SimulExample: A simulated data set
In PHYLOGR: Functions for Phylogenetically Based Statistical Analyses

Description Format Source Examples

A simulated data set; the phylogeny is based in Bauwens and Diaz-Uriarte (1997), such as is included in the file ifsm.pdi (in the Examples directory). But the data are all completely fictitious and have nothing to do with lacertids (or, for that matter, with any other creatures).

This data frame contains the following columns:

sim.counter: the simulation counter
Tips: the name of tips; it matches those for the lacertid examples but, again, is unrelated to those
y: one numeric variable
x1: another numeric variable
x2: ditto
x3: ditto
x4: ditto
x5: guess what? same thing
x6: again
x7: once more
diet: a factor with fictitious levels Carnivore Herbivore Ommnivore

Bauwens, D., and Diaz-Uriarte, R. (1997) Covariation of life-history traits in lacertid lizards: a comparative study. The American Naturalist, 149, 91-11

# a canonical correlation example
data(SimulExample)
ex1.cancor <- cancor.phylog(SimulExample[,c(1,2,3,4,5)],SimulExample[,c(1,2,6,7,8)])
ex1.cancor
summary(ex1.cancor)
plot(ex1.cancor)

$call
cancor.phylog(data1 = SimulExample[, c(1, 2, 3, 4, 5)], data2 = SimulExample[, 
    c(1, 2, 6, 7, 8)])

$CanonicalCorrelations
   sim.counter     corr1      corr2       corr3
1            0 0.9838962 0.88417720 0.051546495
2            1 0.6325809 0.48979735 0.196570291
3            2 0.6824328 0.45178721 0.106355630
4            3 0.7508360 0.35999499 0.211118642
5            4 0.8226840 0.72493791 0.074082867
6            5 0.6884552 0.43660310 0.025980345
7            6 0.6454337 0.60856925 0.033345911
8            7 0.7725224 0.40680581 0.072988125
9            8 0.7959243 0.55203066 0.011985398
10           9 0.8718280 0.40044492 0.172152126
11          10 0.7783081 0.62310191 0.114865358
12          11 0.7137652 0.62992358 0.090824721
13          12 0.8340989 0.59310121 0.137063804
14          13 0.7864354 0.42769122 0.118935582
15          14 0.5347906 0.33700416 0.176490648
16          15 0.8073579 0.25371665 0.149527536
17          16 0.7506524 0.45185979 0.192870716
18          17 0.8063087 0.53263470 0.243448619
19          18 0.6130657 0.56128855 0.185500055
20          19 0.8059692 0.33583561 0.079950441
21          20 0.6210359 0.35395828 0.039643534
22          21 0.8272798 0.57047345 0.038052111
23          22 0.7299834 0.27509378 0.139310853
24          23 0.8081339 0.79097739 0.033994730
25          24 0.6913278 0.41491876 0.360819059
26          25 0.7046790 0.48076965 0.133645492
27          26 0.8250116 0.63290165 0.336269251
28          27 0.7330786 0.44133207 0.151679070
29          28 0.5861245 0.29914254 0.006451618
30          29 0.7630111 0.24503695 0.093865322
31          30 0.8574988 0.70790446 0.059132027
32          31 0.7124877 0.58367100 0.066604244
33          32 0.5190821 0.45489788 0.076462671
34          33 0.7551642 0.39371036 0.172938247
35          34 0.6672596 0.31272087 0.052250977
36          35 0.6044366 0.52234820 0.015956357
37          36 0.7648344 0.57775896 0.123527180
38          37 0.6652652 0.46968713 0.041937443
39          38 0.8543336 0.64290957 0.537501461
40          39 0.7914036 0.44231390 0.174731740
41          40 0.8292692 0.73714721 0.040404313
42          41 0.6953984 0.46395608 0.015520590
43          42 0.8356046 0.39414370 0.222138447
44          43 0.7481198 0.56363464 0.370969997
45          44 0.8139959 0.43544152 0.312974716
46          45 0.7635686 0.56793798 0.083859614
47          46 0.7440929 0.40523754 0.072524796
48          47 0.8662495 0.64159592 0.163946758
49          48 0.7107965 0.51345279 0.371712406
50          49 0.3955801 0.04719332 0.002114502
51          50 0.7003494 0.44649065 0.109016855

$LR.statistic
   sim.counter    lambda
1            0 67.074744
2            1 11.134845
3            2 11.697715
4            3 13.688240
5            4 25.387866
6            5 11.536600
7            6 13.534696
8            7 14.776467
9            8 18.459286
10           9 22.036433
11          10 19.379624
12          11 16.551619
13          12 22.170832
14          13 15.928086
15          14  6.602490
16          15 15.446762
17          16 14.785285
18          17 19.506912
19          18 11.944556
20          19 15.858749
21          20  8.406181
22          21 20.903367
23          22 11.602596
24          23 27.571991
25          24 13.208060
26          25 13.056980
27          26 23.938378
28          27 13.645927
29          28  6.948238
30          29 12.737112
31          30 27.379132
32          31 15.248554
33          32  7.447444
34          33 14.085860
35          34  9.380584
36          35 10.442864
37          36 17.562940
38          37 11.278322
39          38 29.472676
40          39 16.646470
41          40 26.315572
42          41 12.196852
43          42 19.136840
44          43 18.232844
45          44 18.897057
46          45 17.161739
47          46 13.383451
48          47 26.264525
49          48 15.638276
50          49  2.327536
51          50 12.266054

attr(,"class")
[1] "phylog.cancor" "list"         

Call: 
cancor.phylog(data1 = SimulExample[, c(1, 2, 3, 4, 5)], data2 = SimulExample[, 
    c(1, 2, 6, 7, 8)])

Canonical correlations from original data:
    corr1     corr2     corr3 
0.9838962 0.8841772 0.0515465 

LR statistic from original data to test that all canonical correlations are zero:
  lambda 
67.07474 

Test that all canonical correlations are zero:
   P-value 
0.01960784 

'Correlation-wise' P-value for canonical correlations:
     corr1      corr2      corr3 
0.01960784 0.01960784 0.76470588 

'Multiple' P-value for canonical correlations:
     corr1      corr2      corr3 
0.01960784 0.01960784 0.76470588 

Quantiles of canonical correlations' distributions:
          corr1     corr2     corr3
50%   0.7507442 0.4594270 0.1119411
90%   0.8342495 0.6417273 0.3153042
95%   0.8560744 0.7172729 0.3664021
99%   0.8690945 0.7646006 0.4562648
99.9% 0.8715546 0.7883397 0.5293778

Number of simulations used in analyses:  50