gvf.misc.RdThese methods extract information from fitted GVF model(s).
# S3 method for gvf.fit coef(object, ...) # S3 method for gvf.fits coef(object, ...) # S3 method for gvf.fit.gr coef(object, ...) # S3 method for gvf.fits.gr coef(object, ...) # S3 method for gvf.fit residuals(object, ...) # S3 method for gvf.fits residuals(object, ...) # S3 method for gvf.fit.gr residuals(object, ...) # S3 method for gvf.fits.gr residuals(object, ...) # S3 method for gvf.fit fitted(object, ...) # S3 method for gvf.fits fitted(object, ...) # S3 method for gvf.fit.gr fitted(object, ...) # S3 method for gvf.fits.gr fitted(object, ...) # S3 method for gvf.fit predict(object, ...) # S3 method for gvf.fits predict(object, ...) # S3 method for gvf.fit.gr predict(object, ...) # S3 method for gvf.fits.gr predict(object, ...) # S3 method for gvf.fit effects(object, ...) # S3 method for gvf.fits effects(object, ...) # S3 method for gvf.fit.gr effects(object, ...) # S3 method for gvf.fits.gr effects(object, ...) # S3 method for gvf.fit rstandard(model, ...) # S3 method for gvf.fits rstandard(model, ...) # S3 method for gvf.fit.gr rstandard(model, ...) # S3 method for gvf.fits.gr rstandard(model, ...) # S3 method for gvf.fit rstudent(model, ...) # S3 method for gvf.fits rstudent(model, ...) # S3 method for gvf.fit.gr rstudent(model, ...) # S3 method for gvf.fits.gr rstudent(model, ...) # S3 method for gvf.fit anova(object, ...) # S3 method for gvf.fits anova(object, ...) # S3 method for gvf.fit.gr anova(object, ...) # S3 method for gvf.fits.gr anova(object, ...) # S3 method for gvf.fit vcov(object, ...) # S3 method for gvf.fits vcov(object, ...) # S3 method for gvf.fit.gr vcov(object, ...) # S3 method for gvf.fits.gr vcov(object, ...)
| object | An object containing one or more fitted GVF models (see ‘Usage’ for the allowed classes). |
|---|---|
| model | An object containing one or more fitted GVF models (see ‘Usage’ for the allowed classes). |
| ... | Further arguments passed to or from other methods (see corresponding .lm methods). |
These methods can be used to extract information from fitted GVF model(s).
For more details on their usage, please read the help pages of the methods with same name defined on class .lm by package stats (e.g. coef, fitted, etc.).
The requested information, wrapped into an R object whose structure depends on the class of the input fitted GVF model(s) (i.e. arguments object and/or model).
GVF.db to manage ReGenesees archive of registered GVF models, gvf.input and svystat to prepare the input for GVF model fitting, fit.gvf to fit GVF models, plot.gvf.fit to get diagnostic plots for fitted GVF models, drop.gvf.points to drop alleged outliers from a fitted GVF model and simultaneously refit it, and predictCV to predict CV values via fitted GVF models.
#> name Y SE CV VAR #> 1 ind 924101.3 17172.681 0.01858312 294900983 #> 2 sexf 470060.8 12796.386 0.02722283 163747492 #> 3 sexm 454040.5 11359.582 0.02501887 129040102 #> 4 marstatmarried 536680.2 13970.474 0.02603128 195174152 #> 5 marstatunmarried 312654.5 11180.844 0.03576102 125011282 #> 6 marstatwidowed 74766.6 5880.564 0.07865228 34581029summary(ee.AF)#> name Y SE CV #> age10c1 : 1 Min. : 0 Min. : 0 Min. :0.01858 #> age10c10: 1 1st Qu.: 4292 1st Qu.: 1212 1st Qu.:0.08230 #> age10c2 : 1 Median : 16633 Median : 2268 Median :0.14861 #> age10c3 : 1 Mean : 45014 Mean : 2989 Mean :0.22191 #> age10c4 : 1 3rd Qu.: 46046 3rd Qu.: 4006 3rd Qu.:0.27813 #> age10c5 : 1 Max. :924101 Max. :17173 