##################################################
# Implementation of Poisson Class #
# Kaitlin Cornwell #
# August 3, 2018 #
##################################################
###### Poisson
#' Make Poisson Distribution
#'
#' `make_poisson()` creates an object that allows for analysis assuming
#' a binomial distribution.
#'
#' The returned tibble can be row bound with other objects from the
#' `make_distribution()` functions. Then multiple models can be worked
#' with at once.
#'
#' This tibble is the input for `set_priors()` and `fit_model()` functions.
#'
#' @param ... A comma separated list of predictors that will be used in the
#' model, given as unquoted expressions. 1 if fitting an intercept
#' only model
#'
#' @return A tibble with one row. The tibble will contain information stating
#' that it is a poisson model and will contain the right hand side of
#' a model formula by creating a linear combination of predictors
#'
#' @examples
#' make_pois(1)
#' make_pois(x)
#' make_pois(x, y)
#'
#' @export
make_pois <- function(...) {
# the poisson distribution is fit using base::glm
# it takes in a formula and would set family = poisson
# make the list of predictors into expressions
predictors = enexprs(...)
# output the tibble
tibble(model_type = "Poisson", model_eq = write_equation(predictors))
}
### Poisson base/default class
#' Constructor for Poisson distribution
#'
#' @param model_object an object coming from the `fit_model()` function
#' @param outcome the variable to be used as the dependent variable provided
#' as an expression
#' @param group the variable to be used as the grouping variable provided
#' as an expression
#' @param opt the model options provided as a function call to `model_options()`
#'
#' @return An object of class Poisson
Poisson <- function(model_object, outcome, group = NA, opt = NAA) {
# turn outcome and group variable names into expressions
outcome = enexpr(outcome)
group = enexpr(group)
values <- tibble(outcome = c(expr(!!outcome)),
group = c(expr(!!group)),
equation = c(expr(!!model_object$model_eq)))
# if there are model options then put them in the object
if (!is.na(opt))
values <- bind_cols(values, as_tibble(opt))
# set the class
attr(values, "class") <- "Poisson"
}
# Poission fit_object()
# should not run
#' @export
fit_object.Poisson <- function(model_object, model_data) {
stop("You have not defined the type of model fit properly")
}
# Poisson model_prediction()
# should not run
#' @export
model_prediction.Poisson <- function(object, new_values = NULL) {
stop("You have not defined the type of model fit properly")
}
#' @export
simulate_distribution.Poisson <- function(model_object, model_results, values, nsim = 1, seed = NULL) {
stop("You have not defined the type of model fit properly")
}
# Poisson inv_transformation()
# should not run
#' @export
inv_transformation.Poisson <- function(object, model_results, id, predictions) {
stop("You have not defined the type of model fit properly")
}
#' @export
model_distribution.Poisson <- function(model_object, model_results, id, hist = FALSE) {
stop("You have not defined the type of model fit properly")
}
### Poisson frequentist class
#' Constructor for Poisson Frequentist distribution
#'
#' @param model_object an object coming from the `fit_model()` function
#' @param outcome the variable to be used as the dependent variable provided
#' as an expression
#' @param group the variable to be used as the grouping variable provided
#' as an expression
#' @param opt the model options provided as a function call to `model_options()`
#'
#' @return An object of class Poisson.Frequentist
Poisson.Frequentist <- function(model_object, outcome, group = NA, opt = NA) {
# turn the outcome and group variable names into expressions
outcome = enexpr(outcome)
group = enexpr(group)
values <- tibble(outcome = c(expr(!!outcome)),
group = c(expr(!!group)),
equation = c(expr(!!model_object$model_eq)))
# if there are model options then append them to the object
if (!is.na(opt))
values <- bind_cols(values, as_tibble(opt))
# set the class
structure(values, class = c("Poisson.Frequentist", "Poisson"))
}
# Fits the Poisson model with a Frequentist framework
# arguments: model_object - an object of class Poisson.Frequntist
# model_data - the dataset to be used to fit the model
# returns: the S3 object from glm along with relevant model information including type of fit and equation
#' @export
fit_object.Poisson.Frequentist <- function(model_object, model_data) {
