## Registered S3 method overwritten by 'plsdepot':
##   method       from
##   print.nipals ade4
# load sample data
data(genus)

# get variable names from dataset
n <- names(genus)
ny <- n[grep("^gen",n)]    # Y <- names that begins with "gen"
nx <- n[-grep("^gen",n)]   # X <- remaining names

# remove "geology" and "surface" from nx
# as surface is offset and we want to use geology as additional covariate
nx <-nx[!nx%in%c("geology","surface")]

# build multivariate formula
# we also add "lat*lon" as computed covariate to catch spatial spatial patterns
form <- multivariateFormula(ny,c(nx,"I(lat*lon)"),A=c("geology"))

# define family
fam <- rep("poisson",length(ny))

genus.scglr <- scglr(formula=form,data = genus,family=fam, K=4,
 offset=genus$surface)

Display a summary of results

summary(genus.scglr)
## Squared correlations with numerical covariates (in decreasing order):
##                   sc1      sc2       sc3       sc4  best_plane  best_val
## I(lat * lon)  0.66880  0.26787  7.69e-05  2.03e-02         1/2     0.937
## lat           0.70973  0.21923  2.94e-02  1.96e-03         1/2     0.929
## pluvio_1      0.70736  0.18380  4.48e-02  3.06e-07         1/2     0.891
## pluvio_11     0.61328  0.25904  2.55e-02  2.70e-02         1/2     0.872
## pluvio_12     0.69657  0.16913  3.45e-02  2.25e-03         1/2     0.866
## pluvio_8      0.66418  0.19905  6.49e-03  7.62e-02         1/2     0.863
## lon           0.00247  0.06429  6.25e-01  2.19e-01         3/4     0.844
## altitude      0.26936  0.02136  5.68e-01  1.83e-02         1/3     0.837
## pluvio_9      0.46391  0.02146  3.60e-01  4.12e-03         1/3     0.823
## pluvio_2      0.67099  0.13844  3.96e-02  2.09e-02         1/2     0.809
## evi_12        0.01730  0.65767  9.26e-03  1.48e-01         2/4     0.806
## evi_10        0.09297  0.69712  2.76e-04  3.18e-02         1/2     0.790
## evi_21        0.74633  0.04065  9.19e-03  5.80e-03         1/2     0.787
## pluvio_7      0.64409  0.13875  1.95e-02  3.67e-02         1/2     0.783
## evi_22        0.73945  0.00834  9.31e-03  1.79e-02         1/4     0.757
## evi_13        0.00652  0.71963  4.10e-05  3.37e-02         2/4     0.753
## pluvio_5      0.07808  0.02399  5.53e-01  2.00e-01         3/4     0.753
## evi_14        0.09405  0.65146  3.60e-02  2.62e-03         1/2     0.746
## evi_11        0.06628  0.59269  1.13e-02  1.42e-01         2/4     0.734
## evi_8         0.23786  0.48037  6.88e-03  7.95e-03         1/2     0.718
## evi_20        0.61219  0.10471  3.74e-02  2.25e-03         1/2     0.717
## pluvio_6      0.49315  0.21866  2.13e-02  2.92e-02         1/2     0.712
## evi_16        0.23937  0.45836  8.98e-03  1.97e-02         1/2     0.698
## evi_18        0.46185  0.19867  5.50e-05  1.85e-02         1/2     0.661
## pluvio_10     0.06931  0.00133  5.71e-01  1.21e-03         1/3     0.640
## evi_1         0.29570  0.04706  3.45e-01  8.85e-02         1/3     0.640
## evi_23        0.59027  0.00952  4.92e-02  2.13e-02         1/3     0.639
## evi_15        0.19499  0.43739  1.12e-01  3.06e-05         1/2     0.632
## evi_19        0.49159  0.13345  1.76e-02  4.19e-04         1/2     0.625
## evi_4         0.42181  0.00472  1.22e-01  1.73e-01         1/4     0.594
## evi_9         0.24108  0.35092  5.44e-02  5.07e-02         1/2     0.592
## evi_17        0.30351  0.28835  9.40e-04  3.18e-03         1/2     0.592
## pluvio_4      0.05537  0.20147  3.80e-01  1.65e-01         2/3     0.581
## evi_2         0.35567  0.01105  2.23e-01  1.62e-01         1/3     0.578
## evi_3         0.23373  0.00752  3.32e-01  2.33e-01         1/3     0.566
## evi_5         0.31808  0.02263  4.98e-02  2.14e-01         1/4     0.532
## evi_7         0.27081  0.23478  1.96e-01  5.81e-03         1/2     0.506
## pluvio_3      0.08644  0.17868  4.02e-04  1.46e-01         2/4     0.325
## pluvio_yr     0.05313  0.09302  2.11e-01  1.32e-02         2/3     0.304
## evi_6         0.12703  0.08455  1.39e-01  1.36e-01         3/4     0.275
## 
## Squared correlations with linear predictors (in decreasing order):
##             sc1      sc2       sc3      sc4  best_plane  best_val
## gen1   0.000912  0.46237  0.000899  0.53582         2/4     0.998
## gen21  0.582105  0.00166  0.000419  0.41581         1/4     0.998
## gen18  0.418668  0.57730  0.002436  0.00159         1/2     0.996
## gen8   0.015876  0.00621  0.924547  0.05336         3/4     0.978
## gen27  0.525994  