Plot(s) of probabilistic bias analyses

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).

Usage

# S3 method for episensr.probsens
plot(
  x,
  parms = c("rr", "or", "rr_tot", "or_tot", "irr", "irr_tot", "seca", "seexp", "spca",
    "spexp", "or_sel", "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), seca, seexp, spca, or_sel, 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

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.parms = list("trapezoidal", c(.75, .85, .95, 1)),
spca.parms = list("trapezoidal", c(.75, .85, .95, 1)))
#> Chosen prior Se/Sp distributions lead to 784 negative adjusted counts which were discarded.
plot(risk, "rr")
#> Warning: Removed 784 rows containing non-finite outside the scale range (`stat_bin()`).
#> Warning: Removed 784 rows containing non-finite outside the scale range
#> (`stat_density()`).


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.parms = list("beta", c(908, 16)),
seexp.parms = list("beta", c(156, 56)),
spca.parms = list("beta", c(153, 6)),
spexp.parms = list("beta", c(205, 18)),
corr.se = .8,
corr.sp = .8)
plot(odds, "seca")


set.seed(123)
select <- probsens.sel(matrix(c(136, 107, 297, 165),
dimnames = list(c("Melanoma+", "Melanoma-"), c("Mobile+", "Mobile-")),
nrow = 2, byrow = TRUE), reps = 20000,
or.parms = list("triangular", c(.35, 1.1, .43)))
plot(select, "or_sel")


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.parms = list("trapezoidal", c(.4, .45, .55, .6)),
spca.parms = 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")