plot.episensr.probsens: Plot(s) of probabilistic bias analyses

In episensr: Basic Sensitivity Analysis of Epidemiological Results

Plot(s) of probabilistic bias analyses

Description

This takes a probsens-family object and produces the distribution plot of chosen bias parameters, as well as distribution of adjusted measures (with confidence interval). It can also produce a forest plot of relative risks or odds ratios (with probsens(), probsens_conf(), or probsens.sel())

Usage

## S3 method for class 'episensr.probsens'
plot(
  x,
  parms = c("rr", "or", "rr_tot", "or_tot", "forest_rr", "forest_or", "irr", "irr_tot",
    "seca", "seexp", "spca", "spexp", "prev_exp", "prev_nexp", "risk"),
  ...
)

Arguments

x	An object of class "episensr.probsens" returned from the episensr probsens, probsens.sel, probsens_conf, probsens.irr, probsens.irr.conf functions.
parms	Choice between adjusted relative risk (rr) and odds ratio (or), total error relative risk and odds ratio (rr_tot and or_tot), forest plots (forest_rr and forest_or), seca, seexp, spca, and spexp, prev.exp, prev.nexp and risk, irr and irr_tot.
...	Other unused arguments.

See Also

probsens, probsens.sel, probsens_conf, probsens.irr, probsens.irr.conf

Other visualization: plot.episensr.booted(), plot.mbias()

Examples

set.seed(123)
risk <- probsens(matrix(c(45, 94, 257, 945),
dimnames = list(c("BC+", "BC-"), c("Smoke+", "Smoke-")), nrow = 2, byrow = TRUE),
type = "exposure", reps = 20000,
seca = list("trapezoidal", c(.75, .85, .95, 1)),
spca = list("trapezoidal", c(.75, .85, .95, 1)))
plot(risk, "rr")

set.seed(123)
odds <- probsens(matrix(c(45, 94, 257, 945),
dimnames = list(c("BC+", "BC-"), c("Smoke+", "Smoke-")), nrow = 2, byrow = TRUE),
type = "exposure", reps = 20000,
seca = list("beta", c(908, 16)),
seexp = list("beta", c(156, 56)),
spca = list("beta", c(153, 6)),
spexp = list("beta", c(205, 18)),
corr_se = .8,
corr_sp = .8)
plot(odds, "seca")

set.seed(123)
smoke <- probsens(matrix(c(215, 1449, 668, 4296),
dimnames = list(c("BC+", "BC-"), c("Smoke+", "Smoke-")), nrow = 2, byrow = TRUE),
type = "exposure", reps = 20000,
seca = list("uniform", c(.7, .95)),
spca = list("uniform", c(.9, .99)))
plot(smoke, "forest_or")

set.seed(123)
conf <- probsens_conf(matrix(c(105, 85, 527, 93),
dimnames = list(c("HIV+", "HIV-"), c("Circ+", "Circ-")), nrow = 2, byrow = TRUE),
reps = 20000,
prev_exp = list("triangular", c(.7, .9, .8)),
prev_nexp = list("trapezoidal", c(.03, .04, .05, .06)),
risk = list("triangular", c(.6, .7, .63)),
corr_p = .8)
plot(conf, "prev_exp")

set.seed(123)
inc1 <- probsens.irr(matrix(c(2, 67232, 58, 10539000),
dimnames = list(c("GBS+", "Person-time"), c("HPV+", "HPV-")), ncol = 2),
reps = 20000,
seca = list("trapezoidal", c(.4, .45, .55, .6)),
spca = list("constant", 1))
plot(inc1, "irr")

set.seed(123)
inc2 <- probsens.irr.conf(matrix(c(77, 10000, 87, 10000),
dimnames = list(c("D+", "Person-time"), c("E+", "E-")), ncol = 2),
reps = 20000,
prev_exp = list("trapezoidal", c(.01, .2, .3, .51)),
prev_nexp = list("trapezoidal", c(.09, .27, .35, .59)),
risk = list("trapezoidal", c(2, 2.5, 3.5, 4.5)),
corr_p = .8)
plot(inc2, "risk")

episensr documentation built on June 8, 2025, 10:34 a.m.