ptmixed (version 1.1.3)

make.spaghetti: 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

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

# \donttest{
# 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')
# }