Our goal is to produce estimated WG and WS raw scores for each ability level, so that we can produce norms tables for the CDI-CAT that are equivalent norms tables for the full WG and WS forms.

These norms are based on all available data contributed by Sho, Hiromichi, and Yasuyo: 407 WG administrations and 681 WS administrations. (The same data we used to fit the IRT model.) For the by-sex norms, note that there are 0 administrations with missing sex information, so the N per (month) age bin by sex is sometimes quite low.

WG Raw Score Norms

Both Sexes

## new prediction 
## new prediction

## new prediction 
## new prediction
age 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 99 N
12 0 0 0 0 1 1 1 1 1 1 2 2 2 3 3 4 4 6 7 12 151
13 0 0 0 1 1 1 1 2 2 2 3 3 4 5 5 6 8 10 13 20 24
14 0 0 1 1 1 2 2 3 3 4 5 6 7 8 9 11 13 16 22 35 25
15 0 1 1 2 2 3 4 5 6 7 8 9 11 13 16 18 22 28 37 59 73
16 0 1 2 3 4 5 6 8 9 11 14 16 19 22 26 31 38 47 62 96 46
17 1 2 3 4 6 8 11 13 16 19 23 27 32 37 44 52 62 77 100 151 41
18 1 3 5 8 11 14 18 22 27 33 39 45 53 62 72 85 101 123 157 226 130

Males

## new prediction 
## new prediction
age 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 99 N
12 0 0 0 0 0 1 1 1 1 1 2 2 2 2 3 3 4 5 7 11 73
13 0 0 0 1 1 1 1 1 2 2 2 3 3 4 5 6 7 8 11 18 13
14 0 0 1 1 1 2 2 2 3 3 4 5 6 6 8 9 11 14 18 29 11
15 0 1 1 1 2 2 3 4 5 6 7 8 9 11 12 15 18 22 29 46 34
16 0 1 2 2 3 4 5 6 8 9 11 13 15 17 20 24 29 35 47 73 23
17 1 2 3 4 5 7 9 10 13 15 18 21 24 28 33 38 46 56 74 112 14
18 1 3 5 7 9 11 14 17 21 25 29 34 39 45 52 61 72 88 113 166 59

Females

## new prediction 
## new prediction
age 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 99 N
12 0 0 0 0 1 1 1 1 1 2 2 2 3 3 4 4 5 6 8 13 78
13 0 0 0 1 1 1 2 2 2 3 3 4 4 5 6 7 9 11 14 23 11
14 0 0 1 1 2 2 3 3 4 5 5 6 8 9 10 12 15 19 25 40 14
15 0 1 1 2 3 3 4 5 6 8 9 11 13 15 18 21 26 32 43 68 39
16 0 1 2 3 4 6 7 9 11 13 16 19 22 26 31 37 44 55 72 111 23
17 1 2 3 5 7 10 13 16 19 23 27 32 38 45 52 62 74 91 118 176 27
18 1 3 6 9 13 17 22 27 33 39 47 55 64 75 87 102 121 147 186 261 71

WS Raw Score Norms

Both Sexes

save(gam_wg_prod, #gam_wg_m_prod, gam_wg_f_prod, 
     gam_ws_prod, #gam_ws_m_prod, gam_ws_f_prod,
     wg_prod, ws_prod,
     file="models/JP_WG_WS_production_norms_models.Rdata")
## new prediction 
## new prediction

## new prediction 
## new prediction
age 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 99 N
16 1 3 4 6 8 10 13 16 19 22 26 30 35 40 47 55 65 79 103 156 46
17 2 4 6 9 11 14 18 21 25 30 34 40 46 53 61 71 84 101 130 193 41
18 3 6 9 12 16 20 24 29 34 40 46 53 60 69 79 92 107 129 164 238 130
19 4 8 13 17 22 28 33 39 46 53 61 69 79 90 103 118 137 163 204 289 34
20 6 12 18 24 31 38 45 53 61 70 80 91 103 116 132 150 173 203 252 346 57
21 9 17 25 33 42 51 60 70 81 92 104 117 132 148 167 189 216 251 305 408 60
22 12 23 34 45 56 67 79 92 105 119 133 149 167 186 208 234 265 305 364 470 36
23 17 31 45 59 73 87 102 118 134 150 168 187 208 231 256 285 319 363 426 531 36
24 23 41 59 76 94 112 130 148 167 187 208 231 254 280 309 341 378 424 487 585 66
25 30 53 75 97 118 140 161 183 206 229 253 279 305 334 365 399 438 484 545 630 32
26 38 67 95 121 146 172 197 223 249 275 303 331 360 390 423 458 496 541 596 664 35
27 48 84 117 148 178 208 237 267 296 325 355 385 416 448 481 515 552 592 638 687 14
28 58 102 141 178 214 248 281 314 347 378 410 442 473 505 536 568 601 634 669 701 18
29 70 122 169 212 253 292 329 365 400 434 466 498 529 558 587 615 642 667 691 708 18
30 83 145 199 249 296 339 380 419 455 490 522 552 581 607 631 653 673 690 703 710 18

