graphPAF (version 2.0.0)

plot.SAF_summary: Produce plots of sequential and average PAF

Description

Produce plots of sequential and average PAF

Usage

# S3 method for SAF_summary
plot(
  x,
  number_rows = 3,
  max_PAF = 0.4,
  min_PAF = 0,
  point.size = 4,
  axis.text.size = 6,
  title.size = 6,
  axis.title.size = 6,
  ...
)

Value

A ggplot2 plotting object illustrating average sequential PAF by position and average PAF by risk factor.

Arguments

x

An SAF_summary R object produced by running the average_paf function.

number_rows

integer How many rows of plots will be included on the associated figure.

max_PAF

upper limit of y axis on PAF plots (default = 0.4)

min_PAF

lower limit of y axis on PAF plots (default = 0)

point.size

size of points on each individual plot (default=4)

axis.text.size

size of axis labels on each plot (default=6)

title.size

size of title on each individual plot (default=6)

axis.title.size

size of titles on each plot (default=6)

...

Other global arguments inherited by that might be passed to the ggplot routine

References

Ferguson, J., O’Connell, M. and O’Donnell, M., 2020. Revisiting sequential attributable fractions. Archives of Public Health, 78(1), pp.1-9. Ferguson, J., Alvarez-Iglesias, A., Newell, J., Hinde, J. and O’Donnell, M., 2018. Estimating average attributable fractions with confidence intervals for cohort and case–control studies. Statistical methods in medical research, 27(4), pp.1141-1152

Examples

library(splines)
library(survival)
library(parallel)
options(boot.parallel="snow")
options(boot.ncpus=2)
#  Simulated data on occupational and environmental exposure to
# chronic cough from Eide, 1995
# First specify the causal graph, in terms of the parents of each node.  Then put into a list
parent_urban.rural <- c()
parent_smoking.category <- c("urban.rural")
parent_occupational.exposure <- c("urban.rural")
parent_y <- c("urban.rural","smoking.category","occupational.exposure")
parent_list <- list(parent_urban.rural, parent_smoking.category,
parent_occupational.exposure, parent_y)
# also specify nodes of graph, in order from root to leaves
node_vec <- c("urban.rural","smoking.category","occupational.exposure", "y")
model_list=automatic_fit(Hordaland_data,
parent_list=parent_list, node_vec=node_vec, prev=.09)
out <- average_paf(data=model_list[[length(model_list)]]$data,
 model_list=model_list,
parent_list=parent_list, node_vec=node_vec, prev=.09, nperm=10,
riskfactor_vec = c("urban.rural","occupational.exposure"),ci=FALSE)
plot(out)
# \donttest{
# plot with confidence intervals for average and sequential PAF
# (This is probably more useful for more than 2 risk factors).
# Separate axes for each risk factor so confidence intervals can be clearly displayed
out <- average_paf(data=model_list[[length(model_list)]]$data,
 model_list=model_list,
parent_list=parent_list, node_vec=node_vec, prev=.09, nperm=10,
riskfactor_vec = c("urban.rural","occupational.exposure"),ci=TRUE,boot_rep=8)
plot(out)
# Here we plot, with margin of error of point estimate when 50 permutations are used
out <- average_paf(data=model_list[[length(model_list)]]$data,
 model_list=model_list,
parent_list=parent_list, node_vec=node_vec, prev=.09, nperm=50,
riskfactor_vec = c("urban.rural","occupational.exposure"),ci=FALSE,exact=FALSE)
plot(out)
# }