Max. :1.00000 #> (Other) :343 NA's :6 #> VAR #> Min. : 0 #> 1st Qu.: 1470114 #> Median : 5146089 #> Mean : 15409738 #> 3rd Qu.: 16045307 #> Max. :294900983 #># List available registered GVF models: GVF.db#> #> # Registered GVF models currently available: #> #> Model.id GVF.model Estimator.kind Resp.to.CV #> 1 1 log(CV^2) ~ log(Y) Frequency sqrt(exp(resp)) #> 2 2 CV^2 ~ I(1/Y) Frequency sqrt(resp) #> 3 3 CV^2 ~ I(1/Y) + I(1/Y^2) Frequency sqrt(resp) #> 4 4 SE ~ Y + I(Y^2) Total resp/Y #> 5 5 CV ~ I(1/Y) + Y Total resp #>## (A) A *single* fitted GVF model ## # Fit example data to registered GVF model number one: m <- fit.gvf(ee.AF, 1) # Extract some information: coef(m)#> (Intercept) log(Y) #> 5.7518487 -0.9948979fitted(m)#> 1 2 3 4 5 6 #> -7.91464226 -7.24213145 -7.20763272 -7.37399555 -6.83644862 -5.41302091 #> 7 8 9 10 11 12 #> -5.95512677 -6.77718776 -6.96691378 -5.90746547 -4.16078349 -4.93077019 #> 13 14 15 16 17 18 #> -5.51554300 -5.98608773 -6.17966521 -6.36174913 -6.18501900 -5.64967262 #> 19 20 21 22 23 24 #> -4.43775737 -3.89374587 -2.72170942 -6.77340871 -7.10775313 -6.48663997 #> 25 26 27 28 29 30 #> -4.94776887 -5.39417556 -6.51911555 -5.24578784 -5.93722214 -4.59645882 #> 31 32 33 34 35 36 #> -5.20464333 -6.16052664 -5.21832910 -5.39312537 -6.70358729 -6.16458662 #> 37 38 39 40 41 42 #> -4.72252631 -6.66480459 -6.12876694 -4.72429344 -5.35306515 -6.08566265 #> 43 44 45 46 47 48 #> -6.27477769 -5.28566119 -3.31761501 -5.16951305 -6.08948790 -6.27982219 #> 49 50 51 52 53 54 #> -5.14508697 -3.60416817 -4.06492182 -6.27389141 -6.56979161 -5.53343247 #> 55 56 57 58 59 60 #> -3.89397255 -5.76924317 -5.66745967 -5.55009622 -4.19041938 -1.49266011 #> 61 62 63 64 65 66 #> -2.10385929 -4.12071873 -4.55325900 -3.91772470 -2.37890110 -6.08892547 #> 67 68 69 70 71 72 #> -6.45243670 -5.79748739 -6.07864410 -6.38262382 -5.79657106 -4.44095468 #> 73 74 75 76 77 78 #> -4.74499217 -5.85805912 -4.47921550 -5.25077981 -3.86806882 -4.51503269 #> 79 80 81 82 83 84 #> -5.47003679 -4.53261576 -4.73518331 -4.03404178 -4.66242507 -5.80010689 #> 85 86 87 88 89 90 #> -4.62761051 -5.24443307 -3.94417251 -4.51503269 -5.47179442 -4.52480583 #> 91 92 93 94 95 96 #> -4.67080486 -3.54072372 -5.62978583 -5.84203709 -4.96616194 -2.97153815 #> 97 98 99 100 101 102 #> -5.14166693 -4.92500186 -4.92882804 -3.35981806 -1.11257071 -1.85320792 #> 103 104 105 106 107 108 #> -3.22095258 -3.92457366 -3.25857264 -1.63769693 -3.17680320 -5.53659419 #> 109 110 111 112 113 114 #> -5.91691628 -4.70430043 -3.39290090 -5.01347139 -5.02803008 -4.78709991 #> 115 116 117 118 119 120 #> -3.62427665 -0.35133432 -0.60962000 -3.60452059 -3.79874733 -3.19669346 #> 121 122 123 124 125 126 #> -1.73833982 -2.53232948 -3.09215992 -4.84404227 -5.02783216 -5.21720861 #> 127 128 129 130 131 132 #> -5.08313040 -4.70017635 -3.52366318 -2.83335901 -0.93923107 -4.06794542 #> 133 134 135 136 137 138 #> -4.72848579 -4.13926428 -4.32304321 -4.37059429 -4.08781409 -3.05199982 #> 139 140 141 142 143 144 #> -2.04272892 -1.11257071 NA -1.47051037 -0.71746479 -2.52299309 #> 145 146 147 148 149 150 #> -2.53962130 -3.29333288 -3.17295402 -3.18121455 -0.67898118 -1.38145822 #> 151 152 153 154 155 156 #> -0.16271647 -2.12060472 -2.75447895 -4.66042538 -5.00402197 -5.34422806 #> 157 158 159 160 161 162 #> -5.09478929 -4.44632409 -3.23521248 -3.01401479 -2.24898394 -4.00554593 #> 163 164 165 166 167 168 #> -4.56464043 -4.25234263 -4.41763842 -4.11124083 -4.08354495 -3.26612007 #> 169 170 171 172 173 174 #> -2.43410771 