Example MSE object used in the vignette

Description

A dummy example MSE object, with customized stock and fleet, all MPs, and 16 simulations.

Usage

data("ourMSE")

Format

The format is: Formal class 'MSE' [package "DLMtool"] with 17 slots ..@ Name : chr "Stock:Snapper Fleet:Generic_FlatE Observation model:Imprecise_Biased" ..@ nyears : num 50 ..@ proyears: num 20 ..@ nMPs : int 63 ..@ MPs : chr [1:63] "AvC" "BK" "BK_CC" "BK_ML" ... ..@ nsim : num 16 ..@ OM :'data.frame': 16 obs. of 34 variables: .. ..$ RefY : num [1:16] 50.2 120.1 50.8 67.8 78.4 ... .. ..$ M : num [1:16] 0.213 0.239 0.217 0.243 0.241 ... .. ..$ Depletion : num [1:16] 0.311 0.257 0.257 0.199 0.13 ... .. ..$ A : num [1:16] 144.4 656.6 191.9 72.2 146.1 ... .. ..$ BMSY_B0 : num [1:16] 0.325 0.325 0.325 0.296 0.306 ... .. ..$ FMSY_M : num [1:16] 0.579 0.564 0.556 0.664 0.62 ... .. ..$ Mgrad : num [1:16] -0.1677 -0.0985 -0.1874 0.1456 0.0794 ... .. ..$ Msd : num [1:16] 0.0313 0.0253 0.015 0.0364 0.011 ... .. ..$ procsd : num [1:16] 0.475 0.415 0.331 0.343 0.436 ... .. ..$ Esd : num [1:16] 0.293 0.13 0.372 0.106 0.312 ... .. ..$ dFfinal : num [1:16] -0.009183 -0.007048 -0.006787 -0.003407 -0.000569 ... .. ..$ MSY : num [1:16] 55.2 51.1 39.2 48.7 51.8 ... .. ..$ qinc : num [1:16] -0.686 -0.127 1.558 0.78 0.692 ... .. ..$ qcv : num [1:16] 0.159 0.298 0.187 0.241 0.102 ... .. ..$ FMSY : num [1:16] 0.123 0.135 0.121 0.161 0.149 ... .. ..$ Linf : num [1:16] 91.1 89.7 88.9 91.2 89.9 ... .. ..$ K : num [1:16] 0.198 0.205 0.179 0.199 0.21 ... .. ..$ t0 : num [1:16] -0.0367 -0.0296 -0.0333 -0.0322 -0.0319 ... .. ..$ hs : num [1:16] 0.856 0.875 0.856 0.937 0.94 ... .. ..$ Linfgrad : num [1:16] -0.185 0.0209 -0.1721 -0.185 -0.1004 ... .. ..$ Kgrad : num [1:16] -0.0662 0.0667 0.1438 0.0726 0.1547 ... .. ..$ Linfsd : num [1:16] 0.01334 0.01203 0.02384 0.01377 0.00404 ... .. ..$ recgrad : num [1:16] -0.731 2.751 -4.075 -0.234 -1.097 ... .. ..$ Ksd : num [1:16] 0.00443 0.01185 0.01474 0.00921 0.02128 ... .. ..$ ageM : num [1:16] 2.26 1.97 2.25 2.51 2.25 ... .. ..$ V26 : num [1:16] 12.5 10.3 10.8 13.7 11.1 ... .. ..$ V27 : num [1:16] 32 27 31.8 34.8 30.8 ... .. ..$ V28 : num [1:16] 0.798 0.681 0.896 0.687 0.902 ... .. ..$ LFC : num [1:16] 13.4 13.3 10.9 12.1 12.6 ... .. ..$ OFLreal : num [1:16] 51.4 28.4 31.1 40 22.3 ... .. ..$ Spat_targ : num [1:16] 1 1 1 1 1 1 1 1 1 1 ... .. ..$ Frac_area_1 : num [1:16] 0.136 0.138 0.145 0.07 0.133 ... .. ..$ Prob_staying: num [1:16] 0.884 0.911 0.651 0.427 0.658 ... .. ..$ AC : num [1:16] 0.569 0.717 0.763 0.572 0.8 ... ..@ Obs :'data.frame': 16 obs. of 25 variables: .. ..