autoplot.summary.seroincidence.by

Plot method for `summary.seroincidence.by` objects

Description

Plot method for summary.seroincidence.by objects

Usage

## S3 method for class 'summary.seroincidence.by'
autoplot(object, type, ...)

Arguments

object a summary.seroincidence.by object (generated by applying the summary() method to the output of est_seroincidence_by()).

type

character string indicating which type of plot to generate. The implemented options are:

“scatter”: calls strat_ests_scatterplot() to generate a scatterplot
“bar”: calls strat_ests_barplot() to generate a barplot

…

Arguments passed on to strat_ests_scatterplot, strat_ests_barplot

xvar: the name of a stratifying variable in object
alpha: transparency for the points in the graph (1 = no transparency, 0 = fully transparent)
shape: shape argument for geom_point()
dodge_width: width for jitter
CIs: logical, if TRUE, add CI error bars
color_var: character which variable in object to use to determine point color
group_var: character which variable in object to use to connect points with lines (NULL for no lines)
yvar: the name of a stratifying variable in object.
title: a title for the final plot.
xlab: a label for the x-axis of the final plot.
ylab: a label for the y-axis of the final plot.
fill_lab: fill label.
color_palette: optional color palette for bar color.

Value

a ggplot2::ggplot() object

Examples

Code

library("serocalculator")


library(dplyr)
library(ggplot2)

xs_data <-
  sees_pop_data_pk_100

curve <-
  typhoid_curves_nostrat_100 %>%
  filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG"))

noise <-
  example_noise_params_pk

est2 <- est_seroincidence_by(
  strata = c("catchment", "ageCat"),
  pop_data = xs_data,
  sr_params = curve,
  noise_params = noise,
  curve_strata_varnames= NULL,
  noise_strata_varnames = NULL,
  antigen_isos = c("HlyE_IgG", "HlyE_IgA"),
  num_cores = 2 # Allow for parallel processing to decrease run time
)

est2sum <- summary(est2)

est2sum |> autoplot(
    type ="scatter",
    xvar = "ageCat",
    color_var = "catchment",
    CIs = TRUE,
    group_var = "catchment")