gpca: Main function gpca, Generalized Principal Component of...
In GPCSIV: GPCSIV, Generalized Principal Component of Symbolic Interval variables
Description
Usage
Arguments
Value
Author(s)
References
Examples
Description
Performs an analysis in principal axes of multiple tables of symbolic interval variables.
The function uses a class "Resdata" object.
Usage
1
2
3
Arguments
xmin
List of all data frames containing all min of initial data.
These data have the same number of rows and columns.
xmax
List of all data frames containing all max of initial data.
These data have the same number of rows and columns.
reduire
is a logical argument of the Centrage function. To centering without scaling by standard deviation,
use reduire=0. Otherwise use reduire=1.
nomVar
Set the column names of all data frames
axes
a length 2 vector specifying the components to plot
axes2
a length 2 vector specifying the components to plot
nomInd
Set the column row names of all data frames
legend
This function could be used to add legends to plots.
xlim
range for the plotted "x" values, defaulting to the range of the finite values of "x"
ylim
range for the plotted "y" values, defaulting to the range of the finite values of "y"
nametable
Set the column names of the tables
plot3d.table
for visualization in 2D and 3D of tables
Value
Returns a list including:
PC
array containing the projections of the min and max of the average of input interval datasets.
Correl
Correlations based on interval variables - dimensions
Pval2
a matrix containing all the eigenvalues, the percentage of variance
and the cumulative percentage of variance
PCinterval
array list containing the coordinates of the individuals on the principal axes
Author(s)
Brahim Brahim and Sun Makosso-Kallyth
References
S.Makosso-Kallyth, Analysis of m sets of symbolic interval variables. Revue des Nouvelles Technologies de l"Informatique,
vol. RNTI-E25. pp. 97-108, 2013.
Examples
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data(Judge1)
data(Judge2)
data(Judge3)
preparation1<-Resdata(list(Judge1,Judge2,Judge3))
List1min<-preparation1$tablemin
List1max<-preparation1$tablemax
# example 1 with the use of some parameters by default
example1<-gpca(xmin=List1min,xmax=List1max,nomInd=paste('Region',1:6),
nomVar=c('Banana','Coffee','Thea','Cocoa'))
# example 1 with visualization of table containing the coordinates
# of the individuals onto the principal axes
example1<-gpca(xmin=List1min,xmax=List1max,nomInd=paste('Region',1:6),nomVar=c('Banana',
'Coffee','Thea','Cocoa'),axes=c(1,2),axes2=c(1,2,3),plot3d.table=c(1:3),
nametable=paste('Expert',1:3,sep='-'))
# example 1 with visualization of the table 2 and 3 containing
#the coordinates of the individuals onto the principal axes
example1<-gpca(xmin=List1min,xmax=List1max,nomInd=paste('Region',1:6),
nomVar=c('Banana','Coffee','Thea','Cocoa'),axes=c(1,2),
axes2=c(1,2,3),plot3d.table=c(2:3))
#### print numeric output of example1
# input tables onto the axes of the compromise
example1$PCinterval
# Principal components of the compromise
example1$PCCompromise
# Correlation between initial interval variables and principal
#component of the compromise
example1$Correl
# print eigenvalue, % of variance, cumulative % percentage
# of PCA of the compromise
example1$Pval
data(video1)
data(video2)
data(video3)
preparation2<-Resdata(list(video1,video2,video3))
List2min<-preparation2$tablemin
List2max<-preparation2$tablemax
# example2 : analysis of video dataset
example2<-gpca(xmin=List2min,xmax=List2max,nomVar=c('nvisit','nwatch',
'nlike','ncoment','nshare'),
nametable=paste('Video', 1:3))
# example2 : analysis of video dataset with the 3D graphics
example2<-gpca(xmin=List2min,xmax=List2max,nomVar=c('nvisit',
'nwatch','nlike','ncoment','nshare'),nametable=paste('Video', 1:3),
nomInd=paste('Obs',1:10),plot3d.table=c(1,2,3))
data(oils)
preparation3<-Resdata(list(oils))
List3min<-preparation3$tablemin
List3max<-preparation3$tablemax
# example3 Interval Principal component analysis based on min and max
example3<-gpca(xmin=List3min,xmax=List3max,nomInd=rownames(oils),
nomVar=c('Gravity','Freezing','Iodine','Saponification'))
#### print numeric output of example3
# interval Principal components
example3$PCinterval
# Correlation between initial interval variables and principal
#components
example3$Correl
# print eigenvalue, % of variance, cumulative % percentage
# of PCA of the compromise
example3$Pval
# example3 Interval Principal component analysis based on min and max
#with standardisation of variables
example3bis<-gpca(xmin=List3min,xmax=List3max,nomInd=rownames(oils),
nomVar=c('Gravity','Freezing','Iodine','Saponification'),reduire=1)
# interval Principal components
example3bis$PCinterval
# Correlation between initial interval variables and principal
#components
example3bis$Correl
# print eigenvalue, % of variance, cumulative % percentage
# of PCA of the compromise
example3bis$Pval
Example output
Loading required package: scatterplot3d
Loading required package: sqldf
Loading required package: gsubfn
Loading required package: proto
Could not load tcltk. Will use slower R code instead.
