Calculate Net Spread for tows missing net width using a glm.
Source:R/calc_net_spread.R
calc_net_spread.RdThe Marport Deep Sea Technologies Inc. net mensuration system was used during the deployment of each tow to record net spread and net height. Net width was measured as the horizontal distance between two sensors attached immediately forward of the junction of the upper breastline and the dandyline, and net height was measured from the headrope center to the seafloor. A custom-made AFSC bottom contact sensor (accelerometer) attached to the center of the footrope was used to determine tow duration based on footrope contact with the seafloor. Mean calc_net_spread values for estimating area swept for the tow duration were calculated according to the methods described by Lauth and Kotwicki (2014).
Arguments
- dat
data.frame. This can be data from GIDES or race_data.hauls, including these columns: WIRE_OUT, NET_HEIGHT, NET_SPREAD
Details
In race_data, this will manifest as... net_mensuration_code = Net Mensuration Method 0* Unidentified method. Will make racebase.haul$net_mesured = "N". 1 Scanmar net mensuration - don't use, historical 2 NetMind net mensuration - don't use, historical 3 Furuno net mensuration - don't use, historical 4* Estimated from other hauls - when missing net spread, or spread and hieght. Will make racebase.haul$net_mesured = "N". Estimated using GLM 5* Estimated from warp angle - hopefully, will not come up and shouldn't be used 6 Marport net mensuration - shouldn't be there, indicates raw data 7* Marport with sequential outlier rejection, smoothed mean, and adjusted for MKII offset (see AFSC Proc. Report Lauth & Kotwicki 2014). Will make racebase.haul$net_mesured = "Y".
Examples
# Here is an example using 202101 Alaska Night Data from race_data.hauls:
path <- system.file("exdata/calc_net_spread/VESTY202101_RACE_DATA.HAULS.csv", package = "GAPsurvey")
dat <- read.csv(file=path, header=TRUE, sep=",", stringsAsFactors = FALSE)
dat <- dat[dat$PERFORMANCE >= 0 & dat$HAUL_TYPE == 3,]
dat[dat$NET_SPREAD_METHOD != 7, "NET_SPREAD"] <- NA # a normal net width
dat[dat$NET_HEIGHT_METHOD != 6, "NET_HEIGHT"] <- NA # a normal net height
calc_net_spread(dat)
#> $actions
#> method_col method_code n
#> 1 NET_SPREAD_METHOD 7 161
#> 2 NET_HEIGHT_METHOD 4 5
#> 3 NET_SPREAD_METHOD 4 27
#> desc
#> 1 There were 161 hauls successfully completed where the Marport sensor properly caclulated net mensuration using sequential outlier rejection, smoothed mean, and MKII offset adjustments (see AFSC Proc. Report Lauth & Kotwicki 2014).
#> 2 There were 5 missing net height values estimated by averaging the net height of tows with the same wire out scope.
