r - Fit data with proportional dependent variable -


here's simplified version of question example data:

each year, find 40 balls in yard. proportion of them red. i'd model proportion of red balls on time.

library(tidyverse) library(modelr)  # generate proportion data changes year data = tibble(   year = 2011:2020,    reds = 1:10, # red balls   total = 40, # total number of balls   propred = reds / total # proportion of red balls each year )  # fit model model = glm(propred ~ year, xxx_what_goes_here_xxx, data)  # graph model's prediction , data tibble(year = 2000:2030) %>%    modelr::add_predictions(model, "propred") %>%    ggplot() +      aes(y=propred, x=year) +      geom_line() +     geom_point(data=data)

this case can use logistic regression, using cbind(successes, failures) option in formula interface glm:

model <- glm(cbind(reds, total - reds) ~ year, family = 'binomial', data = data)  tibble(year = 2000:2030) %>%      mutate(propred = predict(model, newdata = ., type = 'response')) %>%     ggplot() +      aes(y=propred, x=year) +      geom_line() +     geom_point(data=data)

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