mplus.plot: Plot Mplus GH5 File
In misty: Miscellaneous Functions 'T. Yanagida'
mplus.plot R Documentation
Plot Mplus GH5 File
Description
This function uses the h5file function in the hdf5r package to
read a Mplus GH5 file that is requested by the command PLOT: TYPE IS PLOT2
in Mplus to display trace plots, posterior distribution plots, autocorrelation
plots, posterior predictive check plots based on the "bayesian_data" section, and
the loop plot based on the "loop_data" section of the Mplus GH5 file. By default,
the function displays trace plots if the "bayesian_data" section is available in
the Mplus GH5 File. Otherwise, the function plots the loop plot if the "loop_data"
section is available in the Mplus GH5 file.
Usage
mplus.plot(x, plot = c("none", "trace", "post", "auto", "ppc", "loop"),
param = c("all", "on", "by", "with", "inter", "var", "r2", "new"),
std = c("all", "none", "stdyx", "stdy", "std"), burnin = TRUE,
point = c("all", "none", "m", "med", "map"),
ci = c("none", "eti", "hdi"), chain = 1, conf.level = 0.95,
hist = TRUE, density = TRUE, area = TRUE, alpha = 0.4,
fill = "gray85", nrow = NULL, ncol = NULL,
scales = c("fixed", "free", "free_x", "free_y"),
xlab = NULL, ylab = NULL, xlim = NULL, ylim = NULL,
xbreaks = ggplot2::waiver(), ybreaks = ggplot2::waiver(),
xexpand = ggplot2::waiver(), yexpand = ggplot2::waiver(),
palette = "Set 2", binwidth = NULL, bins = NULL,
density.col = "#0072B2", shape = 21,
point.col = c("#CC79A7", "#D55E00", "#009E73"),
linewidth = 0.6, linetype = "dashed", line.col = "black",
bar.col = "black", bar.width = 0.8, plot.margin = NULL,
legend.title.size = 10, legend.text.size = 10, legend.box.margin = NULL,
saveplot = c("all", "none", "trace", "post", "auto", "ppc", "loop"),
file = "Mplus_Plot.pdf",
file.plot = c("_TRACE", "_POST", "_AUTO", "_PPC", "_LOOP"),
width = NA, height = NA, units = c("in", "cm", "mm", "px"),
dpi = 600, check = TRUE)
Arguments
x
a character string indicating the name of the Mplus
GH5 file (HDF5 format) with or without the file
extension .gh5, e.g., "Mplus_Plot.gh5"
or "Mplus_Plot". Alternatively, a misty.object
of type mplus can be specified, i.e., result
object of the mplus.plot() function.
plot
a character string indicating the type of plot to
display, i.e., "none" for not displaying any
plot, "trace" (default) for displaying trace
plots, post for displaying posterior distribution
plots, "auto" for displaying autocorrelation
plots, "ppc" for displaying posterior predictive
check plots, and "loop" for displaying the
loop plot. The default setting is "trace" if
the "bayesian_data" section is available in the Mplus
GH5 file. Otherwise, the default setting switches to
"loop".
param
character vector indicating which parameters to print
for the trace plots, posterior distribution plots,
and autocorrelation plots, i.e., "all" for all
parameters, "on" (default), for regression
slopes, "by" for factor loadings, "with"
for covariances, "inter" for intercepts and
thresholds, "var" for (residual) variances,
"r2" for r-square, and "new" for
parameters not in the analysis model specified in the
NEW option. The default setting is "on"
if regression slopes are available. Otherwise, the
default setting switches to "by" and to
"with" if factor loadings are not available.
std
a character vector indicating the standardized
parameters to print for the trace plots, posterior
distribution plots, and autocorrelation plots, i.e.,
"all" for all standardized parameters,
"none" (default) for not printing any
standardized parameters, "stdyx" for StdYX
standardized parameters, "stdy" for StdY
standardized parameters, and "std" for StdX
standardized parameters.
burnin
logical: if FALSE, the first half of each chain
is discarded as being part of the burnin phase when
displaying trace plots. The default setting for
plot = "trace" is TRUE. Note that the
first half of each chain is always discarded when
displaying posterior distribution plots (plot = "post") regardless
of the setting of the argument burnin.
point
a character vector indicating the point estimate(s)
to be displayed in the posterior distribution plots,
i.e., "all" for all point estimates, "none"
for not displaying any point estimates, "m"
for the posterior mean estimate, "med" (default)
for the posterior median estimate, and "map"
for the maximum a posteriori estimate.
ci
a character string indicating the type of credible
interval to be displayed in the posterior distribution
plots, i.e., "none" for not displaying any
credible intervals, "eti" (default) for displaying
the equal-tailed intervals and "hdi" for displaying
the highest density interval.
chain
a numerical value indicating the chain to be used for
the autocorrelation plots. By default, the first
chain is used.
