plot.powRICLPM: Plot Results From 'powRICLPM' Object

In powRICLPM: Perform Power Analysis for the RI-CLPM and STARTS Model

Plot Results From powRICLPM Object

Description

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").

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.

Value

A ggplot2 object.

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))

powRICLPM documentation built on Oct. 26, 2024, 5:14 p.m.