View source: R/plot.infl.rma.uni.r
plot.infl.rma.uni | R Documentation |
Function to plot objects of class "infl.rma.uni"
. \loadmathjax
## S3 method for class 'infl.rma.uni'
plot(x, plotinf=TRUE, plotdfbs=FALSE, dfbsnew=FALSE, logcov=TRUE,
layout, slab.style=1, las=0, pch=21, bg, bg.infl, col.na, ...)
x |
an object of class |
plotinf |
logical to specify whether the various case diagnostics should be plotted (the default is |
plotdfbs |
logical to specify whether the DFBETAS values should be plotted (the default is |
dfbsnew |
logical to specify whether a new device should be opened for plotting the DFBETAS values (the default is |
logcov |
logical to specify whether the covariance ratios should be plotted on a log scale (the default is |
layout |
optional vector of two numbers to specify the number of rows and columns for the layout of the figure. |
slab.style |
integer to indicate the style of the x-axis labels: 1 = study number, 2 = study label, 3 = abbreviated study label. Note that study labels, even when abbreviated, may be too long to fit in the margins.) |
las |
integer between 0 and 3 to specify the alignment of the axis labels (see |
pch |
plotting symbol to use. By default, an open circle is used. See |
bg |
optional character string to specify the background color of open plotting symbols. If unspecified, gray is used by default. |
bg.infl |
optional character string to specify the background color when the point is considered influential. If unspecified, red is used by default. |
col.na |
optional character string to specify the color for lines connecting two points with |
... |
other arguments. |
When plotinf=TRUE
, the function plots the (1) externally standardized residuals, (2) DFFITS values, (3) Cook's distances, (4) covariance ratios, (5) leave-one-out \mjseqn\tau^2 estimates, (6) leave-one-out (residual) heterogeneity test statistics, (7) hat values, and (8) weights. If plotdfbs=TRUE
, the DFBETAS values are also plotted either after confirming the page change (if dfbsnew=FALSE
) or on a separate device (if dfbsnew=TRUE
).
A case (which is typically synonymous with study) may be considered to be ‘influential’ if at least one of the following is true:
The absolute DFFITS value is larger than \mjeqn3 \times \sqrtp/(k-p)3*\sqrt(p/(k-p)), where \mjseqnp is the number of model coefficients and \mjseqnk the number of cases.
The lower tail area of a chi-square distribution with \mjseqnp degrees of freedom cut off by the Cook's distance is larger than 50%.
The hat value is larger than \mjeqn3 \times (p/k)3*(p/k).
Any DFBETAS value is larger than \mjseqn1.
Cases which are considered influential with respect to any of these measures are indicated by the color specified for the bg.infl
argument (the default is "red"
).
The cut-offs described above are indicated in the plot with horizontal reference lines. In addition, on the plot of the externally standardized residuals, horizontal reference lines are drawn at -1.96, 0, and 1.96. On the plot of the covariance ratios, a horizontal reference line is drawn at 1. On the plot of leave-one-out \mjseqn\tau^2 estimates, a horizontal reference line is drawn at the \mjseqn\tau^2 estimate based on all cases. On the plot of leave-one-out (residual) heterogeneity test statistics, horizontal reference lines are drawn at the test statistic based on all cases and at \mjseqnk-p, the degrees of freedom of the test statistic. On the plot of the hat values, a horizontal reference line is drawn at \mjseqnp/k. Since the sum of the hat values is equal to \mjseqnp, the value \mjseqnp/k indicates equal hat values for all \mjseqnk cases. Finally, on the plot of weights, a horizontal reference line is drawn at \mjseqn100/k, corresponding to the value for equal weights (in %) for all \mjseqnk cases. Note that all weights will automatically be equal to each other when using unweighted model fitting. Also, the hat values will be equal to the weights (except for their scaling) in models without moderators.
The chosen cut-offs are (somewhat) arbitrary. Substantively informed judgment should always be used when examining the influence of each case on the results.
Wolfgang Viechtbauer wvb@metafor-project.org https://www.metafor-project.org
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03
Viechtbauer, W., & Cheung, M. W.-L. (2010). Outlier and influence diagnostics for meta-analysis. Research Synthesis Methods, 1(2), 112–125. https://doi.org/10.1002/jrsm.11
influence
for the function to compute the various model diagnostics.
### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)
### fit mixed-effects model with absolute latitude and publication year as moderators
res <- rma(yi, vi, mods = ~ ablat + year, data=dat)
### compute the diagnostics
inf <- influence(res)
### plot the values
plot(inf)
### select which plots to show
plot(inf, plotinf=1:4)
plot(inf, plotinf=1:4, layout=c(4,1))
### plot the DFBETAS values
plot(inf, plotinf=FALSE, plotdfbs=TRUE)
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