## Details

This contains the output of makeCycle after a run, which generates information suitable for analysis and plotting of cycle information. Each field corresponds to a column in the complete data frame.

To manipulate the entire data frame,
use the `data`

field, e.g. `final <- run1$cycle$data %>% slice_tail(n=1)`

. 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

`names`

Vector of names of the random parameters

`cycnum`

Vector cycle numbers, which may start at numbers greater than 1 if a non-uniform prior was specified for the run (NPAG only)

`ll`

Vector of -2*Log-likelihood at each cycle

`gamlam`

A tibble of cycle number and gamma or lambda at each cycle for each output equation

`mean`

A tibble of cycle number and the mean of each random parameter at each cycle, normalized to initial mean

`median`

A tibble of cycle number and the median of each random parameter at each cycle, normalized to initial median

`sd`

A tibble of cycle number and the standard deviation of each random parameter at each cycle, normalized to initial standard deviation

`aic`

A vector of Akaike Information Criterion at each cycle

`bic`

A vector of Bayesian (Schwartz) Information Criterion at each cycle

`data`

A data frame combining all the above fields as its columns

## Methods

### Method `new()`

Create new object populated with cycle information

#### Usage

`PM_cycle$new(cycle)`

#### Arguments

`cycle`

The parsed output from makeCycle.

### Method `plot()`

Plot method

#### Arguments

`...`

Arguments passed to plot.PM_cycle

#### Details

See plot.PM_cycle.