Hello everybody,

I have a problem with the model.table of anova.

I have data (datos2) with 4 cluster (V7), calculated with daisy and hclust.

str(datos2)
'data.frame':   56 obs. of  7 variables:
 $ Estacion: Factor w/ 56 levels "Abradelo","AltoDoRodicio",..: 1 2 3 4 5 6
7 8 9 10 ...
 $ Invierno: num  36 53.9 37.1 63.6 12.5 ...
 $ X       : int  643449 616292 562796 669916 625025 631194 511201 524431
641809 590324 ...
 $ X.1     : int  4734442 4684126 4696309 4743224 4723028 4833932 4649389
4701030 4706108 4765782 ...
 $ X.2     : int  826 981 553 1364 432 421 473 424 697 731 ...
 $ X.3     : num  87961 82902 30608 84260 94169 ...
 $ V7      : int  1 1 2 1 3 4 2 2 1 4 ...


I made the anova test:

sig.test <- aov(V7~Invierno+X+X.1+X.2+X.3,data=datos2)

> summary(sig.test)
            Df Sum Sq Mean Sq F value   Pr(>F)
Invierno     1   0.01    0.01   0.014   0.9064
X            1   0.81    0.81   1.784   0.1877
X.1          1  32.61   32.61  72.026 2.97e-11 ***
X.2          1   1.40    1.40   3.098   0.0845 .
X.3          1   0.74    0.74   1.639   0.2063
Residuals   50  22.64    0.45
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>


Now i would like to now the differences between each cluster, for this
model.tables:

mod <- model.tables(sig.test,"mean")


Tables of means
Grand mean

2.178571

 Invierno
Invierno
3.642384106 5.849889625 7.726269316 8.830022075 10.04415011 10.15452539
     2.1615      2.1628      2.1639      2.1646      2.1653      2.1653
 11.9205298 12.25165563 12.47240618 13.35540839 13.90728477 14.34878587
     2.1664      2.1666      2.1667      2.1672      2.1676      2.1678
 14.6799117 15.56291391 16.11479029 18.98454746 19.09492274 20.52980132
     2.1680      2.1686      2.1689      2.1706      2.1707      2.1715
23.73068433 24.28256071 24.39293598 25.05518764 25.71743929  26.1589404
     2.1734      2.1737      2.1738      2.1742      2.1746      2.1749
26.26931567 29.13907285 29.69094923  29.8013245 31.01545254 33.99558499
     2.1749      2.1766      2.1770      2.1770      2.1777      2.1795
 35.7615894 35.98233996 36.42384106 37.08609272  38.0794702 39.18322296
     2.1806      2.1807      2.1810      2.1814      2.1819      2.1826
44.81236203 45.36423841 49.88962472 51.87637969 53.09050773 53.42163355
     2.1859      2.1863      2.1890      2.1901      2.1909      2.1911
53.75275938 53.86313466 54.52538631 54.74613687 57.61589404 58.60927152
     2.1913      2.1913      2.1917      2.1919      2.1936      2.1942
60.26490066 63.35540839 63.57615894 64.56953642
     2.1951      2.1970      2.1971      2.1977

 X
X
497771 505372 508273 510859 511201 513379 516145 518427 524431 525267
526634
2.3693 2.3603 2.3464 2.3529 2.3850 2.3565 2.3218 2.3784 2.3245 2.3400
2.3120
531549 536101 540986 544158 547280 549546 555930 558736 560019 560578
562796
2.3085 2.2778 2.2838 2.2569 2.2480 2.2909 2.2708 2.2397 2.2067 2.2101
2.2390
570876 574209 576464 583167 585343 585393 589733 590324 594370 597892
598421
2.1876 2.2207 2.2228 2.1681 2.1689 2.2040 2.1982 2.1907 2.1252 2.1852
2.1306
598945 612930 613230 615988 616292 618769 623277 625025 625827 631194
632785
2.1654 2.0910 2.1139 2.1112 2.1276 2.0689 2.0768 2.0578 2.1086 2.0921
2.0430
636959 639190 639736 640275 641809 643449 648423 654995 657933 664123
669916
2.0585 2.0225 2.0379 2.0679 2.0327 2.0401 2.0285 1.9861 1.9910 2.0179
2.0076
670351
1.9719

