rnmamod (version 0.4.0)

distr_characteristics: Visualising the distribution of characteristics (Comparisons' comparability for transitivity evaluation)

Description

distr_characteristics uses violin and bar plots to visualise the distribution of each characteristic in the dataset either per comparison or cluster of comparisons.

Usage

distr_characteristics(
  input,
  drug_names,
  rename_char = NULL,
  cluster = NULL,
  label_size = 4,
  title_size = 14,
  axis_title_size = 14,
  axis_text_size = 14,
  axis_x_text_angle = 0,
  legend_text_size = 13
)

Value

distr_characteristics returns a list of plots using the proper plot (violin or bar plot) for each characteristic. The size of the dots in the violin plot (with amalgamated box plots and dots) are proportional to the total sample size of the study: the large the sample size of the study, the larger the size of the corresponding point.

Arguments

input

A data-frame in the long arm-based format. Two-arm trials occupy one row in the data-frame. Multi-arm trials occupy as many rows as the number of possible comparisons among the interventions. The first three columns refer to the trial name, first and second arm of the comparison (their identifier number), respectively. The remaining columns refer to summary characteristics. See 'Details' for specifying the columns.

drug_names

A vector of labels with the name of the interventions in the order they appear in the argument input.

rename_char

A list of two elements: (i) a numeric vector with the position of the characteristics in input, and (ii) a character vector with the names of the characteristics, as they are wished to appear in the title of the plots. This argument is optional, in case the user wants to control the appearance of the titles.

cluster

An object of S3 class comp_clustering that has information on the cluster of each comparison. See 'Value' in comp_clustering. If cluster is not provided, the function presents the distribution of characteristics per comparison; otherwise per cluster. In the latter, the function prints a table with the comparisons and the corresponding cluster.

label_size

A positive integer for the font size of labels in the plots. label_size determines the size argument found in the geom's aesthetic properties in the R-package ggplot2).

title_size

A positive integer for the font size of legend title in the plots. title_size determines the title argument found in the theme's properties in the R-package ggplot2.

axis_title_size

A positive integer for the font size of axis title in the plots. axis_title_size determines the axis.title argument found in the theme's properties in the R-package ggplot2.

axis_text_size

A positive integer for the font size of axis text in the plots. axis_text_size determines the axis.text argument found in the theme's properties in the R-package ggplot2).

axis_x_text_angle

A positive integer for the angle of axis text in the plots. axis_text_angle determines the axis.text.x argument found in the theme's properties in the R-package ggplot2).

legend_text_size

A positive integer for the font size of legend text in the plots. legend_text_size determines the legend.text argument found in the theme's properties in the R-package ggplot2.

Author

Loukia M. Spineli

Details

The correct type mode of columns in input must be ensured to use the function distr_characteristics. The first three columns referring to the trial name, first and second arm of the comparison, respectively, must be character. The remaining columns referring to the characteristics must be double or integer depending on whether the corresponding characteristic refers to a quantitative or qualitative variable. The type mode of each column is assessed by distr_characteristics using the base function typeof.

The interventions should be sorted in an ascending order of their identifier number within the trials so that the first treatment column (second column in input) is the control arm for every pairwise comparison. This is important to ensure consistency in the order of interventions within the comparisons obtained from the other related functions.

See Also

comp_clustering

Examples

# \donttest{
# Fictional dataset
set.seed(13022024)
data_set <- data.frame(Trial_name = as.character(1:(5 + 7 +2)),
                       arm1 = rep(c("1", "2"), c(5 + 7, 2)),
                       arm2 = rep(c("2", "3"), c(5, 7 + 2)),
                       sample = as.numeric(sample(50:300, 5 + 7 + 2)),
                       age = as.numeric(sample(18:50, 5 + 7 + 2)),
                       blinding = factor(rep(c("yes", "no", "yes"), c(5, 7, 2))))

distr_characteristics(input = data_set,
                      drug_names = c("A", "B", "C"))
# }