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Plot Results From powRICLPM Object

Source: R/plot.R
plot.powRICLPM.Rd

Visualizes (using ggplot2) the results from a powRICLPM analysis, for a specific parameter, across all experimental conditions. By default, sample size is plotted on the x-axis, power on the y-axis, with results colored by the number of time points, wrapped by the proportion of between-unit variance, and shaped by the reliability. Optionally, other variables can be mapped to the y-axis, x-axis, color, shape, and facets.

Usage

# S3 method for class 'powRICLPM'
plot(
  x,
  y = "power",
  ...,
  parameter = NULL,
  color_by = "time_points",
  shape_by = "reliability",
  facet_by = "ICC"
)

Arguments

x

A powRICLPM object.

y

(optional) A character string, specifying which outcome is plotted on the y-axis (see "Details").

...

(don't use)

parameter

Character string of length 1, denoting the parameter to visualize the results for.

color_by

Character string of length 1, denoting what variable to map to color (see "Details").

shape_by

Character string of length 1, denoting what variable to map to point shapes (see "Details").

facet_by

Character string of length 1, denoting what variable to facet by (see "Details").

Value

A ggplot2 object.

Details

Mapping Options

The following outcomes can be plotted on the y-axis:

  • average: The average estimate.

  • MSE: The mean square error.

  • coverage: The coverage rate

  • accuracy: The average width of the confidence interval.

  • SD: Standard deviation of parameter estimates.

  • SEAvg: Average standard error.

  • bias: The absolute difference between the average estimate and population value.

The following variables can be mapped to color, shape, and facet:

  • sample_size: Sample size.

  • time_points: Time points.

  • ICC: Intraclass correlation (ICC).

  • reliability: Item-reliablity.

See also

  • give: Extract information (e.g., performance measures) for a specific parameter, across all experimental conditions. This function is used internally by plot.powRICLPM.

Examples

# Visualize power for "wB2~wA1" across simulation conditions
plot(out_preliminary, parameter = "wB2~wA1")



# Visualize bias for "wB2~wA1" across simulation conditions
plot(out_preliminary, y = "bias", parameter = "wB2~wA1")



# Visualize coverage rate for "wB2~wA1" across simulation conditions
plot(out_preliminary, y = "coverage", parameter = "wB2~wA1")



# Visualize MSE for autoregressive effect across simulation conditions
plot(out_preliminary, y = "MSE", parameter = "wA2~wA1")



# Error: No parameter specified
try(plot(out_preliminary))
#> Error in icheck_plot_parameter(parameter, x) : 
#>   No `parameter` was specified:
#> ℹ `plot()` needs to know which specific parameter to create a plot for.