Males

## new prediction 
## new prediction
age 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 99 N
16 1 2 4 6 8 10 12 14 17 19 23 26 30 35 40 47 56 68 88 133 7
17 2 3 6 8 10 13 16 19 22 26 30 34 39 45 52 61 72 86 111 166 3
18 2 5 8 11 14 17 21 25 29 34 39 45 51 59 67 78 91 109 140 204 32
19 3 7 11 15 19 23 28 33 38 44 51 58 66 76 86 99 116 138 174 250 20
20 5 10 14 20 25 31 37 43 50 58 66 75 85 97 110 126 146 172 215 301 32
21 7 13 20 26 33 41 49 57 66 75 86 97 109 123 139 158 182 214 263 359 32
22 9 18 26 35 44 54 64 74 85 97 110 123 139 156 175 197 225 261 317 421 17
23 13 24 35 46 58 70 82 95 109 123 139 156 174 194 217 243 275 316 377 484 17
24 17 32 46 60 75 90 105 121 138 156 174 194 216 239 265 295 331 375 439 544 28
25 23 42 60 78 96 114 133 153 173 194 216 239 264 291 320 353 391 438 502 598 18
26 29 53 76 99 121 144 166 190 213 238 263 290 318 348 380 415 455 501 562 643 16
27 37 68 96 124 151 178 205 233 260 288 317 347 377 409 443 479 517 561 614 676 6
28 47 84 119 153 186 218 250 281 313 344 376 408 440 473 506 541 577 614 656 696 10
29 57 103 146 186 225 263 300 335 370 405 438 472 504 536 567 598 628 657 685 706 12
30 68 124 176 224 270 313 355 395 432 468 503 535 566 594 621 645 667 686 702 710 9

Females

## new prediction 
## new prediction
age 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 99 N
16 1 3 5 7 9 12 15 19 22 26 31 36 42 49 57 67 80 97 127 192 4
17 2 4 7 10 14 17 21 26 31 36 42 48 56 64 74 86 101 123 157 231 8
18 3 7 11 15 20 25 30 35 42 48 56 64 73 83 95 110 128 153 194 277 30
19 5 11 16 22 28 34 41 48 56 64 74 84 95 107 122 139 161 190 236 328 13
20 8 16 23 31 39 47 56 65 75 85 96 109 122 137 155 175 200 233 285 384 25
21 12 23 33 43 53 63 74 86 98 111 124 139 155 173 193 217 245 283 339 442 28
22 18 31 45 58 71 84 97 111 126 141 158 175 194 215 238 265 297 337 397 501 19
23 25 43 60 76 92 108 125 142 159 178 197 217 239 262 288 318 353 396 457 556 19
24 33 56 77 97 117 137 157 177 198 219 241 264 288 315 343 375 411 456 516 605 38
25 43 72 98 122 146 170 193 217 240 265 289 315 342 370 401 434 471 514 570 646 14
26 54 89 121 150 179 206 233 260 287 314 342 369 398 428 459 492 528 568 617 675 19
27 66 109 146 181 214 246 277 307 337 367 396 426 455 485 516 548 581 616 655 694 8
28 78 130 174 215 253 289 324 358 390 421 452 482 512 541 570 598 626 654 682 705 8
29 92 153 205 252 296 336 374 410 445 477 508 537 565 592 617 640 662 682 699 709 6
30 106 177 238 292 341 386 427 465 500 532 562 589 614 636 655 673 687 699 707 711 9

Ability Norms

Replacing 110 inestimable abilities with -3. (Lowest ability in dataset otherwise is -2.53, producing 1-4 words.)

