๐ Tidy tools for academics
Install the development version from Github with:
## install devtools if not already
if (!requireNamespace("devtools", quietly = TRUE)) {
install.packages("devtools")
}
## install tidyreg from Github
devtools::install_github("mkearney/tidyreg")Load the package (it, of course, plays nicely with tidyverse).
## load tidyverse
library(tidyverse)
## load tidyreg
library(tidyreg)Conduct an Ordinary Least Squares (OLS) regression analysis.
polcom %>%
tidy_regression(follow_trump ~ news_1 + ambiv_sexism_1) %>%
tidy_summary()
#> # A tidy model
#> Model formula : follow_trump ~ news_1 + ambiv_sexism_1
#> Model type : Ordinary Least Squares (OLS) regression
#> Model pkg::fun : stats::lm()
#> Model data : 243 (observations) X 3 (variables)
#> $fit
#> fit_stat n df estimate p.value stars
#> F 243 2 3.831 0.023 *
#> R^2 243 - 0.031 -
#> Adj R^2 243 - 0.023 -
#> RMSE 243 - 0.409 -
#> AIC 243 - 260.148 -
#> BIC 243 - 274.121 -
#>
#> $coef
#> term est s.e. est.se p.value stars std.est
#> (Intercept) 0.745 0.097 7.692 <.001 *** <.001
#> news_1 0.022 0.012 1.811 0.071 + 0.048
#> ambiv_sexism_1 -0.038 0.021 -1.870 0.063 + -0.050Conduct a logistic regression analysis for binary (dichotomous) outcomes.
polcom %>%
tidy_regression(follow_trump ~ news_1 + ambiv_sexism_1, type = "logistic") %>%
tidy_summary()
#> # A tidy model
#> Model formula : follow_trump ~ news_1 + ambiv_sexism_1
#> Model type : Logistic regression
#> Model pkg::fun : stats::glm()
#> Model data : 243 (observations) X 3 (variables)
#> $fit
#> fit_stat n df estimate p.value stars
#> ฯ2 243 240 247.442 0.357
#> ฮฯ2 243 2 7.466 0.024 *
#> Nagelkerke R^2 243 - 0.030 -
#> McFadden R^2 243 - 0.029 -
#> RMSE 243 - 2.540 -
#> AIC 243 - 253.442 -
#> BIC 243 - 263.921 -
#>
#> $coef
#> term est s.e. est.se p.value stars std.est
#> (Intercept) 1.133 0.553 2.049 0.040 * <.001
#> news_1 0.127 0.070 1.808 0.071 + 0.195
#> ambiv_sexism_1 -0.229 0.122 -1.872 0.061 + -0.201Conduct a poisson regression analysis for count data.
polcom %>%
mutate(polarize = abs(therm_1 - therm_2)) %>%
tidy_regression(polarize ~ news_1 + ambiv_sexism_1, type = "poisson") %>%
tidy_summary()
#> # A tidy model
#> Model formula : polarize ~ news_1 + ambiv_sexism_1
#> Model type : Poisson regression
#> Model pkg::fun : stats::glm()
#> Model data : 242 (observations) X 3 (variables)
#> $fit
#> fit_stat n df estimate p.value stars
#> ฯ2 242 239 6549.419 <.001 ***
#> ฮฯ2 242 2 399.077 <.001 ***
#> Nagelkerke R^2 242 - 0.808 -
#> McFadden R^2 242 - 0.057 -
#> RMSE 242 - 0.760 -
#> AIC 242 - 7725.222 -
#> BIC 242 - 7735.689 -
#>
#> $coef
#> term est s.e. est.se p.value stars std.est
#> (Intercept) 3.798 0.038 99.448 <.001 *** <.001
#> news_1 0.045 0.005 9.358 <.001 *** 0.881
#> ambiv_sexism_1 -0.126 0.008 -15.852 <.001 *** -2.230Conduct a negative binomial regression analysis for overdispersed count data.
polcom %>%
mutate(polarize = abs(therm_1 - therm_2)) %>%
tidy_regression(polarize ~ news_1 + ambiv_sexism_1, type = "negbinom") %>%
tidy_summary()
#> # A tidy model
#> Model formula : polarize ~ news_1 + ambiv_sexism_1
#> Model type : Negative binomial regression
#> Model pkg::fun : MASS::glm.nb()
#> Model data : 242 (observations) X 3 (variables)
#> $fit
#> fit_stat n df estimate p.value stars
#> ฯ2 242 239 293.328 0.009 **
#> ฮฯ2 242 2 8.440 0.015 *
#> Nagelkerke R^2 242 - 0.034 -
#> McFadden R^2 242 - 0.028 -
#> RMSE 242 - 0.761 -
#> AIC 242 - 2312.391 -
#> BIC 242 - 2326.347 -
#>
#> $coef
#> term est s.e. est.se p.value stars std.est
#> (Intercept) 3.741 0.258 14.510 <.001 *** 3.752
#> news_1 0.053 0.032 1.632 0.103 0.113
#> ambiv_sexism_1 -0.123 0.054 -2.273 0.023 * -0.158polcom %>%
mutate(polarize = abs(therm_1 - therm_2)) %>%
tidy_regression(polarize ~ news_1 + ambiv_sexism_1,
type = "quasipoisson", robust = TRUE) %>%
tidy_summary()
#> # A tidy model
#> Model formula : polarize ~ news_1 + ambiv_sexism_1
#> Model type : [Robust] Poisson regression
#> Model pkg::fun : robust::glmRob()
#> Model data : 242 (observations) X 3 (variables)
#> $fit
#> fit_stat n df estimate p.value stars
#> ฯ2 242 239 6989.543 <.001 ***
#> ฮฯ2 242 2 58782.937 <.001 ***
#> Nagelkerke R^2 242 - 1.000 -
#> McFadden R^2 242 - 0.894 -
#> RMSE 242 - 31.865 -
#> AIC 242 - 2245.147 -
#> BIC 242 - 2259.103 -
#>
#> $coef
#> term est s.e. est.se p.value stars std.est
#> (Intercept) 3.705 0.071 51.968 <.001 *** <.001
#> news_1 0.079 0.010 8.325 <.001 *** 1.279
#> ambiv_sexism_1 -0.241 0.022 -11.179 <.001 *** -2.086
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