# if it is intercept only
if (quo_name(model_object$equation[[1]]) == "1")
# create intercept only equation
mod_formula <- expr(!!model_object$outcome[[1]] ~ 1)
# if it is not intercept only
else
# create the formula putting together the outcome and linear combination of predictors
mod_formula <- expr(!!model_object$outcome[[1]] ~ !!model_object$equation[[1]])
# if there is a grouping variable then group the data
if (!is.na(model_object$group)) {
groups <- model_data %>% select(!!model_object$group[[1]]) %>% distinct()
model_data <- model_data %>% group_by(!!model_object$group[[1]]) %>% nest()
}
else {
model_data <- model_data %>% nest()
}
# fit the model using glm
results <- model_data %>%
rowwise() %>%
do(model_results = as_result(glm(formula = !!mod_formula, family = poisson, data = .$data),
model_data = .$data)) %>%
mutate(model_type = "Poisson", fit_type = "Frequentist", model_eq = model_object$equation)
if (!is.na(model_object$group))
results <- bind_cols(groups, results)
results
}
# Predicts values for the model
# arguments: model_object - an object of type Poisson.Frequentist
# model_results - an S3 object of class glm contianing the fit model information
# id - an id value to be appended to final dataset
# pred_values - the values at which to carry out prediction at
# returns: a tibble with each row a predicted value of the dataset
#' @export
model_prediction.Poisson.Frequentist <- function(model_object, model_results, id, new_values = NULL) {
if (is.na(new_values) %>% all()) {
# get the predictors of the model
preds <- get_predictors(model_object$equation)[-c(1,2)]
# if intercept only model
if ((preds == "1") %>% all())
new_values <- tibble(intercept = rep(model_results$coefficients, times = nrow(model_results$data)))
else
# subset data to be appended to prediction results
new_values <- model_results$data %>% select(!!preds)
}
# using predict.glm function
predict(model_results, new_values) %>%
as_tibble() %>%
bind_cols(new_values) %>%
# add id column
mutate(id = id) %>%
rename(!!model_object$outcome[[1]] := value)
}
# Carries out the inverse transformation of the link function
# arguments: model_object - an object of type Poisson.Frequentist
# model_results - an S3 object of class glm contianing the fit model information
# id - an id value to be appended to final dataset
# values - the values at which to carry out the transformation of
# returns: a tibble with each row the average response at the given predictors
#' @export
inv_transformation.Poisson.Frequentist <- function(model_object, model_results, id, values) {
# carry out the inverse transformation
values %>% mutate(!!model_object$outcome[[1]] := exp(!!model_object$outcome[[1]]))
}
# Simulate data from a fit distribution
# arguments: model_object - an object of type Poisson.Frequentist
# model_results - an S3 object of class glm contianing the fit model information
# values - the values at which to carry out simulation at
# nsim - number of datasets to simulate
# seed - value set for random number generator
# returns: a tibble with each row a simulated value of the dataset
#' @export
simulate_distribution.Poisson.Frequentist <- function(model_objects, model_results, values, nsim = 1, seed = NULL) {
# transform the outcome into the parameter for poisson distribution
values <- values %>% mutate(lambda = exp(!!model_objects$outcome[[1]]))
# carry out the simulation starting with the predicted values
set.seed(seed)
values$lambda %>%
map(~ rpois(nsim, .)) %>%
unlist() %>%
as_tibble() %>%
bind_cols(.,
values %>% slice(rep(1:n(), each = nsim))) %>%
select(-!!model_objects$outcome[[1]], -lambda) %>%
rename(!!model_objects$outcome[[1]] := value)
}
#' @export
model_distribution.Poisson.Frequentist <- function(model_object, model_results, id, hist = FALSE) {
bounds <- model_results$data %>% select(!!model_object$outcome[[1]]) %>% summarise(min = min(.), max = max(.))
graphing_tbl <- tibble(x = bounds$min:bounds$max,
y = dpois(x, lambda = exp(coef(model_results$result)[[1]])),
id = id)
if (hist) {
hist_data <- model_results$data %>%
select(!!model_object$outcome[[1]]) %>%
group_by(!!model_object$outcome[[1]]) %>%
summarise(n = n()) %>%
rename(x = y)
graphing_tbl <- inner_join(graphing_tbl, hist_data, by = "x")
}
model_plot <- ggplot(graphing_tbl, aes(x = x))
if (hist)
model_plot <- model_plot + geom_bar(aes(y = n/sum(n)), stat = "identity")
model_plot +
geom_line(aes(y = y), col = id) +
ggtitle(glue("{id} - Poisson")) +
theme(legend.position = "none") +
labs(y = "Density")
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.