sens_table {ConfoundedMeta}  R Documentation 
Produces table showing the proportion of true effect sizes more extreme than .q
across a grid of bias parameters .muB
and .sigB
(for .meas == "prop"
).
Alternatively, produces a table showing the minimum bias factor (for .meas == "Tmin"
)
or confounding strength (for .meas == "Gmin"
) required to reduce to less than
.r
the proportion of true effects more extreme than .q
.
sens_table(.meas, .q, .r = seq(0.1, 0.9, 0.1), .muB = NULL, .sigB = NULL, .yr, .t2)
.meas 

.q 
True effect size that is the threshold for "scientific significance" 
.r 
For 
.muB 
Mean bias factor on the log scale across studies 
.sigB 
Standard deviation of log bias factor across studies 
.yr 
Pooled point estimate (on log scale) from confounded metaanalysis 
.t2 
Estimated heterogeneity (tau^2) from confounded metaanalysis 
For .meas=="Tmin"
or .meas=="Gmin"
, arguments .muB
and
.sigB
can be left NULL
; .r
can also be NULL
as
it will default to a reasonable range of proportions. Returns a data.frame
whose rows are values of .muB
(for .meas=="prop"
) or of .r
(for .meas=="Tmin"
or .meas=="Gmin"
). Its columns are values of
.sigB
(for .meas=="prop"
) or of .q
(for .meas=="Tmin"
or .meas=="Gmin"
).
Tables for Gmin
will display NaN
for cells corresponding to Tmin
<1,
i.e., for which no bias is required to reduce the effects as specified.
sens_table( .meas="prop", .q=log(1.1), .muB=c( log(1.1), log(1.5), log(2.0) ), .sigB=c(0, 0.1, 0.2), .yr=log(2.5), .t2=0.1 ) sens_table( .meas="Tmin", .q=c( log(1.1), log(1.5) ), .yr=log(1.3), .t2=0.1 ) # will have NaNs in cells with Tmin < 1 (no bias needed) sens_table( .meas="Gmin", .r=0.8, .q=c( log(1.1) ), .yr=log(1.3), .t2=0.1 )