Creates tables to describe different objects (see list of supported objects
in report()).
Arguments
- x
The R object that you want to report (see list of of supported objects above).
- ...
Arguments passed to or from other methods.
Examples
# \donttest{
# Miscellaneous
r <- report_table(sessionInfo())
r
#> Package | Version | Reference
#> ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#> Matrix | 1.5.3 | Bates D, Maechler M, Jagan M (2022). _Matrix: Sparse and Dense Matrix Classes and Methods_. https://Matrix.R-forge.R-project.org/, https://Matrix.R-forge.R-project.org/doxygen/.
#> R | 4.2.2 | R Core Team (2022). _R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. <https://www.R-project.org/>.
#> Rcpp | 1.0.10 | Eddelbuettel D, François R (2011). “Rcpp: Seamless R and C++ Integration.” _Journal of Statistical Software_, *40*(8), 1-18. doi:10.18637/jss.v040.i08 <https://doi.org/10.18637/jss.v040.i08>. Eddelbuettel D (2013). _Seamless R and C++ Integration with Rcpp_. Springer, New York. doi:10.1007/978-1-4614-6868-4 <https://doi.org/10.1007/978-1-4614-6868-4>, ISBN 978-1-4614-6867-7. Eddelbuettel D, Balamuta JJ (2018). “Extending extitR with extitC++: A Brief Introduction to extitRcpp.” _The American Statistician_, *72*(1), 28-36. doi:10.1080/00031305.2017.1375990 <https://doi.org/10.1080/00031305.2017.1375990>.
#> bayestestR | 0.13.0 | Makowski D, Ben-Shachar M, Lüdecke D (2019). “bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework.” _Journal of Open Source Software_, *4*(40), 1541. doi:10.21105/joss.01541 <https://doi.org/10.21105/joss.01541>, <https://joss.theoj.org/papers/10.21105/joss.01541>.
#> dplyr | 1.1.0 | Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A Grammar of Data Manipulation_. https://dplyr.tidyverse.org, https://github.com/tidyverse/dplyr.
#> lavaan | 0.6.13 | Rosseel Y (2012). “lavaan: An R Package for Structural Equation Modeling.” _Journal of Statistical Software_, *48*(2), 1-36. doi:10.18637/jss.v048.i02 <https://doi.org/10.18637/jss.v048.i02>.
#> lme4 | 1.1.31 | Bates D, Mächler M, Bolker B, Walker S (2015). “Fitting Linear Mixed-Effects Models Using lme4.” _Journal of Statistical Software_, *67*(1), 1-48. doi:10.18637/jss.v067.i01 <https://doi.org/10.18637/jss.v067.i01>.
#> performance | 0.10.2 | Lüdecke D, Ben-Shachar M, Patil I, Waggoner P, Makowski D (2021). “performance: An R Package for Assessment, Comparison and Testing of Statistical Models.” _Journal of Open Source Software_, *6*(60), 3139. doi:10.21105/joss.03139 <https://doi.org/10.21105/joss.03139>.
#> report | 0.5.6 | Makowski D, Lüdecke D, Patil I, Thériault R (2023). “Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption.” _CRAN_. <https://easystats.github.io/report/>.
#> rstanarm | 2.21.3 | Goodrich B, Gabry J, Ali I, Brilleman S (2022). “rstanarm: Bayesian applied regression modeling via Stan.” R package version 2.21.3, <https://mc-stan.org/rstanarm/>. Brilleman S, Crowther M, Moreno-Betancur M, Buros Novik J, Wolfe R (2018). “Joint longitudinal and time-to-event models via Stan.” StanCon 2018. 10-12 Jan 2018. Pacific Grove, CA, USA., <https://github.com/stan-dev/stancon_talks/>.
