~ trt + week + hilo + (1 | ID), data=data.glmm, family=binomial)
model_summary(list(glmm1, glmm2)) # "std" is not applicable to glmm
#>
#> Model Summary
#>
#> ─────────────────────────────────────────────
#> (1) y (2) y
#> ─────────────────────────────────────────────
#> (Intercept) 3.144 *** 3.399 ***
#> (0.622) (0.736)
#> trtdrug -1.320 * -0.928
#> (0.642) (0.802)
#> trtdrug+ -0.795 -1.061
#> (0.652) (0.751)
#> week -0.144 ** -0.144 **
#> (0.051) (0.051)
#> hilolo -0.656
#> (0.876)
#> ─────────────────────────────────────────────
#> AIC 207.771 209.217
#> BIC 224.739 229.579
#> Num. obs. 220 220
#> Num. groups: ID 50 50
#> Var: ID (Intercept) 1.314 1.254
#> ─────────────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#>
model_summary(list(glmm1, glmm2), file="GLMM Models.doc")
#> ✔ Table saved to '/home/runner/work/bruceR/bruceR/docs/reference/GLMM Models.doc'
#>
unlink("GLMM Models.doc") # delete file for code check
#### Example 5: Multinomial Logistic Model ####
library(nnet)
d = airquality
d$Month = as.factor(d$Month) # Factor levels: 5, 6, 7, 8, 9
mn1 = multinom(Month ~ Temp, data=d, Hess=TRUE)
#> # weights: 15 (8 variable)
#> initial value 246.244001
#> iter 10 value 196.357608
#> final value 194.725454
#> converged
mn2 = multinom(Month ~ Temp + Wind + Ozone, data=d, Hess=TRUE)
#> # weights: 25 (16 variable)
#> initial value 186.694798
#> iter 10 value 168.210554
#> iter 20 value 139.276111
#> iter 30 value 137.478576
#> final value 137.466340
#> converged
model_summary(mn1)
#>
#> Model Summary
#>
#> ───────────────────────────────────────────────────────────────
#> 6 7 8 9
#> ───────────────────────────────────────────────────────────────
#> (Intercept) -20.275 *** -29.489 *** -29.629 *** -16.484 ***
#> (4.089) (4.730) (4.738) (3.731)
#> Temp 0.280 *** 0.393 *** 0.395 *** 0.231 ***
#> (0.056) (0.063) (0.063) (0.052)
#> ───────────────────────────────────────────────────────────────
#> AIC 405.451 405.451 405.451 405.451
#> BIC 429.694 429.694 429.694 429.694
#> Num. obs. 153 153 153 153
#> K 5 5 5 5
#> ───────────────────────────────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#>
#> Not enough model terms in the conditional part of the model to check for
#> multicollinearity.
model_summary(mn2)
#>
#> Model Summary
#>
#> ───────────────────────────────────────────────────────────────
#> 6 7 8 9
#> ───────────────────────────────────────────────────────────────
#> (Intercept) -27.752 *** -27.079 *** -27.212 *** -17.641 ***
#> (7.316) (5.944) (5.948) (4.772)
#> Temp 0.346 *** 0.364 *** 0.363 *** 0.261 ***
#> (0.094) (0.076) (0.076) (0.063)
#> Wind 0.223 0.027 0.040 -0.007
#> (0.147) (0.138) (0.137) (0.120)
#> Ozone -0.039 -0.016 -0.014 -0.032
#> (0.027) (0.016) (0.015) (0.017)
#> ───────────────────────────────────────────────────────────────
#> AIC 306.933 306.933 306.933 306.933
#> BIC 350.990 350.990 350.990 350.990
#> Num. obs. 116 116 116 116
#> K 5 5 5 5
#> ───────────────────────────────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#>
#> # Check for Multicollinearity
#>
#> Low Correlation
#>
#> Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
#> Temp 2.84 [2.24, 3.72] 1.68 0.35 [0.27, 0.45]
#> Wind 1.95 [1.60, 2.52] 1.40 0.51 [0.40, 0.63]
#> Ozone 2.36 [1.89, 3.06] 1.54 0.42 [0.33, 0.53]
#>
model_summary(mn2, file="Multinomial Logistic Model.doc")
#> ✔ Table saved to '/home/runner/work/bruceR/bruceR/docs/reference/Multinomial Logistic Model.doc'
#>
unlink("Multinomial Logistic Model.doc") # delete file for code check