plot_study_dissimilarities {rnmamod} | R Documentation |
Plot Gower's disimilarity values for each study (Transitivity evaluation)
Description
Illustrating the range of Gower's dissimilarity values for each study in the network, as well as their between- and within-comparison dissimilarities
Usage
plot_study_dissimilarities(
results,
axis_title_size = 12,
axis_text_size = 12,
strip_text_size = 11,
label_size = 3.5
)
Arguments
results |
An object of S3 class |
axis_title_size |
A positive integer for the font size of axis title
(both axes). |
axis_text_size |
A positive integer for the font size of axis text (both
axes). |
strip_text_size |
A positive integer for the font size of facet labels.
|
label_size |
A positive integer for the font size of labels appearing on
each study-specific segment. |
Details
The range of Gower's dissimilarity values for each study versus the remaining studies in the network for a set of clinical and methodological characteristics that may act as effect modifiers. Gower's dissimilarities take values from 0 to 1, with 0 and 1 implying perfect similarity and perfect dissimilarity, respectively.
The unique dissimilarity values appear as dotted, vertical, grey lines on each study
Value
A horizontal bar plot illustrating the range of Gower's dissimilarity
values for each study with those found in other comparisons. The
study names appear on the y-axis in the order they appear in results
and the dissimilarity values appear on the x-axis. Red and blue points refer
to the (average) within-comparison and between-comparison dissimilarity,
respectively, for each study.
A data-frame on the (average) within-comparison and between-comparison
dissimilarities for each study alongside the study name and comparison.
The last two columns refer to the within-comparison and between-comparison
dissimilarities, respectively, after replacing with the maximum value in the
multi-arm trials. These two columns should be used as a covariate in the
function study_perc_contrib
to obtain the
percentage contribution of each study based on the covariate values.
Author(s)
Loukia M. Spineli
References
Gower J. General Coefficient of Similarity and Some of Its Properties. Biometrics 1971;27(4):857–71. doi: 10.2307/2528823
See Also
comp_clustering
, study_perc_contrib
Examples
# Fictional dataset
data_set <- data.frame(Trial_name = paste("study", as.character(1:7)),
arm1 = c("1", "1", "1", "1", "1", "2", "2"),
arm2 = c("2", "2", "2", "3", "3", "3", "3"),
sample = c(140, 145, 150, 40, 45, 75, 80),
age = c(18, 18, 18, 48, 48, 35, 35),
blinding = factor(c("yes", "yes", "yes", "no", "no", "no", "no")))
# Obtain comparison dissimilarities (informative = TRUE)
res <- comp_clustering(input = data_set,
drug_names = c("A", "B", "C"),
threshold = 0.13, # General research setting
informative = TRUE,
get_plots = TRUE)
plot_study_dissimilarities(results = res,
axis_title_size = 12,
axis_text_size = 12,
strip_text_size = 11,
label_size = 3.5)