conf.level
a numeric value between 0 and 1 indicating the
confidence level of the credible interval (default is
0.95).
hist
logical: if TRUE (default), histograms are
drawn in the posterior probability plots.
density
logical: if TRUE (default), density curves are
drawn in the posterior probability plots.
area
logical: if TRUE (default), statistical not
significant and statistical significant area is
filled with a different color and vertical lines are
drawn.
alpha
a numeric value between 0 and 1 for the alpha
argument (default is 0.4) for the annotate,
geom_histogram, geom_bar, and
geom_ribbon function.
fill
a character string indicating the color for the
"fill" argument (default is "gray85")
for the annotate, geom_histogram,
geom_bar, and geom_point functions.
nrow
a numeric value indicating the nrow argument
(default is NULL) for the facet_wrap
function.
ncol
a numeric value indicating the ncol argument
(default is 2) for the facet_wrap function.
scales
a character string indicating the scales argument
(default is "free") for the facet_wrap
function.
xlab
a character string indicating the name argument
for the scale_x_continuous function. Note that
the default setting depends on the type of plot,
e.g., "" for the trace plots and "Lag"
for the autocorrelation plots.
ylab
a character string indicating the name argument
for the scale_y_continuous function. Note that
the default setting depends on the type of plot,
e.g., "" for the trace plots and "Autocorrelation"
for the autocorrelation plots.
xlim
a numeric vector with two elements indicating the
limits argument (default it NULL) for
the scale_x_continuous function.
ylim
a numeric vector with two elements indicating the
limits argument (default it NULL) for
the scale_y_continuous function.
xbreaks
a numeric vector indicating the breaks argument
(default is ggplot2::waiver()) for the
scale_x_continuous function.
ybreaks
a numeric vector indicating the breaks argument
(default is ggplot2::waiver()) for the
scale_y_continuous function.
xexpand
a numeric vector with two elements indicating the
expand argument (default is (0.02, 0))
for the scale_x_continuous function.
yexpand
a numeric vector with two elements indicating the
expand argument for the scale_y_continuous
function. Note that the default setting depends
on the type of plot, e.g., (0.02, 0) for the
trace plots and expansion(mult = c(0, 0.05))
for the posterior distribution plots.
palette
a character string indicating the palette name (default
is "Set 2") for the hcl.colors function.
Note that the character string must be one of
hcl.pals().
binwidth
a numeric value indicating the binwidth argument
(default is to use the number of bins in bins
argument) for the geom_histogram function.
bins
a numeric value indicating the bins argument
(default is 30) for the geom_histogram
function.
density.col
a character string indicating the color argument
(default is "#0072B2") for the geom_density
function.
shape
a numeric value indicating the shape argument
(default is 21) for the geom_point
function.
point.col
a character vector with three elements indicating the
values argument (default is
c("#CC79A7", "#D55E00", "#009E73")) for the
scale_color_manual function.
linewidth
a numeric value indicating the linewidth argument
(default is 0.6) for the geom_vline function.
linetype
a numeric value indicating the linetype argument
(default is "dashed") for the geom_vline
function.
line.col
a character string indicating the color argument
(default is "black") for the geom_vline
function.
bar.col
a character string indicating the color argument
(default is "black") for the geom_bar
function.
bar.width
a character string indicating the width argument
(default = 0.8)for the geom_bar function.
plot.margin
a numeric vector indicating the plot.margin
argument for the theme function. Note that the
default setting depends on the type of the plot, e.g.,
c(4, 15, -10, 0) for the trace plots, and
c(4, 15, 4, 4) for the autocorrelation plots.
legend.title.size
a numeric value indicating the legend.title
argument (default is element_text(size = 10))
for the theme function.
legend.text.size
a numeric value indicating the legend.text
argument (default is element_text(size = 10))
for the theme function.
legend.box.margin
a numeric vector indicating the legend.box.margin
argument for the theme function. Note that the
default setting depends on the type of plot, e.g.,
c(-16, 6, 6, 6) for the trace plots, and
c(-25, 6, 6, 6) for the posterior distribution
plots with displaying point estimates.
saveplot
a character vector indicating the plot to be saved,
i.e., "all" for saving all plots, "none"
(default) for not saving any plots, "trace"
for saving the trace plots, post for the saving
the posterior distribution plots, "auto" for
saving the autocorrelation plots, "ppc" for
saving the posterior predictive check plots, and
"loop" for saving the loop plot.
file
a character string indicating the filename
argument (default is "Mplus_Plot.pdf") including
the file extension for the ggsave function.
