pub_bias_plot: pub_bias_plot

In daniel1noble/orchaRd: Orchard Plots and Prediction Intervals for Meta-analysis and Meta-regression

pub_bias_plot

Description

This function adds the bias corrected mean and confidence intervals to an existing orchard plot that displays the overall meta-analytic mean effect size.

Usage

pub_bias_plot(
  plot,
  fe_model,
  v_model = NULL,
  col = c("red", "blue"),
  plotadj = -0.05,
  textadj = 0.05,
  branch.size = 1.2,
  trunk.size = 3
)

Arguments

plot	The orchard plot object to add the bias corrected mean and confidence intervals to. This plot needs to be a plot that displays the raw and a single meta-analytic mean (overall mean), confidence interval and predcition interval.
fe_model	The meta-analytic model (rma object) produced from a two-step correction (sensu Yang et al. 2023) (Step 1: Fixed effect model) with a robust corretion to correct the meta-analytic mean for publication bias (selection bias) and the dependency in the data.
v_model	An optional argument. The meta-analytic model (rma object) to deal with publication bias using the meta-regression approach proposed by Nakagawa et al. 2023. This model can have fixed and/or random effects, with one random effect being sampling error (se or v). The intercept from this model can be considred the corrected meta-analytic mean when sample size is infinite or sampling error is zero.
col	The colour of the mean and confidence intervals.
plotadj	The adjustment to the x-axis position of the mean and confidence intervals.
textadj	The adjustment to the y-axis position of the mean and confidence intervals for the text displaying the type of correction.
branch.size	Size of the confidence intervals.
trunk.size	Size of the mean, or central point.

Value

An orchard plot with the corrected meta-analytic mean and confidence intervals added.

Author(s)

Daniel Noble - [e-mail]

Examples

## Not run: 
	# Data
		data(english)
		# We need to calculate the effect sizes, in this case d
		english <- escalc(measure = "SMD", n1i = NStartControl, sd1i = SD_C, m1i = MeanC, n2i = NStartExpt, sd2i = SD_E, m2i = MeanE, 
						var.names=c("SMD","vSMD"),
						data = english)

	# Our MLMA model
		english_MA1 <- rma.mv(yi = SMD, V = vSMD, random = list( ~ 1 | StudyNo, ~ 1 | EffectID),test = "t", data = english)

	# Step 1: Fit the fixed effect model
		english_MA2 <- rma.mv(yi = SMD, V = vSMD, data = english, test = "t")

		english_MA3 <- rma(yi = SMD, vi = vSMD, data = english, test = "t", method = "FE")

	# Step 2: Correct for dependency 
		english_MA2_1 <- robust(english_MA2, cluster = english$StudyNo, clubSandwich=TRUE)

	# Step 3: Testing modified eggers. Need intercept
		english_MA4 <- rma.mv(yi = SMD, V = vSMD, mod = ~vSMD, random = list( ~ 1 | StudyNo, ~ 1 | EffectID),test = "t", data = english)

	# Now plot the results

		plot <- orchard_plot(english_MA1, group = "StudyNo",  xlab = "Standardized Mean Difference")
		plot2 <- pub_bias_plot(plot, english_MA2_1)
		plot3 <- pub_bias_plot(plot, english_MA2_1, english_MA4)
		
## End(Not run)

daniel1noble/orchaRd documentation built on May 12, 2024, 7:46 a.m.