This function provides visual diagnostic tools for assessing multiply imputed datasets imputed with 'mixgb' or other imputers through overimputations. It supports visualisation for both training and test set.
Usage
vismi_overimp(
obj,
x = NULL,
y = NULL,
z = NULL,
m = NULL,
imp_idx = NULL,
integerAsFactor = FALSE,
num_plot = "cv",
fac_plot = "cv",
train_color_pal = NULL,
test_color_pal = NULL,
stack_y = FALSE,
diag_color = NULL,
seed = 2025,
...
)Arguments
- obj
Overimputation object of class 'overimp' created by the
overimp()function.- x
A character string specifying the name of the variable to plot on the x
- y
A character string specifying the name of the variable to plot on the y
- z
A character string specifying the name of the variable to plot on the z
- m
A single positive integer specifying the number of imputed datasets to plot. It should be smaller than the total number of imputed datasets in the object.
- imp_idx
A vector of integers specifying the indices of imputed datasets to plot.
- integerAsFactor
A logical indicating whether integer variables should be treated as factors. Default is FALSE (treated as numeric).
- num_plot
A character string specifying the type of plot for numeric variables.
- fac_plot
A character string specifying the type of plot for categorical variables.
- train_color_pal
A vector of colors for the training data. If NULL, default colors will be used.
- test_color_pal
A vector of colors for the test data. If NULL, default colors will be used.
- stack_y
A logical indicating whether to stack y values in certain plots. Default is FALSE.
- diag_color
A character string specifying the color of the diagonal line in scatter plots. Default is NULL.
- seed
An integer specifying the random seed for reproducibility. Default is 2025.
- ...
Additional arguments to customize the plots, such as position, point_size, linewidth, alpha, xlim, ylim, boxpoints, width.