Note that one of ".eps", ".ps",
".tex", ".pdf" (default), ".jpeg",
".tiff", ".png", ".bmp",
".svg" or ".wmf" needs to be specified
as file extension in the file argument.
file.plot
a character vector with five elements for distinguishing
different types of plots. By default, the character
string specified in the argument "file"
("Mplus_Plot") is concatenated with "_TRACE"
("Mplus_Plot_TRACE") for the trace plots,
"_POST" ("Mplus_Plot_POST") for
the posterior distribution plots, "_AUTO"
("Mplus_Plot_AUTO") for the autocorrelation
plots, "_PPC" ("Mplus_Plot_PPC") for the
posterior predictive check plots, and "_LOOP"
("Mplus_Plot_LOOP") for the loop plot.
width
a numeric value indicating the width argument
(default is the size of the current graphics device)
for the ggsave function.
height
a numeric value indicating the height argument
(default is the size of the current graphics device)
for the ggsave function.
units
a character string indicating the units
argument (default is in) for the ggsave
function.
dpi
a numeric value indicating the dpi argument
(default is 600) for the ggsave function.
check
logical: if TRUE (default), argument
specification is checked.
Value
Returns an object of class misty.object, which is a list with following
entries:
call
function call
type
type of analysis
x
Mplus GH5 file
args
specification of function arguments
data
list with posterior distribution of each parameter estimate
in wide and long format (post), autocorrelation for
each parameter estimate in wide and long format (auto),
data for the posterior predictive check (ppc),
and data for the loop plot (loop)
plot
list with the trace plots (trace, posterior distribution
plots (post), autocorrelation plots (auto),
posterior predictive check plots (ppc), and
loop plot (loop)
Author(s)
Takuya Yanagida
References
Muthen, L. K., & Muthen, B. O. (1998-2017). Mplus User's Guide (8th ed.).
Muthen & Muthen.
See Also
read.mplus, write.mplus, mplus,
mplus.update, mplus.print, mplus.bayes,
mplus.run, mplus.lca
Examples
## Not run:
#----------------------------------------------------------------------------
# Mplus Example 3.18: Moderated Mediation with a Plot of the Indirect Effect
#..........
# Trace Plots
# Example 1a: Default setting
mplus.plot("ex3.18.gh5")
# Example 1b: Exclude first half of each chain
mplus.plot("ex3.18.gh5", burnin = FALSE)
# Example 1c: Print all parameters
mplus.plot("ex3.18.gh5", param = "all")
# Example 1d: Print user-specified parameters
mplus.plot("ex3.18.gh5", param = "param")
# Example 1e: Arrange panels in three columns
mplus.plot("ex3.18.gh5", ncol = 3)
# Example 1f: Specify "Pastel 1" palette for the hcl.colors function
mplus.plot("ex3.18.gh5", palette = "Pastel 1")
#..........
# Posterior Distribution Plots
# Example 2a: Default setting, i.e., posterior median and equal-tailed interval
mplus.plot("ex3.18.gh5", plot = "post")
# Example 2b: Display posterior mean and maximum a posteriori
mplus.plot("ex3.18.gh5", plot = "post", point = c("m", "map"))
# Example 2c: Display maximum a posteriori and highest density interval
mplus.plot("ex3.18.gh5", plot = "post", point = "map", ci = "hdi")
# Example 2d: Do not display any point estimates and credible interval
mplus.plot("ex3.18.gh5", plot = "post", point = "none", ci = "none")
# Example 2d: Do not display histograms
mplus.plot("ex3.18.gh5", plot = "post", hist = FALSE)
#..........
# Autocorrelation Plots
# Example 3a: Default setting, i.e., first chain
mplus.plot("ex3.18.gh5", plot = "auto")
# Example 3b: Use second chain
mplus.plot("ex3.18.gh5", plot = "auto", chain = 2)
# Example 3b: Modify limits and breaks of the y-axis
mplus.plot("ex3.18.gh5", plot = "auto",
ylim = c(-0.05, 0.05), ybreaks = seq(-0.1, 0.1, by = 0.025))
#..........
# Posterior Predictive Check Plots
# Example 4a: Default setting, i.e., 95% Interval
mplus.plot("ex3.18.gh5", plot = "ppc")
# Example 4b: Default setting, i.e., 99% Interval
mplus.plot("ex3.18.gh5", plot = "ppc", conf.level = 0.99)
#..........
# Loop Plot
# Example 5a: Default setting
mplus.plot("ex3.18.gh5", plot = "loop")
# Example 5b: Do not fill area and draw vertical lines
mplus.plot("ex3.18.gh5", plot = "loop", area = FALSE)
#..........
