gamma-classify.R

library(ggplot2)
library(MASS)

setwd('~/Downloads/jtls_and_more/')
jtl <- read_csv("~/Downloads/jtls_and_more/sample.jtl.csv")

df <- subset(jtl, jtl$lb == 'HomePage_01')
ggplot(jtl, aes(t)) +
  facet_wrap( ~ lb, ncol = 3) +
  geom_histogram(bins = 75) +
  xlim(0, 1000)

# Fit parameters (to avoid errors, set lower bounds to zero)
fit.params <- fitdistr(jtl$t, "gamma", lower = c(0, 0))
ggplot(data = data, aes(x = x, y = y)) +
  geom_point(size = 3) +
  geom_line(aes(
    x = data$x,
    y = dgamma(data$x, fit.params$estimate["shape"], fit.params$estimate["rate"])
  ), color = "red", size = 1) +
  theme_classic()

random-gamma.R

library(ggplot2)
library(MASS)

# Generate gamma rvs
x <- rgamma(100000, shape = 2, rate = 0.2)
den <- density(x)
data <- data.frame(x = den$x, y = den$y)

# Plot density as points
ggplot(data = data, aes(x = x, y = y)) +
  geom_point(size = 3) +
  theme_classic()

# Fit parameters (to avoid errors, set lower bounds to zero)
fit.params <- fitdistr(x, "gamma", lower = c(0, 0))

# Plot using density points
ggplot(data = data, aes(x = x, y = y)) +
  geom_point(size = 3) +
  geom_line(aes(
    x = data$x,
    y = dgamma(data$x, fit.params$estimate["shape"], fit.params$estimate["rate"])
  ), color = "red", size = 1) +
  theme_classic()