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 4.2.2 (2022-10-31)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.1 LTS
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#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
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#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] ggmice_0.0.1.9000
#>
#> loaded via a namespace (and not attached):
#> [1] Rcpp_1.0.10 highr_0.10 bslib_0.4.2 compiler_4.2.2
#> [5] pillar_1.8.1 jquerylib_0.1.4 tools_4.2.2 digest_0.6.31
#> [9] gtable_0.3.1 lattice_0.20-45 jsonlite_1.8.4 evaluate_0.20
#> [13] memoise_2.0.1 lifecycle_1.0.3 tibble_3.1.8 pkgconfig_2.0.3
#> [17] rlang_1.0.6 cli_3.6.0 yaml_2.3.7 pkgdown_2.0.7
#> [21] xfun_0.36 fastmap_1.1.0 withr_2.5.0 stringr_1.5.0
#> [25] dplyr_1.0.10 knitr_1.42 desc_1.4.2 generics_0.1.3
#> [29] fs_1.6.0 vctrs_0.5.2 sass_0.4.5 systemfonts_1.0.4
#> [33] grid_4.2.2 tidyselect_1.2.0 rprojroot_2.0.3 glue_1.6.2
#> [37] mice_3.15.0 R6_2.5.1 textshaping_0.3.6 fansi_1.0.4
#> [41] rmarkdown_2.20 farver_2.1.1 ggplot2_3.4.0 tidyr_1.3.0
#> [45] purrr_1.0.1 magrittr_2.0.3 MASS_7.3-58.1 scales_1.2.1
#> [49] backports_1.4.1 htmltools_0.5.4 colorspace_2.1-0 labeling_0.4.2
#> [53] ragg_1.2.5 utf8_1.2.2 stringi_1.7.12 munsell_0.5.0
#> [57] cachem_1.0.6 broom_1.0.3