plot.powRICLPM {powRICLPM} | R Documentation |
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 |
y |
(optional) A |
... |
(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))