This is a simple function to grab the residuals of a linear model.

grab_resids(lm_obj)

Arguments

lm_obj

the linear model object created by the lm() function.

Examples

library(palmerpenguins) fit <- lm(bill_length_mm ~ ., data = penguins[, -8]) grab_resids(fit)
#> 1 2 3 5 6 7 #> -0.37428738 2.06412394 2.65477581 -1.41371500 -1.26080756 1.87925818 #> 8 13 14 15 16 17 #> -2.51410782 4.52941922 -2.37256702 -7.47570053 -0.77882023 0.53797192 #> 18 19 20 21 22 23 #> 0.50575778 -2.68471463 4.29719335 1.22944162 -1.67036139 -2.41228497 #> 24 25 26 27 28 29 #> -1.71447856 -0.33548976 -2.78356977 1.10596784 3.36550148 1.64792180 #> 30 31 32 33 34 35 #> 0.69127063 3.79940081 -1.67183077 2.72205947 1.37209065 -0.64922057 #> 36 37 38 39 40 41 #> -2.09063574 -1.52822242 5.49547978 0.85585365 -0.59854469 0.23956810 #> 42 43 44 45 46 47 #> 0.66059798 -0.63376186 2.96013544 1.02463438 -1.06985263 2.19385526 #> 49 50 51 52 53 54 #> -1.10862196 1.32558259 2.27069207 -0.64101987 -2.61243683 0.51766027 #> 55 56 57 58 59 60 #> -2.37043204 1.18981102 1.67679269 0.08309600 0.55014910 -3.02232871 #> 61 62 63 64 65 66 #> -0.94128076 -0.69536065 0.44235144 0.56258809 0.09204279 1.23078897 #> 67 68 69 70 71 72 #> -1.83951600 0.51497977 -0.76186687 0.18140540 -4.47784299 -0.46950021 #> 73 74 75 76 77 78 #> 1.80185438 4.72519809 -2.13490452 1.87591348 3.40630756 -2.85462800 #> 79 80 81 82 83 84 #> -0.64409476 1.26567845 -2.33511219 1.69095881 -1.22607141 -5.90214215 #> 85 86 87 88 89 90 #> 0.26025483 1.03681560 -3.71578705 -0.40386745 -1.71842918 1.35867460 #> 91 92 93 94 95 96 #> -2.37784066 -0.07403902 -2.46776702 -0.42025495 -0.35834836 -0.82234639 #> 97 98 99 100 101 102 #> 0.51074180 -0.42502689 -2.04196312 3.02216849 -3.04813479 -1.56989722 #> 103 104 105 106 107 108 #> 1.54888670 -3.35623782 0.43667372 0.04650971 0.25051816 -2.57800388 #> 109 110 111 112 113 114 #> 1.67866379 1.29228199 -0.05080576 3.90607011 2.21009310 0.68228109 #> 115 116 117 118 119 120 #> 0.59012874 1.82847608 2.11847945 -3.98913772 -1.33707840 1.56100715 #> 121 122 123 124 125 126 #> -0.54256463 -3.01222770 3.95519667 0.39636464 -0.97436945 -0.48128281 #> 127 128 129 130 131 132 #> 1.52505546 0.58201410 2.11854096 2.55755963 1.15007805 2.63743149 #> 133 134 135 136 137 138 #> -0.75082871 -3.56788165 1.31636703 1.57748652 -1.16042713 -0.87787685 #> 139 140 141 142 143 144 #> 0.71263510 -0.52878672 3.17817642 1.83474286 -3.71623196 1.51693859 #> 145 146 147 148 149 150 #> 0.86990191 -0.26686528 -1.03148465 -0.27043135 -1.42484029 -1.94858656 #> 151 152 153 154 155 156 #> -0.93048168 0.80692170 1.19393737 -0.49868551 3.64662467 0.66313656 #> 157 158 159 160 161 162 #> -0.99442257 1.51895996 -0.25120144 -2.19585333 -1.41961569 -1.79485865 #> 163 164 165 166 167 168 #> -4.52626920 -0.30685223 0.07373080 -0.57227623 0.86645667 -0.28004174 #> 169 170 171 172 173 174 #> -2.54877268 -0.99303570 0.71737972 -0.50559076 1.13373974 -1.01429638 #> 175 176 177 178 180 181 #> 1.17261880 -2.30705990 -2.79335809 -2.35062417 -1.21492477 2.92439037 #> 182 183 184 185 186 187 #> 0.65665566 -1.85765796 -2.48435238 -0.51427324 8.51261140 2.38711695 #> 188 189 190 191 192 193 #> -1.08191429 -3.08121266 -5.15122717 -0.65645924 0.28360609 -1.55425890 #> 194 195 196 197 198 199 #> -0.46219910 0.52899302 1.48841972 0.83773939 -2.36434156 0.77692581 #> 200 201 202 203 204 205 #> 0.79206844 -0.82304483 -3.67722700 1.08429029 -0.21237300 0.01045703 #> 206 207 208 209 210 211 #> 1.09493899 0.38532334 -3.83307716 -0.89262689 -3.15877566 -1.83512927 #> 212 213 214 215 216 217 #> 0.77962737 0.74550698 -2.82216623 0.48376385 3.96649296 -0.17692308 #> 218 220 221 222 223 224 #> -0.84902061 -1.00742826 -2.54618015 1.23908551 1.63627855 -2.50843486 #> 225 226 227 228 229 230 #> -0.81650170 -0.05914526 0.39031197 -1.91655129 2.32371446 0.96968467 #> 231 232 233 234 235 236 #> -0.43366014 -5.11412014 3.59872548 3.10526436 1.76059162 0.41535893 #> 237 238 239 240 241 242 #> -0.52588893 0.24722526 -2.45973321 2.69607412 1.87889348 1.55294561 #> 243 244 245 246 247 248 #> 1.08163073 1.92349298 -0.13635807 -0.46897563 -1.44307309 1.29907909 #> 249 250 251 252 253 254 #> 0.85876658 0.40592066 3.55210613 1.37376658 2.12044050 5.43742633 #> 255 256 258 259 260 261 #> 0.81205012 -0.65316642 -2.38356174 -3.80394455 4.02961104 -1.71987772 #> 262 263 264 265 266 267 #> -0.36734911 4.10597741 -0.77932746 -2.30801744 1.21744817 0.74287531 #> 268 270 271 273 274 275 #> 4.52941529 -1.43876245 1.47654698 0.90793792 0.58173975 -1.01285991 #> 276 277 278 279 280 281 #> 0.96301924 -0.63667121 -0.37489801 1.49324137 -1.72719580 2.35476083 #> 282 283 284 285 286 287 #> -2.80844739 0.11289381 1.40881645 -2.34754708 0.88245345 -0.90006178 #> 288 289 290 291 292 293 #> 1.35816352 0.31239673 1.53765467 -0.93867103 -0.41335064 0.35391086 #> 294 295 296 297 298 299 #> 11.43908990 -0.75459279 -1.25510387 -3.90250815 -0.38693805 -2.55111355 #> 300 301 302 303 304 305 #> 0.57100706 -0.42830874 1.43601293 2.76700397 -0.89352434 -0.85348080 #> 306 307 308 309 310 311 #> 0.94901880 -5.17531407 2.65567801 -3.76748135 0.13463797 -0.01083723 #> 312 313 314 315 316 317 #> -0.22300083 -0.24294444 -0.67790280 1.01873478 1.73311924 -2.39853041 #> 318 319 320 321 322 323 #> -0.63221804 1.02360402 -1.64309630 3.29718354 -0.21488077 3.20990977 #> 324 325 326 327 328 329 #> -2.94534551 2.69138625 2.23907152 1.11865158 0.77511833 -1.43359299 #> 330 331 332 333 334 335 #> -0.38193180 -3.94788347 2.45261485 -1.20637577 -1.84206584 -0.27190444 #> 336 337 338 339 340 341 #> -2.15320737 0.77849787 0.12993803 -1.53593949 4.46500632 -4.28130911 #> 342 343 344 #> -0.04117198 -0.61029557 2.11006640