WG: Both Sexes

## GAMLSS-RS iteration 1: Global Deviance = 983.8628 
## GAMLSS-RS iteration 2: Global Deviance = 983.3841 
## GAMLSS-RS iteration 3: Global Deviance = 983.383 
## GAMLSS-RS iteration 4: Global Deviance = 983.383
## new prediction 
## new prediction

## new prediction 
## new prediction
age 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 99 N
12 -3.45 -3.21 -3.05 -2.92 -2.81 -2.71 -2.62 -2.53 -2.45 -2.36 -2.28 -2.20 -2.11 -2.02 -1.92 -1.81 -1.68 -1.52 -1.28 -0.83 151
13 -3.19 -2.95 -2.79 -2.66 -2.55 -2.45 -2.36 -2.27 -2.19 -2.11 -2.02 -1.94 -1.85 -1.76 -1.66 -1.55 -1.42 -1.26 -1.02 -0.57 24
14 -2.94 -2.70 -2.54 -2.41 -2.30 -2.20 -2.11 -2.02 -1.94 -1.85 -1.77 -1.69 -1.60 -1.51 -1.41 -1.30 -1.17 -1.01 -0.77 -0.32 25
15 -2.69 -2.45 -2.29 -2.16 -2.05 -1.95 -1.86 -1.77 -1.69 -1.60 -1.52 -1.44 -1.35 -1.26 -1.16 -1.05 -0.92 -0.76 -0.52 -0.07 73
16 -2.44 -2.20 -2.04 -1.91 -1.80 -1.70 -1.61 -1.53 -1.44 -1.36 -1.27 -1.19 -1.10 -1.01 -0.91 -0.80 -0.67 -0.51 -0.27 0.18 46
17 -2.20 -1.96 -1.80 -1.67 -1.56 -1.46 -1.37 -1.29 -1.20 -1.12 -1.03 -0.95 -0.86 -0.77 -0.67 -0.56 -0.43 -0.27 -0.03 0.42 41
18 -1.97 -1.73 -1.57 -1.44 -1.33 -1.23 -1.14 -1.05 -0.96 -0.88 -0.80 -0.71 -0.63 -0.53 -0.44 -0.32 -0.20 -0.03 0.21 0.66 130

WG: Males

## GAMLSS-RS iteration 1: Global Deviance = 451.2308 
## GAMLSS-RS iteration 2: Global Deviance = 451.1478 
## GAMLSS-RS iteration 3: Global Deviance = 451.1477
## new prediction 
## new prediction
age 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 99 N
12 -3.51 -3.28 -3.12 -2.99 -2.88 -2.79 -2.70 -2.61 -2.53 -2.45 -2.37 -2.28 -2.20 -2.11 -2.01 -1.90 -1.78 -1.62 -1.38 -0.94 73
13 -3.27 -3.03 -2.87 -2.75 -2.64 -2.54 -2.45 -2.36 -2.28 -2.20 -2.12 -2.03 -1.95 -1.86 -1.76 -1.65 -1.52 -1.36 -1.13 -0.68 13
14 -3.02 -2.79 -2.63 -2.50 -2.39 -2.29 -2.20 -2.11 -2.03 -1.95 -1.87 -1.78 -1.70 -1.60 -1.51 -1.40 -1.27 -1.11 -0.87 -0.42 11
15 -2.78 -2.54 -2.38 -2.25 -2.14 -2.04 -1.95 -1.87 -1.78 -1.70 -1.62 -1.53 -1.45 -1.35 -1.26 -1.15 -1.02 -0.86 -0.62 -0.17 34
16 -2.53 -2.30 -2.13 -2.01 -1.90 -1.80 -1.71 -1.62 -1.54 -1.45 -1.37 -1.29 -1.20 -1.11 -1.01 -0.90 -0.77 -0.61 -0.37 0.08 23
17 -2.29 -2.05 -1.89 -1.76 -1.65 -1.55 -1.46 -1.37 -1.29 -1.21 -1.13 -1.04 -0.95 -0.86 -0.76 -0.65 -0.53 -0.36 -0.13 0.32 14
18 -2.05 -1.81 -1.65 -1.52 -1.41 -1.31 -1.22 -1.13 -1.05 -0.97 -0.88 -0.80 -0.71 -0.62 -0.52 -0.41 -0.28 -0.12 0.12 0.57 59

