I have a pretty large model (MLM_LAI_10_DIC), with more than 7000 values. When I use cld() on lsmeans() on my model, I get too many groups. Is there any way to apply cld() on just some of the values? Like making a filter.
> cld_ecosistema<-cld(lsmeans(MLM_LAI_10_DIC,~Severidad.x*num.mes|ecosistema_b))
> cld_ecosistema
ecosistema_b = bosque:
Severidad.x num.mes lsmean SE df lower.CL upper.CL .group
Severidad alta 61 0.239 0.0707 159 0.09941 0.379 1234
Severidad alta 37 0.266 0.0729 178 0.12246 0.410 12345678
Severidad baja 61 0.286 0.0729 178 0.14212 0.430 12 56 90A
Severidad alta 73 0.286 0.0707 159 0.14673 0.426 12345678 0 B
Severidad baja 37 0.293 0.0729 178 0.14878 0.436 12 56 90A
Severidad baja 73 0.294 0.0707 159 0.15447 0.434 12 56 90A
No quemado 61 0.310 0.0717 168 0.16803 0.451 1 3 5 7 9 C
No quemado 37 0.340 0.0717 168 0.19808 0.481 1 3 5 7 9 C
No quemado 73 0.344 0.0707 159 0.20401 0.483 1 3 5 7 9 C
Severidad alta 1 0.386 0.0989 467 0.19161 0.580 1234567890ABCD
Severidad alta 13 0.412 0.0707 159 0.27261 0.552 567890ABCD
Severidad baja 1 0.442 0.0743 192 0.29498 0.588 1234567890ABCD
Severidad baja 49 0.451 0.0707 159 0.31107 0.590 1234567890ABCD
Severidad alta 49 0.458 0.0707 159 0.31855 0.598 9 A CD
Severidad alta 25 0.474 0.0707 159 0.33461 0.614 9 A CD
Severidad baja 13 0.484 0.0707 159 0.34451 0.624 34 78 BCD
Severidad baja 25 0.489 0.0717 168 0.34692 0.630 34 78 BCD
Severidad baja 85 0.513 0.0707 159 0.37283 0.652 34 78 BCD
Severidad alta 85 0.519 0.0707 159 0.37966 0.659 9 A CD
No quemado 49 0.529 0.0707 159 0.38922 0.669 2 4 6 8 0AB D
No quemado 25 0.624 0.0707 159 0.48404 0.763 6 8 0AB D
No quemado 13 0.640 0.0707 159 0.50076 0.780 6 8 0AB D
No quemado 85 0.647 0.0707 159 0.50754 0.787 0AB D
No quemado 1 0.650 0.0717 168 0.50834 0.792 0AB D
ecosistema_b = higrofilo:
Severidad.x num.mes lsmean SE df lower.CL upper.CL .group
Severidad baja 73 0.281 0.0707 159 0.14177 0.421 1234
Severidad alta 61 0.288 0.0707 159 0.14796 0.427 1 5
Severidad alta 73 0.294 0.0707 159 0.15465 0.434 1 5
Severidad baja 61 0.302 0.0717 167 0.16022 0.443 1234
Severidad alta 37 0.356 0.0707 159 0.21655 0.496 12 567890A
Severidad alta 13 0.432 0.0707 159 0.29222 0.572 123 567890ABCDEFG
Severidad baja 37 0.448 0.0707 159 0.30864 0.588 1234567 BC HI
No quemado 73 0.464 0.0707 159 0.32472 0.604 123456 89 B DE H JK
Severidad alta 1 0.465 0.0787 236 0.31013 0.620 1234567890ABCDEFGHIJKLM
Severidad baja 49 0.508 0.0707 159 0.36815 0.648 5678 0 BCD F HIJ L
Severidad alta 49 0.517 0.0707 159 0.37754 0.657 234 67890ABCDEFGHIJKLM
Severidad baja 85 0.556 0.0707 159 0.41630 0.696 567890ABCDEFGHIJKLMN
No quemado 61 0.562 0.0717 167 0.42034 0.703 1234567890ABCDEFGHIJKLM
Severidad alta 25 0.566 0.0707 159 0.42588 0.705 34 BCDEFGHIJKLMN
Severidad alta 85 0.648 0.0707 159 0.50806 0.787 4 HIJKLMN
Severidad baja 25 0.668 0.0707 159 0.52877 0.808 890A DEFG JKLMN
No quemado 37 0.681 0.0707 159 0.54096 0.820 7 0A C FG I LM
Severidad baja 13 0.691 0.0707 159 0.55082 0.830 9 A E G K MN
No quemado 49 0.698 0.0707 159 0.55810 0.838 7 0A C FG I LM
Severidad baja 1 0.702 0.0717 167 0.56053 0.844 9 A E G K MN
No quemado 85 0.924 0.0707 159 0.78471 1.064 N
No quemado 25 1.109 0.0707 159 0.96957 1.249 O
