Example MSE object used in the vignette

Description

A dummy example MSE object, with blue shark, generic fleet and imprecise and biased observation model, four MPs, and 16 simulations.

Usage

data("SnapMSE")

Format

The format is: Formal class 'MSE' [package "DLMtool"] with 17 slots ..@ Name : chr "Stock:Blue_shark Fleet:Generic_fleet Observation model:Imprecise_Biased" ..@ nyears : num 50 ..@ proyears: num 30 ..@ nMPs : int 4 ..@ MPs : chr [1:4] "Fratio" "DCAC" "Fdem" "DD" ..@ nsim : num 16 ..@ OM :'data.frame': 16 obs. of 34 variables: .. ..$ RefY : num [1:16] 15916 7401 10480 2887 8534 ... .. ..$ M : num [1:16] 0.172 0.175 0.167 0.242 0.185 ... .. ..$ Depletion : num [1:16] 0.397 0.555 0.361 0.338 0.57 ... .. ..$ A : num [1:16] 63144 1191 34716 118 4138 ... .. ..$ BMSY_B0 : num [1:16] 0.38 0.346 0.304 0.337 0.349 ... .. ..$ FMSY_M : num [1:16] 0.497 0.577 0.83 0.423 0.478 ... .. ..$ Mgrad : num [1:16] -0.1167 -0.2097 -0.0849 0.197 -0.0967 ... .. ..$ Msd : num [1:16] 0.06411 0.08061 0.09191 0.00586 0.04776 ... .. ..$ procsd : num [1:16] 0.248 0.213 0.152 0.233 0.25 ... .. ..$ Esd : num [1:16] 0.315 0.398 0.214 0.333 0.38 ... .. ..$ dFfinal : num [1:16] 0.00832 -0.00412 0.00492 0.01095 -0.00757 ... .. ..$ MSY : num [1:16] 2573 3845 4674 2884 4262 ... .. ..$ qinc : num [1:16] -0.331 0.525 -1.985 0.71 0.915 ... .. ..$ qcv : num [1:16] 0.181 0.218 0.151 0.251 0.133 ... .. ..$ FMSY : num [1:16] 0.0855 0.1011 0.1387 0.1025 0.0882 ... .. ..$ Linf : num [1:16] 197 196 201 197 201 ... .. ..$ K : num [1:16] 0.226 0.232 0.239 0.238 0.218 ... .. ..$ t0 : num [1:16] -1.032 -1.027 -0.971 -1.011 -0.959 ... .. ..$ hs : num [1:16] 0.487 0.647 0.769 0.656 0.638 ... .. ..$ Linfgrad : num [1:16] 0.1212 -0.0666 -0.1746 -0.1365 0.1226 ... .. ..$ Kgrad : num [1:16] 0.0116 0.1432 -0.2061 -0.1219 -0.0717 ... .. ..$ Linfsd : num [1:16] 0.0044 0.01507 0.02219 0.01226 0.00046 ... .. ..$ recgrad : num [1:16] -8.58 1.41 1.21 -1.32 9.24 ... .. ..$ Ksd : num [1:16] 0.0206 0.0245 0.0159 0.0173 0.0198 ... .. ..$ ageM : num [1:16] 4.37 3.81 3.8 4.33 4.12 ... .. ..$ V26 : num [1:16] 35.7 46 31.1 33.4 44 ... .. ..$ V27 : num [1:16] 147 103 117 108 117 ... .. ..$ V28 : num [1:16] 0.164 0.456 0.297 0.797 0.695 ... .. ..$ LFC : num [1:16] 55.9 48.9 60 52.2 58 ... .. ..$ OFLreal : num [1:16] 2035 4356 5837 3427 4114 ... .. ..$ Spat_targ : num [1:16] 1 1 1 1 1 1 1 1 1 1 ... .. ..$ Frac_area_1 : num [1:16] 0.0952 0.1028 0.0983 0.0997 0.1023 ... .. ..$ Prob_staying: num [1:16] 0.85 0.833 0.879 0.878 0.829 ... .. ..$ AC : num [1:16] 0.12 0.705 0.611 0.146 0.639 ... ..@ Obs :'data.frame': 16 obs. of 25 variables: .. ..$ Cbias : num [1:16] 0.604 1.099 0.787 1.049 0.999 ... .. ..$ Csd : num [1:16] 0.375 0.289 0.429 0.512 0.262 ... .. ..$ CAA_nsamp : num [1:16] 89 80 86 65 73 88 97 54 70 57 ... .. ..