0.26815625 0.41371570 0.26815625 -0.07818422 -3.13217222 #> 175 176 177 178 179 180 #> -2.63655039 -3.12971887 -3.08811970 -2.98083035 -1.57051306 -1.45456457 #> 181 182 183 184 185 186 #> -0.35176539 -5.57334670 -5.91912941 -5.21678081 -5.02983503 -5.30395829 #> 187 188 189 190 191 192 #> -4.82181737 -3.28203165 -4.15845237 -3.10973647 -5.54253725 -5.81852194 #> 193 194 195 196 197 198 #> -5.28230862 -5.03065811 -5.30194876 -4.67855800 -3.40149006 -4.04370095 #> 199 200 201 202 203 204 #> -3.26583217 -3.71627317 -4.21589684 -5.41469482 -3.70446326 -4.70820755 #> 205 206 207 208 209 210 #> -3.37298702 -4.10112971 -4.85977113 -4.02396005 -4.16856736 -3.31279258 #> 211 212 213 214 215 216 #> -3.65979318 -4.60025821 -3.59973171 -4.19257705 -2.77999153 -3.32909435 #> 217 218 219 220 221 222 #> -4.51635422 -3.49820422 -3.77673839 -2.81738149 -2.18762987 -3.30891774 #> 223 224 225 226 227 228 #> -2.43304424 -2.67589197 -1.01859902 -1.22771123 -2.89366595 -1.48441164 #> 229 230 231 232 233 234 #> -1.80508587 -3.49228690 -4.01076183 -5.25487524 -4.07588546 -4.69957138 #> 235 236 237 238 239 240 #> -3.41800438 -4.01299818 -4.93327770 -4.07075472 -4.11557933 -2.89245975 #> 241 242 243 244 245 246 #> -3.75064385 -4.70492964 -3.42888639 -4.21796906 -2.93939443 -3.49729193 #> 247 248 249 250 251 252 #> -4.41363511 -3.23258404 -3.53259125 -1.76716877 -2.15735239 -3.39672522 #> 253 254 255 256 257 258 #> -2.56624341 -2.52967776 -0.89116730 -1.22771123 -2.86485733 -2.16796436 #> 259 260 261 262 263 264 #> -2.46719214 -2.51897527 -2.64688131 -2.09869830 -4.40501556 -4.89742546 #> 265 266 267 268 269 270 #> -4.16346432 -4.78253618 -4.97434572 -4.41050174 -3.86674757 -4.22635978 #> 271 272 273 274 275 276 #> -3.33157711 -1.25346255 -2.59251166 -1.00807090 -3.94336258 -4.38076729 #> 277 278 279 280 281 282 #> -3.69968636 -3.85648297 -3.98626547 -3.61908968 -3.85338666 -3.97322746 #> 283 284 285 286 287 288 #> -3.65532599 -1.80513588 -2.65848549 -2.14956609 -0.57544980 -0.24279167 #> 289 290 291 292 293 294 #> NA -1.09035840 -0.68068570 -0.38127588 -1.64318433 -2.54795084 #> 295 296 297 298 299 300 #> -1.97839366 -2.60509934 -3.30216686 -2.32128390 -1.63253602 -2.89200704 #> 301 302 303 304 305 306 #> -1.09035840 NA -1.49327600 0.35370140 -1.75409639 -2.54060066 #> 307 308 309 310 311 312 #> -1.72801932 -4.50483775 -4.66533528 -4.06982601 -4.77828644 -5.02110693 #> 313 314 315 316 317 318 #> -4.63324716 -3.47896137 -4.00956268 -3.15516928 -2.23229729 -2.56995928 #> 319 320 321 322 323 324 #> -2.01873510 -3.93344448 -4.07821445 -3.71699797 -3.92587374 -4.25318405 #> 325 326 327 328 329 330 #> -3.47898926 -3.78129731 -3.93611387 -3.23912441 -2.12318685 -3.06751409 #> 331 332 333 334 335 336 #> -2.05917130 NA NA -0.35133432 NA -0.07818422 #> 337 338 339 340 341 342 #> 0.26815625 -1.87367339 -2.99334400 -2.35011928 -2.75569139 -2.95634310 #> 343 344 345 346 347 348 #> -2.29544408 -1.83900445 -2.65542694 -1.39883513 -0.50189202 -1.07360226 #> 349 #> -0.13094481## (B) A *a set* of GVF models fitted to the same data ## # Fit example data to registered GVF models for frequencies (i.e. number 1:3): mm <- fit.gvf(ee.AF, 1:3) # Extract some information: r.mod <- residuals(mm) lapply(r.mod, head)#> [[1]] #> 1 2 3 4 5 6 #> -0.05636149 0.03473291 -0.16861685 0.07708302 0.17465511 0.32758362 #> #> [[2]] #> 1 2 3 4 5 6 #> -0.01311595 -0.01300715 -0.01314289 -0.01299811 -0.01276340 -0.01064978 #> #> [[3]] #> 1 2 3 4 5 6 #> 0.0004512901 0.0004519346 0.0003084251 0.0004882947 