spaghetti: Spaghetti plot

In kkholst/lava: Latent Variable Models

Spaghetti plot

Description

Spaghetti plot for longitudinal data

Usage

spaghetti(
  formula,
  data = NULL,
  id = "id",
  group = NULL,
  type = "o",
  lty = 1,
  pch = NA,
  col = 1:10,
  alpha = 0.3,
  lwd = 1,
  level = 0.95,
  trend.formula = formula,
  tau = NULL,
  trend.lty = 1,
  trend.join = TRUE,
  trend.delta = 0.2,
  trend = !is.null(tau),
  trend.col = col,
  trend.alpha = 0.2,
  trend.lwd = 3,
  trend.jitter = 0,
  legend = NULL,
  by = NULL,
  xlab = "Time",
  ylab = "",
  add = FALSE,
  ...
)

Arguments

formula	Formula (response ~ time)
data	data.frame
id	Id variable
group	group variable
type	Type (line 'l', stair 's', ...)
lty	Line type
pch	Colour
col	Colour
alpha	transparency (0-1)
lwd	Line width
level	Confidence level
trend.formula	Formula for trendline
tau	Quantile to estimate (trend)
trend.lty	Trend line type
trend.join	Trend polygon
trend.delta	Length of limit bars
trend	Add trend line
trend.col	Colour of trend line
trend.alpha	Transparency
trend.lwd	Trend line width
trend.jitter	Jitter amount
legend	Legend
by	make separate plot for each level in 'by' (formula, name of column, or vector)
xlab	Label of X-axis
ylab	Label of Y-axis
add	Add to existing device
...	Additional arguments to lower level arguments

Author(s)

Klaus K. Holst

Examples

if (interactive() & requireNamespace("mets")) {
K <- 5
y <- "y"%++%seq(K)
m <- lvm()
regression(m,y=y,x=~u) <- 1
regression(m,y=y,x=~s) <- seq(K)-1
regression(m,y=y,x=~x) <- "b"
N <- 50
d <- sim(m,N); d$z <- rbinom(N,1,0.5)
dd <- mets::fast.reshape(d); dd$num <- dd$num+3
spaghetti(y~num,dd,id="id",lty=1,col=Col(1,.4),
          trend.formula=~factor(num),trend=TRUE,trend.col="darkblue")
dd$num <- dd$num+rnorm(nrow(dd),sd=0.5) ## Unbalance
spaghetti(y~num,dd,id="id",lty=1,col=Col(1,.4),
          trend=TRUE,trend.col="darkblue")
spaghetti(y~num,dd,id="id",lty=1,col=Col(1,.4),
           trend.formula=~num+I(num^2),trend=TRUE,trend.col="darkblue")
}

kkholst/lava documentation built on March 4, 2023, 10:35 p.m.