$ CAA_ESS : num [1:16] 14 13 17 13 15 20 15 12 15 19 ... .. ..$ CAL_nsamp : num [1:16] 77.3 70.6 78.5 96.8 73.9 ... .. ..$ CAL_ESS : num [1:16] 20 11 17 19 16 16 20 14 15 19 ... .. ..$ Isd : num [1:16] 0.274 0.341 0.47 0.528 0.342 ... .. ..$ Dbias : num [1:16] 0.762 1.424 1.64 0.697 0.599 ... .. ..$ Derr : num [1:16] 0.0747 0.0884 0.0918 0.0827 0.0928 ... .. ..$ Mbias : num [1:16] 0.749 0.727 2.711 0.437 0.981 ... .. ..$ FMSY_Mbias : num [1:16] 0.862 1.336 0.273 1.268 0.683 ... .. ..$ BMSY_B0bias: num [1:16] 0.685 0.941 1.192 1.006 1.446 ... .. ..$ lenMbias : num [1:16] 0.828 0.734 1.033 0.672 0.816 ... .. ..$ LFCbias : num [1:16] 0.922 0.881 1.1 0.993 1.093 ... .. ..$ LFSbias : num [1:16] 0.884 0.958 0.86 1 1.041 ... .. ..$ Abias : num [1:16] 0.538 3.303 3.903 0.969 0.975 ... .. ..$ Aerr : num [1:16] 0.311 0.288 0.393 0.317 0.21 ... .. ..$ Kbias : num [1:16] 1.265 0.905 1.052 0.974 0.899 ... .. ..$ t0bias : num [1:16] 1.043 0.956 1.091 0.994 1.103 ... .. ..$ Linfbias : num [1:16] 0.805 0.975 0.964 0.895 1.037 ... .. ..$ hbias : num [1:16] 0.89 0.945 0.921 1.19 1.007 ... .. ..$ Irefbias : num [1:16] 1.928 0.836 0.845 0.581 0.706 ... .. ..$ Crefbias : num [1:16] 0.876 1.052 0.792 0.553 0.672 ... .. ..$ Brefbias : num [1:16] 0.334 0.458 1.194 1.003 1.656 ... .. ..$ betas : num [1:16] 2.988 2.751 0.453 2.727 2.308 ... ..@ B_BMSY : num [1:16, 1:4, 1:30] 0.638 1.17 1.758 1.359 0.861 ... ..@ F_FMSY : num [1:16, 1:4, 1:30] 0.373 2.854 1.198 0.668 0.83 ... ..@ B : num [1:16, 1:4, 1:30] 34017 67403 97638 44286 49632 ... ..@ FM : num [1:16, 1:4, 1:30] 0.0319 0.2886 0.1661 0.0685 0.0732 ... ..@ C : num [1:16, 1:4, 1:30] 649 15647 11473 2852 3300 ... ..@ TAC : num [1:16, 1:4, 1:30] 649 15647 11473 2852 3300 ... ..@ SSB_hist: num [1:16, 1:46, 1:50, 1:2] 3.144 2.76 12.83 19.055 0.222 ... ..@ CB_hist : num [1:16, 1:46, 1:50, 1:2] 0 0 0 0 0 0 0 0 0 0 ... ..@ FM_hist : num [1:16, 1:46, 1:50, 1:2] 0 0 0 0 0 0 0 0 0 0 ...

Examples

data(SnapMSE)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(DLMtool)
Loading required package: snowfall
Loading required package: snow
Loading required package: boot
Loading required package: MASS
Loading required package: parallel

Attaching package: 'parallel'

The following objects are masked from 'package:snow':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, clusterSplit, makeCluster, parApply,
    parCapply, parLapply, parRapply, parSapply, splitIndices,
    stopCluster

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/DLMtool/SnapMSE.Rd_%03d_medium.png", width=480, height=480)
> ### Name: SnapMSE
> ### Title: Example MSE object used in the vignette
> ### Aliases: SnapMSE
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(SnapMSE)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>