Old friends
Create the ggmice equivalent of mice
plots
How to re-create the output of the plotting functions from
mice with ggmice. In alphabetical order of the
mice functions.
First load the ggmice, mice, and
ggplot2 packages, some incomplete data and a
mids object into your workspace.
bwplot
Box-and-whisker plot of observed and imputed data.
# ggmice equivalent
ggmice(imp, aes(x = .imp, y = hgt)) +
geom_boxplot() +
labs(x = "Imputation number")
# extended reproduction with ggmice
ggmice(imp, aes(x = .imp, y = hgt)) +
stat_boxplot(geom = "errorbar", linetype = "dashed") +
geom_boxplot(outlier.colour = "grey", outlier.shape = 1) +
labs(x = "Imputation number") +
theme(legend.position = "none")
densityplot
Density plot of observed and imputed data.
# extended reproduction with ggmice
ggmice(imp, aes(x = hgt, group = .imp, linewidth = .where)) +
geom_density() +
scale_linewidth_manual(
values = c("observed" = 1, "imputed" = 0.5),
guide = "none"
) +
theme(legend.position = "none")
fluxplot
Influx and outflux plot of multivariate missing data patterns.
md.pattern
Missing data pattern plot.
# extended reproduction with ggmice
plot_pattern(dat, square = TRUE) +
theme(
legend.position = "none",
axis.title = element_blank(),
axis.title.x.top = element_blank(),
axis.title.y.right = element_blank()
)
plot.mids
Plot the trace lines of the MICE algorithm.
stripplot
Stripplot of observed and imputed data.
# ggmice equivalent
ggmice(imp, aes(x = .imp, y = hgt)) +
geom_jitter(width = 0.25) +
labs(x = "Imputation number")
# extended reproduction with ggmice (not recommended)
ggmice(imp, aes(x = .imp, y = hgt)) +
geom_jitter(
shape = 1,
width = 0.1,
na.rm = TRUE,
data = data.frame(
hgt = dat$hgt,
.imp = factor(rep(1:imp$m, each = nrow(dat))),
.where = "observed"
)
) +
geom_jitter(shape = 1, width = 0.1) +
labs(x = "Imputation number") +
theme(legend.position = "none")
xyplot
Scatterplot of observed and imputed data.
# extended reproduction with ggmice
ggmice(imp, aes(age, hgt)) +
geom_point(size = 2, shape = 1) +
theme(legend.position = "none")
Extensions
Interactive plots
To make ggmice visualizations interactive, the
plotly package can be used. For example, an interactive
influx and outflux plot may be more legible than a static one.
Plot multiple variables
You may want to create a plot visualizing the imputations of multiple
variables as one object. To visualize multiple variables at once, the
variable names are saved in a vector. This vector is used together with
the functional programming package purrr and visualization
package patchwork to map() over the variables
and subsequently wrap_plots to create a single figure.
# load packages
library(purrr)
library(patchwork)
# create vector with variable names
vrb <- names(dat)Display box-and-whisker plots for all variables.
# ggmice equivalent
p <- map(vrb, ~ {
ggmice(imp, aes(x = .imp, y = .data[[.x]])) +
geom_boxplot() +
scale_x_discrete(drop = FALSE) +
labs(x = "Imputation number")
})
wrap_plots(p, guides = "collect") &
theme(legend.position = "bottom")
Display density plots for all variables.
# ggmice equivalent
p <- map(vrb, ~ {
ggmice(imp, aes(x = .data[[.x]], group = .imp)) +
geom_density()
})
wrap_plots(p, guides = "collect") &
theme(legend.position = "bottom")
Display strip plots for all variables.
# ggmice equivalent
p <- map(vrb, ~ {
ggmice(imp, aes(x = .imp, y = .data[[.x]])) +
geom_jitter() +
labs(x = "Imputation number")
})
wrap_plots(p, guides = "collect") &
theme(legend.position = "bottom")
This is the end of the vignette. This document was generated using:
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