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Creates tables to describe different objects (see list of supported objects in report()).

Usage

report_table(x, ...)

Arguments

x

The R object that you want to report (see list of of supported objects above).

...

Arguments passed to or from other methods.

Value

An object of class report_table().

Examples

# \donttest{
library(report)

# Miscellaneous
r <- report_table(sessionInfo())
r
#> Package     | Version |                                                                                                                                                                                                                                                Reference
#> --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#> Matrix      |   1.4.1 |                                                                                                                                                                                                                                                         
#> R           |   4.2.0 |                                                                                    R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
#> Rcpp        | 1.0.8.3 |                                                                                           Dirk Eddelbuettel and Romain Francois (2011). Rcpp: Seamless R and C++ Integration. Journal of Statistical Software, 40(8), 1-18, <doi:10.18637/jss.v040.i08>.
#> bayestestR  |  0.12.1 |               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
#> dplyr       |   1.0.9 |                                                                                                                                                                                                                                                         
#> lavaan      |  0.6.11 |                                                                                          Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. https://doi.org/10.18637/jss.v048.i02
#> lme4        |  1.1.29 |                                                                                                                                                                                                                                                         
#> performance |   0.9.1 |                                                        Lüdecke et al., (2021). performance: An R Package for Assessment, Comparison and Testing of Statistical Models. Journal of Open Source Software, 6(60), 3139. https://doi.org/10.21105/joss.03139
#> report      | 0.5.1.2 | Makowski, D., Ben-Shachar, M.S., Patil, I. & Lüdecke, D. (2020). Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption. CRAN. Available from https://github.com/easystats/report. doi: .
#> 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.
summary(r)
#> Package     | Version
#> ---------------------
#> Matrix      |   1.4.1
#> R           |   4.2.0
#> Rcpp        | 1.0.8.3
#> bayestestR  |  0.12.1
#> dplyr       |   1.0.9
#> lavaan      |  0.6.11
#> lme4        |  1.1.29
#> performance |   0.9.1
#> report      | 0.5.1.2
#> 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(mpg ~ am, data = mtcars))
#> Warning: Unable to retrieve data from htest object. Using t_to_d() approximation.
#> Welch Two Sample t-test
#> 
#> Parameter | Group | Mean_Group1 | Mean_Group2 | Difference |          95% CI | t(18.33) |     p |     d |          d  CI
#> ------------------------------------------------------------------------------------------------------------------------
#> mpg       |    am |       17.15 |       24.39 |      -7.24 | [-11.28, -3.21] |    -3.77 | 0.001 | -1.76 | [-2.82, -0.67]
#> 
#> Alternative hypothesis: two.sided

# ANOVAs
report_table(aov(Sepal.Length ~ Species, data = iris))
#> For one-way between subjects designs, partial eta squared is equivalent to eta squared.
#> Returning eta squared.
#> 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
#> 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
#> BIC         |             |                |       |       |            |                   | 29.63
#> Tjur's R2   |             |                |       |       |            |                   |  0.53
#> Sigma       |             |                |       |       |            |                   |  0.87
#> Log_loss    |             |                |       |       |            |                   |  0.35

# Mixed models
if (require("lme4")) {
  model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
  report_table(model)
}
#> Package 'merDeriv' needs to be installed to compute confidence intervals
#>   for random effect parameters.
#> 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

# Bayesian models
if (require("rstanarm")) {
  model <- stan_glm(Sepal.Length ~ Species, data = iris, refresh = 0, iter = 600)
  report_table(model, effectsize_method = "basic")
}
#> Parameter         | Median |       95% CI |   pd | % in ROPE |  Rhat |    ESS |                 Prior | Std. Median | Std_Median 95% CI |     Fit
#> -------------------------------------------------------------------------------------------------------------------------------------------------
#> (Intercept)       |   5.00 | [4.86, 5.14] | 100% |        0% | 0.999 | 959.00 | Normal (5.84 +- 2.07) |        0.00 |      [0.00, 0.00] |        
#> Speciesversicolor |   0.94 | [0.73, 1.14] | 100% |        0% | 0.999 | 877.00 | Normal (0.00 +- 4.38) |        0.53 |      [0.42, 0.65] |        
#> Speciesvirginica  |   1.59 | [1.38, 1.78] | 100% |        0% | 0.998 | 906.00 | Normal (0.00 +- 4.38) |        0.91 |      [0.79, 1.02] |        
#>                   |        |              |      |           |       |        |                       |             |                   |        
#> ELPD              |        |              |      |           |       |        |                       |             |                   | -115.95
#> LOOIC             |        |              |      |           |       |        |                       |             |                   |  231.90
#> WAIC              |        |              |      |           |       |        |                       |             |                   |  231.85
#> R2                |        |              |      |           |       |        |                       |             |                   |    0.62
#> R2 (adj.)         |        |              |      |           |       |        |                       |             |                   |    0.61
#> Sigma             |        |              |      |           |       |        |                       |             |                   |    0.52

# Structural Equation Models (SEM)
if (require("lavaan")) {
  structure <- " ind60 =~ x1 + x2 + x3
                 dem60 =~ y1 + y2 + y3
                 dem60 ~ ind60 "
  model <- lavaan::sem(structure, data = PoliticalDemocracy)
  report_table(model)
}
#> Warning: No column names that matched the required search pattern were found.
#> 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
# }