library(urca)
data("UKpppuip")
data = UKpppuip[,1:5] # n = 5.
plot(ts(data))
est = ca.jo(data, type = 'trace', K = 2, spec = "transitory", ecdet = "const")
summary(est)

# Eigenvectors, normalised to first column:
  #   (These are the cointegration relations)
  #
  # p1.l1       p2.l1     e12.l1      i1.l1      i2.l1
  # p1.l1   1.0000000   1.0000000   1.000000  1.0000000  1.0000000
  # p2.l1  -0.7416024  -0.9458357  -1.397222 -1.9840046 -1.3797522
  # e12.l1 -1.0345469  -2.1091638  -2.333572  0.6975493 -0.6373520
  # i1.l1  -2.9971374 -22.8591633  10.827331 -0.8242170  2.0956201
  # i2.l1  -2.8613965  10.4695052 -12.817530  1.7284994  0.6975215
  #
  # Weights W:
  #   (This is the loading matrix)
  #
  # p1.l1        p2.l1       e12.l1         i1.l1        i2.l1
  # p1.d  -0.066164427 -0.003506203  0.002625976  0.0006857297 -0.013125003
  # p2.d  -0.083429841  0.007530168  0.008082641  0.0317811111  0.006745236
  # e12.d -0.003804601  0.032942284  0.019624465 -0.1025097997 -0.035013372
  # i1.d   0.004030585  0.012228142 -0.001300394  0.0133270315 -0.022664334
  # i2.d   0.050269357 -0.003983539  0.017150064  0.0452773744 -0.011868206

est@PI
  # p1.l1       p2.l1       e12.l1       i1.l1       i2.l1
  # p1.d  -0.07948393  0.06546368  0.078561030  0.27881486  0.11098623
  # p2.d  -0.02929068 -0.02890440  0.069438097  0.15337170  0.27360208
  # e12.d -0.08876102  0.19593347 -0.160529533 -0.51803397 -0.09737199
  # i1.d   0.00562103 -0.00790769 -0.003184998 -0.36416531  0.14038424
  # i2.d   0.09684505 -0.13092988 -0.044477555  0.06389597 -0.33538412