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Generates summary statistics of final population model parameters.


# S3 method for PMfinal
summary(object, lower = 0.025, upper = 0.975, ...)



The PMfinal object made after an NPAG or IT2B, e.g. final.1 after run 1.


Desired lower confidence interval boundary. Default is 0.025. Ignored for IT2B objects.


Desired upper confidence interval boundary. Default is 0.975. Ignored for IT2B objects.


Not used.


The output is a data frame. For NPAG this has 4 columns:


The value of the summary statistic


The name of the parameter


Either WtMed for weighted median, or MAWD for MAWD (see details)


Requested lower, 0.5 (median), and upper quantiles

For IT2B this has 6 columns:


Parameter mean value


Standard error of the mean


Error of the mean divided by mean


Variance of the parameter values


Standard error of the variance


Name of the summary statistic


For NPAG runs, this function will generate weighted medians as central tendencies of the population points with a 95\ and the median absolute weighted deviation (MAWD) from the median as a measure of the variance, with its 95\ 95\ sample from a normal distribution. To estimate these non-parametric summaries, the function uses a Monte Carlo simulation approach, creating 1000 x npoint samples with replacement from the weighted marginal distribution of each parameter, where npoint is the number of support points in the model. As an example, if there are 100 support points, npoint = 100, and for Ka, there will be 1000 sets of 100 samples drawn from the weighted marginal distribution of the values for Ka. For each of the 1,000 sets of npoint values, the median and MAWD are calculated, with MAWD equal to the median absolute difference between each point and the median of that set. The output is npoint estimates of the weighted median and npoint estimates of the MAWD for each parameter, from which the median, 2.5th, and 97.5th percentiles can be found as point estimates and 95\ interval limits, respectively, of both the weighted median and MAWD.

For IT2B runs, the function will return the mean and variance of each parameter, and the standard errors of these terms, using SE (mean) = SD/sqrt(nsub) and SE (var) = var * sqrt(2/(nsub-1)).

See also


Michael Neely


#> Weighted Medians (95% credibility interval)
#> Ka: 0.7027 (0.5068 - 0.8484)
#> Ke: 0.0439 (0.0351 - 0.0652)
#> V: 72.4752 (64.6925 - 99.1551)
#> Tlag1: 1.1133 (0.7438 - 1.7913)
#> Median Absolute Weighed Differences (dispersion measure) (95% credibility interval)
#> Ka: 0.1653 (0.0486 - 0.3151)
#> Ke: 0.0148 (0.0047 - 0.0216)
#> V: 21.0102 (6.3874 - 37.2509)
#> Tlag1: 0.5253 (0.1913 - 0.8148)
#> <IT2B>
#>   Inherits from: <PM_final>
#>   Public:
#>     ab: 1e-08 1e-08 1e-08 1e-08 3.05586550968077 0.1250126778470 ...
#>     clone: function (deep = FALSE) 
#>     data: PMfinal, IT2B, list
#>     gridpts: NULL
#>     initialize: function (final) 
#>     nsub: 20
#>     plot: function (...) 
#>     popCV: data.frame
#>     popCor: data.frame
#>     popCov: data.frame
#>     popMean: data.frame
#>     popMedian: data.frame
#>     popPoints: NULL
#>     popRanFix: NULL
#>     popSD: data.frame
#>     popVar: data.frame
#>     postCor: NULL
#>     postCov: NULL
#>     postMean: data.frame
#>     postMed: data.frame
#>     postPoints: NULL
#>     postSD: data.frame
#>     postVar: data.frame
#>     shrinkage: data.frame
#>     summary: function (...)