In aarmiller/predictBoundCP: Find Change-Point using Prediction Bound

knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(predictBoundCP)

Overview of change-point method

This package contains the scripts necessary to find a change point by finding the point where the observed data systematically deviate outside of, and above, the prediction bound of a model fit to a training period before the diagnostic opportunity window.

The following steps are applied to find the change-point:

1. Specify the training period [low,high] days prior to diagnosis, where low is > the furthest point in the "feasible" opportunity window

2. Fit a model to the data over the period [low,high].

3. Generate forecasts over the period [0, low-1] along with a corresponding prediction interval

4. Find the change-point cp where the observed number of SSD visits exceeds the upper prediction bound for all points t<cp prior to diagnosis

Example

SSD Visit Count Data

The package contains a count dataset corresponding to SSD visits prior to HSV encephalitis. The change-point functions require a count dataset with two variables: days_since_dx is the number of days since diagnosis (negative values before diagnosis) and n is the number of visits

visit_counts

The following figure depicts the trend in visit counts prior to HSV diagnosis, for the 180 days prior to diagnosis

plot_ssd_curve(count_data = visit_counts,
               max_window = 180)

Finding a change-point

The main function is fit_cp_model(). This function takes an SSD visit count dataset (visit_count), low and high values for the training window, a model to fit over the training window, the confidence level for the prediction bound. The function also has an option to return a plot of the fitted model along with prediction bounds and the change-point.

The current options for models includes:

• lm - Linear Model
• exp - Exponential Model
• lm_period - Linear Model with 7-day Periodicity
• exp_period - Exponential Model with 7-day Periodicity
• auto_arima - Auto ARIMA Model

``` {r warning=FALSE, message=FALSE}

Linear Model

mod1 <- fit_cp_model(count_data = visit_counts, low = 40, high = 180, # training window model = "lm", # model level = .95, # prediction bound plot = TRUE)

Exponential Model

mod2 <- fit_cp_model(count_data = visit_counts, low = 40, high = 180, # training window model = "exp", # model level = .95, # prediction bound plot = TRUE)

Linear Model with Periodicity

mod3 <- fit_cp_model(count_data = visit_counts, low = 40, high = 180, # training window model = "lm_period", # model level = .95, # prediction bound plot = TRUE)

Exponential Model with Periodicity

mod4 <- fit_cp_model(count_data = visit_counts, low = 40, high = 180, # training window model = "exp_period", # model level = .95, # prediction bound plot = TRUE)

Auto ARIMA Model

mod5 <- fit_cp_model(count_data = visit_counts, low = 40, high = 180, # training window model = "auto_arima", # model level = .95, # prediction bound plot = TRUE)

We can compare the results of the different models

``` {r}
gridExtra::grid.arrange(mod1$plot, mod2$plot,
                        mod3$plot, mod4$plot, 
                        nrow = 2)

mod5$plot

Profiling different change-points

The function fit_cp_profile() creates a profile of change-points using a range of different models, training windows and prediction bounds.

``` {r warning=FALSE, message=FALSE} profile_res <- fit_cp_profile(count_data = visit_counts, low = 40, high = 180)

We can view the change-points and the results across the various parameters:
``` {r}
profile_res

Or we can plot the results across different lower bounds used in the training data: ``` {r} profile_res %>% ggplot2::ggplot(aes(low,cp,color=model)) + ggplot2::geom_line() + ggplot2::facet_wrap(~bound) + ggplot2::theme_minimal() + ggplot2::theme(legend.position="bottom") ````

aarmiller/predictBoundCP documentation built on Dec. 31, 2020, 6:38 p.m.