make.spaghetti: Generate a spaghetti plot to visualize longitudinal data

In ptmixed: Poisson-Tweedie Generalized Linear Mixed Model

Generate a spaghetti plot to visualize longitudinal data

Description

A spaghetti plot, or trajectory plot, is a plot that allows to compare across individuals or groups the trajectories of a longitudinal outcome

Usage

make.spaghetti(x, y, id, group = NULL, data, col = NULL, pch = 16,
  lty = 1, lwd = 1, title = "", xlab = NA, ylab = NA,
  legend.title = "", xlim = NULL, ylim = NULL, cex.axis = 1,
  cex.title = 1, cex.lab = 1, cex.leg = 1, margins = NULL,
  legend.inset = -0.3, legend.space = 1)

Arguments

x	the time variable (numeric vector)
y	the longitudinal outcome (numeric vector)
id	the subject indicator
group	the group that each subject belongs to (optional, do not specify if not relevant)
data	a data frame containing x, y, id and optionally group
col	a vector of colors (optional)
pch	dot type
lty	line type
lwd	line width
title	plot title
xlab	label for the x axis
ylab	label for the y axis
legend.title	legend title
xlim	limits for the x axis
ylim	limits for the y axis
cex.axis	font size for the axes
cex.title	title font size
cex.lab	font size for axis labels
cex.leg	font size for the legend
margins	use this argument if you want to overwrite the default function margins
legend.inset	moves legend more to the left / right (default is -0.3)
legend.space	interspace between lines in the legend (default is 1)

Author(s)

Mirko Signorelli

References

Signorelli, M., Spitali, P., Tsonaka, R. (2021). Poisson-Tweedie mixed-effects model: a flexible approach for the analysis of longitudinal RNA-seq data. Statistical Modelling, 21 (6), 520-545. URL: https://doi.org/10.1177/1471082X20936017

Examples

# generate example data
set.seed(123)
n = 12; t = 6
id = rep(1:n, each = t)
rand.int = rep(rnorm(n, sd = 0.5), each = t)
group = rep(c(0,1), each = n*t/2)
time = rep(0:(t-1), n)
offset = rnorm(n*t, sd = 0.3)
beta = c(3, 0, 0.1, 0.3)
X = model.matrix(~group + time + group*time)
mu = 2^(X %*% beta + rand.int + offset)
y = rpois(n*t, lambda = mu)
group = ifelse(group == 0, 'control', 'treatment')
data.long = data.frame(y, group, time, id, offset)
rm(list = setdiff(ls(), 'data.long'))

# create plot
make.spaghetti(x = time, y, id, group, 
data = data.long, title = 'spaghetti plot')

ptmixed documentation built on Aug. 18, 2022, 5:06 p.m.