Graphics Programs Reference
In-Depth Information
we observe that PC
1
(fi rst column) has high negative loads in the fi rst three
variables
amp
,
pyr
and
pla
(fi rst to third row), and high positive loads in the
fi fth variable
qtz
(fi fth row). PC
2
(second column) has high negative loads in
the vein minerals
fl u
,
sph
and
gal
, and again a positive load in
qtz
. We create
a number of plots of the PCs, where we also observe signifi cant loads of the
other PCs.
subplot(2,2,1),plot(1:9,pcs(:,1),'o'),axis([1 9 -1 1])
text((1:9)+0.2,pcs(:,1),minerals,'FontSize',8),hold
plot(1:9,zeros(9,1),'r'), title('PC 1')
subplot(2,2,2),plot(1:9,pcs(:,2),'o'),axis([1 9 -1 1])
text((1:9)+0.2,pcs(:,2),minerals,'FontSize',8),hold
plot(1:9,zeros(9,1),'r'),title('PC 2')
subplot(2,2,3),plot(1:9,pcs(:,3),'o'),axis([1 9 -1 1])
text((1:9)+0.2,pcs(:,3),minerals,'FontSize',8),hold
plot(1:9,zeros(9,1),'r'),title('PC 3')
subplot(2,2,4),plot(1:9,pcs(:,4),'o'),axis([1 9 -1 1])
text((1:9)+0.2,pcs(:,4),minerals,'FontSize',8),hold
plot(1:9,zeros(9,1),'r'),title('PC 4')
The loads of the index minerals and their relationship to the PCs can be used
to interpret the relative infl uence of the source rocks. PC
1
characterized by
strong contributions of
amp
,
pyr
and
pla
, and a contribution with opposite
sign of
qtz
probably describes the amount of magmatic rock clasts in the
sediment. The second principal component PC
2
is clearly dominated by hy-
drothermal minerals hence suggesting the detrital input from the vein. PC
3
and PC
4
show a mixed and contradictory pattern of loads and are therefore
not easy to interpret. We will see later that this observation is in line with a
rather weak and mixed signal from the sandstone source on the sediments.
An alternative way to plot of the loads is a bivariate plot of two principal
components. We ignore PC
3
and PC
4
at this point and concentrate on PC
1
and PC
2
.
plot(pcs(:,1),pcs(:,2),'o')
text(pcs(:,1)+0.02,pcs(:,2),minerals,'FontSize',14), hold
x=get(gca,'XLim'); y=get(gca,'YLim');
plot(x,zeros(size(x)),'r')
plot(zeros(size(y)),y,'r')
xlabel('First Principal Component Scores')
ylabel('Second Principal Component Scores')
Here we observe the same relationships on a single plot that were previously
shown on several graphs (Fig. 9.3). It is also possible to plot the data set as
functions of the new variables. This needs the second output of
princomp
