Template file to add report support for new objects. Check-out the vignette on Supporting New Models.
Usage
# Default S3 method
report(x, ...)
# Default S3 method
report_effectsize(x, ...)
# Default S3 method
report_table(x, ...)
# Default S3 method
report_statistics(x, ...)
# Default S3 method
report_parameters(x, ...)
# Default S3 method
report_intercept(x, ...)
# Default S3 method
report_model(x, ...)
# Default S3 method
report_random(x, ...)
# Default S3 method
report_priors(x, ...)
# Default S3 method
report_performance(x, ...)
# Default S3 method
report_info(x, ...)
# Default S3 method
report_text(x, ...)Value
An object of class report().
See also
Specific components of reports (especially for stats models):
Other types of reports:
Methods:
Template file for supporting new models:
report.default()
Examples
library(report)
# Add a reproducible example instead of the following
model <- lm(Sepal.Length ~ Petal.Length * Species, data = iris)
r <- report(model)
r
#> We fitted a linear model (estimated using OLS) to predict Sepal.Length with
#> Petal.Length and Species (formula: Sepal.Length ~ Petal.Length * Species). The
#> model explains a statistically significant and substantial proportion of
#> variance (R2 = 0.84, F(5, 144) = 151.71, p < .001, adj. R2 = 0.83). The model's
#> intercept, corresponding to Petal.Length = 0 and Species = setosa, is at 4.21
#> (95% CI [3.41, 5.02], t(144) = 10.34, p < .001). Within this model:
#>
#> - The effect of Petal Length is statistically non-significant and positive
#> (beta = 0.54, 95% CI [-4.76e-03, 1.09], t(144) = 1.96, p = 0.052; Std. beta =
#> 1.16, 95% CI [-0.01, 2.32])
#> - The effect of Species [versicolor] is statistically significant and negative
#> (beta = -1.81, 95% CI [-2.99, -0.62], t(144) = -3.02, p = 0.003; Std. beta =
#> -0.88, 95% CI [-2.41, 0.65])
#> - The effect of Species [virginica] is statistically significant and negative
#> (beta = -3.15, 95% CI [-4.41, -1.90], t(144) = -4.97, p < .001; Std. beta =
#> -1.75, 95% CI [-3.32, -0.18])
#> - The effect of Petal Length × Species [versicolor] is statistically
#> non-significant and positive (beta = 0.29, 95% CI [-0.30, 0.87], t(144) = 0.97,
#> p = 0.334; Std. beta = 0.61, 95% CI [-0.63, 1.85])
#> - The effect of Petal Length × Species [virginica] is statistically
#> non-significant and positive (beta = 0.45, 95% CI [-0.12, 1.03], t(144) = 1.56,
#> p = 0.120; Std. beta = 0.97, 95% CI [-0.26, 2.19])
#>
#> Standardized parameters were obtained by fitting the model on a standardized
#> version of the dataset. 95% Confidence Intervals (CIs) and p-values were
#> computed using a Wald t-distribution approximation.
summary(r)
#> We fitted a linear model to predict Sepal.Length with Petal.Length and Species.
#> The model's explanatory power is substantial (R2 = 0.84, adj. R2 = 0.83). The
#> model's intercept is at 4.21 (95% CI [3.41, 5.02]). Within this model:
#>
#> - The effect of Petal Length is statistically non-significant and positive
#> (beta = 0.54, 95% CI [-4.76e-03, 1.09], t(144) = 1.96, p = 0.052, Std. beta =
#> 1.16)
#> - The effect of Species [versicolor] is statistically significant and negative
#> (beta = -1.81, 95% CI [-2.99, -0.62], t(144) = -3.02, p = 0.003, Std. beta =
#> -0.88)
#> - The effect of Species [virginica] is statistically significant and negative
#> (beta = -3.15, 95% CI [-4.41, -1.90], t(144) = -4.97, p < .001, Std. beta =
#> -1.75)
#> - The effect of Petal Length × Species [versicolor] is statistically
#> non-significant and positive (beta = 0.29, 95% CI [-0.30, 0.87], t(144) = 0.97,
#> p = 0.334, Std. beta = 0.61)
#> - The effect of Petal Length × Species [virginica] is statistically
#> non-significant and positive (beta = 0.45, 95% CI [-0.12, 1.03], t(144) = 1.56,
#> p = 0.120, Std. beta = 0.97)
as.data.frame(r)
#> Parameter | Coefficient | 95% CI | t(144)
#> ---------------------------------------------------------------------------
#> (Intercept) | 4.21 | [ 3.41, 5.02] | 10.34
#> Petal Length | 0.54 | [ 0.00, 1.09] | 1.96
#> Species [versicolor] | -1.81 | [-2.99, -0.62] | -3.02
#> Species [virginica] | -3.15 | [-4.41, -1.90] | -4.97
#> Petal Length × Species [versicolor] | 0.29 | [-0.30, 0.87] | 0.97
#> Petal Length × Species [virginica] | 0.45 | [-0.12, 1.03] | 1.56
#> | | |
#> AIC | | |
#> AICc | | |
#> BIC | | |
#> R2 | | |
#> R2 (adj.) | | |
#> Sigma | | |
#>
#> Parameter | p | Std. Coef. | Std. Coef. 95% CI | Fit
#> --------------------------------------------------------------------------------------
#> (Intercept) | < .001 | 0.49 | [-1.03, 2.01] |
#> Petal Length | 0.052 | 1.16 | [-0.01, 2.32] |
#> Species [versicolor] | 0.003 | -0.88 | [-2.41, 0.65] |
#> Species [virginica] | < .001 | -1.75 | [-3.32, -0.18] |
#> Petal Length × Species [versicolor] | 0.334 | 0.61 | [-0.63, 1.85] |
#> Petal Length × Species [virginica] | 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
summary(as.data.frame(r))
#> Parameter | Coefficient | 95% CI | t(144)
#> ---------------------------------------------------------------------------
#> (Intercept) | 4.21 | [ 3.41, 5.02] | 10.34
#> Petal Length | 0.54 | [ 0.00, 1.09] | 1.96
#> Species [versicolor] | -1.81 | [-2.99, -0.62] | -3.02
#> Species [virginica] | -3.15 | [-4.41, -1.90] | -4.97
#> Petal Length × Species [versicolor] | 0.29 | [-0.30, 0.87] | 0.97
#> Petal Length × Species [virginica] | 0.45 | [-0.12, 1.03] | 1.56
#> | | |
#> AICc | | |
#> R2 | | |
#> R2 (adj.) | | |
#> Sigma | | |
#>
#> Parameter | p | Std. Coef. | Fit
#> ------------------------------------------------------------------
#> (Intercept) | < .001 | 0.49 |
#> Petal Length | 0.052 | 1.16 |
#> Species [versicolor] | 0.003 | -0.88 |
#> Species [virginica] | < .001 | -1.75 |
#> Petal Length × Species [versicolor] | 0.334 | 0.61 |
#> Petal Length × Species [virginica] | 0.120 | 0.97 |
#> | | |
#> AICc | | | 107.56
#> R2 | | | 0.84
#> R2 (adj.) | | | 0.83
#> Sigma | | | 0.34