# Diagnostic of assessments in MSE: did Assess models converge during MSE?

Source:`R/diagnostic.R`

`diagnostic.Rd`

Diagnostic check for convergence of Assess models during closed-loop simulation. Use when the MP was
created with make_MP with argument `diagnostic = "min"`

or `"full"`

.
This function summarizes and plots the diagnostic information.

## Arguments

- MSE
An object of class MSE created by

`MSEtool::runMSE()`

.- MP
Optional, a character vector of MPs that use assessment models.

- gradient_threshold
The maximum magnitude (absolute value) desired for the gradient of the likelihood.

- figure
Logical, whether a figure will be drawn.

- ...
Arguments to pass to

`diagnostic`

.

## Value

A matrix with diagnostic performance of assessment models in the MSE. If `figure = TRUE`

,
a set of figures: traffic light (red/green) plots indicating whether the model converged (defined if a positive-definite
Hessian matrix was obtained), the optimizer reached pre-specified iteration limits (as passed to `stats::nlminb()`

),
and the maximum gradient of the likelihood in each assessment run. Also includes the number of optimization iterations
function evaluations reported by `stats::nlminb()`

for each application of the assessment model.

## Examples

```
# \donttest{
OM <- MSEtool::testOM; OM@proyears <- 20
myMSE <- runMSE(OM, MPs = "SCA_4010")
#> ✔ Checking MPs
#> ℹ Checking OM for completeness
#> ✔ Loading operating model
#> ✔ Optimizing for user-specified movement
#> ✔ Calculating MSY reference points for each year
#> ✔ Optimizing for user-specified depletion in last historical year
#> ✔ Calculating historical stock and fishing dynamics
#> ✔ Calculating per-recruit reference points
#> ✔ Calculating B-low reference points
#> ✔ Calculating reference yield - best fixed F strategy
#> ✔ Simulating observed data
#> ✔ Running forward projections
#> ✔ 1 / 1 Running MSE for SCA_4010
#>
|=== | 5 % ~22s
|====== | 11% ~10s
|======== | 16% ~07s
|=========== | 21% ~05s
|============== | 26% ~07s
|================ | 32% ~05s
|=================== | 37% ~04s
|====================== | 42% ~03s
|======================== | 47% ~04s
|=========================== | 53% ~03s
|============================= | 58% ~03s
|================================ | 63% ~02s
|=================================== | 68% ~03s
|===================================== | 74% ~02s
|======================================== | 79% ~02s
|=========================================== | 84% ~01s
|============================================= | 89% ~01s
|================================================ | 95% ~00s
|==================================================| 100% elapsed=08s
#>
diagnostic(myMSE)
#> ✔ Creating plots for MP:
#> SCA_4010
#> SCA_4010
#> Percent positive-definite Hessian 100
#> Percent iteration limit reached 0
#> Percent max. gradient < 0.1 80
#> Median iterations 753
#> Median function evaluations 1130
# How to get all the reporting
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
conv_statistics <- lapply(1:myMSE@nMPs, function(m) {
lapply(1:myMSE@nsim, function(x) {
myMSE@PPD[[m]]@Misc[[x]]$diagnostic %>%
mutate(MP = myMSE@MPs[m], Simulation = x)
}) %>% bind_rows()
}) %>% bind_rows()
# }
```