Loading required package: RSQLite
dev.new(): using pdf(file="Rplots1.pdf")
dev.new(): using pdf(file="Rplots2.pdf")
dev.new(): using pdf(file="Rplots3.pdf")
dev.new(): using pdf(file="Rplots4.pdf")
dev.new(): using pdf(file="Rplots5.pdf")
dev.new(): using pdf(file="Rplots6.pdf")
dev.new(): using pdf(file="Rplots7.pdf")
dev.new(): using pdf(file="Rplots8.pdf")
dev.new(): using pdf(file="Rplots9.pdf")
dev.new(): using pdf(file="Rplots10.pdf")
dev.new(): using pdf(file="Rplots11.pdf")
dev.new(): using pdf(file="Rplots12.pdf")
dev.new(): using pdf(file="Rplots13.pdf")
dev.new(): using pdf(file="Rplots14.pdf")
dev.new(): using pdf(file="Rplots15.pdf")
dev.new(): using pdf(file="Rplots16.pdf")
dev.new(): using pdf(file="Rplots17.pdf")
dev.new(): using pdf(file="Rplots18.pdf")
dev.new(): using pdf(file="Rplots19.pdf")
dev.new(): using pdf(file="Rplots20.pdf")
dev.new(): using pdf(file="Rplots21.pdf")
dev.new(): using pdf(file="Rplots22.pdf")
dev.new(): using pdf(file="Rplots23.pdf")
dev.new(): using pdf(file="Rplots24.pdf")
[[1]]
PCMin.1 PCmax.1 PCMin.2 PCmax.2 PCMin.3 PCmax.3
1 -1.9043828 -1.6906276 -1.6973097 -1.46980541 -0.32840685 -0.23713980
2 0.6648543 0.7170355 0.1383440 0.18996763 -0.29651786 -0.22931097
3 1.8233001 1.9320340 0.1132950 0.14814026 0.05462642 0.09074404
4 1.9751944 1.9985675 -0.1480274 -0.05822476 0.07702845 0.07849832
5 -1.4453316 -1.3771256 0.2951176 0.53211899 -0.08157594 0.09690224
6 -1.5565107 -1.1370076 0.9751102 0.98127362 0.32916072 0.44599125
PCMin.4 PCmax.4
1 -0.322242415 0.05587024
2 -0.062027447 0.22145976
3 -0.007930257 0.10923748
4 -0.759909103 -0.66987506
5 0.431136944 0.56441518
6 0.135657847 0.30420683
[[2]]
PCMin.1 PCmax.1 PCMin.2 PCmax.2 PCMin.3 PCmax.3
1 -1.3721097 -1.2850463 -1.8447834 -1.2446613 -0.27484546 -0.09065018
2 0.1005696 0.2463789 -1.3713815 -0.9102382 -0.37663737 0.08622497
3 1.6325415 1.6735795 0.4749717 0.9530985 0.06097001 0.37048551
4 1.9974705 2.0757157 0.2746279 0.7343251 -0.01208360 0.40077155
5 -1.1616927 -0.9340642 0.7951728 0.7952078 -0.24061029 -0.05928570
6 -1.6112550 -1.3620879 0.6115381 0.7321224 0.06483170 0.07082887
PCMin.4 PCmax.4
1 0.70485335 0.82128562
2 -0.41096683 0.02181701
3 0.51543312 0.67702477
4 -0.48010353 -0.43990933
5 -1.05654835 -0.38698116
6 -0.05866723 0.09276254
[[3]]
PCMin.1 PCmax.1 PCMin.2 PCmax.2 PCMin.3 PCmax.3
1 -0.3580956 -0.1361526 -0.8274940 -0.8201524 0.59519733 0.95264381
2 0.7892446 0.8717568 -0.2017383 -0.1120540 -0.67004036 -0.66255124
3 2.6832210 2.8291702 0.1402089 0.2305543 -0.52305273 -0.38570872
4 0.1711216 0.1768957 -0.4451009 -0.2965103 0.64426819 0.69736516
