r lifecycle::badge("stable")
Compare parameters, convergence, -2*log likelihood, AIC and bias and imprecision of population and posterior predictions.
Arguments
- x
The first PM_result object you wish to compare. Unlike the legacy PMcompare this function only uses objects already loaded with PM_load. This will serve as the reference output for P-value testing (see details).
- y
The second PM_result object to compare.
- ...
Additional PM_result objects to compare. See details. Also, parameters to be passed to plot.PM_op if
plotis true as well as to mtsknn.eq. Order does not matter.- icen
Can be either "median" for the predictions based on medians of
pred.typeparameter value distributions, or "mean". Default is "median".- outeq
Number of the output equation to compare; default is 1
- plot
Boolean operator selecting whether to generate observed vs. predicted plots for each data object as in plot.PM_op.
Value
A data frame with the following objects for each model to analyze:
- run
The run number of the data
- type
NPAG or IT2B data
- nsub
Number of subjects in the model
- nvar
Number of random parameters in the model
- par
Names of random parameters
- cycles
Number of cycles run
- converge
Boolean value if convergence occurred.
- ll
Final cycle -2*Log-likelihood
- aic
Final cycle Akaike Information Criterion
- bic
Final cycle Bayesian (Schwartz) Information Criterion
- popBias
Bias, or mean weighted prediction error of predictions based on population parameters minus observations
- popImp
Imprecision, or bias-adjusted mean weighted squared error of predictions based on population parameters minus observations
- popPerRMSE
Percent root mean squared error of predictions based on population parameters minus observations
- postBias
Bias, or mean weighted prediction error of predictions - observations based on posterior parameters
- postImp
Imprecision, or bias-adjusted mean weighted squared error of predictions - observations based on posterior parameters
- postPerRMSE
Percent root mean squared error of predictions based on posterior parameters minus observations
- pval
P-value for each model compared to the first. See details.
Details
Objects can be specified separated by commas, e.g. PM_compare(run1, run2, run3)
followed by any arguments you wish to plot.PMop, mtsknn.eq.
P-values are based on comparison using the nearest neighbors
approach if all models are non-parametrics. Models may only be compared on
parameters that are included in the first model. The P-value is the
comparison between each model and the first model in
the list. Missing P-values are when a model has no parameter names
in common with the first model, and for the first model compared to itself,
or when models from IT2B runs are included. Significant P-values indicate
that the null hypothesis should be rejected, i.e. the joint distributions
between the two compared models for that parameter are significantly different.