WG: Females

## GAMLSS-RS iteration 1: Global Deviance = 525.079 
## GAMLSS-RS iteration 2: Global Deviance = 524.8681 
## GAMLSS-RS iteration 3: Global Deviance = 524.8677
## new prediction 
## new prediction
age 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 99 N
12 -3.36 -3.12 -2.96 -2.83 -2.72 -2.62 -2.53 -2.44 -2.36 -2.28 -2.19 -2.11 -2.02 -1.93 -1.83 -1.72 -1.60 -1.44 -1.20 -0.75 78
13 -3.11 -2.87 -2.71 -2.58 -2.47 -2.37 -2.28 -2.19 -2.11 -2.03 -1.94 -1.86 -1.77 -1.68 -1.58 -1.47 -1.35 -1.18 -0.95 -0.50 11
14 -2.86 -2.62 -2.46 -2.33 -2.22 -2.12 -2.03 -1.94 -1.86 -1.78 -1.69 -1.61 -1.52 -1.43 -1.33 -1.22 -1.10 -0.94 -0.70 -0.25 14
15 -2.61 -2.37 -2.21 -2.08 -1.97 -1.87 -1.78 -1.70 -1.61 -1.53 -1.45 -1.36 -1.28 -1.19 -1.09 -0.98 -0.85 -0.69 -0.45 0.00 39
16 -2.37 -2.13 -1.97 -1.84 -1.73 -1.63 -1.54 -1.45 -1.37 -1.29 -1.20 -1.12 -1.03 -0.94 -0.84 -0.73 -0.61 -0.45 -0.21 0.24 23
17 -2.13 -1.89 -1.73 -1.60 -1.49 -1.39 -1.30 -1.21 -1.13 -1.05 -0.96 -0.88 -0.79 -0.70 -0.60 -0.49 -0.37 -0.21 0.03 0.48 27
18 -1.89 -1.65 -1.49 -1.36 -1.25 -1.15 -1.06 -0.98 -0.89 -0.81 -0.73 -0.64 -0.56 -0.46 -0.37 -0.26 -0.13 0.03 0.27 0.72 71

WS: Both Sexes

## new prediction 
## new prediction

## new prediction 
## new prediction
age 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 99 N
16 -1.80 -1.64 -1.53 -1.44 -1.37 -1.30 -1.24 -1.18 -1.13 -1.07 -1.01 -0.96 -0.90 -0.84 -0.77 -0.69 -0.61 -0.50 -0.34 -0.03 46
17 -1.68 -1.52 -1.41 -1.32 -1.25 -1.18 -1.12 -1.06 -1.00 -0.95 -0.89 -0.84 -0.78 -0.72 -0.65 -0.57 -0.49 -0.38 -0.22 0.09 41
18 -1.56 -1.40 -1.29 -1.20 -1.13 -1.06 -1.00 -0.94 -0.88 -0.83 -0.77 -0.72 -0.66 -0.59 -0.53 -0.45 -0.37 -0.26 -0.10 0.21 130
19 -1.44 -1.28 -1.17 -1.08 -1.01 -0.94 -0.88 -0.82 -0.76 -0.71 -0.65 -0.60 -0.54 -0.48 -0.41 -0.33 -0.25 -0.14 0.02 0.33 34
20 -1.32 -1.16 -1.05 -0.97 -0.89 -0.82 -0.76 -0.70 -0.65 -0.59 -0.54 -0.48 -0.42 -0.36 -0.29 -0.22 -0.13 -0.02 0.14 0.44 57
21 -1.21 -1.05 -0.94 -0.85 -0.78 -0.71 -0.65 -0.59 -0.53 -0.48 -0.42 -0.37 -0.31 -0.24 -0.18 -0.10 -0.02 0.09 0.25 0.56 60
22 -1.10 -0.94 -0.83 -0.74 -0.67 -0.60 -0.54 -0.48 -0.43 -0.37 -0.31 -0.26 -0.20 -0.14 -0.07 0.00 0.09 0.20 0.36 0.67 36
23 -1.00 -0.84 -0.73 -0.64 -0.57 -0.50 -0.44 -0.38 -0.33 -0.27 -0.21 -0.16 -0.10 -0.04 0.03 0.10 0.19 0.30 0.46 0.77 36
24 -0.91 -0.75 -0.64 -0.55 -0.48 -0.41 -0.35 -0.29 -0.23 -0.18 -0.12 -0.06 -0.01 0.06 0.12 0.20 0.28 0.39 0.55 0.86 66
25 -0.83 -0.66 -0.55 -0.47 -0.39 -0.33 -0.26 -0.21 -0.15 -0.09 -0.04 0.02 0.08 0.14 0.21 0.28 0.37 0.48 0.64 0.94 32
26 -0.75 -0.59 -0.48 -0.39 -0.32 -0.25 -0.19 -0.13 -0.07 -0.02 0.04 0.10 0.16 0.22 0.28 0.36 0.45 0.55 0.72 1.02 35
27 -0.68 -0.51 -0.41 -0.32 -0.24 -0.18 -0.12 -0.06 0.00 0.06 0.11 0.17 0.23 0.29 0.36 0.43 0.52 0.63 0.79 1.09 14
28 -0.61 -0.45 -0.34 -0.25 -0.18 -0.11 -0.05 0.01 0.07 0.12 0.18 0.24 0.29 0.36 0.42 0.50 0.58 0.69 0.86 1.16 18
29 -0.54 -0.38 -0.27 -0.19 -0.11 -0.04 0.02 0.08 0.13 0.19 0.24 0.30 0.36 0.42 0.49 0.56 0.65 0.76 0.92 1.22 18
30 -0.48 -0.32 -0.21 -0.12 -0.05 0.02 0.08 0.14 0.20 0.25 0.31 0.37 0.43 0.49 0.55 0.63 0.71 0.82 0.99 1.29 18