0.0005849223 0.0016497883 #>#> [[1]] #> 1 2 3 4 5 6 #> -0.13777389 0.08467353 -0.41101035 0.18800930 0.42518703 0.79467755 #> #> [[2]] #> 1 2 3 4 5 6 #> -0.2249911 -0.2231242 -0.2254528 -0.2229693 -0.2189426 -0.1826822 #> #> [[3]] #> 1 2 3 4 5 6 #> 0.008803815 0.008816321 0.006016741 0.009525650 0.011410552 0.032181511 #>#> [[1]] #> 1 2 3 4 5 6 #> -0.13757555 0.08455018 -0.41050895 0.18774316 0.42467572 0.79424727 #> #> [[2]] #> 1 2 3 4 5 6 #> -0.2246776 -0.2228131 -0.2251387 -0.2226583 -0.2186367 -0.1824230 #> #> [[3]] #> 1 2 3 4 5 6 #> 0.008790860 0.008803347 0.006007887 0.009511633 0.011393762 0.032134199 #>## (C) a *single* GVF model fitted to *grouped* data ## # We have at our disposal the following survey design object on household data: exdes#> Stratified 2 - Stage Cluster Sampling Design (with replacement) #> - [55] strata #> - [1307, 2372] clusters #> #> Call: #> e.svydesign(data = example, ids = ~towcod + famcod, strata = ~SUPERSTRATUM, #> weights = ~weight, fpc = NULL, self.rep.str = NULL, check.data = TRUE)# Use function svystat to prepare *grouped* estimates and errors of counts # to be fitted separately (here groups are regions): ee.g <- svystat(exdes, y=~ind, by=~age5c:marstat:sex, combo=3, group=~regcod) str(ee.g)#> List of 3 #> $ 6 :Classes ‘gvf.input’ and 'data.frame': 69 obs. of 5 variables: #> ..$ name: chr [1:69] "6:ind" "6.1:ind" "6.2:ind" "6.3:ind" ... #> ..$ Y : num [1:69] 293458 40903 93388 118888 34964 ... #> ..$ SE : num [1:69] 8924 4080 6249 5682 4605 ... #> ..$ CV : num [1:69] 0.0304 0.0998 0.0669 0.0478 0.1317 ... #> ..$ VAR : num [1:69] 79634317 16648310 39046122 32287405 21209323 ... #> ..- attr(*, "y.vars")= chr "ind" #> ..- attr(*, "stats.kind")= chr "Absolute Frequency" #> ..- attr(*, "has.Deff")= logi FALSE #> $ 7 :Classes ‘gvf.input’ and 'data.frame': 71 obs. of 5 variables: #> ..$ name: chr [1:71] "7:ind" "7.1:ind" "7.2:ind" "7.3:ind" ... #> ..$ Y : num [1:71] 410672 55834 131681 148350 63303 ... #> ..$ SE : num [1:71] 12714 4814 6149 6490 5796 ... #> ..$ CV : num [1:71] 0.031 0.0862 0.0467 0.0437 0.0916 ... #> ..$ VAR : num [1:71] 1.62e+08 2.32e+07 3.78e+07 4.21e+07 3.36e+07 ... #> ..- attr(*, "y.vars")= chr "ind" #> ..- attr(*, "stats.kind")= chr "Absolute Frequency" #> ..- attr(*, "has.Deff")= logi FALSE #> $ 10:Classes ‘gvf.input’ and 'data.frame': 71 obs. of 5 variables: #> ..$ name: chr [1:71] "10:ind" "10.1:ind" "10.2:ind" "10.3:ind" ... #> ..$ Y : num [1:71] 219971 32191 69506 89226 24631 ... #> ..$ SE : num [1:71] 7322 2678 4026 3620 3092 ... #> ..$ CV : num [1:71] 0.0333 0.0832 0.0579 0.0406 0.1255 ... #> ..$ VAR : num [1:71] 53611574 7170100 16205547 13102640 9562313 ... #> ..- attr(*, "y.vars")= chr "ind" #> ..- attr(*, "stats.kind")= chr "Absolute Frequency" #> ..- attr(*, "has.Deff")= logi FALSE #> - attr(*, "group.vars")= chr "regcod" #> - attr(*, "class")= chr [1:2] "gvf.input.gr" "list"# Fit registered GVF model number one separately inside groups: m.g <- fit.gvf(ee.g, 1) # Extract some information: coef(m.g)#> $`6` #> (Intercept) log(Y) #> 5.3879239 -0.9527893 #> #> $`7` #> (Intercept) log(Y) #> 6.152759 -1.017113 #> #> $`10` #> (Intercept) log(Y) #> 5.721254 -1.016518 #>fitted(m.g)#> $`6` #> 1 2 3 4 5 6 7 #> -6.6072082 -4.7297156 -5.5162894 -5.7463127 -4.5802393 -2.7852877 -6.1037229 #> 8 9 10 11 12 13 14 #> -5.5982478 -3.9829423 -5.9516953 -5.9418491 -2.8957376 -5.0484764 -5.3589888 #> 15 16 17 18 19 20 21 #> -4.3162872 -2.5609311 -4.5526282 -4.5079693 -4.4373039 -2.6740531 -0.6715748 #> 22 23 24 25 26 27 28 #> -1.1646902 -2.4712860 -3.3505789 -2.4483286 -0.6011303 -4.1437910 -4.8095031 #> 29 30 31 32 33 34 35 #> -5.0901459 -4.0221788 -1.7109306 -3.9884707 -4.9000776 -5.0816140 -3.8051175 #> 36 37 38 39 40 41 42 #> -2.4124761 -5.4579382 -4.9374304 -3.2636019 -5.4284327 -4.9382186 -3.3780043 #> 43 44 45 46 47 48 49 #> -2.5328415 -4.3390561 -4.7005983 -3.8235700 -1.3208910 -3.8969423 -3.8137399 #> 50 51 52 53 54 55 56 #> -3.8107746 -1.8492150 -0.6715748 -1.1646902 -1.6941180 -2.6153204 -1.6839204 #> 57 58 59 60 61 62 63 #> -1.8003358 -4.4346534 -4.6965285 -3.4521967 -2.2582971 -3.8874440 -3.8801937 #> 64 65 66 67 68 69 #> -3.7417364 -2.1538047 -1.9148517 -2.7595388 -1.8816501 -0.6011303 #> #> $`7` #> 1 2 3 4 5 6 #> -6.99399032 -4.96442693 -5.83711622 -5.95834561 -5.09212083 -3.35771748 #> 7 8 9 10 11 12 #> -6.43370715 -5.85390036 -4.62687621 -6.32404106 -6.25266929 -3.08564238 #> 13 14 15 16 17 18 #> -5.32758352 -5.54213793 -4.64847258 -3.07147276 -4.76818014 -4.65337420 #> 19 20 21 22 23 24 #> -4.47568754 -3.38080340 0.02426166 -0.86637359 -3.29031945 -3.64694862 #> 25 26 27 28 29 30 #> -3.27535925 -1.76708909 -4.39095250 -5.14294763 -5.25989368 -4.43897109 #> 31 32 33 34 35 36 #> -2.73852031 -4.10831008 -5.12114954 -5.24673650 -4.33246521 -2.55898367 #> 37 38 39 40 41 42 #> -5.77882532 -5.14991780 -3.97883341 -5.67597136 -5.14786340 -3.86151966 #> 43 44 45 46 47 48 #> -2.43350987 -4.73430734 -4.81294517 -4.04825715 -2.37792617 -4.20611250 #> 49 50 51 52 53 54 #> -3.80280168 -3.78947255 -2.44537316 0.02426166 -0.42341027 -2.33237034 #> 55 56 57 58 59 60 #> -3.10342756 -2.68410911 -1.25414524 -2.32485603 -4.49703472 -4.86075053 #> 61 62 63 64 65 66 #> -3.82661911 -2.35487021 -3.89680383 -4.07568040 -3.75153022 -2.86353513 #> 67 68 69 70 71 #> 0.19254470 -2.78770886 -2.74988175 -2.44224632 -0.82510043 #> #> $`10` #> 1 2 3 4 5 6 7 #> -6.7831910 -4.8296433 -5.6120747 -5.8659592 -4.5575219 -2.8107631 -6.2243600 #> 8 9 10 11 12 13 14 #> -5.7162326 -4.1203687 -6.0790623 -6.0781261 -2.8326613 -5.0674153 -5.4866815 #> 15 16 17 18 19 20 21 #> -4.1780306 -2.5323222 -4.6491527 -4.4888673 -4.4043996 -3.0113331 -0.5145579 #> 22 23 24 25 26 27 28 #> -0.9711817 -3.0891098 -3.2187986 -2.1439956 -0.7762453 -4.1507637 -4.9428079 #> 29 30 31 32 33 34 35 #> -5.1336983 -3.9087738 -1.4159864 -4.0986620 -4.8708758 -5.1882937 -3.7938290 #> 36 37 38 39 40 41 42 #> -2.5134777 -5.4857363 -5.0821898 -3.3329034 -5.5526881 -4.9358173 -3.4923913 #> 43 44 45 46 47 48 49 #> -2.2998942 -4.4095300 -4.6619358 -3.5595649 -1.1855662 -3.9356736 -3.8533255 #> 50 51 52 53 54 55 56 #> -3.8903492 -2.3518674 -0.5451502 -2.1769752 -2.5273168 -1.2696419 0.2057991 #> 57 58 59 60 61 62 63 #> -1.9211599 -4.3138568 -4.8895218 -3.3793235 -2.2181933 -3.9533614 -3.7101805 #> 64 65 66 67 68 69 70 #> -3.4651031 -2.2595082 -0.5145579 0.1183949 -2.5567788 -2.5009155 -1.5848222 #> 71 #> -0.2893791 #>## (D) a *set of* GVF models fitted to *grouped* data ## # Fit all registered GVF models for frequencies (i.e. number 1:3) separately # inside groups: mm.g <- fit.gvf(ee.g, 1:3) # Extract some information: coef(mm.g)#> $`6` #> $`6`[[1]] #> (Intercept) log(Y) #> 5.3879239 -0.9527893 #> #> $`6`[[2]] #> (Intercept) I(1/Y) #> 9.833598e-03 2.709174e+02 #> #> $`6`[[3]] #> (Intercept) I(1/Y) I(1/Y^2) #> -7.947002e-03 4.457342e+02 -1.071654e+05 #> #> #> $`7` #> $`7`[[1]] #> (Intercept) log(Y) #> 6.152759 -1.017113 #> #> $`7`[[2]] #> (Intercept) I(1/Y) #> 4.293585e-03 3.815012e+02 #> #> $`7`[[3]] #> (Intercept) I(1/Y) I(1/Y^2) #> 1.325770e-03 4.162739e+02 -1.453588e+04 #> #> #> $`10` #> $`10`[[1]] #> (Intercept) log(Y) #> 5.721254 -1.016518 #> #> $`10`[[2]] #> (Intercept) I(1/Y) #> 5.101619e-03 2.552963e+02 #> #> $`10`[[3]] #> (Intercept) I(1/Y) I(1/Y^2) #> -3.756025e-03 3.062188e+02 -1.480137e+04 #> #>fitted(mm.g)#> $`6` #> $`6`[[1]] #> 1 2 3 4 5 6 7 #> -6.6072082 -4.7297156 -5.5162894 -5.7463127 -4.5802393 -2.7852877 -6.1037229 #> 8 9 10 11 12 13 14 #> -5.5982478 -3.9829423 -5.9516953 -5.9418491 -2.8957376 -5.0484764 -5.3589888 #> 15 16 17 18 19 20 21 #> -4.3162872 -2.5609311 -4.5526282 -4.5079693 -4.4373039 -2.6740531 -0.6715748 #> 22 23 24 25 26 27 28 #> -1.1646902 -2.4712860 -3.3505789 -2.4483286 -0.6011303 -4.1437910 -4.8095031 #> 29 30 31 32 33 34 35 #> -5.0901459 -4.0221788 -1.7109306 -3.9884707 -4.9000776 -5.0816140 -3.8051175 #> 36 37 38 39 40 41 42 #> -2.4124761 -5.4579382 -4.9374304 -3.2636019 -5.4284327 -4.9382186 -3.3780043 #> 43 44 45 46 47 48 49 #> -2.5328415 -4.3390561 -4.7005983 -3.8235700 -1.3208910 -3.8969423 -3.8137399 #> 50 51 52 53 54 55 56 #> -3.8107746 -1.8492150 -0.6715748 -1.1646902 -1.6941180 -2.6153204 -1.6839204 #> 57 58 59 60 61 62 63 #> -1.8003358 -4.4346534 -4.6965285 -3.4521967 -2.2582971 -3.8874440 -3.8801937 #> 64 65 66 67 68 69 #> -3.7417364 -2.1538047 -1.9148517 -2.7595388 -1.8816501 -0.6011303 #> #> $`6`[[2]] #> 1 2 3 4 5 6 7 #> 0.01075679 0.01645694 0.01273459 0.01211236 0.01758198 0.06081062 0.01139958 #> 8 9 10 11 12 13 14 #> 0.01249548 0.02433690 0.01167048 0.01168957 0.05523088 0.01457364 0.01325533 #> 15 16 17 18 19 20 21 #> 0.02005531 0.07434541 0.01780980 0.01819256 0.01883609 0.06712332 0.47846770 #> 22 23 24 25 26 27 28 #> 0.28912986 0.08070985 0.03799841 0.08243835 0.51442919 0.02208399 0.01592489 #> 29 30 31 32 33 34 35 #> 0.01437080 0.02375178 0.16726132 0.02425299 0.01537251 0.01441161 0.02731281 #> 36 37 38 39 40 41 42 #> 0.08522245 0.01291781 0.01515956 0.04069049 0.01301481 0.01515516 0.03719927 #> 43 44 45 46 47 48 49 #> 0.07627563 0.01981394 0.01666248 0.02697755 0.24689815 0.02570689 0.02715535 #> 50 51 52 53 54 55 56 #> 0.02720934 0.14599351 0.47846770 0.28912986 0.17006389 0.07076594 0.17178803 #> 57 58 59 60 61 62 63 #> 0.15316096 0.01886117 0.01669171 0.03514919 0.09846427 0.02586592 0.02598838 #> 64 65 66 67 68 69 #> 0.02851511 0.10873743 0.13692938 0.06220705 0.14143632 0.51442919 #> #> $`6`[[3]] #> 1 2 3 4 5 #> -0.0064293453 0.0028861870 -0.0031863565 -0.0042053928 0.0047135683 #> 6 7 8 9 10 #> 0.0721300539 -0.0053741127 -0.0035778167 0.0156078322 -0.0049297416 #> 11 12 13 14 15 #> -0.0048984510 0.0637349964 -0.0001811293 -0.0023343984 0.0087180000 #> 16 17 18 19 20 #> 0.0921162488 0.0050831762 0.0057037927 0.0067462604 0.0815182921 #> 21 22 23 24 25 #> 0.4424231347 0.3376760966 0.1013293885 0.0372337052 0.1038111423 #> 26 27 28 29 30 #> 0.4504882133 0.0119891684 0.0020206824 -0.0005121004 0.0146694219 #> 31 32 33 34 35 #> 0.2148790410 0.0154733226 0.0011212420 -0.0004454969 0.0203650662 #> 36 37 38 39 40 #> 0.1077901551 -0.0028865148 0.0007742681 0.0414309258 -0.0027278032 #> 41 42 43 44 45 #> 0.0007670902 0.0359836779 0.0949229176 0.0083279927 0.0032203103 #> 46 47 48 49 50 #> 0.0198304185 0.3000332151 0.0178010758 0.0201139916 0.0202000891 #> 51 52 53 54 55 #> 0.1890043684 0.4424231347 0.3376760966 0.2181901764 0.0868826362 #> 56 57 58 59 60 #> 0.2202157974 0.1978719323 0.0067868614 0.0032678211 0.0327684307 #> 61 62 63 64 65 #> 0.1264054194 0.0180553184 0.0182510510 