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Plot a population, its power, its smallest effects or a plan

Source: R/plot-power.R
plot_power.Rd

Draws the objects of the power analysis with the style of the other uSDT plots.

Usage

# S3 method for class 'usdt_population'
plot(
  x,
  trials = NULL,
  signal = 0.5,
  dichotomize = c("none", "influence", "awareness", "both"),
  caption = TRUE,
  ...
)

# S3 method for class 'usdt_power'
plot(x, caption = TRUE, ...)

# S3 method for class 'usdt_mdes'
plot(x, caption = TRUE, ...)

# S3 method for class 'usdt_plan'
plot(x, caption = TRUE, ...)

Arguments

x

A usdt_population, usdt_power, usdt_mdes or usdt_plan object.

trials

For a population, the number of trials per subject in each task, as in usdt_power(), to add the distribution of the measured \(d'\).

signal, dichotomize

For a population with trials, as in usdt_power().

caption

Logical. Show the caption that explains the plot under it (default TRUE).

...

Ignored.

Value

A ggplot object. Its underlying data frame is stored in $data.

Details

  • A usdt_population shows, for each scenario, the bivariate normal distribution of the true \(d'\) across subjects (ellipses holding 50% and 90% of them) with the latent regression of influence on awareness \(d'\) and its intercept at \(d'_A = 0\). With trials, a dashed ellipse adds the distribution of the \(d'\) that those trials measure, wider by the measurement error, which is what attenuates the observed correlation.

  • A usdt_power shows power against the number of subjects, or of trials when the subjects are fixed, one line per hypothesis. A simulation adds its 95% Wilson intervals. A single design shows one point per hypothesis.

  • A usdt_mdes shows the power along the values searched, with the smallest detectable effect and the population value marked.

  • A usdt_plan with subjects and trials free shows the cost of the cheapest closed-form design for each number of trials (a line for one free task, a map for two), with the chosen design marked. With one value free, it shows the approximate power (or the expected interval width) along it.

See also

usdt_population(), usdt_power(), usdt_mdes(), usdt_plan()

Examples

pop <- usdt_population(dprime    = c(awareness = 0.15, influence = 0.30),
                       sd_dprime = c(awareness = 0.35, influence = 0.30),
                       cor_dprime = 0.3, criterion = 0.1)
plot(pop, trials = c(awareness = 64, influence = 200),
     dichotomize = "influence")


plot(usdt_power(pop, subjects = seq(20, 300, 40),
                trials = c(awareness = 64, influence = 200),
                dichotomize = "influence"))


plot(usdt_mdes(pop, subjects = 80,
               trials = c(awareness = 64, influence = 200),
               hypothesis = "intercept > 0", dichotomize = "influence"))


plot(usdt_plan(pop, hypothesis = "diff = 0", subjects = c(20, 300),
               trials = list(awareness = c(32, 96), influence = 200),
               cost = c(subject = 600, awareness = 4, influence = 1.5),
               dichotomize = "influence"))