# Save Plots
# Example 6a: Save all plots in pdf format
mplus.plot("ex3.18.gh5", saveplot = "all")
# Example 6b: Save all plots in png format with 300 dpi
mplus.plot("ex3.18.gh5", saveplot = "all", file = "Mplus_Plot.png", dpi = 300)
# Example 6a: Save loop plot, specify width and height of the plot
mplus.plot("ex3.18.gh5", plot = "none", saveplot = "loop",
width = 7.5, height = 7)
#----------------------------------------------------------------------------
# Plot from misty.object
# Create misty.object
object <- mplus.plot("ex3.18.gh5", plot = "none")
# Trace plot
mplus.plot(object, plot = "trace")
# Posterior distribution plot
mplus.plot(object, plot = "post")
# Autocorrelation plot
mplus.plot(object, plot = "auto")
# Posterior predictive check plot
mplus.plot(object, plot = "ppc")
# Loop plot
mplus.plot(object, plot = "loop")
#----------------------------------------------------------------------------
# Create Plots Manually
# Load ggplot2 package
library(ggplot2)
# Create misty object
object <- mplus.plot("ex3.18.gh5", plot = "none")
#..........
# Example 7: Trace Plots
# Extract data in long format
data.post <- object$data$post$long
# Extract ON parameters
data.trace <- data.post[grep(" ON ", data.post$param), ]
# Plot
ggplot(data.trace, aes(x = iter, y = value, color = chain)) +
annotate("rect", xmin = 0, xmax = 15000, ymin = -Inf, ymax = Inf,
alpha = 0.4, fill = "gray85") +
geom_line() +
facet_wrap(~ param, ncol = 2, scales = "free") +
scale_x_continuous(name = "", expand = c(0.02, 0)) +
scale_y_continuous(name = "", expand = c(0.02, 0)) +
scale_colour_manual(name = "Chain",
values = hcl.colors(n = 2, palette = "Set 2")) +
theme_bw() +
guides(color = guide_legend(nrow = 1, byrow = TRUE)) +
theme(plot.margin = margin(c(4, 15, -10, 0)),
legend.position = "bottom",
legend.title = element_text(size = 10),
legend.text = element_text(size = 10),
legend.box.margin = margin(c(-16, 6, 6, 6)),
legend.background = element_rect(fill = "transparent"))
#..........
# Example 8: Posterior Distribution Plots
# Extract data in long format
data.post <- object$data$post$long
# Extract ON parameters
data.post <- data.post[grep(" ON ", data.post$param), ]
# Discard burn-in iterations
data.post <- data.post[data.post$iter > 15000, ]
# Drop factor levels
data.post$param <- droplevels(data.post$param,
exclude = c("[Y]", "[M]", "Y", "M", "INDIRECT", "MOD"))
# Plot
ggplot(data.post, aes(x = value)) +
geom_histogram(aes(y = after_stat(density)), color = "black", alpha = 0.4,
fill = "gray85") +
geom_density(color = "#0072B2") +
geom_vline(data = data.frame(param = unique(data.post$param),
stat = tapply(data.post$value, data.post$param, median)),
aes(xintercept = stat, color = "Median"), linewidth = 0.6) +
geom_vline(data = data.frame(param = unique(data.post$param),
low = tapply(data.post$value, data.post$param,
function(y) quantile(y, probs = 0.025))),
aes(xintercept = low), linetype = "dashed", linewidth = 0.6) +
geom_vline(data = data.frame(param = unique(data.post$param),
upp = tapply(data.post$value, data.post$param,
function(y) quantile(y, probs = 0.975))),
aes(xintercept = upp), linetype = "dashed", linewidth = 0.6) +
facet_wrap(~ param, ncol = 2, scales = "free") +
scale_x_continuous(name = "", expand = c(0.02, 0)) +
scale_y_continuous(name = "Probability Density, f(x)",
expand = expansion(mult = c(0L, 0.05))) +
scale_color_manual(name = "Point Estimate", values = c(Median = "#D55E00")) +
labs(caption = "95% Equal-Tailed Interval") +
theme_bw() +
theme(plot.margin = margin(c(4, 15, -8, 4)),
plot.caption = element_text(hjust = 0.5, vjust = 7),
legend.position = "bottom",
legend.title = element_text(size = 10),
legend.text = element_text(size = 10),
legend.box.margin = margin(c(-30, 6, 6, 6)),
legend.background = element_rect(fill = "transparent"))
#..........
# Example 9: Autocorrelation Plots
# Extract data in long format
data.auto <- object$data$auto$long
# Select first chain
data.auto <- data.auto[data.auto$chain == 1, ]
# Extract ON parameters
data.auto <- data.auto[grep(" ON ", data.auto$param), ]
# Plot
ggplot(data.auto, aes(x = lag, y = cor)) +
geom_bar(stat = "identity", alpha = 0.4, color = "black", fill = "gray85",
width = 0.8) +
facet_wrap(~ param, ncol = 2) +
scale_x_continuous(name = "Lag", breaks = seq(1, 30, by = 2), expand = c(0.02, 0)) +
scale_y_continuous(name = "Autocorrelation", limits = c(-0.1, 0.1),
breaks = seq(-0.1, 1., by = 0.05), expand = c(0.02, 0)) +
theme_bw() +
theme(plot.margin = margin(c(4, 15, 4, 4)))
#..........