#> 3 w ~ 20.835 - 559.975/s - 0.729 * h: For 27 hauls, the net width was estimated using a generalized linear model. The both predictor variables and their interaction were significant (P < 0.001)
#>
#> $glm_summary
#> est0 se tvalue prob var
#> (Intercept) 20.8345889 0.3579184 58.210439 1.179885e-108 (Intercept)
#> INVSCOPE -559.9746315 38.3797512 -14.590366 4.416356e-31 INVSCOPE
#> NET_HEIGHT -0.7289979 0.1812255 -4.022601 8.899050e-05 NET_HEIGHT
#> est case var0
#> (Intercept) 20.835 very signifcant (P < 0.001) intercept
#> INVSCOPE -559.975 very signifcant (P < 0.001) inversed scope
#> NET_HEIGHT -0.729 very signifcant (P < 0.001) net height
#>
#> $dat
#> HAUL NET_SPREAD NET_HEIGHT WIRE_OUT NET_SPREAD_METHODS NET_HEIGHT_METHOD
#> 2 3 14.83635 2.627722 137.16 4 4
#> 3 186 17.86335 2.209000 411.48 4 6
#> 4 185 17.92678 2.122000 411.48 4 6
#> 5 22 17.45922 2.230000 320.04 4 6
#> 6 39 17.19329 2.194773 274.32 4 4
#> 7 40 17.18947 2.200000 274.32 4 6
#> 8 62 15.00017 2.403000 137.16 4 6
#> 9 71 17.91849 1.600000 320.04 4 6
#> 10 73 18.04389 1.728000 365.76 4 6
#> 11 74 17.95641 1.848000 365.76 4 6
#> 12 75 17.97999 2.049000 411.48 4 6
#> 13 76 18.25751 1.855000 457.20 4 6
#> 14 78 18.22639 1.711000 411.48 4 6
#> 15 93 14.83635 2.627722 137.16 4 4
#> 16 96 14.83635 2.627722 137.16 4 4
#> 17 124 17.07211 2.361000 274.32 4 6
#> 18 125 16.52242 2.555000 228.60 4 6
#> 19 127 17.16177 2.238000 274.32 4 6
#> 20 130 16.54429 2.525000 228.60 4 6
#> 21 132 16.52752 2.548000 228.60 4 6
#> 22 155 17.75375 2.126000 365.76 4 6
#> 23 157 17.65096 2.267000 365.76 4 6
#> 24 158 17.80186 2.060000 365.76 4 6
#> 25 162 17.70928 2.187000 365.76 4 6
#> 26 175 17.38997 2.325000 320.04 4 6
#> 27 179 17.80649 2.287000 411.48 4 6
#> 28 180 17.82472 2.262000 411.48 4 6
#> 39 153 17.17460 2.343000 365.76 7 6
#> 40 154 18.06890 2.172000 411.48 7 6
#> 41 156 17.26440 2.022000 365.76 7 6
#> 42 160 17.65670 2.246000 320.04 7 6
#> 43 161 17.72740 2.305000 365.76 7 6
#> 44 163 16.91930 2.240000 365.76 7 6
#> 45 164 17.80950 2.166000 365.76 7 6
#> 46 165 17.85030 2.193000 320.04 7 6
#> 47 166 17.89860 2.234000 320.04 7 6
#> 48 167 17.89930 2.080000 365.76 7 6
#> 49 168 17.82630 2.109000 365.76 7 6
#> 50 169 18.03190 2.031000 365.76 7 6
#> 51 170 17.90640 2.016000 320.04 7 6
#> 52 171 17.30380 2.134000 320.04 7 6
#> 53 172 17.63530 2.083000 320.04 7 6
#> 54 173 18.04400 2.273000 320.04 7 6
#> 55 174 17.73700 2.184000 320.04 7 6
#> 56 176 17.63460 2.077000 365.76 7 6
#> 57 177 18.20400 2.234000 365.76 7 6
#> 58 178 18.22860 2.259000 365.76 7 6
#> 59 181 19.17180 2.186000 411.48 7 6
#> 60 182 17.95510 1.952000 411.48 7 6
#> 61 183 18.34570 2.080000 411.48 7 6
#> 62 184 17.71440 2.077000 365.76 7 6