0.0222796915 0.1404946393 #> 66 67 68 69 #> 0.1775754192 0.0742168427 0.1832882084 0.4504882133 #> #> #> $`7` #> $`7`[[1]] #> 1 2 3 4 5 6 #> -6.99399032 -4.96442693 -5.83711622 -5.95834561 -5.09212083 -3.35771748 #> 7 8 9 10 11 12 #> -6.43370715 -5.85390036 -4.62687621 -6.32404106 -6.25266929 -3.08564238 #> 13 14 15 16 17 18 #> -5.32758352 -5.54213793 -4.64847258 -3.07147276 -4.76818014 -4.65337420 #> 19 20 21 22 23 24 #> -4.47568754 -3.38080340 0.02426166 -0.86637359 -3.29031945 -3.64694862 #> 25 26 27 28 29 30 #> -3.27535925 -1.76708909 -4.39095250 -5.14294763 -5.25989368 -4.43897109 #> 31 32 33 34 35 36 #> -2.73852031 -4.10831008 -5.12114954 -5.24673650 -4.33246521 -2.55898367 #> 37 38 39 40 41 42 #> -5.77882532 -5.14991780 -3.97883341 -5.67597136 -5.14786340 -3.86151966 #> 43 44 45 46 47 48 #> -2.43350987 -4.73430734 -4.81294517 -4.04825715 -2.37792617 -4.20611250 #> 49 50 51 52 53 54 #> -3.80280168 -3.78947255 -2.44537316 0.02426166 -0.42341027 -2.33237034 #> 55 56 57 58 59 60 #> -3.10342756 -2.68410911 -1.25414524 -2.32485603 -4.49703472 -4.86075053 #> 61 62 63 64 65 66 #> -3.82661911 -2.35487021 -3.89680383 -4.07568040 -3.75153022 -2.86353513 #> 67 68 69 70 71 #> 0.19254470 -2.78770886 -2.74988175 -2.44224632 -0.82510043 #> #> $`7`[[2]] #> 1 2 3 4 5 6 #> 0.005222553 0.011126385 0.007190738 0.006865212 0.010320222 0.037455775 #> 7 8 9 10 11 12 #> 0.005905101 0.007143322 0.013815593 0.006088570 0.006219050 0.047626292 #> 13 14 15 16 17 18 #> 0.009074769 0.008165479 0.013615544 0.048234194 0.012580513 0.013570728 #> 19 20 21 22 23 24 #> 0.015341598 0.036711555 0.926239339 0.388368037 0.039727677 0.029247873 #> 25 26 27 28 29 30 #> 0.040252711 0.162717717 0.016301427 0.010026462 0.009403788 0.015747703 #> 31 32 33 34 35 36 #> 0.065251842 0.020147981 0.010150651 0.009470322 0.017012156 0.077020038 #> 37 38 39 40 41 42 #> 0.007361624 0.009987309 0.022300304 0.007688104 0.009998821 0.024501713 #> 43 44 45 46 47 48 #> 0.086568629 0.012861138 0.012223700 0.021112251 0.091189960 0.018694474 #> 49 50 51 52 53 54 #> 0.025702660 0.025985070 0.085614574 0.926239339 0.597977297 0.095170468 #> 55 56 57 58 59 60 #> 0.046875166 0.068601648 0.266619895 0.095844341 0.015112139 0.011859600 #> 61 62 63 64 65 66 #> 0.025207155 0.093182225 0.023812703 0.020664847 0.026809529 0.058201533 #> 67 68 69 70 71 #> 1.092121224 0.062373992 0.064574710 0.085864958 0.404273825 #> #> $`7`[[3]] #> 1 2 3 4 5 6 #> 0.002339325 0.008776698 0.004486152 0.004131134 0.007898090 0.037400769 #> 7 8 9 10 11 12 #> 0.003083912 0.004434442 0.011706628 0.003284041 0.003426365 0.048420600 #> 13 14 15 16 17 18 #> 0.006540462 0.005549079 0.011488721 0.049078611 0.010361170 0.011439903 #> 19 20 21 22 23 24 #> 0.013368589 0.036593590 0.922413577 0.405674914 0.039864184 0.028492380 #> 25 26 27 28 29 30 #> 0.040433329 0.171683185 0.014413694 0.007577900 0.006899146 0.013810796 #> 31 32 33 34 35 36 #> 0.067469084 0.018600146 0.007713265 0.006971675 0.015187448 0.080152795 #> 37 38 39 40 41 42 #> 0.004672512 0.007535224 0.020941369 0.005028539 0.007547772 0.023335028 #> 43 44 45 46 47 48 #> 0.090423898 0.010666901 0.009972412 0.019649160 0.095388369 0.017018548 #> 49 50 51 52 53 54 #> 0.024640447 0.024947381 0.089398470 0.922413577 0.613920716 0.099661016 #> 55 56 57 58 59 60 #> 0.047607455 0.071082308 0.280689608 0.100384033 0.013118716 0.009575689 #> 61 62 63 64 65 66 #> 0.024101872 0.097527247 0.022585950 0.019162460 0.025843348 0.059857040 #> 67 68 69 70 71 #> 1.070118853 0.064363141 