 X.1
X.1
4639803 4644255 4648384 4649389 4658771 4664595 4669787 4669863 4674072
4675170
  0.871   0.969   0.964   1.144   1.293   1.143   1.334   1.199   1.554
1.500
4676811 4684126 4685177 4688943 4691376 4692421 4695132 4696309 4698702
4701030
  1.360   1.425   1.565   1.612   1.425   1.802   1.533   1.721   1.822
1.870
4702027 4703493 4703623 4706108 4711638 4714422 4717609 4718574 4720490
4723028
  1.853   1.623   1.728   1.726   2.095   2.102   1.855   2.057   2.046
2.013
4732806 4734442 4741204 4741500 4743224 4747354 4751251 4757557 4761493
4765782
  2.264   2.097   2.281   2.418   2.123   2.512   2.469   2.681   2.561
2.600
4771652 4775147 4777923 4780523 4786917 4788103 4798361 4799728 4812691
4815885
  2.817   2.828   2.809   2.755   2.881   3.046   3.225   3.060   3.173
3.421
4822868 4824470 4827467 4833932 4835419 4839683
  3.330   3.385   3.417   3.460   3.535   3.647

.
.
.


str(mod)
List of 2
 $ tables:List of 6
  ..$ Grand mean: num 2.18
  ..$ Invierno  : mtable [1:52(1d)] 2.16 2.16 2.16 2.16 2.17 ...
  .. ..- attr(*, "dimnames")=List of 1
  .. .. ..$ Invierno: chr [1:52] "3.642384106" "5.849889625" "7.726269316"
"8.830022075" ...
  ..$ X         : mtable [1:56(1d)] 2.37 2.36 2.35 2.35 2.39 ...
  .. ..- attr(*, "dimnames")=List of 1
  .. .. ..$ X: chr [1:56] "497771" "505372" "508273" "510859" ...
  ..$ X.1       : mtable [1:56(1d)] 0.871 0.969 0.964 1.144 1.293 ...
  .. ..- attr(*, "dimnames")=List of 1
  .. .. ..$ X.1: chr [1:56] "4639803" "4644255" "4648384" "4649389" ...
  ..$ X.2       : mtable [1:55(1d)] 2.29 2.41 2.23 2.13 2.51 ...
  .. ..- attr(*, "dimnames")=List of 1
  .. .. ..$ X.2: chr [1:55] "14" "25" "30" "34" ...
  ..$ X.3       : mtable [1:56(1d)] 2.21 2.17 2.14 2.11 2.03 ...
  .. ..- attr(*, "dimnames")=List of 1
  .. .. ..$ X.3: chr [1:56] "28.51498374" "55.82394391" "75.75059895"
"101.2102529" ...
 $ n     : NULL
 - attr(*, "type")= chr "means"
 - attr(*, "class")= chr [1:2] "tables_aov" "list.of"



Warnmeldungen:
1: In replications(paste("~", xx), data = mf) :
  non-factors ignored: Invierno
2: In replications(paste("~", xx), data = mf) :
  nicht-Faktoren ignoriert: X
3: In replications(paste("~", xx), data = mf) :
  nicht-Faktoren ignoriert: X.1
4: In replications(paste("~", xx), data = mf) :
  nicht-Faktoren ignoriert: X.2
5: In replications(paste("~", xx), data = mf) :
  nicht-Faktoren ignoriert: X.3


But i have this errors. I hope someone can explain me why and what to do
for solve it. Thank you! Best regards

Dominic

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