# Example 10: Posterior Predictive Check (PPC) Plots
# Extract data
data.ppc <- object$data$ppc
# Scatter plot
ppc.scatter <- ggplot(data.ppc, aes(x = obs, y = rep)) +
geom_point(shape = 21, fill = "gray85") +
geom_abline(slope = 1) +
scale_x_continuous("Observed", limits = c(0, 45), breaks = seq(0, 45, by = 5),
expand = c(0.02, 0)) +
scale_y_continuous("Recpliated", limits = c(0, 45), breaks = seq(0, 45, by = 5),
expand = c(0.02, 0)) +
theme_bw() +
theme(plot.margin = margin(c(2, 15, 4, 4)))
# Histogram
ppc.hist <- ggplot(data.ppc, aes(x = diff)) +
geom_histogram(color = "black", alpha = 0.4, fill = "gray85") +
geom_vline(xintercept = mean(data.ppc$diff), color = "#CC79A7") +
geom_vline(xintercept = quantile(data.ppc$diff, probs = 0.025),
linetype = "dashed", color = "#CC79A7") +
geom_vline(xintercept = quantile(data.ppc$diff, probs = 0.975),
linetype = "dashed", color = "#CC79A7") +
scale_x_continuous("Observed - Replicated", expand = c(0.02, 0)) +
scale_y_continuous("Count", expand = expansion(mult = c(0L, 0.05))) +
theme_bw() +
theme(plot.margin = margin(c(2, 15, 4, 4)))
# Combine plots using the patchwork package
patchwork::wrap_plots(ppc.scatter, ppc.hist)
#..........
# Example 11: Loop Plot
# Extract data
data.loop <- object$data$loop
# Plot
plot.loop <- ggplot(data.loop, aes(x = xval, y = estimate)) +
geom_line(linewidth = 0.6, show.legend = FALSE) +
geom_line(aes(xval, low)) +
geom_line(aes(xval, upp)) +
scale_x_continuous("MOD", expand = c(0.02, 0)) +
scale_y_continuous("INDIRECT", expand = c(0.02, 0)) +
scale_fill_manual("Statistical Significance",
values = hcl.colors(n = 2, palette = "Set 2")) +
theme_bw() +
theme(plot.margin = margin(c(4, 15, -6, 4)),
legend.position = "bottom",
legend.title = element_text(size = 10),
legend.text = element_text(size = 10),
legend.box.margin = margin(-10, 6, 6, 6),
legend.background = element_rect(fill = "transparent"))
# Significance area
for (i in unique(data.loop$group)) {
plot.loop <- plot.loop + geom_ribbon(data = data.loop[data.loop$group == i, ],
aes(ymin = low, ymax = upp, fill = sig), alpha = 0.4)
}
# Vertical lines
plot.loop + geom_vline(data = data.loop[data.loop$change == 1, ],
aes(xintercept = xval, color = sig), linewidth = 0.6,
linetype = "dashed", show.legend = FALSE)
## End(Not run)
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| mplus.plot | R Documentation |
Plot Mplus GH5 File
Description
This function uses the h5file function in the hdf5r package to
read a Mplus GH5 file that is requested by the command PLOT: TYPE IS PLOT2
in Mplus to display trace plots, posterior distribution plots, autocorrelation
plots, posterior predictive check plots based on the "bayesian_data" section, and
the loop plot based on the "loop_data" section of the Mplus GH5 file. By default,
the function displays trace plots if the "bayesian_data" section is available in
the Mplus GH5 File. Otherwise, the function plots the loop plot if the "loop_data"
section is available in the Mplus GH5 file.
