docs/examples.R

In holub008/gdlm: Build linear models through gradient descent

library(gdlm)
library(ggplot2)

data(mpg)

(p1 <- ggplot(mpg) + geom_point(aes(hwy, cty)))
m <- gdlm(cty ~ hwy, mpg, loss = LS_LOSS())
summary(m)
(p2 <- p1 + geom_abline(aes(intercept = m$estimators[1], 
                            slope = m$estimators[2]), color = 'black'))


m_negative_penalty <- gdlm(cty ~ hwy, mpg, loss = LS_LOSS(.1))
summary(m_negative_penalty)
p2 + geom_abline(aes(intercept = m_negative_penalty$estimators[1], 
                     slope = m_negative_penalty$estimators[2]), color ='red')


m_lad <- gdlm(cty ~ hwy, mpg, loss = LAD_LOSS())
m_lad_negative_penalty <- gdlm(cty ~ hwy, mpg, loss = LAD_LOSS(loss_asymmetry = .1))
p1 +
  geom_abline(aes(intercept = m_lad$estimators[1], slope = m_lad$estimators[2]), color = 'blue') +
  geom_abline(aes(intercept = m_lad_negative_penalty$estimators[1],
                  slope = m_lad_negative_penalty$estimators[2]), color ='red') + ggtitle('Least Absolute Deviation Fits')


car_data <- mpg
car_data$is_big_car <- mpg$class %in% c('suv', 'pickup', 'minivan')
m_log <- gdlm(is_big_car ~ hwy, car_data, loss = LOGISTIC_LOSS())
summary(m_log)
log_odds <- predict(m_log)
ggplot() + geom_histogram(aes(log_odds, fill = car_data$is_big_car))


m_log_shrunk <- gdlm(is_big_car ~ hwy, car_data, 
                     loss = compose_regularization(LOGISTIC_LOSS(), elastic_net_parameter = .5, lambda = 1e-3))
summary(m_log_shrunk)
log_odds <- predict(m_log_shrunk)
ggplot() + geom_histogram(aes(log_odds, fill = car_data$is_big_car))