Hello,
rpy 1.25
ubuntu 6.10
python 2.4
The code below can be copied and pasted in the R interpreter or an empty
file, as appropriate, to be run easily. The python code imports
dataframe.py that is included as an example in the rpy distribution.
My question is:
Running the R code in the interpreter prints the list of hubertgamma
values as:
0.6982304 0.6501113 0.5739095 0.5301628 0.4861548 0.4826513
0.4579655 0.4465557 0.4329692 0.4092727
Running the RPy code in a shell prints the list of hubertgamma values
as:
[0.052761567599982385, -0.0045159859941235163, -0.047153537691912426,
-0.018242422413064344, -0.022733831895949543, -0.0033926040834361161,
-0.020580660750514498, -0.018014109040888696, -0.012575979384130119]
Why aren't these two lists the same? I think I am missing something in
the conversion.
Myles
# R code
#================================================================
# from http://addictedtor.free.fr/packages
require(fpc)
require(A2R)
d.usa <- dist(USArrests, "euc")
h.usa <- hclust(d.usa, method="ward")
set.seed(1)
some.factor <- letters[1:4][rbinom(50, prob=0.5, size=3)+1]
hubertgamma <- rep(0,10)
for(i in 1:10){
hubertgamma[i] <- cluster.stats(d.usa,
cutree(h.usa, k=i+1),
G2 = FALSE,
G3 = FALSE,
silhouette = FALSE)$hubertgamma
}
A2Rplot(h.usa,
k=3,
fact.sup=some.factor,
criteria=hubertgamma,
boxes = FALSE,
col.up = "gray",
col.down = c("orange","royalblue","green3"))
hubertgamma
#prints 0.6982304 0.6501113 0.5739095 0.5301628
# 0.4861548 0.4826513 0.4579655
# 0.4465557 0.4329692 0.4092727
# python code using RPy to put in a module
#=========================================
from rpy import *
from dataframe import * # dataframe.py comes with the rpy
distribution
class_table['data.frame'] = DataFrame
class A(object):
def build(self):
set_default_mode(NO_CONVERSION) # so that, hclust finds more
than one item in rdist
d = r('USArrests')
self.rdist = r.dist(d, method = "euclidean")
self.dend = r.hclust(self.rdist, method="ward")
r.require('fpc')
r.require('A2R')
hubertgamma = []
set_default_mode(CLASS_CONVERSION)
f=['a','b','c','d']
self.somefactor = [f[int(i)] for i in r.rbinom(50, prob=0.5,
size=3)]
self.hubertgamma = []
for i in range(1,10): # not zero-based, could cause a problem
self.hubertgamma.append(r.cluster_stats(self.rdist,
r.cutree(self.dend, k=i+1),
G2 = False,
G3 = False,
silhouette = False)['hubertgamma'])
r.x11()
r.A2Rplot(self.dend,
k = 3,
fact_sup=self.somefactor,
criteria=self.hubertgamma,
boxes = True,
col_up = "gray",
col_down = ["orange","royalblue", "green3"])
#r.dev_off()
return self.hubertgamma
if __name__ == '__main__':
obj = A()
print obj.build()
#prints [0.052761567599982385, -0.0045159859941235163,
# -0.047153537691912426, -0.018242422413064344,
# -0.022733831895949543, -0.0033926040834361161,
# -0.020580660750514498, -0.018014109040888696,
# -0.012575979384130119]
-------------------------------------------------------------------------
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