Usage
mplus.plot(x, plot = c("none", "trace", "post", "auto", "ppc", "loop"),
param = c("all", "on", "by", "with", "inter", "var", "r2", "new"),
std = c("all", "none", "stdyx", "stdy", "std"), burnin = TRUE,
point = c("all", "none", "m", "med", "map"),
ci = c("none", "eti", "hdi"), chain = 1, conf.level = 0.95,
hist = TRUE, density = TRUE, area = TRUE, alpha = 0.4,
fill = "gray85", nrow = NULL, ncol = NULL,
scales = c("fixed", "free", "free_x", "free_y"),
xlab = NULL, ylab = NULL, xlim = NULL, ylim = NULL,
xbreaks = ggplot2::waiver(), ybreaks = ggplot2::waiver(),
xexpand = ggplot2::waiver(), yexpand = ggplot2::waiver(),
palette = "Set 2", binwidth = NULL, bins = NULL,
density.col = "#0072B2", shape = 21,
point.col = c("#CC79A7", "#D55E00", "#009E73"),
linewidth = 0.6, linetype = "dashed", line.col = "black",
bar.col = "black", bar.width = 0.8, plot.margin = NULL,
legend.title.size = 10, legend.text.size = 10, legend.box.margin = NULL,
saveplot = c("all", "none", "trace", "post", "auto", "ppc", "loop"),
file = "Mplus_Plot.pdf",
file.plot = c("_TRACE", "_POST", "_AUTO", "_PPC", "_LOOP"),
width = NA, height = NA, units = c("in", "cm", "mm", "px"),
dpi = 600, check = TRUE)
Arguments
x |
a character string indicating the name of the Mplus
GH5 file (HDF5 format) with or without the file
extension |
plot |
a character string indicating the type of plot to
display, i.e., |
param |
character vector indicating which parameters to print
for the trace plots, posterior distribution plots,
and autocorrelation plots, i.e., |
std |
a character vector indicating the standardized
parameters to print for the trace plots, posterior
distribution plots, and autocorrelation plots, i.e.,
|
burnin |
logical: if |
point |
a character vector indicating the point estimate(s)
to be displayed in the posterior distribution plots,
i.e., |
ci |
a character string indicating the type of credible
interval to be displayed in the posterior distribution
plots, i.e., |
chain |
a numerical value indicating the chain to be used for the autocorrelation plots. By default, the first chain is used. |
conf.level |
a numeric value between 0 and 1 indicating the
confidence level of the credible interval (default is
|
hist |
logical: if |
density |
logical: if |
area |
logical: if |
alpha |
a numeric value between 0 and 1 for the |
fill |
a character string indicating the color for the
|
nrow |
a numeric value indicating the |
ncol |
a numeric value indicating the |
scales |
a character string indicating the |
xlab |
a character string indicating the |
ylab |
a character string indicating the |
xlim |
a numeric vector with two elements indicating the
|
ylim |
a numeric vector with two elements indicating the
|
xbreaks |
a numeric vector indicating the |
ybreaks |
a numeric vector indicating the |
xexpand |
a numeric vector with two elements indicating the
|
yexpand |
a numeric vector with two elements indicating the
|
palette |
a character string indicating the palette name (default
is |
binwidth |
a numeric value indicating the |
bins |
a numeric value indicating the |
density.col |
a character string indicating the |
shape |
a numeric value indicating the |
point.col |
a character vector with three elements indicating the
|
linewidth |
a numeric value indicating the |
linetype |
a numeric value indicating the |
line.col |
a character string indicating the |
bar.col |
a character string indicating the |
bar.width |
a character string indicating the |
plot.margin |
a numeric vector indicating the |
legend.title.size |
a numeric value indicating the |
legend.text.size |
a numeric value indicating the |
legend.box.margin |
a numeric vector indicating the |
saveplot |
a character vector indicating the plot to be saved,
i.e., |
file |
a character string indicating the |
file.plot |
a character vector with five elements for distinguishing
different types of plots. By default, the character
string specified in the argument |
width |
a numeric value indicating the |
height |
a numeric value indicating the |
units |
a character string indicating the |
dpi |
a numeric value indicating the |
check |
logical: if |
Value
Returns an object of class misty.object, which is a list with following
entries:
call |
function call |
type |
type of analysis |
x |
Mplus GH5 file |
args |
specification of function arguments |
data |
list with posterior distribution of each parameter estimate
in wide and long format ( |
plot |
list with the trace plots ( |
Author(s)
Takuya Yanagida
References
Muthen, L. K., & Muthen, B. O. (1998-2017). Mplus User's Guide (8th ed.). Muthen & Muthen.
See Also
read.mplus, write.mplus, mplus,
mplus.update, mplus.print, mplus.bayes,
mplus.run, mplus.lca
Examples
## Not run:
#----------------------------------------------------------------------------
# Mplus Example 3.18: Moderated Mediation with a Plot of the Indirect Effect
#..........
# Trace Plots
# Example 1a: Default setting
mplus.plot("ex3.18.gh5")
# Example 1b: Exclude first half of each chain
mplus.plot("ex3.18.gh5", burnin = FALSE)
# Example 1c: Print all parameters
mplus.plot("ex3.18.gh5", param = "all")
# Example 1d: Print user-specified parameters
mplus.plot("ex3.18.gh5", param = "param")
# Example 1e: Arrange panels in three columns
mplus.plot("ex3.18.gh5", ncol = 3)
# Example 1f: Specify "Pastel 1" palette for the hcl.colors function
mplus.plot("ex3.18.gh5", palette = "Pastel 1")
#..........
# Posterior Distribution Plots
# Example 2a: Default setting, i.e., posterior median and equal-tailed interval
mplus.plot("ex3.18.gh5", plot = "post")
# Example 2b: Display posterior mean and maximum a posteriori
mplus.plot("ex3.18.gh5", plot = "post", point = c("m", "map"))
# Example 2c: Display maximum a posteriori and highest density interval
mplus.plot("ex3.18.gh5", plot = "post", point = "map", ci = "hdi")
# Example 2d: Do not display any point estimates and credible interval
mplus.plot("ex3.18.gh5", plot = "post", point = "none", ci = "none")
# Example 2d: Do not display histograms
mplus.plot("ex3.18.gh5", plot = "post", hist = FALSE)
#..........