$ Cbias : num [1:16] 0.659 1.007 0.745 1.866 1.27 ... .. ..$ Csd : num [1:16] 0.202 0.311 0.554 0.575 0.589 ... .. ..$ CAA_nsamp : num [1:16] 88 59 86 71 97 81 87 80 97 99 ... .. ..$ CAA_ESS : num [1:16] 13 19 18 19 13 12 19 15 15 18 ... .. ..$ CAL_nsamp : num [1:16] 77.1 71.2 72.3 77.8 74 ... .. ..$ CAL_ESS : num [1:16] 19 19 16 17 15 19 15 14 14 19 ... .. ..$ Isd : num [1:16] 0.512 0.289 0.439 0.543 0.352 ... .. ..$ Dbias : num [1:16] 0.621 2.023 0.765 1.325 0.648 ... .. ..$ Derr : num [1:16] 0.0663 0.0647 0.0921 0.1788 0.1261 ... .. ..$ Mbias : num [1:16] 1.108 1.769 1.271 0.901 0.397 ... .. ..$ FMSY_Mbias : num [1:16] 1.208 0.98 1.189 2.058 0.583 ... .. ..$ BMSY_B0bias: num [1:16] 0.897 0.947 0.998 0.942 0.953 ... .. ..$ lenMbias : num [1:16] 1.087 0.898 1.067 0.821 0.842 ... .. ..$ LFCbias : num [1:16] 0.939 0.96 0.896 1.042 0.903 ... .. ..$ LFSbias : num [1:16] 0.977 1.006 0.926 1.252 0.76 ... .. ..$ Abias : num [1:16] 0.331 2.987 0.272 0.231 1.854 ... .. ..$ Aerr : num [1:16] 0.449 0.271 0.262 0.302 0.39 ... .. ..$ Kbias : num [1:16] 0.899 1.166 1.03 1.157 0.841 ... .. ..$ t0bias : num [1:16] 1.019 1 0.982 0.954 1.096 ... .. ..$ Linfbias : num [1:16] 0.925 0.927 0.929 1.038 0.971 ... .. ..$ hbias : num [1:16] 1.021 0.977 1.029 0.983 0.969 ... .. ..$ Irefbias : num [1:16] 1.264 0.718 1.281 0.902 0.952 ... .. ..$ Crefbias : num [1:16] 0.839 0.576 0.921 0.908 1.1 ... .. ..$ Brefbias : num [1:16] 0.655 1.141 1.095 0.725 1.58 ... .. ..$ betas : num [1:16] 0.389 0.521 0.366 1.689 0.357 ... ..@ B_BMSY : num [1:16, 1:63, 1:20] 1.096 0.472 0.956 0.828 0.371 ... ..@ F_FMSY : num [1:16, 1:63, 1:20] 0.447 1.344 0.802 2.214 2.207 ... ..@ B : num [1:16, 1:63, 1:20] 579 223 346 305 144 ... ..@ FM : num [1:16, 1:63, 1:20] 0.0552 0.1816 0.0969 0.3566 0.3297 ... ..@ C : num [1:16, 1:63, 1:20] 29.5 38 33.5 116.3 54 ... ..@ TAC : num [1:16, 1:63, 1:20] 29.5 38 33.5 116.3 54 ... ..@ SSB_hist: num [1:16, 1:34, 1:50, 1:2] 0.0125 0.0513 0.0463 0.0164 0.0113 ... ..@ CB_hist : num [1:16, 1:34, 1:50, 1:2] 0 0 0 0 0 0 0 0 0 0 ... ..@ FM_hist : num [1:16, 1:34, 1:50, 1:2] 0 0 0 0 0 0 0 0 0 0 ...

Examples

data(ourMSE)

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)

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> 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/ourMSE.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ourMSE
> ### Title: Example MSE object used in the vignette
> ### Aliases: ourMSE
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(ourMSE)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>