inst/testdata/test_examples.R

In bbolker/cpcbp: common principal components/back-projection analysis

## pure-numeric examples
flury1=matrix(c(45,10,0,5,0,0,10,
  45,5,0,0,0,0,5,45,10,0,0,5,0,10,45,0,
                0,0,0,0,0,16.4,-4.8,0,0,0,0,-4.8,13.6),6)
flury2=matrix(c(27.50,-12.50,-0.50,-4.50,-2.04,3.72,-12.50,27.50,-4.50,-0.5,2.04,-3.72,-0.50,-4.50,24.50,-9.50,-3.72,-2.04,-4.5,-0.50,-9.50,24.50,3.72,2.04,-2.04,2.04,-3.72,3.72,54.76,-4.68,3.72,-3.72,-2.04,2.04,-4.68,51.24),6)
test_flury=list(flury1,flury2)
rm(flury1,flury2) ## cleanup
                
##Real Examples
test_iris<-vector("list",length=3)
test_iris[[1]]<-var(iris[51:100,1:4])
test_iris[[2]]<-var(iris[101:150,1:4])
test_iris[[3]]<-var(iris[1:50,1:4])
n_iris <- rep(49,3)

marten1=matrix(c(1.1544,0.9109,1.0330,0.7993,0.9109,2.0381,0.7056,1.4083,1.0330,0.7056,1.2100,0.7958,0.7993,1.4083,0.7958,2.0277),4)
marten2=matrix(c(0.9617,0.2806,0.9841,0.6775,0.2806,1.8475,0.3129,1.2960,0.9841,0.3129,1.2804,0.7923,0.6775,1.2960,0.7923,1.7819),4)
n_marten <- c(91,46)
test_marten=list(marten1,marten2)

turtle1=matrix(c(1.1072,0.8019,0.8160,0.8019,0.6417,0.6005,0.8160,0.6005,0.6773),3)
turtle2=matrix(c(2.6391,2.0124,2.5443,2.0124,1.6190,1.9782,2.5443,1.9782,2.5899),3)
n_turtle=rep(23,2)
test_turtle=list(turtle1,turtle2)

bank1=matrix(c(0.1326,0.0859,0.0567,0.0491,0.0859,0.1263,0.0582,0.0306,0.0567,0.0582,0.4132,-0.2635,0.0491,0.0306,-0.2635,0.4212),4)
bank2=matrix(c(0.0641,0.0489,0.0289,-0.0130,0.0489,0.0940,-0.0109,0.0071,0.0289,-0.0109,0.7242,-0.4330,-0.013,0.0071,-0.433,0.4039),4)
n_bank=c(99,84)
test_bank=list(bank1,bank2)

vole1=matrix(c(112.01,106.64,52.97,106.64,108.13,54.75,52.97,54.75,33.86),3)
vole2=matrix(c(86.08,81.66,40.24,81.66,85.54,42.08,40.24,42.08,26.66),3)
vole3=matrix(c(65.4,60.23,24.69,60.23,62.27,23.47,24.69,23.47,16.33),3)
vole4=matrix(c(88.66,79.11,41.32,79.11,80.57,38.81,41.32,38.81,23.97),3)
test_vole=list(vole1,vole2,vole3,vole4)
n_vole=c(172,140,87,75)