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). 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)))
#> ℹ Calculating observed measures
#> ⠙ Assign probability distributions
#> ✔ Assign probability distributions [10ms]
#> 
#> ⠙ Simple bias analysis
#> ✔ Simple bias analysis [36ms]
#> 
#> ⠙ Incorporating random error
#> ! Chosen Se/Sp distributions lead to 821 impossible values which were discarded.
#> ⠙ Incorporating random error

#> ⠹ Incorporating random error
#> ✔ Incorporating random error [96ms]
#> 
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)
#> ℹ Calculating observed measures
#> ⠙ Assign probability distributions
#> ✔ Assign probability distributions [143ms]
#> 
#> ⠙ Simple bias analysis
#> ✔ Simple bias analysis [35ms]
#> 
#> ⠙ Incorporating random error
#> ⠹ Incorporating random error
#> ✔ Incorporating random error [101ms]
#> 
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)))
#> ℹ Calculating observed measures
#> ⠙ Assign probability distributions
#> ✔ Assign probability distributions [9ms]
#> 
#> ⠙ Simple bias analysis
#> ✔ Simple bias analysis [32ms]
#> 
#> ⠙ Incorporating random error
#> ⠹ Incorporating random error
#> ✔ Incorporating random error [100ms]
#> 
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)
#> ℹ Calculating observed measures
#> ⠙ Simple bias analysis
#> ! Samplings lead to 1801 instances in which
#> sampled cell counts were zero and discarded.
#> ⠙ Simple bias analysis

#> ✔ Simple bias analysis [99ms]
#> 
#> ⠙ Incorporating random error
#> ✔ Incorporating random error [39ms]
#> 
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))
#> ℹ Calculating observed measures
#> ⠙ Assign probability distributions
#> ✔ Assign probability distributions [8ms]
#> 
#> ⠙ Bias analysis
#> ✔ Bias analysis [76ms]
#> 
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)
#> ℹ Calculating observed measures
#> ⠙ Assign probability distributions
#> ✔ Assign probability distributions [13ms]
#> 
#> ⠙ Bias analysis
#> ✔ Bias analysis [100ms]
#> 
plot(inc2, "risk")