0.44516  0.022142  0.00670         1/2     0.971
## gen7   0.214801  0.73265  0.002168  0.05038         1/2     0.947
## gen22  0.704343  0.03946  0.232465  0.02374         1/3     0.937
## gen16  0.809616  0.01703  0.126117  0.04724         1/3     0.936
## gen25  0.770678  0.16066  0.030781  0.03788         1/2     0.931
## gen4   0.776168  0.07545  0.013762  0.13462         1/4     0.911
## gen17  0.114859  0.41245  0.470803  0.00189         2/3     0.883
## gen13  0.617553  0.03093  0.094788  0.25673         1/4     0.874
## gen20  0.557628  0.30612  0.124115  0.01214         1/2     0.864
## gen12  0.456888  0.39584  0.000520  0.14675         1/2     0.853
## gen11  0.700466  0.14993  0.000347  0.14926         1/2     0.850
## gen23  0.620545  0.09738  0.214517  0.06755         1/3     0.835
## gen2   0.304118  0.51641  0.177271  0.00220         1/2     0.821
## gen14  0.481295  0.00746  0.302236  0.20900         1/3     0.784
## gen19  0.149902  0.60341  0.108745  0.13794         1/2     0.753
## gen6   0.000284  0.26622  0.376885  0.35661         3/4     0.733
## gen3   0.496492  0.23665  0.090771  0.17608         1/2     0.733
## gen15  0.427298  0.15050  0.305844  0.11635         1/3     0.733
## gen9   0.003411  0.26844  0.433794  0.29436         3/4     0.728
## gen24  0.434008  0.25733  0.274629  0.03404         1/3     0.709
## gen10  0.480673  0.19625  0.149038  0.17404         1/2     0.677
## gen5   0.228834  0.16479  0.374878  0.23150         3/4     0.606
## gen26  0.295846  0.23466  0.232525  0.23697         1/4     0.533
## 
## Coefficients for dependant variables:
##                 gen1    gen2    gen3    gen4    gen5     gen6    gen7    gen8
## (intercept) -1.13391 -2.4178 -3.1632 -2.3851 -2.4478 -2.87852 -1.1772 -0.1396
## sc1          0.00354 -0.1071 -0.1725 -0.1978 -0.0687 -0.00139 -0.0622  0.0212
## sc2         -0.10061  0.1762  0.1504  0.0778  0.0736  0.05385  0.1449  0.0168
## sc3          0.00569  0.1324  0.1194 -0.0426 -0.1424 -0.08216 -0.0101  0.2623
## sc4         -0.18554  0.0197 -0.2222 -0.1781  0.1495  0.10676  0.0651 -0.0842
## geology2    -0.25134  0.0148 -0.0635 -0.5522  0.2506 -0.31190 -0.5414 -0.8644
## geology3     0.33681  0.2404  0.4513 -0.8408 -1.5774  0.90862 -2.0432 -0.1712
## geology5    -0.26846 -0.0477 -0.5602 -0.7021 -0.0530  0.18820 -0.2256  1.0874
## geology6    -0.26108  0.4777  0.3120 -1.4617 -0.3235  0.61212 -0.3824 -0.4185
##                 gen9   gen10    gen11    gen12    gen13  gen14   gen15   gen16
## (intercept) -2.51093  1.0455  0.81833  0.33917  0.58020 -1.352 -2.0073 -1.9672
## sc1          0.00569  0.1176 -0.07320 -0.03545 -0.03029 -0.121 -0.1284 -0.2184
## sc2         -0.06375 -0.0948  0.04274  0.04165  0.00856  0.019  0.0961  0.0400
## sc3          0.10393  0.1060  0.00264  0.00194 -0.01921 -0.155 -0.1758 -0.1395
## sc4         -0.11436 -0.1530 -0.07306  0.04344 -0.04223  0.172  0.1448 -0.1141
## geology2     0.60261 -0.3671 -0.15431  0.21443 -0.27642  0.505  0.5783 -0.1490
## geology3    -0.46136 -0.0110 -0.22531 -1.32704 -0.16668  0.887  0.5519 -0.0155
## geology5     0.45431  0.6989 -0.47412 -0.73740 -0.61361 -2.824 -1.1225 -1.6327
## geology6     0.63890 -0.8604  0.03874 -0.48033 -0.11401  1.082  0.5012  0.4271
##                gen17   gen18   gen19   gen20    gen21   gen22   gen23   gen24
## (intercept) -2.45334 -4.1827 -0.2651 -1.8914 -1.09273 -3.2709 -0.9284 -0.4525
## sc1         -0.05727 -0.2508  0.0729  0.0995 -0.06685 -0.2103 -0.0944 -0.0833
## sc2          0.13696  0.3717  0.1846 -0.0930  0.00451  0.0628  0.0472  0.0810
## sc3          0.18765 -0.0310  0.1005  0.0760 -0.00290 -0.1956 -0.0898  0.1073
## sc4         -0.01589  0.0335 -0.1512 -0.0317 -0.12217  0.0835  0.0674  0.0504
## geology2     0.19925  0.7547  0.1999 -0.4999  0.09073  0.1596  0.1743  0.1299
## geology3    -0.00803  0.1827 -0.2987  0.0823  0.66335  0.5874  0.2995  0.7990
## geology5     0.71780 -1.5620  1.3689  0.7728  0.14004 -1.4318 -0.3998 -0.1794
## geology6     0.95852  0.7697  0.0359 -0.4193  0.30955  0.6599  0.4938  0.5605
##               gen25   gen26   gen27
## (intercept)  0.0902  1.0427  0.6591
## sc1         -0.1611 -0.0384 -0.1429
## sc2          0.0928  0.0432  0.1659
## sc3          0.0521  0.0551 -0.0474
## sc4         -0.0772 -0.0743  0.0349
## geology2    -0.1312 -0.4234 -0.4130
## geology3     0.8697 -2.6665 -4.0113
## geology5    -0.8717  0.2328 -1.3482
## geology6     0.3861 -0.6387 -1.1895

