Dear Prof. Exoffier and everyone in google group,
I'm using fsc2 to infer the population history of my studying species.
My pop.tpl is as follows, the mutation rate is for per site per generation, generation time is 2 years:
//Parameters for the coalescence simulation program : simcoal.exe
2 samples to simulate :
//Population effective sizes (number of genes)
NPOP1
NPOP2
//Samples sizes and samples age
58
18
//Growth rates: negative growth implies population expansion
0
0
//Number of migration matrices : 0 implies no migration between demes
2
//migration matrix 0
0 MIG21
MIG12 0
//migration matrix 1: No migration
0 0
0 0
//historical event: time, source, sink, migrants, new deme size, growth rate, migr mat index
2 historical event
TBOT 1 1 0 RESBOT 0 1
TDIV 1 0 1 RESIZE 0 1
//Number of independent loci [chromosome]
1 0
//Per chromosome: Number of contiguous linkage Block: a block is a set of contiguous loci
1
//per Block:data type, number of loci, per gen recomb and mut rates
FREQ 1 0 3e-8
My pop.est is as follows:
// Priors and rules file
// *********************
[PARAMETERS]
//#isInt? #name #dist.#min #max
//all N are in number of haploid individuals
1 NPOP1 unif 100 1000000 output
1 NPOP2 unif 100 NPOP1 output paramInRange
1 NBOT unif 100 NPOP2 output paramInRange
1 TBOT unif 14000 15000 output
1 TDIV unif TBOT 15000 output paramInRange
1 ANCSIZE unif 100 1000000 output
0 MIG21 logunif 1e-9 1e-2 output
0 MIG12 logunif 1e-9 1e-2 output
[COMPLEX PARAMETERS]
0 RESBOT = NBOT/NPOP2 output
0 RESIZE = ANCSIZE/NPOP1 output
The SFS file:
1 observation
d0_0 d0_1 d0_2 d0_3 d0_4 d0_5 d0_6 d0_7 d0_8 d0_9 d0_10 d0_11 d0_12 d0_13 d0_14 d0_15 d0_16 d0_17 d0_18 d0_19 d0_20 d0_21 d0_22 d0_23 d0_24 d0_25 d0_26 d0_27 d0_28 d0_29 d0_30 d0_31 d0_32 d0_33 d0_34 d0_35 d0_36 d0_37 d0_38 d0_39 d0_40 d0_41 d0_42 d0_43 d0_44 d0_45 d0_46 d0_47 d0_48 d0_49 d0_50 d0_51 d0_52 d0_53 d0_54 d0_55 d0_56 d0_57 d0_58
d1_0 1045 81862 37623 21095 13212 8691 6280 4534 3440 2594 2082 1602 1206 1053 850 699 601 490 397 355 299 293 217 188 167 130 114 109 99 80 65 69 52 49 39 44 27 27 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
d1_1 11309 3308 2459 1922 1629 1397 1152 992 822 753 647 559 537 446 408 311 291 244 249 244 192 157 170 126 91 114 108 96 89 74 75 62 59 73 56 39 32 13.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
d1_2 3425 1870 1344 1164 982 875 779 695 653 555 509 436 402 372 305 278 282 265 207 209 175 179 144 154 115 108 105 100 96 99 74 57 77 51 58 67 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
d1_3 1545 1084 833 753 730 602 547 520 477 416 392 382 319 306 283 229 239 207 214 161 145 157 160 157 126 106 105 105 91 93 76 74 74 71 55 24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
d1_4 874 651 564 545 485 478 420 406 363 342 337 337 319 290 248 242 219 227 190 187 155 163 150 132 135 123 115 116 107 109 79 93 72 89 37 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
d1_5 535 456 412 333 343 376 351 303 338 257 276 250 254 231 250 195 180 203 179 154 168 144 146 127 150 122 125 100 117 116 99 111 89 49 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
d1_6 290 277 280 252 252 274 239 264 240 243 235 216 214 223 189 179 170 185 169 164 148 140 136 140 153 132 131 143 107 115 92 106 50.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
d1_7 191 183 204 218 211 198 212 184 162 191 208 200 190 186 179 187 156 168 162 146 138 150 141 135 126 129 112 126 112 121 110 58 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
d1_8 119 126 160 144 143 151 158 160 157 166 140 152 179 139 144 149 123 149 141 156 154 131 145 122 118 133 120 125 110 119 60.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
d1_9 53 87 83 95 104 134 111 110 124 123 130 126 125 140 116 133 111 115 137 126 111 118 120 121 96 117 120 129 141 86 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
d1_10 48 55 63 57 86 84 65 102 91 85 95 113 118 99 90 102 98 112 109 110 118 128 121 119 124 128 120 119 60.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
d1_11 25 24 40 54 52 55 61 63 72 71 88 79 91 83 85 86 95 98 91 115 108 96 108 90 96 103 107 58 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
d1_12 18 22 28 29 45 36 44 61 55 56 70 66 68 82 73 59 95 77 87 72 93 84 76 75 104 105 50.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
d1_13 12 19 15 27 21 36 40 46 37 52 47 52 55 58 72 64 63 66 76 80 64 74 75 68 93 49 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
d1_14 4 14 11 18 23 19 30 22 26 40 36 38 41 54 41 61 48 54 65 68 66 62 72 73 37 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
d1_15 1 3 9 13 13 14 24 22 14 30 21 31 30 41 52 35 47 42 50 41 64 70 75 24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
d1_16 4 4 2 4 11 9 14 9 10 19 19 24 18 34 37 32 34 24 42 45 39 53 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
d1_17 1 0 0 1 3 8 9 9 6 7 11 10 18 19 18 19 21 32 28 37 47 13.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
d1_18 7 0 3 5 5 3 6 6 4 8 4 10 11 12 11 17 17 17 26 29 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
The command I used, which means 100 runs of randon seeds:
for i in {1..100}
do
/fsc27093 -t pop.tpl -e pop.est -c 10 -m -n 100000 -l 20 -L 100 -M -q
done
This is the best model to this SFS file I tested now...... The estimated likelihood is very stable among different seeds and is the closest model to observed likelihood. For example, in run45, it gets the best estimated likelihood:
NPOP1 TBOT ANCSIZE MIG21 MIG12 NPOP2 NBOT TDIV RESBOT RESIZE MaxEstLhood MaxObsLhood
9557128 14437 19627384 2.18959e-07 1.97659e-05 98315 1281 14783 0.0130291 2.0536906 -508577.745 -494057.057
However, you can see in this .bestlhoods that the population size is far too large that is almost impossible.
This is very awkward. Could you please help me figure out the issue? Thanks a a lot in advance.