5 -1.6111417 -1.3813749 0.4810522 0.5803501 -0.05201749 0.04808676
6 -2.1383522 -1.8962929 0.5154993 0.7553851 -0.35929054 -0.28490018
PCMin.4 PCmax.4
1 -0.63003738 -0.50809007
2 -0.07937279 0.09854730
3 -0.55899758 -0.43656706
4 1.02971630 1.13318317
5 -0.24486740 0.07855425
6 -0.13973020 0.25766145
PCMin.1 PCmax.1 PCMin.2 PCmax.2 PCMin.3 PCmax.3
1 -1.1402776 -1.1085272 -1.4540819 -1.1806535 0.05871343 0.14688618
2 0.5182229 0.6117237 -0.4483638 -0.3073363 -0.42532957 -0.29094805
3 2.0463542 2.1449279 0.2428252 0.4439310 -0.09003743 -0.02060773
4 1.3831869 1.4151349 -0.1061668 0.1265300 0.23689430 0.39172172
5 -1.4060553 -1.2308549 0.5568918 0.6027814 -0.12473457 0.02856776
6 -1.7687059 -1.4651295 0.7027704 0.8208726 0.03636408 0.05250986
PCMin.4 PCmax.4
1 -0.04182638 0.082372826
2 -0.18412235 0.113941355
3 0.03669898 0.062701182
4 -0.05670071 -0.005598472
5 -0.13785964 -0.066903872
6 0.08574639 0.111550691
Component 1 Component 2 Component 3 Component 4
Banana 0.9475776 0.28760035 -0.06420047 -0.123535371
Coffee -0.9788854 0.01704021 -0.20342589 -0.010529542
Thea -0.9082117 -0.40071121 0.10574003 -0.058318485
Cocoa -0.7314283 0.67698066 0.08178909 -0.004518791
eigenvalue percentage of variance cumulative percentage of variance
comp 1 4.10714018 78.9645203 78.96452
comp 2 0.98804906 18.9963859 97.96091
comp 3 0.08818825 1.6955212 99.65643
comp 4 0.01787006 0.3435726 100.00000
dev.new(): using pdf(file="Rplots25.pdf")
dev.new(): using pdf(file="Rplots26.pdf")
dev.new(): using pdf(file="Rplots27.pdf")
dev.new(): using pdf(file="Rplots28.pdf")
dev.new(): using pdf(file="Rplots29.pdf")
dev.new(): using pdf(file="Rplots30.pdf")
dev.new(): using pdf(file="Rplots31.pdf")
dev.new(): using pdf(file="Rplots32.pdf")
dev.new(): using pdf(file="Rplots33.pdf")
dev.new(): using pdf(file="Rplots34.pdf")
dev.new(): using pdf(file="Rplots35.pdf")
dev.new(): using pdf(file="Rplots36.pdf")
dev.new(): using pdf(file="Rplots37.pdf")
dev.new(): using pdf(file="Rplots38.pdf")
dev.new(): using pdf(file="Rplots39.pdf")
dev.new(): using pdf(file="Rplots40.pdf")
dev.new(): using pdf(file="Rplots41.pdf")
dev.new(): using pdf(file="Rplots42.pdf")
PCMin.1 PCmax.1 PCMin.2 PCmax.2 PCMin.3 PCmax.3
1 -2.777942976 -1.86800934 -0.72797797 1.1742529 -0.9088893 -0.1684641
2 -1.427877361 -1.19250193 -1.37224148 -1.0972948 -0.5752133 -0.4181994
3 -0.118956988 -0.07267297 -0.31996162 -0.3167105 0.4211059 0.4495401
4 -1.033084069 -0.27195335 -0.33313137 0.9257008 -0.1160384 0.3729293
5 -0.875403208 -0.46821222 -0.04131050 1.3592194 0.4381686 1.1656965