Percent of variance captured by components

screeplot(genus.scglr)
## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?

Correlation plot for first two components

plot(genus.scglr, title="Correlation plot showing covariates")
plot(genus.scglr,predictors=TRUE,covariates=FALSE, title="Correlation plot showing predictors")

Comparing combinations of components

pairs(genus.scglr,ncol=2)

Using grouped covariates (theme)

n <- names(genus)
n <-n[!n%in%c("geology","surface","lon","lat","forest","altitude")]
ny <- n[grep("^gen",n)]    # Y <- names that begins with "gen"
nx1 <- n[grep("^evi",n)]   # theme 1 with EVI covariates
nx2 <- n[-c(grep("^evi",n),grep("^gen",n))] # theme 2 with pluviometry covariates

form <- multivariateFormula(ny,nx1,nx2,A=c("geology"))
print(form)
## Multivariate formula 
##     gen1 + gen2 + gen3 + gen4 + gen5 + gen6 + gen7 + gen8 + gen9 + 
##     gen10 + gen11 + gen12 + gen13 + gen14 + gen15 + gen16 + gen17 + 
##     gen18 + gen19 + gen20 + gen21 + gen22 + gen23 + gen24 + gen25 + 
##     gen26 + gen27 ~ evi_1 + evi_2 + evi_3 + evi_4 + evi_5 + evi_6 + 
##     evi_7 + evi_8 + evi_9 + evi_10 + evi_11 + evi_12 + evi_13 + 
##     evi_14 + evi_15 + evi_16 + evi_17 + evi_18 + evi_19 + evi_20 + 
##     evi_21 + evi_22 + evi_23 | pluvio_yr + pluvio_1 + pluvio_2 + 
##     pluvio_3 + pluvio_4 + pluvio_5 + pluvio_6 + pluvio_7 + pluvio_8 + 
##     pluvio_9 + pluvio_10 + pluvio_11 + pluvio_12 | geology 
##   Response: 
##      Y   =  gen1 + gen2 + gen3 + gen4 + gen5 + gen6 + gen7 + gen8 + gen9 + 
##     gen10 + gen11 + gen12 + gen13 + gen14 + gen15 + gen16 + gen17 + 
##     gen18 + gen19 + gen20 + gen21 + gen22 + gen23 + gen24 + gen25 + 
##     gen26 + gen27 
##   Covariates:
##      T1  =  evi_1 + evi_2 + evi_3 + evi_4 + evi_5 + evi_6 + evi_7 + evi_8 + 
##     evi_9 + evi_10 + evi_11 + evi_12 + evi_13 + evi_14 + evi_15 + 
##     evi_16 + evi_17 + evi_18 + evi_19 + evi_20 + evi_21 + evi_22 + 
##     evi_23 
##      T2  =  pluvio_yr + pluvio_1 + pluvio_2 + pluvio_3 + pluvio_4 + pluvio_5 + 
##     pluvio_6 + pluvio_7 + pluvio_8 + pluvio_9 + pluvio_10 + pluvio_11 + 
##     pluvio_12 
##      A   =  geology
testthm <-scglrTheme(form,data=genus,H=c(2,3),family="poisson",offset = genus$surface)

Specialized screeplot (one for each theme)

screeplot(testthm)
## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?

## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?

Specialized correlation plot (one for each theme)

plot(testthm)