pct (version 0.9.1)

model_pcycle_pct_2020: Model cycling levels as a function of explanatory variables

Description

Model cycling levels as a function of explanatory variables

Usage

model_pcycle_pct_2020(pcycle, distance, gradient, weights)

Arguments

pcycle

The proportion of trips by bike, e.g. 0.1, meaning 10%

distance

Vector distance numeric values of routes in km (switches to km if more than 100).

gradient

Vector gradient numeric values of routes.

weights

The weights used in the model, typically the total number of people per OD pair

Examples

# NOT RUN {
# l = get_pct_lines(region = "isle-of-wight")
# l = get_pct_lines(region = "cambridgeshire")
l = wight_lines_pct
pcycle = l$bicycle / l$all
pcycle_dutch = l$dutch_slc / l$all
m1 = model_pcycle_pct_2020(
  pcycle,
  distance = l$rf_dist_km,
  gradient = l$rf_avslope_perc - 0.78,
  weights = l$all
  )
m2 = model_pcycle_pct_2020(
  pcycle_dutch, distance = l$rf_dist_km,
  gradient = l$rf_avslope_perc - 0.78,
  weights = l$all
)
m3 = model_pcycle_pct_2020(
  pcycle_dutch, distance = l$rf_dist_km,
  gradient = l$rf_avslope_perc - 0.78,
  weights = rep(1, nrow(l))
)
m1
plot(l$rf_dist_km, pcycle, cex = l$all / 100, ylim = c(0, 0.5))
points(l$rf_dist_km, m1$fitted.values, col = "red")
points(l$rf_dist_km, m2$fitted.values, col = "blue")
points(l$rf_dist_km, pcycle_dutch, col = "green")
cor(l$dutch_slc, m2$fitted.values * l$all)^2 # 95% captured
# identical means:
mean(l$dutch_slc)
mean(m2$fitted.values * l$all)
pct_coefficients_2020 = c(
  alpha = -4.018 + 2.550,
  d1 = -0.6369 -0.08036,
  d2 = 1.988,
  d3 = 0.008775,
  h1 = -0.2555,
  i1 = 0.02006,
  i2 = -0.1234
)
pct_coefficients_2020
m2$coef
plot(pct_coefficients_2020, m2$coeff)
cor(pct_coefficients_2020, m2$coeff)^2
cor(pct_coefficients_2020, m3$coeff)^2 # explains 95%+ variability in params
# }