summary(r)
#> Package | Version
#> ---------------------
#> Matrix | 1.5.3
#> R | 4.2.2
#> Rcpp | 1.0.10
#> bayestestR | 0.13.0
#> dplyr | 1.1.0
#> lavaan | 0.6.13
#> lme4 | 1.1.31
#> performance | 0.10.2
#> report | 0.5.6
#> rstanarm | 2.21.3
# Data
report_table(iris$Sepal.Length)
#> Mean | SD | Median | MAD | Min | Max | n_Obs | Skewness | Kurtosis | percentage_Missing
#> --------------------------------------------------------------------------------------------
#> 5.84 | 0.83 | 5.80 | 1.04 | 4.30 | 7.90 | 150 | 0.31 | -0.55 | 0.00
report_table(as.character(round(iris$Sepal.Length, 1)))
#> n_Entries | n_Obs | n_Missing | percentage_Missing
#> --------------------------------------------------
#> 35.00 | 150 | 0 | 0.00
report_table(iris$Species)
#> Level | n_Obs | percentage_Obs
#> -----------------------------------
#> setosa | 50 | 33.33
#> versicolor | 50 | 33.33
#> virginica | 50 | 33.33
report_table(iris)
#> Variable | Level | n_Obs | percentage_Obs | Mean | SD | Median | MAD | Min | Max | Skewness | Kurtosis | percentage_Missing
#> -----------------------------------------------------------------------------------------------------------------------------------------
#> Sepal.Length | | 150 | | 5.84 | 0.83 | 5.80 | 1.04 | 4.30 | 7.90 | 0.31 | -0.55 | 0.00
#> Sepal.Width | | 150 | | 3.06 | 0.44 | 3.00 | 0.44 | 2.00 | 4.40 | 0.32 | 0.23 | 0.00
#> Petal.Length | | 150 | | 3.76 | 1.77 | 4.35 | 1.85 | 1.00 | 6.90 | -0.27 | -1.40 | 0.00
#> Petal.Width | | 150 | | 1.20 | 0.76 | 1.30 | 1.04 | 0.10 | 2.50 | -0.10 | -1.34 | 0.00
#> Species | setosa | 50 | 33.33 | | | | | | | | |
#> Species | versicolor | 50 | 33.33 | | | | | | | | |
#> Species | virginica | 50 | 33.33 | | | | | | | | |
# h-tests
report_table(t.test(mtcars$mpg ~ mtcars$am))
#> Welch Two Sample t-test
#>
#> Parameter | Group | Mean_Group1 | Mean_Group2 | Difference | 95% CI | t(18.33) | p | Cohen's d | Cohens_d CI
#> ---------------------------------------------------------------------------------------------------------------------------------
#> mtcars$mpg | mtcars$am | 17.15 | 24.39 | -7.24 | [-11.28, -3.21] | -3.77 | 0.001 | -1.41 | [-2.26, -0.53]
#>
#> Alternative hypothesis: two.sided
# ANOVAs
report_table(aov(Sepal.Length ~ Species, data = iris))
#> Parameter | Sum_Squares | df | Mean_Square | F | p | Eta2 | Eta2 95% CI
#> -----------------------------------------------------------------------------------
#> Species | 63.21 | 2 | 31.61 | 119.26 | < .001 | 0.62 | [0.54, 1.00]
#> Residuals | 38.96 | 147 | 0.27 | | | |
# GLMs
report_table(lm(Sepal.Length ~ Petal.Length * Species, data = iris))
#> Parameter | Coefficient | 95% CI | t(144) | p | Std. Coef. | Std. Coef. 95% CI | Fit
#> ------------------------------------------------------------------------------------------------------------------------------
#> (Intercept) | 4.21 | [ 3.41, 5.02] | 10.34 | < .001 | 0.49 | [-1.03, 2.01] |
#> Petal Length | 0.54 | [ 0.00, 1.09] | 1.96 | 0.052 | 1.16 | [-0.01, 2.32] |
#> Species [versicolor] | -1.81 | [-2.99, -0.62] | -3.02 | 0.003 | -0.88 | [-2.41, 0.65] |
#> Species [virginica] | -3.15 | [-4.41, -1.90] | -4.97 | < .001 | -1.75 | [-3.32, -0.18] |
#> Petal Length × Species [versicolor] | 0.29 | [-0.30, 0.87] | 0.97 | 0.334 | 0.61 | [-0.63, 1.85] |
#> Petal Length × Species [virginica] | 0.45 | [-0.12, 1.03] | 1.56 | 0.120 | 0.97 | [-0.26, 2.19] |
#> | | | | | | |
#> AIC | | | | | | | 106.77
#> AICc | | | | | | | 107.56
#> BIC | | | | | | | 127.84
#> R2 | | | | | | | 0.84
#> R2 (adj.) | | | | | | | 0.83
#> Sigma | | | | | | | 0.34
report_table(glm(vs ~ disp, data = mtcars, family = "binomial"))
#> Parameter | Coefficient | 95% CI | z | p | Std. Coef. | Std. Coef. 95% CI | Fit
