Hi all,
This isn't crucial to my work at the moment since I am not
using the PIC option of ace to do ancestral character
estimation. But while trying it out I noticed a very weird
result that I can't explain...basically when I run ace on my
raw trait values, I get the same sized confidence interval
(97.5% CI minus 2.5% confidence interval) for all of my
(drastically different) traits.
E.g.:
===
s1n s1t s2en s2et s2in s2it
20 4.852213 4.852213 4.852213 4.852213 4.852213
21 2.445078 2.445078 2.445078 2.445078 2.445078
22 3.200703 3.200703 3.200703 3.200703 3.200703
23 2.960947 2.960947 2.960947 2.960947 2.960947
24 2.240474 2.240474 2.240474 2.240474 2.240474
25 2.654838 2.654838 2.654838 2.654838 2.654838
===
When I transform the data in various ways, the problem goes
away, even though the rest of the code is identical
(copy-paste identical, except for the input data), so I have
(I think!) excluded some idiotic error on my part. Although
the result is so strange, I can't imagine what would cause it.
An example of the expected result:
=
s1n s1t s2en s2et s2in s2it
20 63.84491 1.4760944 44.97236 0.6305324 47.01539
21 32.17208 0.7438184 22.66201 0.3177314 23.69152
22 42.11451 0.9736874 29.66546 0.4159229 31.01312
23 38.95982 0.9007511 27.44330 0.3847672 28.69001
24 29.47992 0.6815757 20.76565 0.2911437 21.70901
25 34.93208 0.8076297 24.60616 0.3449892 25.72398
=
I got the same results on 2 different computers, on APE 2.4
and 2.7, so I doubt it is something specific to my computer.
The code below should load the data & analysis from scratch
& reproduce the weirdness. Thoughts welcome!
Nick
==
library(ape)
#
# Setup
#
# as.data.frame shortcut function
adf <- function(x)
{
return(as.data.frame(x, row.names=NULL,
stringsAsFactors=FALSE))
}
#
# Read in the ultrametric tree
#
trstring =
"(((tessulatus:0.5015255487,(((gloriamaris:0.3047954399,(ammiralis:0.2417876544,(dalli:0.1923168953,textile:0.1923168953):0.04947075908):0.06300778553):0.07060254417,(furvus:0.3149966906,((crocatus:0.2453337596,omaria:0.2453337596):0.009171713364,(aulicus:0.1887595997,episcopatus:0.1887595997):0.06574587326):0.06049121772):0.06040129347):0.07403402886,(bandanus:0.06858755301,marmoreus:0.06858755301):0.38084446):0.05209353574):0.06339155354,((laterculatus:0.4407953441,(arenatus:0.1438581051,pulicarius:0.1438581051):0.296937239):0.06667390592,(consors:0.3221133412,(aurisiacus:0.2279167829,stercusmuscarum:0.2279167829):0.09419655831):0.1853559088):0.05744785226):0.4350828977,orbignyi:1);"
chtr2 = read.tree(file="", trstring)
num_internal_nodes = chtr2$Nnode
treenodes = c(20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
31, 32, 33, 34, 35, 36, 37)
#
# Read in trait values
#
tmpdata = c(8, 15, 15, 15, 15, 8, 15, 15, 15, 15, 15, 15, 6,
15, 15, 10, 15, 15, 8, 0.42, 0.28, 0.19, 0.28, 0.28, 0.42,
0.3, 0.28, 0.28, 0.28, 0.25, 0.25, 0.3, 0.3, 0.3, 0.35, 0.3,
0.3, 0.42, 6.7, 14, 17, 14, 14, 6.7, 5, 14, 14, 14, 10, 10,
5, 5, 5, 5, 5, 5, 6.7, 0.033, 0.17, 0.3, 0.17, 0.17, 0.033,
0.1, 0.17, 0.17, 0.17, 0.17, 0.17, 0.01, 0.1, 0.1, 0.2, 0.1,
0.1, 0.033, 8.3, 12.7, 3, 12.7, 12.7, 8.3, 5, 12.7, 12.7,
12.7, 20, 20, 5, 5, 5, 10, 5, 5, 8.3, 0.074, 0.1, 0.54, 0.1,
0.1, 0.074, 0.06, 0.1, 0.1, 0.1, 0.35, 0.35, 0.06, 0.06,
0.06, 0.15, 0.06, 0.06, 0.074, 30, 19.5, 20, 19.5, 19.5, 31,
7.9, 25, 19.5, 19.5, 40, 40, 5, 8, 8, 6, 8, 22, 30, 0.4,
0.1, 0.37, 0.1, 0.1, 0.37, 0.14, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.15, 0.15, 0.1, 0.15, 0.03, 0.4, 5, 4.5, 5, 7, 4.5, 5,
3.5, 5, 4, 4, 6, 6, 2.5, 3.5, 3.5, 1.5, 3.1, 1.3, 5, 0.015,
0.005, 0.008, 0.006, 0.006, 0.006, 0.02, 0.0065, 0.006,
0.0065, 0.007, 0.01, 0.009, 0.0044, 0.02, 0.06, 0.015,
0.0067, 0.008, 0.029, 0.007, 0.041, 0.007, 0.007, 0.018,
0.1, 0.007, 0.007, 0.007, 0.12, 0.18, 0.03, 0.012, 0.055,
0.13, 0.041, 0.009, 0.015, 5, 3.665, 3.96, 3.665, 3.665, 5,
2.28, 3.665, 3.665, 3.665, 3.79, 3.79, 3.28, 2.28, 2.28, 1,
2.28, 2.28, 5, 0.02, 0.21, 0, 0.21, 0.21, 0.02, 0, 0.21,
0.21, 0.21, 0.45, 0.45, 0, 0, 0, 0, 0, 0, 0.02, 4, 2.665,
2.96, 2.665, 2.665, 4, 1.28, 2.665, 2.665, 2.665, 2.79,
2.79, 2.28, 1.28, 1.28, 0, 1.28, 1.28, 4, 0.01, 0.33, 0.17,
0.33, 0.33, 0.01, 0.32, 0.33, 0.33, 0.33, 0.56, 0.56, 0.3,
0.32, 0.32, 0, 0.3, 0.3, 0.01, 1.2, 1.2, 6.82, 1.2, 1.2,
1.2, 1.78, 1.2, 1.2, 1.2, 2.47, 2.47, 2.58, 1.78, 1.78, 1,
1.78, 1.78, 1.2, 0.15, 0.085, 0, 0.085, 0.085, 0.15, 0,
0.085, 0.085, 0.085, 0.03, 0.03, 0, 0, 0, 0, 0, 0, 0.15,
0.2, 0.2, 5.82, 0.2, 0.2, 0.2, 0.78, 0.2, 0.2, 0.2, 1.47,
1.47, 1.58, 0.78, 0.78, 0, 0.78, 0.78, 0.2, 0.14, 0.18,
0.04, 0.18, 0.18, 0.14, 0.32, 0.18, 0.18, 0.18, 0.25, 0.25,
0.4, 0.32, 0.32, 0, 0.29, 0.29, 0.14, 0.03, 0.03, 0
