S3 method for class powRICLPM. summary.powRICLPM summarizes the setup and results of the powRICLPM analysis. Depending on the arguments that are set, summary.powRICLPM provides a different summary (see "Details").
Usage
# S3 method for class 'powRICLPM'
summary(
object,
...,
parameter = NULL,
sample_size = NULL,
time_points = NULL,
ICC = NULL,
reliability = NULL
)Arguments
- object
A
powRICLPMobject.- ...
(don't use)
- parameter
Character string of length 1 denoting the parameter to visualize the results for.
- sample_size
(optional) An
integer, denoting the sample size of the experimental condition of interest.- time_points
(optional) An
integer, denoting the number of time points of the experimental condition of interest.- ICC
(optional) A
double, denoting the proportion of variance at the between-unit level of the experimental condition of interest.- reliability
(optional) An
integer, denoting the reliability of the indicators of the experimental condition of interest.
Details
summary.powRICLPM provides a different summary of the powRICLPM object, depending on the additional arguments that are set:
When
sample_size = ...,time_points = ...,ICC = ..., andreliabilityare set: Estimation information and results for all parameters across experimental conditions.When
parameter = "..."is set: Estimation information and results for a specific parameter across all experimental conditions.No additional arguments: Characteristics of the different experimental conditions are summarized, as well as session info (information that applies to all conditions, such the number of replications, etc.).
Interpretation Output
Depending on the arguments that you set, summary() prints a table with different analysis outcomes in the columns and where each row refers to a different experimental condition. The following information is available:
Sample size,Time points,ICC,Reliability: The experimental condition that the row refers to.Population: The true value of the parameter.Avg: The average (across replications) parameter estimate.Bias: The difference between the population value and the average parameter estimate.Min: The lowest (across replications) parameter estimate.SD: The standard deviation of the parameter estimate over replications.SEAvg: The average (across replications) standard error of the parameter estimate.MSE: The parameter mean square error, combining a parameter's bias and efficiency.Accuracy: The average (across replications) width of the confidence interval.Cover: The coverage rate, representing the proportion of times (across replications) the true parameter estimate fell in the confidence interval.Power: The proportion of times (across replications) the confidence interval did not contain zero.Error: The number of replications that failed to run (i.e.,lavaan()produced an error).Not converged: The number of replications that did not converge to a solution.Inadmissible: The number of replications that converged to an inadmissible solution (e.g., a variance estimated to be lower than zero).
Examples
# Get setup of powRICLPM analysis and convergence issues
summary(out_preliminary)
#> powRICLPM (0.2.0) simulated power for 16 experimental conditions.
#>
#>
#> Table: SUMMARY OF ANALYSIS PER EXPERIMENTAL CONDITION
#>
#> Sample size Time points ICC Reliability Error Not converged Inadmissible
#> ------------ ------------ ---- ------------ ------ -------------- -------------
#> 500 4 0.3 1.0 0 0 0
#> 700 4 0.3 1.0 0 0 0
#> 500 5 0.3 1.0 0 0 0
#> 700 5 0.3 1.0 0 0 0
#> 500 4 0.5 1.0 0 0 0
#> 700 4 0.5 1.0 0 0 0
#> 500 5 0.5 1.0 0 0 0
#> 700 5 0.5 1.0 0 0 0
#> 500 4 0.3 0.8 0 0 0
#> 700 4 0.3 0.8 0 0 0
#> 500 5 0.3 0.8 0 0 0
#> 700 5 0.3 0.8 0 0 0
#> 500 4 0.5 0.8 0 0 0
#> 700 4 0.5 0.8 0 0 0
#> 500 5 0.5 0.8 0 0 0
#> 700 5 0.5 0.8 0 0 0
# Performance measures for "wB2~wA1" parameter across experimental conditions
summary(out_preliminary, parameter = "wB2~wA1")
#>
#>
#> Table: SIMULATION RESULTS FOR wB2~wA1
#>
#> Sample size Time points ICC Reliability Population Avg Min SD SE Avg MSE Accuracy Cover Power Error Not converged Inadmissible NA
