Plots latent and observed trajectories for latent growth models.
Usage
plot_growth(
x,
items = NULL,
growth_variables = NULL,
time_scale = NULL,
bw = FALSE,
rawdata = FALSE,
estimated = TRUE,
alpha_range = c(0, 0.1),
jitter_lines = NULL
)Arguments
- x
An oject for which a method exists.
- items
Character vector. Indicate the names of the observed variables for the growth trajectory to plot. If NULL (default), all observed variables are used. Use this option to plot one trajectory when a model contains multiple latent growth trajectories.
- growth_variables
Character vector. Indicate the names of the latent variables for the growth trajectory to plot. If NULL (default), all latent growth variables are used. Use this option to plot one trajectory when a model contains multiple latent growth trajectories.
- time_scale
Numeric vector. In case some of the loadings of the growth model are freely estimated, provide the correct time scale here (e.g., c(0, 1, 2)).
- bw
Logical. Should the plot be black and white (for print), or color?
- rawdata
Logical. Should raw data (observed trajectories) be plotted in the background? Setting this to TRUE might result in long plotting times. Requires including the Mplus syntax 'SAVEDATA: FILE IS "filename"; SAVE = cprobabilities' in the Mplus input.
- estimated
Logical. Should the Mplus estimates growth trajectories be displayed? Defaults to TRUE.
- alpha_range
Numeric vector. The minimum and maximum values of alpha (transparency) for the raw data. Minimum should be 0; lower maximum values of alpha can help reduce overplotting.
- jitter_lines
Numeric. Indicate the amount (expressed in fractions of a standard deviation of all observed data) by which the observed trajectories should be vertically jittered. Like alpha_range, this parameter helps control overplotting.
Examples
if (FALSE) { # \dontrun{
data("empathy")
df <- empathy[1:6]
mx_growth_mixture(model = "i =~ 1*ec1 + 1*ec2 + 1*ec3 +1*ec4 +1*ec5 +1*ec6
s =~ 0*ec1 + 1*ec2 + 2*ec3 +3*ec4 +4*ec5 +5*ec6
ec1 ~~ vec1*ec1
ec2 ~~ vec2*ec2
ec3 ~~ vec3*ec3
ec4 ~~ vec4*ec4
ec5 ~~ vec5*ec5
ec6 ~~ vec6*ec6
i ~~ 0*i
s ~~ 0*s
i ~~ 0*s",
classes = 2,
data = df) -> res
plot_growth(res, rawdata = FALSE)
} # }