WS: Males

## new prediction 
## new prediction
age 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 99 N
16 -1.88 -1.70 -1.59 -1.49 -1.41 -1.34 -1.28 -1.21 -1.15 -1.09 -1.03 -0.97 -0.91 -0.84 -0.77 -0.69 -0.60 -0.48 -0.31 0.02 7
17 -1.77 -1.60 -1.48 -1.39 -1.31 -1.24 -1.17 -1.11 -1.05 -0.99 -0.93 -0.86 -0.80 -0.73 -0.66 -0.58 -0.49 -0.37 -0.20 0.13 3
18 -1.66 -1.49 -1.37 -1.28 -1.20 -1.13 -1.06 -1.00 -0.94 -0.88 -0.82 -0.76 -0.69 -0.63 -0.56 -0.48 -0.38 -0.27 -0.09 0.23 32
19 -1.56 -1.38 -1.27 -1.17 -1.09 -1.02 -0.96 -0.89 -0.83 -0.77 -0.71 -0.65 -0.59 -0.52 -0.45 -0.37 -0.28 -0.16 0.01 0.34 20
20 -1.45 -1.28 -1.16 -1.07 -0.99 -0.92 -0.85 -0.79 -0.73 -0.67 -0.61 -0.55 -0.48 -0.42 -0.35 -0.27 -0.17 -0.06 0.12 0.44 32
21 -1.35 -1.18 -1.06 -0.97 -0.89 -0.82 -0.75 -0.69 -0.62 -0.56 -0.50 -0.44 -0.38 -0.31 -0.24 -0.16 -0.07 0.05 0.22 0.55 32
22 -1.25 -1.08 -0.96 -0.87 -0.79 -0.72 -0.65 -0.59 -0.52 -0.46 -0.40 -0.34 -0.28 -0.21 -0.14 -0.06 0.03 0.15 0.32 0.65 17
23 -1.15 -0.98 -0.86 -0.77 -0.69 -0.62 -0.55 -0.49 -0.43 -0.37 -0.31 -0.25 -0.18 -0.12 -0.05 0.03 0.13 0.24 0.42 0.74 17
24 -1.06 -0.89 -0.77 -0.67 -0.60 -0.52 -0.46 -0.39 -0.33 -0.27 -0.21 -0.15 -0.09 -0.02 0.05 0.13 0.22 0.34 0.51 0.84 28
25 -0.97 -0.79 -0.68 -0.58 -0.50 -0.43 -0.36 -0.30 -0.24 -0.18 -0.12 -0.06 0.00 0.07 0.14 0.22 0.32 0.43 0.61 0.93 18
26 -0.87 -0.70 -0.58 -0.49 -0.41 -0.34 -0.27 -0.21 -0.15 -0.09 -0.03 0.03 0.10 0.16 0.23 0.31 0.41 0.52 0.70 1.02 16
27 -0.78 -0.61 -0.49 -0.40 -0.32 -0.25 -0.18 -0.12 -0.06 0.00 0.06 0.12 0.19 0.25 0.33 0.41 0.50 0.62 0.79 1.12 6
28 -0.69 -0.52 -0.40 -0.31 -0.23 -0.16 -0.09 -0.03 0.04 0.10 0.16 0.22 0.28 0.35 0.42 0.50 0.59 0.71 0.88 1.21 10
29 -0.60 -0.43 -0.31 -0.22 -0.14 -0.06 0.00 0.07 0.13 0.19 0.25 0.31 0.37 0.44 0.51 0.59 0.68 0.80 0.97 1.30 12
30 -0.51 -0.33 -0.22 -0.12 -0.04 0.03 0.09 0.16 0.22 0.28 0.34 0.40 0.46 0.53 0.60 0.68 0.77 0.89 1.07 1.39 9

