mlm_spaghetti_plot {bmlm} | R Documentation |
Plot population-level fitted values and X values, for M and Y.
mlm_spaghetti_plot(mod = NULL, d = NULL, id = "id", x = "x", m = "m", y = "y", level = 0.95, n = 12, binary_y = FALSE, mx = "fitted", fixed = TRUE, random = TRUE, h_jitter = 0, v_jitter = 0, bar_width = 0.2, bar_size = 0.75, n_samples = NA)
mod |
A multilevel mediation model estimated with |
d |
A |
id |
Name of id variable (identifying subjects) in data ( |
x |
Name of X variable in |
m |
Name of M variable in |
y |
Name of Y variable in |
level |
X level for Credible Intervals. (Defaults to .95.) |
n |
Number of points along X to evaluate fitted values on. See details. |
binary_y |
Set to TRUE if the outcome variable (Y) is 0/1. |
mx |
Should the X axis of the M-Y figure be "fitted" values, or "data" values. Defaults to "fitted". |
fixed |
Should the population-level ("fixed") fitted values be shown? |
random |
Should the subject-level ("random") fitted values be shown? |
h_jitter |
Horizontal jitter of points. Defaults to 0. |
v_jitter |
Vertical jitter of points. Defaults to 0. |
bar_width |
Width of the error bars. Defaults to 0.2. |
bar_size |
Thickness of the error bars. Defaults to 0.75. |
n_samples |
Number of MCMC samples to use in calculating fitted values. See details. |
If n = 2
, the fitted values will be represented as points
with X
line with a Confidence Ribbon instead.
If a very large model is fitted with a large number of MCMC iterations,
the function might take a long time to run. In these cases, users can set
n_samples
to a smaller value (e.g. 1000), in which case the fitted
values (and the CIs) will be based on a random subset of n_samples
MCMC samples. The default value is NA, meaning that all MCMC samples are
used.
A list of two ggplot2 objects.
Matti Vuorre mv2521@columbia.edu