## Details

Contains the results of makeFinal, which is a list suitable for analysis and plotting of final cycle population values.

However, if you wish to manipulate the entire data frame,
use the `data`

field, e.g. `probs <- run1$final$data$popPoints %>% select(prob)`

.
This will select the probabilities of the support points. If
you are unfamiliar with the `%>%`

pipe function, please type `help("%>%", "magrittr")`

into the R console and look online for instructions/tutorials in tidyverse, a
powerful approach to data manipulation upon which Pmetrics is built.

## Public fields

`popPoints`

(NPAG only) Data frame of the final cycle joint population density of grid points with column names equal to the name of each random parameter plus

*prob*for the associated probability of that point`popMean`

The final cycle mean for each random parameter distribution

`popSD`

The final cycle standard deviation for each random parameter distribution

`popCV`

The final cycle coefficient of variation (SD/Mean) for each random parameter distribution

`popVar`

The final cycle variance for each random parameter distribution

`popCov`

The final cycle random parameter covariance matrix

`popCor`

The final cycle random parameter correlation matrix

`popMedian`

The final cycle median values for each random parameter, i.e. those that have unknown mean and unknown variance, both of which are fitted during the run

`popRanFix`

The final cycle median values for each parameter that is random but fixed to be the same for all subjects, i.e. unknown mean, zero variance, with only mean fitted in the run

`postPoints`

(NPAG only) Data frame of posterior population points for each of the first 100 subject, with columns id, point, parameters and probability. The first column is the subject, the second column has the population point number, followed by the values for the parameters in that point and the probability.

`postMean`

A

*nsub*x*npar*data frame containing the means of the posterior distributions for each parameter.`postSD`

A

*nsub*x*npar*data frame containing the SDs of the posterior distributions for each parameter.`postVar`

A

*nsub*x*npar*data frame containing the variances of the posterior distributions for each parameter.`postCov`

NPAG only: An array of dimensions

*npar*x*npar*x*nsub*that contains the covariances of the posterior distributions for each parameter and subject.*`postCor`

NPAG only: An array of dimensions

*npar*x*npar*x*nsub*that contains the correlations of the posterior distributions for each parameter and subject.`postMed`

A

*nsub*x*npar*data frame containing the medians of the posterior distributions for each parameter.*`shrinkage`

A data frame with the shrinkage for each parameter.

`popVar`

is comprised of variance(EBE) + variance(EBD), where EBE is the Empirical Bayes Estimate or mean of the posterior distribution for the parameter. EBD is the Empirical Bayes Distribution, or the full Bayesian posterior distribution. In other words, if Bayesian posterior distributions are wide for a given parameter due to sparse or uninformative sampling, then most of the population variance is due to this variance and shrinkage of the EBE variance is high because individual posterior estimates shrink towards the population mean.`gridpts`

(NPAG only) Initial number of support points

`nsub`

Number of subjects

`ab`

Matrix of boundaries for random parameter values

`data`

A data frame combining all the above fields as its columns

## Methods

### Method `new()`

Create new object populated with final cycle information

#### Usage

`PM_final$new(final)`

#### Arguments

`final`

The parsed output from makeFinal.

### Method `summary()`

Summary method

#### Arguments

`...`

Arguments passed to summary.PMfinal

#### Details

See summary.PMfinal.