determining how good a correlation is in R -


i'm working set of data , i've obtained correlations (using pearson's correlation coefficient). there r function or package determine how correlation permutation tests? or there other way this?

the example data:

data a

  structure(list(a = c(4.7671948292, 5.057230067, 5.3789958351,    6.1564088085, 4.8594252454, 5.8761895664, 4.4854758124, 4.7528916483,    4.4210848845, 3.9850111524), b = c(4.5852526479, 4.9673151031,    5.1601803995, 6.3082498288, 4.5796519129, 5.665788171, 4.2886052774,    4.4678455852, 4.4444468354, 3.8911975809)), .names = c("a",    "b"), row.names = c("901_at", "902_at", "903_at",     "904_at", "905_at", "906_at", "907_at", "908_at",     "909_at", "910_s_at"), class = "data.frame")

data b

      structure(list(a = c(5.5552465406, 5.8527484565, 8.3272537274,        6.4436035152, 5.597121724, 7.7741738479, 4.9931115346, 5.3852788212,        6.0292060458, 4.8351702985),b = c(5.6748698406, 6.8504588796,        9.4375062219, 7.6984745916, 5.7246927142, 9.0156741296, 4.8601744963,       5.4403609238, 6.842929093, 5.474543968)), .names = c("a", "b"      ), row.names = c("901_at", "902_at", "903_at", "904_at",      "905_at", "906_at", "907_at", "908_at", "909_at",      "910_s_at"), class = "data.frame")

the correlation calculated :

   cor1<-cor(data a, data b)

how permutation tests validate same?

here of packages know ,are powerfull:-

edit:- explaning bit better

cart(classification , regression tree)-rpart package(you can construct decision tree on binary non binary data set depend on result require,in case non binary.)

bnet(bayesian network):-deal package(it based on bayes theorem defined causal relationship.)

naive bayes classifier:-e1071 package,for basic understanding navie bayes classifier!

there still many correlation in r.


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