plot_1num2fac {mixgb} | R Documentation |
Plot observed values versus m sets of imputed values for one specified numeric variable and two factors using ggplot2.
plot_1num2fac(
imputation.list,
var.fac,
var.num,
con.fac,
original.data,
true.data = NULL,
color.pal = NULL,
shape = FALSE
)
imputation.list |
A list of |
var.fac |
A factor variable on the x-axis |
var.num |
A numeric variable on the y-axis |
con.fac |
The name of a factor to condition on |
original.data |
The original data with missing data |
true.data |
The true data without missing values. This is generally unknown in practice. If the true data is known (e.g., in cases where it is generated by simulation), it can be specified in this argument. The output will then have an extra panel called |
color.pal |
A vector of hex color codes for the observed and m sets of imputed values panels. The vector should be of length |
shape |
Whether to plot shapes for different types of missing values. By default, this is set to FALSE to speed up plotting. We only recommend using 'shape = TRUE' for small datasets. |
Boxplots with overlaying data points
# create some extra missing values in factor variables "HSSEX" and "DMARETHN"
nhanes3_NA <- createNA(nhanes3, var.names = c("HSSEX", "DMARETHN"), p = 0.1)
# obtain m multiply datasets
params <- list(max_depth = 3, subsample = 0.8, nthread = 2)
imputed.data <- mixgb(data = nhanes3_NA, m = 3, xgb.params = params, nrounds = 30)
# plot the multiply imputed values for variables "BMPRECUM" versus "HSSEX" conditional on "DMARETHN"
plot_1num2fac(
imputation.list = imputed.data, var.fac = "HSSEX", var.num = "BMPRECUM",
con.fac = "DMARETHN", original.data = nhanes3_NA
)