#> 63 187 17.69960 2.038000 365.76 7 6
#> 64 188 17.84100 2.052000 365.76 7 6
#> 65 189 18.36430 2.225000 320.04 7 6
#> 66 190 17.89060 2.090000 274.32 7 6
#> 67 191 18.83180 1.968000 274.32 7 6
#> 68 192 17.46850 2.266000 274.32 7 6
#> 69 193 17.28910 2.285000 228.60 7 6
#> 70 194 17.46780 2.278000 228.60 7 6
#> 71 195 17.40940 2.351000 228.60 7 6
#> 103 79 17.10380 1.842000 411.48 7 6
#> 104 80 17.27980 1.827000 320.04 7 6
#> 105 81 16.65240 1.903000 274.32 7 6
#> 106 82 15.83280 2.174000 228.60 7 6
#> 107 83 15.52930 2.013000 228.60 7 6
#> 108 84 16.48600 1.860000 228.60 7 6
#> 109 85 16.31050 1.917000 228.60 7 6
#> 110 86 15.99340 1.888000 182.88 7 6
#> 111 87 15.80450 2.250000 228.60 7 6
#> 112 88 15.71270 2.299000 182.88 7 6
#> 113 89 15.62770 2.401000 182.88 7 6
#> 114 90 15.07380 2.254000 137.16 7 6
#> 115 91 15.75430 2.166000 137.16 7 6
#> 116 92 15.45820 2.171000 137.16 7 6
#> 117 94 15.28100 2.148000 137.16 7 6
#> 118 95 15.35290 2.105000 137.16 7 6
#> 119 97 15.37120 2.454000 137.16 7 6
#> 120 98 15.82680 2.563000 137.16 7 6
#> 121 99 15.76280 1.911000 137.16 7 6
#> 122 100 16.43660 2.198000 182.88 7 6
#> 123 101 16.71100 2.107000 182.88 7 6
#> 124 102 14.94470 2.025000 182.88 7 6
#> 125 103 16.60100 2.058000 182.88 7 6
#> 126 104 17.11460 1.820000 228.60 7 6
#> 127 105 16.61830 2.025000 228.60 7 6
#> 128 106 16.68170 1.858000 228.60 7 6
#> 129 107 16.39690 1.914000 228.60 7 6
#> 130 108 16.23050 2.292256 228.60 7 4
#> 131 109 17.24800 2.105000 228.60 7 6
#> 132 110 16.16760 2.041000 228.60 7 6
#> 133 111 15.97460 2.093000 228.60 7 6
#> 134 112 15.52170 2.191000 228.60 7 6
#> 135 113 16.57390 2.099000 274.32 7 6
#> 136 114 15.57140 2.313000 228.60 7 6
#> 137 115 15.64320 2.316000 274.32 7 6
#> 138 117 16.55170 1.943000 274.32 7 6
#> 139 118 17.19710 1.905000 320.04 7 6
#> 140 119 16.47080 2.087000 228.60 7 6
#> 141 120 16.10490 2.134000 228.60 7 6
#> 142 121 15.67950 1.986000 182.88 7 6
#> 143 122 15.10710 2.223000 182.88 7 6
#> 144 123 15.97710 1.977000 228.60 7 6
#> 145 126 16.59840 2.554000 274.32 7 6
#> 146 128 17.23980 2.637000 274.32 7 6
#> 147 129 16.30940 2.701000 228.60 7 6
#> 148 131 17.07920 2.561000 228.60 7 6
#> 149 133 16.89850 2.652000 228.60 7 6
#> 150 134 16.34050 2.543000 228.60 7 6
#> 151 135 17.50510 2.864000 228.60 7 6
#> 152 136 18.53180 2.660000 228.60 7 6
#> 153 137 16.72080 3.029000 228.60 7 6
#> 154 138 16.98170 2.740000 228.60 7 6
#> 155 139 16.60090 2.868000 228.60 7 6
#> 156 140 16.38400 2.904000 228.60 7 6
#> 157 142 18.23070 2.173000 274.32 7 6
#> 158 143 18.24750 2.116000 274.32 7 6
#> 159 144 16.85120 2.248000 274.32 7 6
#> 160 145 18.21740 2.235000 274.32 7 6
#> 161 146 18.09960 2.209000 274.32 7 6
#> 162 147 17.75310 2.054000 274.32 7 6