No quemado 13 1.132 0.0707 159 0.99240 1.272 O
No quemado 1 1.150 0.0707 159 1.00979 1.289 O
ecosistema_b = matorral:
Severidad.x num.mes lsmean SE df lower.CL upper.CL .group
Severidad baja 61 0.166 0.0830 281 0.00288 0.330 1234567890AB
Severidad baja 37 0.181 0.0766 213 0.02963 0.332 1234567890AB
No quemado 37 0.208 0.0717 167 0.06668 0.350 1 3 6
Severidad baja 73 0.235 0.0707 159 0.09487 0.374 1234567890AB
No quemado 61 0.237 0.0728 178 0.09318 0.381 1234 67
Severidad alta 73 0.243 0.0707 159 0.10367 0.383 12 5 9
Severidad alta 61 0.266 0.0717 168 0.12405 0.407 12345 890
Severidad baja 25 0.276 0.0746 194 0.12891 0.423 1234567890AB
No quemado 73 0.279 0.0707 159 0.13933 0.419 12345678 A
Severidad baja 1 0.280 0.0792 240 0.12381 0.436 1234567890AB
Severidad alta 37 0.287 0.0744 192 0.14064 0.434 12345 890
Severidad baja 85 0.323 0.0731 180 0.17827 0.467 1234567890AB
No quemado 49 0.344 0.0707 159 0.20456 0.484 1234567890AB
Severidad baja 49 0.350 0.0707 159 0.21066 0.490 1234567890AB
Severidad baja 13 0.363 0.0719 169 0.22147 0.505 1234567890AB
No quemado 1 0.386 0.0707 159 0.24666 0.526 2 45 7890AB
No quemado 25 0.417 0.0707 159 0.27763 0.557 5 890AB
Severidad alta 13 0.437 0.0707 159 0.29685 0.576 34 678 0AB
No quemado 13 0.442 0.0707 159 0.30221 0.582 5 890AB
Severidad alta 49 0.484 0.0707 159 0.34456 0.624 67 AB
No quemado 85 0.488 0.0717 168 0.34599 0.629 90 B
Severidad alta 25 0.491 0.0707 159 0.35145 0.631 67 AB
Severidad alta 85 0.560 0.0707 159 0.42014 0.700 67 AB
Severidad alta 1 nonEst NA NA NA NA
ecosistema_b = xerofito:
Severidad.x num.mes lsmean SE df lower.CL upper.CL .group
Severidad baja 61 0.157 0.0717 168 0.01520 0.298 123
Severidad baja 73 0.169 0.0707 159 0.02927 0.309 123456
Severidad baja 37 0.184 0.0707 159 0.04401 0.323 123456
No quemado 61 0.218 0.0707 159 0.07837 0.358 123 789
No quemado 37 0.223 0.0707 159 0.08319 0.363 123 789
Severidad alta 73 0.225 0.0707 159 0.08493 0.364 1 4 7 0
No quemado 73 0.235 0.0707 159 0.09571 0.375 123 789
Severidad baja 13 0.250 0.0707 159 0.11038 0.390 1234567890AB
Severidad alta 61 0.259 0.0707 159 0.11939 0.399 12 45 78 0A
Severidad baja 1 0.280 0.0762 210 0.13011 0.430 1234567890ABCD
Severidad baja 25 0.286 0.0717 167 0.14426 0.427 1234567890ABCD
Severidad baja 49 0.295 0.0707 159 0.15558 0.435 1234567890ABCD
Severidad alta 37 0.306 0.0707 159 0.16610 0.446 12 45 78 0A
No quemado 1 0.329 0.0707 159 0.18974 0.469 123 789 C
Severidad baja 85 0.358 0.0707 159 0.21801 0.497 7890ABCD
No quemado 25 0.360 0.0707 159 0.22026 0.500 123 789 C
No quemado 49 0.365 0.0707 159 0.22532 0.505 1234567890ABCD
Severidad alta 13 0.366 0.0707 159 0.22579 0.505 1234567890AB
No quemado 13 0.375 0.0707 159 0.23519 0.515 1234567890ABCD
Severidad alta 49 0.425 0.0707 159 0.28540 0.565 23 56 89 AB
Severidad alta 25 0.512 0.0707 159 0.37239 0.652 3 6 9 BCD
No quemado 85 0.529 0.0707 159 0.38887 0.668 456 0AB D
Severidad alta 85 0.618 0.0707 159 0.47877 0.758 CD
Severidad alta 1 nonEst NA NA NA NA
(1) It is possible to specify cld() such that it returns the difference between "num.mes=1" and "num.mes=85"? (2) And get letters just between those two "num.mes" values?
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