Uncertainty examples with mtcars ## -- Attaching packages ------------------------------------------------------------------------------------------------------------ tidyverse 1.2.1 --
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## v tibble 1.4.2 v dplyr 0.7.6
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## x dplyr::filter() masks stats::filter()
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library(modelr )
library(brms ) ## Loading required package: Rcpp
## Loading 'brms' package (version 2.4.3). Useful instructions
## can be found by typing help('brms'). A more detailed introduction
## to the package is available through vignette('brms_overview').
## Run theme_set(theme_default()) to use the default bayesplot theme.
## NOTE: As of tidybayes version 1.0, several functions, arguments, and output column names
## have undergone significant name changes in order to adopt a unified naming scheme.
## See help('tidybayes-deprecated') for more information.
mtcars %> %
ggplot(aes(x = hp , y = mpg )) +
geom_point()
m_linear = brm(mpg ~ hp , data = mtcars )## Compiling the C++ model
## Start sampling
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mtcars %> %
data_grid(hp = seq_range(hp , n = 101 )) %> %
add_fitted_draws(m_linear , n = 100 ) %> %
ggplot(aes(x = hp )) +
geom_line(aes(y = .value , group = .draw ), alpha = .2 ) +
geom_point(aes(y = mpg ), data = mtcars )
mtcars %> %
data_grid(hp = seq_range(hp , n = 101 )) %> %
add_fitted_draws(m_linear , n = 100 ) %> %
ggplot(aes(x = hp , y = mpg )) +
geom_line(aes(y = .value )) +
geom_point(data = mtcars ) +
transition_manual(.draw )
m_loglog = brm(mpg ~ hp , data = mtcars , family = lognormal )## Compiling the C++ model
## Start sampling
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mtcars %> %
data_grid(hp = seq_range(hp , n = 101 )) %> %
add_fitted_draws(m_loglog , n = 100 ) %> %
ggplot(aes(x = hp , y = mpg )) +
geom_line(aes(y = .value )) +
geom_point(data = mtcars ) +
transition_manual(.draw )
mtcars_clean = mtcars %> %
mutate(transmission = factor (am , labels = c(" automatic" " manual" m_loglog_trans = brm(mpg ~ hp * transmission , data = mtcars_clean , family = lognormal )## Compiling the C++ model
## recompiling to avoid crashing R session
## Start sampling
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mtcars_clean %> %
data_grid(hp = seq_range(hp , n = 101 ), transmission ) %> %
add_fitted_draws(m_loglog_trans , n = 100 ) %> %
ggplot(aes(x = hp , y = mpg , color = transmission )) +
geom_line(aes(y = .value , group = paste(transmission , .draw )), alpha = .1 ) +
geom_point(data = mtcars_clean )
mtcars_clean %> %
data_grid(hp = seq_range(hp , n = 101 ), transmission ) %> %
add_fitted_draws(m_loglog_trans , n = 100 ) %> %
ggplot(aes(x = hp , y = mpg , color = transmission )) +
geom_line(aes(y = .value )) +
geom_point(data = mtcars_clean ) +
transition_manual(.draw )
