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 package, some incomplete data and a mice::mids object into your workspace.
bwplot
Box-and-whisker plot of observed and imputed data.
# original plot
mice::bwplot(imp, bmi ~ .imp)
# ggmice equivalent
ggmice(imp, ggplot2::aes(x = .imp, y = bmi)) +
ggplot2::geom_boxplot() +
ggplot2::labs(x = "Imputation number")
# extended reproduction with ggmice
ggmice(imp, ggplot2::aes(x = .imp, y = bmi)) +
ggplot2::stat_boxplot(geom = 'errorbar', linetype = "dashed") +
ggplot2::geom_boxplot(outlier.colour = "grey", outlier.shape = 1) +
ggplot2::labs(x = "Imputation number") +
ggplot2::theme(legend.position = "none")
densityplot
Density plot of observed and imputed data.
# original plot
mice::densityplot(imp, ~bmi)
# ggmice equivalent
ggmice(imp, ggplot2::aes(x = bmi, group = .imp)) +
ggplot2::geom_density() 
# extended reproduction with ggmice
ggmice(imp, ggplot2::aes(x = bmi, group = .imp, size = .where)) +
ggplot2::geom_density() +
ggplot2::scale_size_manual(values = c("observed" = 1, "imputed" = 0.5),
guide = "none") +
ggplot2::theme(legend.position = "none")
fluxplot
Influx and outflux plot of multivariate missing data patterns.
# original plot
mice::fluxplot(dat)
# ggmice equivalent
plot_flux(dat)
md.pattern
Missing data pattern plot.
# original plot
md <- mice::md.pattern(dat)
# ggmice equivalent
plot_pattern(dat)
# extended reproduction with ggmice
plot_pattern(dat, square = TRUE) +
ggplot2::theme(legend.position = "none",
axis.title = ggplot2::element_blank(),
axis.title.x.top = ggplot2::element_blank(),
axis.title.y.right = ggplot2::element_blank())
plot.mids
Plot the trace lines of the MICE algorithm.
# original plot
plot(imp, bmi ~ .it | .ms)
# ggmice equivalent
plot_trace(imp, "bmi")
stripplot
Stripplot of observed and imputed data.
# original plot
mice::stripplot(imp, bmi ~ .imp)
# ggmice equivalent
ggmice(imp, ggplot2::aes(x = .imp, y = bmi)) +
ggplot2::geom_jitter(width = 0.25) +
ggplot2::labs(x = "Imputation number")
# extended reproduction with ggmice (not recommended)
ggmice(imp, ggplot2::aes(x = .imp, y = bmi)) +
ggplot2::geom_jitter(
shape = 1,
width = 0.1,
na.rm = TRUE,
data = data.frame(
bmi = dat$bmi,
.imp = factor(rep(1:imp$m, each = nrow(dat))),
.where = "observed"
)
) +
ggplot2::geom_jitter(shape = 1, width = 0.1) +
ggplot2::labs(x = "Imputation number") +
ggplot2::theme(legend.position = "none")
xyplot
Scatterplot of observed and imputed data.
# original plot
mice::xyplot(imp, bmi ~ age)
# ggmice equivalent
ggmice(imp, ggplot2::aes(age, bmi)) +
ggplot2::geom_point()
# extended reproduction with ggmice
ggmice(imp, ggplot2::aes(age, bmi)) +
ggplot2::geom_point(size = 2, shape = 1) +
ggplot2::theme(legend.position = "none")
This is the end of the vignette. This document was generated using:
sessionInfo()
#> R version 4.2.0 (2022-04-22)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.4 LTS
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#> BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
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#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
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#> other attached packages:
#> [1] ggmice_0.0.1.9000
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#> loaded via a namespace (and not attached):
#> [1] tidyselect_1.1.2 xfun_0.31 bslib_0.3.1 purrr_0.3.4
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#> [17] withr_2.5.0 lifecycle_1.0.1 stringr_1.4.0 munsell_0.5.0
#> [21] gtable_0.3.0 ragg_1.2.2 memoise_2.0.1 evaluate_0.15
#> [25] labeling_0.4.2 knitr_1.39 fastmap_1.1.0 fansi_1.0.3
#> [29] highr_0.9 broom_0.8.0 Rcpp_1.0.8.3 backports_1.4.1
#> [33] scales_1.2.0 cachem_1.0.6 desc_1.4.1 jsonlite_1.8.0
#> [37] farver_2.1.0 systemfonts_1.0.4 fs_1.5.2 textshaping_0.3.6
#> [41] ggplot2_3.3.6 digest_0.6.29 stringi_1.7.6 dplyr_1.0.9
#> [45] rprojroot_2.0.3 grid_4.2.0 cli_3.3.0 tools_4.2.0
#> [49] magrittr_2.0.3 sass_0.4.1 tibble_3.1.7 mice_3.14.0
#> [53] crayon_1.5.1 tidyr_1.2.0 pkgconfig_2.0.3 MASS_7.3-57
#> [57] ellipsis_0.3.2 rmarkdown_2.14 R6_2.5.1 compiler_4.2.0