# Autocorrelation Plots
# Example 3a: Default setting, i.e., first chain
mplus.plot("ex3.18.gh5", plot = "auto")
# Example 3b: Use second chain
mplus.plot("ex3.18.gh5", plot = "auto", chain = 2)
# Example 3b: Modify limits and breaks of the y-axis
mplus.plot("ex3.18.gh5", plot = "auto",
ylim = c(-0.05, 0.05), ybreaks = seq(-0.1, 0.1, by = 0.025))
#..........
# Posterior Predictive Check Plots
# Example 4a: Default setting, i.e., 95% Interval
mplus.plot("ex3.18.gh5", plot = "ppc")
# Example 4b: Default setting, i.e., 99% Interval
mplus.plot("ex3.18.gh5", plot = "ppc", conf.level = 0.99)
#..........
# Loop Plot
# Example 5a: Default setting
mplus.plot("ex3.18.gh5", plot = "loop")
# Example 5b: Do not fill area and draw vertical lines
mplus.plot("ex3.18.gh5", plot = "loop", area = FALSE)
#..........
# Save Plots
# Example 6a: Save all plots in pdf format
mplus.plot("ex3.18.gh5", saveplot = "all")
# Example 6b: Save all plots in png format with 300 dpi
mplus.plot("ex3.18.gh5", saveplot = "all", file = "Mplus_Plot.png", dpi = 300)
# Example 6a: Save loop plot, specify width and height of the plot
mplus.plot("ex3.18.gh5", plot = "none", saveplot = "loop",
width = 7.5, height = 7)
#----------------------------------------------------------------------------
# Plot from misty.object
# Create misty.object
object <- mplus.plot("ex3.18.gh5", plot = "none")
# Trace plot
mplus.plot(object, plot = "trace")
# Posterior distribution plot
mplus.plot(object, plot = "post")
# Autocorrelation plot
mplus.plot(object, plot = "auto")
# Posterior predictive check plot
mplus.plot(object, plot = "ppc")
# Loop plot
mplus.plot(object, plot = "loop")
#----------------------------------------------------------------------------
# Create Plots Manually
# Load ggplot2 package
library(ggplot2)
# Create misty object
object <- mplus.plot("ex3.18.gh5", plot = "none")
#..........
# Example 7: Trace Plots
# Extract data in long format
data.post <- object$data$post$long
# Extract ON parameters
data.trace <- data.post[grep(" ON ", data.post$param), ]
# Plot
ggplot(data.trace, aes(x = iter, y = value, color = chain)) +
annotate("rect", xmin = 0, xmax = 15000, ymin = -Inf, ymax = Inf,
alpha = 0.4, fill = "gray85") +
geom_line() +
facet_wrap(~ param, ncol = 2, scales = "free") +
scale_x_continuous(name = "", expand = c(0.02, 0)) +
scale_y_continuous(name = "", expand = c(0.02, 0)) +
scale_colour_manual(name = "Chain",
values = hcl.colors(n = 2, palette = "Set 2")) +
theme_bw() +
guides(color = guide_legend(nrow = 1, byrow = TRUE)) +
theme(plot.margin = margin(c(4, 15, -10, 0)),
legend.position = "bottom",
legend.title = element_text(size = 10),
legend.text = element_text(size = 10),
legend.box.margin = margin(c(-16, 6, 6, 6)),
legend.background = element_rect(fill = "transparent"))
#..........
# Example 8: Posterior Distribution Plots
# Extract data in long format
data.post <- object$data$post$long
# Extract ON parameters
data.post <- data.post[grep(" ON ", data.post$param), ]
# Discard burn-in iterations
data.post <- data.post[data.post$iter > 15000, ]
# Drop factor levels
data.post$param <- droplevels(data.post$param,
exclude = c("[Y]", "[M]", "Y", "M", "INDIRECT", "MOD"))
# Plot
ggplot(data.post, aes(x = value)) +
geom_histogram(aes(y = after_stat(density)), color = "black", alpha = 0.4,
fill = "gray85") +
geom_density(color = "#0072B2") +
geom_vline(data = data.frame(param = unique(data.post$param),
stat = tapply(data.post$value, data.post$param, median)),
aes(xintercept = stat, color = "Median"), linewidth = 0.6) +
geom_vline(data = data.frame(param = unique(data.post$param),
low = tapply(data.post$value, data.post$param,
function(y) quantile(y, probs = 0.025))),
aes(xintercept = low), linetype = "dashed", linewidth = 0.6) +
geom_vline(data = data.frame(param = unique(data.post$param),
upp = tapply(data.post$value, data.post$param,
function(y) quantile(y, probs = 0.975))),
aes(xintercept = upp), linetype = "dashed", linewidth = 0.6) +
facet_wrap(~ param, ncol = 2, scales = "free") +
scale_x_continuous(name = "", expand = c(0.02, 0)) +
scale_y_continuous(name = "Probability Density, f(x)",
expand = expansion(mult = c(0L, 0.05))) +
scale_color_manual(name = "Point Estimate", values = c(Median = "#D55E00")) +
labs(caption = "95% Equal-Tailed Interval") +
theme_bw() +
theme(plot.margin = margin(c(4, 15, -8, 4)),
plot.caption = element_text(hjust = 0.5, vjust = 7),
legend.position = "bottom",
legend.title = element_text(size = 10),
legend.text = element_text(size = 10),
legend.box.margin = margin(c(-30, 6, 6, 6)),
legend.background = element_rect(fill = "transparent"))
#..........