#> ---------------------------------------------------------------------------------------------------
#> (Intercept) | 4.14 | [ 1.81, 7.44] | 2.98 | 0.003 | -0.85 | [-2.42, 0.27] |
#> disp | -0.02 | [-0.04, -0.01] | -3.03 | 0.002 | -2.68 | [-4.90, -1.27] |
#> | | | | | | |
#> AIC | | | | | | | 26.70
#> AICc | | | | | | | 27.11
#> BIC | | | | | | | 29.63
#> Tjur's R2 | | | | | | | 0.53
#> Sigma | | | | | | | 0.87
#> Log_loss | | | | | | | 0.35
# }
# \donttest{
# Mixed models
library(lme4)
model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
report_table(model)
#> Parameter | Coefficient | 95% CI | t(146) | p | Effects | Group | Std. Coef. | Std. Coef. 95% CI | Fit
#> ------------------------------------------------------------------------------------------------------------------------------
#> (Intercept) | 2.50 | [1.19, 3.82] | 3.75 | < .001 | fixed | | -1.46e-13 | [-1.49, 1.49] |
#> Petal Length | 0.89 | [0.76, 1.01] | 13.93 | < .001 | fixed | | 1.89 | [ 1.63, 2.16] |
#> | 1.08 | | | | random | Species | | |
#> | 0.34 | | | | random | Residual | | |
#> | | | | | | | | |
#> AIC | | | | | | | | | 127.79
#> AICc | | | | | | | | | 128.07
#> BIC | | | | | | | | | 139.84
#> R2 (conditional) | | | | | | | | | 0.97
#> R2 (marginal) | | | | | | | | | 0.66
#> Sigma | | | | | | | | | 0.34
# }
# \donttest{
# Bayesian models
library(rstanarm)
model <- suppressWarnings(stan_glm(Sepal.Length ~ Species, data = iris, refresh = 0, iter = 600))
report_table(model, effectsize_method = "basic")
#> Parameter | Median | 95% CI | pd | Rhat | ESS | Prior | Std. Median | Std_Median 95% CI | Fit
#> --------------------------------------------------------------------------------------------------------------------------------------
#> (Intercept) | 5.01 | [4.86, 5.14] | 100% | 0.999 | 890.00 | Normal (5.84 +- 2.07) | 0.00 | [0.00, 0.00] |
#> Speciesversicolor | 0.93 | [0.72, 1.13] | 100% | 0.998 | 856.00 | Normal (0.00 +- 4.38) | 0.53 | [0.41, 0.65] |
#> Speciesvirginica | 1.58 | [1.38, 1.79] | 100% | 1.000 | 1062.00 | Normal (0.00 +- 4.38) | 0.90 | [0.79, 1.02] |
#> | | | | | | | | |
#> ELPD | | | | | | | | | -115.88
#> LOOIC | | | | | | | | | 231.75
#> WAIC | | | | | | | | | 231.70
#> R2 | | | | | | | | | 0.61
#> R2 (adj.) | | | | | | | | | 0.61
#> Sigma | | | | | | | | | 0.52
# }
# \donttest{
# Structural Equation Models (SEM)
library(lavaan)
structure <- "ind60 =~ x1 + x2 + x3
dem60 =~ y1 + y2 + y3
dem60 ~ ind60"
model <- lavaan::sem(structure, data = PoliticalDemocracy)
suppressWarnings(report_table(model))
#> Parameter | Coefficient | 95% CI | z | p | Component | Fit
#> ----------------------------------------------------------------------------------
#> ind60 =~ x1 | 1.00 | [1.00, 1.00] | | < .001 | Loading |
#> ind60 =~ x2 | 2.18 | [1.91, 2.45] | 15.59 | < .001 | Loading |
#> ind60 =~ x3 | 1.82 | [1.52, 2.12] | 11.96 | < .001 | Loading |
#> dem60 =~ y1 | 1.00 | [1.00, 1.00] | | < .001 | Loading |
#> dem60 =~ y2 | 1.04 | [0.66, 1.43] | 5.33 | < .001 | Loading |
#> dem60 =~ y3 | 0.98 | [0.65, 1.30] | 5.89 | < .001 | Loading |
#> dem60 ~ ind60 | 1.37 | [0.53, 2.21] | 3.20 | 0.001 | Regression |
#> | | | | | |
#> Chi2 | | | | | | 7.98
#> Chi2_df | | | | | | 8.00
#> p_Chi2 | | | | | | 0.44
#> p_Baseline | | | | | | 0.00
#> GFI | | | | | | 0.97
#> AGFI | | | | | | 0.91
#> NFI | | | | | | 0.97
#> NNFI | | | | | | 1.00
#> CFI | | | | | | 1.00
#> RMSEA | | | | | | 0.00
#> RMSEA_CI_low | | | | | | 0.00
#> RMSEA_CI_high | | | | | | 0.14
#> p_RMSEA | | | | | | 0.57
#> RMR | | | | | | 0.10
#> SRMR | | | | | | 0.03
#> RFI | | | | | | 0.95
#> PNFI | | | | | | 0.52
#> IFI | | | | | | 1.00
#> RNI | | | | | | 1.00
#> Loglikelihood | | | | | | -778.27
#> AIC | | | | | | 1582.54
#> BIC | | | | | | 1612.67
#> BIC (adj.) | | | | | | 1571.69
# }