PlotBeads {rankinma}

Illustrate beading plot

Description

PlotBeads() is a function for illustrating beading plot.

Usage

PlotBeads(
  data,
  scaleX = "Numeric",
  txtValue = "Effects",
  color = NULL,
  whichRoB = "None",
  lgcBlind = FALSE,
  szPnt = NULL,
  szFntTtl = NULL,
  szFntTtlX = NULL,
  szFntX = NULL,
  szFntY = NULL,
  szFntTxt = NULL,
  szFntLgnd = NULL,
  rotateTxt = 60
)

Arguments

data

DATA of metrics for treatment ranking.

scaleX

STRING for indicating scale on the x axis.

txtValue

STRING for indicating labels of metrics or effects on each point.

color

LIST of colors for treatments in a network meta-analysis.

whichRoB

STRING for indicating how to display risk of bias for each treatment.

lgcBlind

LOGIC value for indicating whether to display with color-blind friendly.

szPnt

NUMERIC value for indicating point size of ranking metrics.

szFntTtl

NUMERIC value for indicating font size of main title.

szFntTtlX

NUMERIC value for indicating font size of title on X-axis.

szFntX

NUMERIC value for indicating font size of numeric scale on X-axis.

szFntY

NUMERIC value for indicating font size of outcome name(s).

szFntTxt

NUMERIC value for indicating font size of value of each point.

szFntLgnd

NUMERIC value for indicating legend font size.

rotateTxt

NUMERIC value between 0 and 360 for rotating labels of text values of each point.

Value

PlotBeads() returns a beading plot.

Author(s)

Chiehfeng Chen & Enoch Kang

References

Chen, C., Chuang, Y.C., Chan, E., Chen, J.H., Hou, W.H., & Kang, E. (2023). Beading plot: A novel graphics for ranking interventions in network evidence. PREPRINT (Version 1) available at Research Square.

See Also

GetMetrics, SetMetrics

Examples

## Not run:
#library(netmeta)
#data(Senn2013)
#nma <- netmeta(TE, seTE, treat1, treat2,
#studlab, data = Senn2013, sm = "SMD")

# Get SUCRA
#nma.1 <- GetMetrics(nma, outcome = "HbA1c.random", prefer = "small", metrics = "SUCRA",
#model = "random", simt = 1000)
#nma.2 <- GetMetrics(nma, outcome = "HbA1c.common", prefer = "small", metrics = "SUCRA",
#model = "common", simt = 1000)

# Combine metrics of multiple outcomes
#dataMetrics <- rbind(nma.1, nma.2)

# Set data for rankinma
#dataRankinma <- SetMetrics(dataMetrics, tx = tx, outcome = outcome,
#metrics = SUCRA, metrics.name = "SUCRA")

# Illustrate beading plot
#PlotBeads(data = dataRankinma)
## End(Not run)