# Example 9: Autocorrelation Plots
# Extract data in long format
data.auto <- object$data$auto$long
# Select first chain
data.auto <- data.auto[data.auto$chain == 1, ]
# Extract ON parameters
data.auto <- data.auto[grep(" ON ", data.auto$param), ]
# Plot
ggplot(data.auto, aes(x = lag, y = cor)) +
geom_bar(stat = "identity", alpha = 0.4, color = "black", fill = "gray85",
width = 0.8) +
facet_wrap(~ param, ncol = 2) +
scale_x_continuous(name = "Lag", breaks = seq(1, 30, by = 2), expand = c(0.02, 0)) +
scale_y_continuous(name = "Autocorrelation", limits = c(-0.1, 0.1),
breaks = seq(-0.1, 1., by = 0.05), expand = c(0.02, 0)) +
theme_bw() +
theme(plot.margin = margin(c(4, 15, 4, 4)))
#..........
# Example 10: Posterior Predictive Check (PPC) Plots
# Extract data
data.ppc <- object$data$ppc
# Scatter plot
ppc.scatter <- ggplot(data.ppc, aes(x = obs, y = rep)) +
geom_point(shape = 21, fill = "gray85") +
geom_abline(slope = 1) +
scale_x_continuous("Observed", limits = c(0, 45), breaks = seq(0, 45, by = 5),
expand = c(0.02, 0)) +
scale_y_continuous("Recpliated", limits = c(0, 45), breaks = seq(0, 45, by = 5),
expand = c(0.02, 0)) +
theme_bw() +
theme(plot.margin = margin(c(2, 15, 4, 4)))
# Histogram
ppc.hist <- ggplot(data.ppc, aes(x = diff)) +
geom_histogram(color = "black", alpha = 0.4, fill = "gray85") +
geom_vline(xintercept = mean(data.ppc$diff), color = "#CC79A7") +
geom_vline(xintercept = quantile(data.ppc$diff, probs = 0.025),
linetype = "dashed", color = "#CC79A7") +
geom_vline(xintercept = quantile(data.ppc$diff, probs = 0.975),
linetype = "dashed", color = "#CC79A7") +
scale_x_continuous("Observed - Replicated", expand = c(0.02, 0)) +
scale_y_continuous("Count", expand = expansion(mult = c(0L, 0.05))) +
theme_bw() +
theme(plot.margin = margin(c(2, 15, 4, 4)))
# Combine plots using the patchwork package
patchwork::wrap_plots(ppc.scatter, ppc.hist)
#..........
# Example 11: Loop Plot
# Extract data
data.loop <- object$data$loop
# Plot
plot.loop <- ggplot(data.loop, aes(x = xval, y = estimate)) +
geom_line(linewidth = 0.6, show.legend = FALSE) +
geom_line(aes(xval, low)) +
geom_line(aes(xval, upp)) +
scale_x_continuous("MOD", expand = c(0.02, 0)) +
scale_y_continuous("INDIRECT", expand = c(0.02, 0)) +
scale_fill_manual("Statistical Significance",
values = hcl.colors(n = 2, palette = "Set 2")) +
theme_bw() +
theme(plot.margin = margin(c(4, 15, -6, 4)),
legend.position = "bottom",
legend.title = element_text(size = 10),
legend.text = element_text(size = 10),
legend.box.margin = margin(-10, 6, 6, 6),
legend.background = element_rect(fill = "transparent"))
# Significance area
for (i in unique(data.loop$group)) {
plot.loop <- plot.loop + geom_ribbon(data = data.loop[data.loop$group == i, ],
aes(ymin = low, ymax = upp, fill = sig), alpha = 0.4)
}
# Vertical lines
plot.loop + geom_vline(data = data.loop[data.loop$change == 1, ],
aes(xintercept = xval, color = sig), linewidth = 0.6,
linetype = "dashed", show.legend = FALSE)
## End(Not run)
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