6 -0.001737265 0.22466975 -0.04997672 0.3970405 0.2423624 0.4398678
7 2.449889906 2.56662173 0.23544143 0.5165745 -0.4774781 -0.3122823
8 2.155007810 2.71216248 -0.77216778 0.4225431 -0.4380188 -0.1150868
PCMin.4 PCmax.4
1 0.2160371 0.342281865
2 -0.1358097 -0.098052444
3 -0.1196108 -0.005565342
4 -0.2648157 -0.207579595
5 0.1884373 0.406702409
6 -0.3927452 -0.346653211
7 -0.1547665 -0.084390324
8 0.2713041 0.385226101
Component 1 Component 2 Component 3 Component 4
Gravity -0.9483807 -0.1604614 0.1845905 -0.20187270
Freezing 0.9362286 -0.0627428 -0.2991362 -0.17336920
Iodine -0.8118563 -0.4627703 -0.3511809 0.05835227
Saponification 0.7251872 -0.6469225 0.2344409 0.02514482
eigenvalue percentage of variance cumulative percentage of variance
comp 1 5.1816748 74.023926 74.02393
comp 2 1.1591118 16.558740 90.58267
comp 3 0.5282316 7.546166 98.12883
comp 4 0.1309818 1.871168 100.00000
dev.new(): using pdf(file="Rplots43.pdf")
dev.new(): using pdf(file="Rplots44.pdf")
PCMin.1 PCmax.1 PCMin.2 PCmax.2 PCMin.3 PCmax.3
1 -88.902038 -79.261269 -8.314733 46.032639 -6.962809 25.248600
2 -89.251536 -86.082264 -27.061680 -17.939659 3.369509 4.002519
3 2.986508 3.269241 -4.370036 3.002492 -8.975449 -5.408637
4 -1.799106 -1.267630 -4.021274 7.897671 -7.189329 -4.439322
5 16.974239 28.792078 10.964405 22.444283 -26.267866 -17.159775
6 23.693967 26.537784 -1.223200 6.328128 -5.379904 -1.374109
7 68.817983 75.029000 -10.800809 -3.631286 12.000982 17.238101
8 46.075834 54.387210 -9.786896 -9.520046 7.193457 14.104032
PCMin.4 PCmax.4
1 -0.013877666 -0.001677701
2 0.001647901 0.004104825
3 0.002363158 0.005346489
4 0.007669855 0.008832632
5 -0.010859778 -0.006642694
6 0.013436084 0.015013543
7 -0.003321378 0.005846788
8 -0.015861174 -0.012020886
Component 1 Component 2 Component 3 Component 4
Gravity -0.7986670 0.23710871 -0.43018703 3.476343e-01
Freezing 0.6916349 -0.48693247 0.53342098 5.077643e-07
Iodine -0.9966081 -0.07716975 0.02858404 -3.144872e-08
Saponification 0.4228250 -0.78155566 -0.45868261 -1.040742e-07
eigenvalue percentage of variance cumulative percentage of variance
comp 1 5.926919e+03 8.724434e+01 87.24434
comp 2 5.413405e+02 7.968542e+00 95.21289
comp 3 3.252110e+02 4.787111e+00 100.00000
comp 4 1.744089e-04 2.567302e-06 100.00000
GPCSIV documentation built on May 1, 2019, 9:04 p.m.
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Description Usage Arguments Value Author(s) References Examples
Description
Performs an analysis in principal axes of multiple tables of symbolic interval variables. The function uses a class "Resdata" object.
