Create reports for (general) linear models.
Usage
# S3 method for lm
report(x, include_effectsize = TRUE, effectsize_method = "refit", ...)
# S3 method for lm
report_effectsize(x, effectsize_method = "refit", ...)
# S3 method for lm
report_table(x, include_effectsize = TRUE, ...)
# S3 method for lm
report_statistics(
x,
table = NULL,
include_effectsize = TRUE,
include_diagnostic = TRUE,
...
)
# S3 method for lm
report_parameters(
x,
table = NULL,
include_effectsize = TRUE,
include_intercept = TRUE,
...
)
# S3 method for lm
report_intercept(x, table = NULL, ...)
# S3 method for lm
report_model(x, table = NULL, ...)
# S3 method for lm
report_performance(x, table = NULL, ...)
# S3 method for lm
report_info(
x,
effectsize = NULL,
include_effectsize = FALSE,
parameters = NULL,
...
)
# S3 method for lm
report_text(x, table = NULL, ...)
# S3 method for merMod
report_random(x, ...)Arguments
- x
Object of class
lmorglm.- include_effectsize
If
FALSE, won't include effect-size related indices (standardized coefficients, etc.).- effectsize_method
See documentation for
effectsize::effectsize().- ...
Arguments passed to or from other methods.
- table
Provide the output of
report_table()to avoid its re-computation.- include_diagnostic
If
FALSE, won't include diagnostic related indices for Bayesian models (ESS, Rhat).- include_intercept
If
FALSE, won't include the intercept.- effectsize
Provide the output of
report_effectsize()to avoid its re-computation.- parameters
Provide the output of
report_parameters()to avoid its re-computation.
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:
Examples
# \donttest{
library(report)
# Linear models
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) | 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
summary(as.data.frame(r))
#> Parameter | Coefficient | 95% CI | t(144) | p | Std. Coef. | Fit
#> ----------------------------------------------------------------------------------------------------------
#> (Intercept) | 4.21 | [ 3.41, 5.02] | 10.34 | < .001 | 0.49 |
#> Petal Length | 0.54 | [ 0.00, 1.09] | 1.96 | 0.052 | 1.16 |
#> Species [versicolor] | -1.81 | [-2.99, -0.62] | -3.02 | 0.003 | -0.88 |
#> Species [virginica] | -3.15 | [-4.41, -1.90] | -4.97 | < .001 | -1.75 |
#> Petal Length × Species [versicolor] | 0.29 | [-0.30, 0.87] | 0.97 | 0.334 | 0.61 |
#> Petal Length × Species [virginica] | 0.45 | [-0.12, 1.03] | 1.56 | 0.120 | 0.97 |
#> | | | | | |
#> AICc | | | | | | 107.56
#> R2 | | | | | | 0.84
#> R2 (adj.) | | | | | | 0.83
#> Sigma | | | | | | 0.34
# Logistic models
model <- glm(vs ~ disp, data = mtcars, family = "binomial")
r <- report(model)
r
#> We fitted a logistic model (estimated using ML) to predict vs with disp
#> (formula: vs ~ disp). The model's explanatory power is substantial (Tjur's R2 =
#> 0.53). The model's intercept, corresponding to disp = 0, is at 4.14 (95% CI
#> [1.81, 7.44], p = 0.003). Within this model:
#>
#> - The effect of disp is statistically significant and negative (beta = -0.02,
#> 95% CI [-0.04, -0.01], p = 0.002; Std. beta = -2.68, 95% CI [-4.90, -1.27])
#>
#> 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 z-distribution approximation.
summary(r)
#> We fitted a logistic model to predict vs with disp. The model's explanatory
#> power is substantial (Tjur's R2 = 0.53). The model's intercept is at 4.14 (95%
#> CI [1.81, 7.44]). Within this model:
#>
#> - The effect of disp is statistically significant and negative (beta = -0.02,
#> 95% CI [-0.04, -0.01], p = 0.002, Std. beta = -2.68)
as.data.frame(r)
#> 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 | | | | | | | 1.00
#> Log_loss | | | | | | | 0.35
summary(as.data.frame(r))
#> Parameter | Coefficient | 95% CI | z | p | Std. Coef. | Fit
#> -------------------------------------------------------------------------------
#> (Intercept) | 4.14 | [ 1.81, 7.44] | 2.98 | 0.003 | -0.85 |
#> disp | -0.02 | [-0.04, -0.01] | -3.03 | 0.002 | -2.68 |
#> | | | | | |
#> AICc | | | | | | 27.11
#> Tjur's R2 | | | | | | 0.53
#> Sigma | | | | | | 1.00
#> Log_loss | | | | | | 0.35
# }
# \donttest{
# Mixed models
library(lme4)
model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
r <- report(model)
r
#> We fitted a linear mixed model (estimated using REML and nloptwrap optimizer)
#> to predict Sepal.Length with Petal.Length (formula: Sepal.Length ~
#> Petal.Length). The model included Species as random effect (formula: ~1 |
#> Species). The model's total explanatory power is substantial (conditional R2 =
#> 0.97) and the part related to the fixed effects alone (marginal R2) is of 0.66.
#> The model's intercept, corresponding to Petal.Length = 0, is at 2.50 (95% CI
#> [1.19, 3.82], t(146) = 3.75, p < .001). Within this model:
#>
#> - The effect of Petal Length is statistically significant and positive (beta =
#> 0.89, 95% CI [0.76, 1.01], t(146) = 13.93, p < .001; Std. beta = 1.89, 95% CI
#> [1.63, 2.16])
#>
#> 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 mixed model to predict Sepal.Length with Petal.Length. The
#> model included Species as random effect. The model's total explanatory power is
#> substantial (conditional R2 = 0.97) and the part related to the fixed effects
#> alone (marginal R2) is of 0.66. The model's intercept is at 2.50 (95% CI [1.19,
#> 3.82]). Within this model:
#>
#> - The effect of Petal Length is statistically significant and positive (beta =
#> 0.89, 95% CI [0.76, 1.01], t(146) = 13.93, p < .001, Std. beta = 1.89)
as.data.frame(r)
#> 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
summary(as.data.frame(r))
#> Parameter | Coefficient | 95% CI | t(146) | p | Effects | Group | Std. Coef. | Fit
#> ----------------------------------------------------------------------------------------------------------
#> (Intercept) | 2.50 | [1.19, 3.82] | 3.75 | < .001 | fixed | | -1.46e-13 |
#> Petal Length | 0.89 | [0.76, 1.01] | 13.93 | < .001 | fixed | | 1.89 |
#> | 1.08 | | | | random | Species | |
#> | 0.34 | | | | random | Residual | |
#> | | | | | | | |
#> AICc | | | | | | | | 128.07
#> R2 (conditional) | | | | | | | | 0.97
#> R2 (marginal) | | | | | | | | 0.66
#> Sigma | | | | | | | | 0.34
# }