#> 163 148 18.15540 2.120000 320.04 7 6
#> 164 149 17.98690 2.092000 320.04 7 6
#> 165 150 17.93780 2.021000 320.04 7 6
#> 166 151 19.46440 2.002000 320.04 7 6
#> 167 4 15.25780 2.467000 182.88 7 6
#> 168 5 14.36380 2.934000 137.16 7 6
#> 169 6 15.27740 2.729000 182.88 7 6
#> 170 7 14.69230 3.084000 137.16 7 6
#> 171 8 14.67880 2.706000 137.16 7 6
#> 172 9 14.93400 2.718000 137.16 7 6
#> 173 10 15.27550 2.681000 137.16 7 6
#> 174 11 15.96140 2.429000 182.88 7 6
#> 175 12 14.74250 2.874000 137.16 7 6
#> 176 13 13.82240 2.893000 137.16 7 6
#> 177 14 15.12340 2.695000 182.88 7 6
#> 178 15 14.93340 2.822000 137.16 7 6
#> 179 16 15.26170 2.688000 182.88 7 6
#> 180 17 15.65620 2.489000 182.88 7 6
#> 181 19 15.36790 2.572000 182.88 7 6
#> 182 20 15.23390 2.694000 182.88 7 6
#> 183 21 16.17880 2.467000 228.60 7 6
#> 184 23 16.68610 2.427000 274.32 7 6
#> 185 24 16.41350 2.445000 228.60 7 6
#> 186 25 15.51110 2.530000 182.88 7 6
#> 187 26 15.70080 2.379000 182.88 7 6
#> 188 27 15.82300 2.555000 137.16 7 6
#> 189 28 16.42010 2.439000 182.88 7 6
#> 190 29 14.76970 2.709000 137.16 7 6
#> 191 30 12.79130 3.414000 137.16 7 6
#> 192 31 15.49810 2.530000 137.16 7 6
#> 193 32 15.18150 2.869000 137.16 7 6
#> 194 33 15.37120 2.792000 137.16 7 6
#> 195 34 15.94770 2.386000 137.16 7 6
#> 196 35 16.50700 2.606000 182.88 7 6
#> 197 36 16.26920 2.395000 182.88 7 6
#> 198 37 16.64200 2.406000 228.60 7 6
#> 199 38 16.52550 2.492000 228.60 7 6
#> 200 41 17.08090 2.333000 320.04 7 6
#> 201 42 16.89840 2.316000 320.04 7 6
#> 202 43 16.18790 2.308000 320.04 7 6
#> 203 44 17.22730 2.374000 320.04 7 6
#> 204 45 17.56640 2.306000 274.32 7 6
#> 205 46 16.68040 2.226000 274.32 7 6
#> 206 47 16.17150 2.412000 228.60 7 6
#> 207 48 16.42820 2.348000 228.60 7 6
#> 208 49 15.92490 2.410000 182.88 7 6
#> 209 50 15.71800 2.559000 182.88 7 6
#> 210 51 15.87330 2.437000 182.88 7 6
#> 211 52 15.10380 2.709000 137.16 7 6
#> 212 53 14.66880 2.792000 137.16 7 6
#> 213 54 15.05630 2.662000 137.16 7 6
#> 214 55 13.95290 3.082000 137.16 7 6
#> 215 56 14.57480 2.768000 137.16 7 6
#> 216 57 14.30510 2.963000 137.16 7 6
#> 217 58 12.56630 3.249000 137.16 7 6
#> 218 59 14.91530 2.698000 137.16 7 6
#> 219 60 15.28120 2.655000 137.16 7 6
#> 220 61 15.22370 2.883000 137.16 7 6
#> 221 63 15.58820 1.933000 137.16 7 6
#> 222 64 15.92360 2.062000 137.16 7 6
#> 223 65 16.76920 1.860000 182.88 7 6
#> 224 66 17.35500 1.719000 228.60 7 6
#> 225 67 17.90080 1.861000 228.60 7 6
#> 226 68 17.36020 1.761000 228.60 7 6
#> 227 69 17.46470 1.820000 228.60 7 6
#> 228 70 16.92950 1.716000 274.32 7 6
#> 229 72 17.78080 1.740000 320.04 7 6
#> 230 77 18.56800 1.814000 411.48 7 6
#> WIRE_OUT_METHODS
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#>