0.066738432 0.089667603 0.421784949 #> #> #> $`10` #> $`10`[[1]] #> 1 2 3 4 5 6 7 #> -6.7831910 -4.8296433 -5.6120747 -5.8659592 -4.5575219 -2.8107631 -6.2243600 #> 8 9 10 11 12 13 14 #> -5.7162326 -4.1203687 -6.0790623 -6.0781261 -2.8326613 -5.0674153 -5.4866815 #> 15 16 17 18 19 20 21 #> -4.1780306 -2.5323222 -4.6491527 -4.4888673 -4.4043996 -3.0113331 -0.5145579 #> 22 23 24 25 26 27 28 #> -0.9711817 -3.0891098 -3.2187986 -2.1439956 -0.7762453 -4.1507637 -4.9428079 #> 29 30 31 32 33 34 35 #> -5.1336983 -3.9087738 -1.4159864 -4.0986620 -4.8708758 -5.1882937 -3.7938290 #> 36 37 38 39 40 41 42 #> -2.5134777 -5.4857363 -5.0821898 -3.3329034 -5.5526881 -4.9358173 -3.4923913 #> 43 44 45 46 47 48 49 #> -2.2998942 -4.4095300 -4.6619358 -3.5595649 -1.1855662 -3.9356736 -3.8533255 #> 50 51 52 53 54 55 56 #> -3.8903492 -2.3518674 -0.5451502 -2.1769752 -2.5273168 -1.2696419 0.2057991 #> 57 58 59 60 61 62 63 #> -1.9211599 -4.3138568 -4.8895218 -3.3793235 -2.2181933 -3.9533614 -3.7101805 #> 64 65 66 67 68 69 70 #> -3.4651031 -2.2595082 -0.5145579 0.1183949 -2.5567788 -2.5009155 -1.5848222 #> 71 #> -0.2893791 #> #> $`10`[[2]] #> 1 2 3 4 5 6 #> 0.006262210 0.013032243 0.008774641 0.007962861 0.015466583 0.062891036 #> 7 8 9 10 11 12 #> 0.007112713 0.008416923 0.021036027 0.007421731 0.007423869 0.061659431 #> 13 14 15 16 17 18 #> 0.011378188 0.009256860 0.020157308 0.081100758 0.014573140 0.016190804 #> 19 20 21 22 23 24 #> 0.017151628 0.052543001 0.558289715 0.358110892 0.049048515 0.043784636 #> 25 26 27 28 29 30 #> 0.116458291 0.432733623 0.020566625 0.012196731 0.010981977 0.024723345 #> 31 32 33 34 35 36 #> 0.233004053 0.021379949 0.012716994 0.010674485 0.027072420 0.082522789 #> 37 38 39 40 41 42 #> 0.009260725 0.011287621 0.039677280 0.008995617 0.012245692 0.034656644 #> 43 44 45 46 47 48 #> 0.100625194 0.017090964 0.014454777 0.032766719 0.290987740 0.024210910 #> 49 50 51 52 53 54 #> 0.025823386 0.025082234 0.095863958 0.541889479 0.112903455 0.081475899 #> 55 56 57 58 59 60 #> 0.268293687 1.128764938 0.143751653 0.018274192 0.012578579 0.038133864 #> 61 62 63 64 65 66 #> 0.108619706 0.023881277 0.028956851 0.035460786 0.104496716 0.558289715 #> 67 68 69 70 71 #> 1.036185246 0.079294094 0.083485502 0.198127709 0.695464805 #> #> $`10`[[3]] #> 1 2 3 4 5 #> -0.0023642444 0.0057421933 0.0006465699 -0.0003259265 0.0086519824 #> 6 7 8 9 10 #> 0.0648019067 -0.0013447080 0.0002180680 0.0152990696 -0.0009743558 #> 11 12 13 14 15 #> -0.0009717937 0.0633566226 0.0037635497 0.0012241169 0.0142512615 #> 16 17 18 19 20 #> 0.0860905533 0.0075843539 0.0095171308 0.0106645569 0.0526371003 #> 21 22 23 24 25 #> 0.5902776349 0.3913661363 0.0485181135 0.0423030550 0.1269962625 #> 26 27 28 29 30 #> 0.4676441895 0.0147393848 0.0047428768 0.0032894018 0.0196921016 #> 31 32 33 34 35 #> 0.2578094942 0.0157090759 0.0053651761 0.0029213758 0.0224875452 #> 36 37 38 39 40 #> 0.0877466833 0.0012287459 0.0036551742 0.0374447610 0.0009112427 #> 41 42 43 44 45 #> 0.0048014459 0.0314958071 0.1087488966 0.0105921234 0.0074428880 #> 46 47 48 49 50 #> 0.0292534676 0.3205933157 0.0190819621 0.0210014832 0.0201193485 #> 51 52 53 54 55 #> 0.1032393876 0.5746657038 0.1229092879 0.0865275411 0.2962024280 #> 56 57 58 59 60 #> 1.0573001480 0.1581840723 0.0120046018 0.0051996264 0.0356171848 #> 61 62 63 64 65 #> 0.1179766625 0.0186894154 0.0247282426 0.0324494052 0.1132212743 #> 66 67 68 69 70 #> 0.5902776349 0.9915568842 0.0839851465 0.0888673600 0.2193104564 #> 71 #> 0.7160749720 #> #>