WS: Females

## new prediction 
## new prediction
age 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 99 N
16 -1.64 -1.50 -1.40 -1.32 -1.26 -1.20 -1.14 -1.09 -1.04 -0.99 -0.94 -0.89 -0.84 -0.79 -0.73 -0.66 -0.59 -0.49 -0.35 -0.08 4
17 -1.52 -1.38 -1.28 -1.20 -1.14 -1.08 -1.02 -0.97 -0.92 -0.87 -0.82 -0.77 -0.72 -0.67 -0.61 -0.54 -0.46 -0.37 -0.22 0.04 8
18 -1.40 -1.26 -1.16 -1.08 -1.02 -0.96 -0.90 -0.85 -0.80 -0.75 -0.70 -0.65 -0.60 -0.55 -0.49 -0.42 -0.34 -0.25 -0.10 0.17 30
19 -1.28 -1.14 -1.04 -0.96 -0.90 -0.84 -0.78 -0.73 -0.68 -0.63 -0.58 -0.53 -0.48 -0.42 -0.37 -0.30 -0.22 -0.13 0.02 0.29 13
20 -1.16 -1.02 -0.92 -0.85 -0.78 -0.72 -0.67 -0.61 -0.56 -0.51 -0.46 -0.41 -0.36 -0.31 -0.25 -0.18 -0.11 -0.01 0.13 0.40 25
21 -1.05 -0.91 -0.81 -0.73 -0.67 -0.61 -0.55 -0.50 -0.45 -0.40 -0.35 -0.30 -0.25 -0.19 -0.13 -0.07 0.01 0.11 0.25 0.52 28
22 -0.94 -0.80 -0.70 -0.62 -0.56 -0.50 -0.44 -0.39 -0.34 -0.29 -0.24 -0.19 -0.14 -0.09 -0.03 0.04 0.12 0.21 0.36 0.63 19
23 -0.84 -0.70 -0.60 -0.52 -0.46 -0.40 -0.34 -0.29 -0.24 -0.19 -0.14 -0.09 -0.04 0.01 0.07 0.14 0.22 0.31 0.46 0.72 19
24 -0.75 -0.61 -0.51 -0.43 -0.37 -0.31 -0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.16 0.23 0.31 0.40 0.55 0.81 38
25 -0.67 -0.53 -0.43 -0.35 -0.29 -0.23 -0.17 -0.12 -0.07 -0.02 0.03 0.08 0.13 0.18 0.24 0.31 0.39 0.48 0.63 0.89 14
26 -0.60 -0.46 -0.36 -0.28 -0.22 -0.16 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.31 0.38 0.46 0.55 0.70 0.97 19
27 -0.54 -0.40 -0.30 -0.22 -0.16 -0.10 -0.04 0.01 0.06 0.11 0.16 0.21 0.26 0.32 0.38 0.44 0.52 0.62 0.76 1.03 8
28 -0.48 -0.34 -0.24 -0.17 -0.10 -0.04 0.01 0.07 0.12 0.17 0.22 0.27 0.32 0.37 0.43 0.50 0.57 0.67 0.81 1.08 8
29 -0.43 -0.29 -0.19 -0.11 -0.05 0.01 0.07 0.12 0.17 0.22 0.27 0.32 0.37 0.43 0.48 0.55 0.63 0.72 0.87 1.14 6
30 -0.38 -0.24 -0.14 -0.06 0.00 0.06 0.12 0.17 0.22 0.27 0.32 0.37 0.42 0.48 0.54 0.60 0.68 0.77 0.92 1.19 9 
save(gam_wg_ab, #gam_wg_m_ab, gam_wg_f_ab, 
     gam_ws_ab, #gam_ws_m_ab, gam_ws_f_ab,
     wg_prod, ws_prod,
     file="models/JP_WG_WS_ability_norms_models.Rdata")