mkearney / Tidyreg
🎓 Tidy regression tools for academics
Stars: ✠19
Programming Languages
r
7636 projects
Labels
Projects that are alternatives of or similar to Tidyreg
Math Php
Powerful modern math library for PHP: Features descriptive statistics and regressions; Continuous and discrete probability distributions; Linear algebra with matrices and vectors, Numerical analysis; special mathematical functions; Algebra
Stars: ✠2,009 (+10473.68%)
Mutual labels: statistics, regression
Morpheus Core
The foundational library of the Morpheus data science framework
Stars: ✠203 (+968.42%)
Mutual labels: statistics, regression
Data Science Toolkit
Collection of stats, modeling, and data science tools in Python and R.
Stars: ✠169 (+789.47%)
Mutual labels: statistics, regression
Interactive machine learning
IPython widgets, interactive plots, interactive machine learning
Stars: ✠140 (+636.84%)
Mutual labels: statistics, regression
data-science-notes
Open-source project hosted at https://makeuseofdata.com to crowdsource a robust collection of notes related to data science (math, visualization, modeling, etc)
Stars: ✠52 (+173.68%)
Mutual labels: statistics, regression
Machine Learning With Python
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
Stars: ✠2,197 (+11463.16%)
Mutual labels: statistics, regression
Peroxide
Rust numeric library with R, MATLAB & Python syntax
Stars: ✠191 (+905.26%)
Mutual labels: statistics, regression
Metriculous
Measure and visualize machine learning model performance without the usual boilerplate.
Stars: ✠71 (+273.68%)
Mutual labels: statistics, regression
hdfe
No description or website provided.
Stars: ✠22 (+15.79%)
Mutual labels: statistics, regression
Simple Statistics
simple statistics for node & browser javascript
Stars: ✠2,679 (+14000%)
Mutual labels: statistics, regression
Appelpy
Applied Econometrics Library for Python
Stars: ✠136 (+615.79%)
Mutual labels: statistics, regression
Glm.jl
Generalized linear models in Julia
Stars: ✠358 (+1784.21%)
Mutual labels: statistics, regression
Tidyversity
🎓 Tidy tools for academics
Stars: ✠155 (+715.79%)
Mutual labels: statistics, regression
Openml R
R package to interface with OpenML
Stars: ✠81 (+326.32%)
Mutual labels: statistics, regression
Uci Ml Api
Simple API for UCI Machine Learning Dataset Repository (search, download, analyze)
Stars: ✠190 (+900%)
Mutual labels: statistics, regression
Owl
Owl - OCaml Scientific and Engineering Computing @ http://ocaml.xyz
Stars: ✠919 (+4736.84%)
Mutual labels: statistics, regression
Mlj.jl
A Julia machine learning framework
Stars: ✠982 (+5068.42%)
Mutual labels: statistics, regression
Mathnet Numerics
Math.NET Numerics
Stars: ✠2,688 (+14047.37%)
Mutual labels: statistics, regression
interactive-simple-linear-regression
A PureScript, browser-based implementation of simple linear regression.
Stars: ✠15 (-21.05%)
Mutual labels: statistics, regression
tidyreg 
🎓 Tidy tools for academics
*** This package is in very early development. Feedback is encouraged!!! ***
Installation
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)
Regression models
Ordinary Least Squares (OLS)
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 + -0.050
Logistic (dichotomous)
Conduct 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
#> χ2 243 240 247.442 0.357
#> Δχ2 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 + -0.201
Poisson (count)
Conduct 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
#> χ2 242 239 6549.419 <.001 ***
#> Δχ2 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 *** -2.230
Negative binomial (overdispersed)
Conduct 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
#> χ2 242 239 293.328 0.009 **
#> Δχ2 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 * -0.158
Robust and quasi- models
polcom %>%
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
#> χ2 242 239 6989.543 <.001 ***
#> Δχ2 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
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].