#> ------------ ------------ ---- ------------ ----------- ------ ------- ------- ------- ------ --------- ------ ------ ------ -------------- ------------- ---
#> 500 4 0.3 0.8 0.1 0.092 -0.008 -0.034 0.051 0.056 0.003 0.218 0.96 0.34 0 0 0
#> 500 4 0.3 1.0 0.1 0.101 0.001 -0.037 0.061 0.057 0.004 0.225 0.90 0.44 0 0 0
#> 500 4 0.5 0.8 0.1 0.078 -0.022 -0.072 0.058 0.061 0.004 0.238 0.91 0.23 0 0 0
#> 500 4 0.5 1.0 0.1 0.093 -0.007 -0.047 0.063 0.065 0.004 0.254 0.95 0.32 0 0 0
#> 500 5 0.3 0.8 0.1 0.091 -0.009 -0.026 0.046 0.053 0.002 0.208 0.98 0.40 0 0 0
#> 500 5 0.3 1.0 0.1 0.102 0.002 -0.036 0.057 0.054 0.003 0.213 0.94 0.50 0 0 0
#> 500 5 0.5 0.8 0.1 0.084 -0.016 -0.097 0.055 0.057 0.003 0.222 0.93 0.37 0 0 0
#> 500 5 0.5 1.0 0.1 0.093 -0.007 -0.039 0.062 0.059 0.004 0.233 0.95 0.34 0 0 0
#> 700 4 0.3 0.8 0.1 0.072 -0.028 -0.023 0.043 0.048 0.003 0.186 0.97 0.33 0 0 0
#> 700 4 0.3 1.0 0.1 0.105 0.005 0.011 0.048 0.049 0.002 0.192 0.98 0.58 0 0 0
#> 700 4 0.5 0.8 0.1 0.074 -0.026 -0.036 0.052 0.052 0.003 0.203 0.94 0.33 0 0 0
#> 700 4 0.5 1.0 0.1 0.102 0.002 -0.054 0.060 0.054 0.004 0.214 0.90 0.49 0 0 0
#> 700 5 0.3 0.8 0.1 0.083 -0.017 -0.025 0.046 0.045 0.002 0.176 0.92 0.39 0 0 0
#> 700 5 0.3 1.0 0.1 0.098 -0.002 0.018 0.041 0.046 0.002 0.179 0.98 0.59 0 0 0
#> 700 5 0.5 0.8 0.1 0.077 -0.023 -0.051 0.049 0.047 0.003 0.185 0.90 0.40 0 0 0
#> 700 5 0.5 1.0 0.1 0.099 -0.001 -0.030 0.051 0.049 0.003 0.193 0.92 0.57 0 0 0
# Performance measures for all parameters, for specific experimental condition
summary(out_preliminary, sample_size = 700, time_points = 4, ICC = .3, reliability = 1)
#>
#>
#>
#> Table: SUMMARY OF ANALYSIS
#>
#> Number of replications
#> ---------------------- -----------------------
#> Requested: 100
#> Completed: 100
#> Convergence issues: 0
#> Inadmissible results: 0
#>
#>
#>
#> Table: SUMMARY OF SIMULATION CONDITION
#>
#> Value
#> ----------------------------- ------
#> Skewness: 0
#> Kurtosis: 0
#> Constraints: none
#> Bounds: FALSE
#> Estimated measurement error: FALSE
#> Significance criterion: 0.05
#>
#>
#>
#> Table: SIMULATION RESULTS
#>
#> Population Avg Min SD SE Avg MSE Accuracy Cover Power NA
#> ----------- ----------- ------ ------- ------- ------- ------ --------- ------ ------ -----
#> RI_A~~RI_A 0.429 0.426 -0.003 0.261 0.058 0.055 0.003 0.217 0.97 1.00
#> RI_B~~RI_B 0.429 0.425 -0.003 0.269 0.071 0.062 0.005 0.243 0.89 1.00
#> RI_A~~RI_B 0.129 0.125 -0.004 0.012 0.055 0.045 0.003 0.178 0.89 0.69
#> wA2~wA1 0.200 0.200 0.000 0.051 0.060 0.054 0.004 0.210 0.94 0.94
#> wA2~wB1 0.150 0.150 0.000 0.017 0.053 0.051 0.003 0.200 0.93 0.85
#> wB2~wA1 0.100 0.105 0.005 0.011 0.048 0.049 0.002 0.192 0.98 0.58
#> wB2~wB1 0.300 0.299 -0.001 0.116 0.056 0.054 0.003 0.210 0.94 0.99
#> wA3~wA2 0.200 0.197 -0.003 0.013 0.055 0.054 0.003 0.213 0.97 0.95
#> wA3~wB2 0.150 0.160 0.010 0.020 0.054 0.052 0.003 0.205 0.93 0.85
#> wB3~wA2 0.100 0.101 0.001 -0.030 0.051 0.050 0.003 0.195 0.96 0.56
#> wB3~wB2 0.300 0.295 -0.005 0.139 0.063 0.056 0.004 0.218 0.92 1.00
#> wA4~wA3 0.200 0.196 -0.004 0.083 0.050 0.051 0.003 0.202 0.95 0.97
#> wA4~wB3 0.150 0.153 0.003 0.035 0.045 0.050 0.002 0.195 0.97 0.90
#> wB4~wA3 0.100 0.103 0.003 -0.018 0.048 0.047 0.002 0.185 0.94 0.56
#> wB4~wB3 0.300 0.302 0.002 0.176 0.053 0.051 0.003 0.199 0.92 1.00
#> wA1~~wA1 1.000 0.996 -0.004 0.844 0.081 0.074 0.006 0.288 0.90 1.00
#> wB1~~wB1 1.000 1.006 0.006 0.826 0.081 0.078 0.006 0.306 0.95 1.00
#> wA1~~wB1 0.300 0.302 0.002 0.174 0.063 0.057 0.004 0.224 0.92 1.00
#> wA2~~wA2 0.919 0.923 0.003 0.781 0.066 0.063 0.004 0.248 0.93 1.00
#> wA3~~wA3 0.919 0.918 -0.002 0.764 0.059 0.062 0.003 0.244 0.95 1.00
#> wA4~~wA4 0.919 0.915 -0.004 0.783 0.061 0.059 0.004 0.230 0.92 1.00
#> wB2~~wB2 0.882 0.872 -0.010 0.722 0.062 0.059 0.004 0.232 0.88 1.00
#> wB3~~wB3 0.882 0.892 0.010 0.763 0.057 0.059 0.003 0.233 0.98 1.00
#> wB4~~wB4 0.882 0.869 -0.013 0.730 0.057 0.055 0.003 0.215 0.95 1.00
#> wA2~~wB2 0.212 0.214 0.001 0.104 0.046 0.044 0.002 0.173 0.95 1.00
#> wA3~~wB3 0.212 0.214 0.001 0.123 0.043 0.044 0.002 0.172 0.97 1.00
#> wA4~~wB4 0.212 0.213 0.001 0.090 0.041 0.041 0.002 0.161 0.95 1.00