Usage
1 2 3 |
Arguments
xmin |
List of all data frames containing all min of initial data. These data have the same number of rows and columns. |
xmax |
List of all data frames containing all max of initial data. These data have the same number of rows and columns. |
reduire |
is a logical argument of the Centrage function. To centering without scaling by standard deviation, use reduire=0. Otherwise use reduire=1. |
nomVar |
Set the column names of all data frames |
axes |
a length 2 vector specifying the components to plot |
axes2 |
a length 2 vector specifying the components to plot |
nomInd |
Set the column row names of all data frames |
legend |
This function could be used to add legends to plots. |
xlim |
range for the plotted "x" values, defaulting to the range of the finite values of "x" |
ylim |
range for the plotted "y" values, defaulting to the range of the finite values of "y" |
nametable |
Set the column names of the tables |
plot3d.table |
for visualization in 2D and 3D of tables |
Value
Returns a list including:
PC |
array containing the projections of the min and max of the average of input interval datasets. |
Correl |
Correlations based on interval variables - dimensions |
Pval2 |
a matrix containing all the eigenvalues, the percentage of variance and the cumulative percentage of variance |
PCinterval |
array list containing the coordinates of the individuals on the principal axes |
Author(s)
Brahim Brahim and Sun Makosso-Kallyth
References
S.Makosso-Kallyth, Analysis of m sets of symbolic interval variables. Revue des Nouvelles Technologies de l"Informatique, vol. RNTI-E25. pp. 97-108, 2013.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 | data(Judge1)
data(Judge2)
data(Judge3)
preparation1<-Resdata(list(Judge1,Judge2,Judge3))
List1min<-preparation1$tablemin
List1max<-preparation1$tablemax
# example 1 with the use of some parameters by default
example1<-gpca(xmin=List1min,xmax=List1max,nomInd=paste('Region',1:6),
nomVar=c('Banana','Coffee','Thea','Cocoa'))
# example 1 with visualization of table containing the coordinates
# of the individuals onto the principal axes
example1<-gpca(xmin=List1min,xmax=List1max,nomInd=paste('Region',1:6),nomVar=c('Banana',
'Coffee','Thea','Cocoa'),axes=c(1,2),axes2=c(1,2,3),plot3d.table=c(1:3),
nametable=paste('Expert',1:3,sep='-'))
# example 1 with visualization of the table 2 and 3 containing
#the coordinates of the individuals onto the principal axes
example1<-gpca(xmin=List1min,xmax=List1max,nomInd=paste('Region',1:6),
nomVar=c('Banana','Coffee','Thea','Cocoa'),axes=c(1,2),
axes2=c(1,2,3),plot3d.table=c(2:3))
#### print numeric output of example1
# input tables onto the axes of the compromise
example1$PCinterval
# Principal components of the compromise
example1$PCCompromise
# Correlation between initial interval variables and principal
#component of the compromise
example1$Correl
# print eigenvalue, % of variance, cumulative % percentage
# of PCA of the compromise
example1$Pval
data(video1)
data(video2)
data(video3)
preparation2<-Resdata(list(video1,video2,video3))
List2min<-preparation2$tablemin
List2max<-preparation2$tablemax
# example2 : analysis of video dataset
example2<-gpca(xmin=List2min,xmax=List2max,nomVar=c('nvisit','nwatch',
'nlike','ncoment','nshare'),
nametable=paste('Video', 1:3))
# example2 : analysis of video dataset with the 3D graphics
example2<-gpca(xmin=List2min,xmax=List2max,nomVar=c('nvisit',
'nwatch','nlike','ncoment','nshare'),nametable=paste('Video', 1:3),
nomInd=paste('Obs',1:10),plot3d.table=c(1,2,3))
data(oils)
preparation3<-Resdata(list(oils))
List3min<-preparation3$tablemin
List3max<-preparation3$tablemax
# example3 Interval Principal component analysis based on min and max
example3<-gpca(xmin=List3min,xmax=List3max,nomInd=rownames(oils),
nomVar=c('Gravity','Freezing','Iodine','Saponification'))
#### print numeric output of example3
# interval Principal components
example3$PCinterval
# Correlation between initial interval variables and principal
#components
example3$Correl
# print eigenvalue, % of variance, cumulative % percentage
# of PCA of the compromise
example3$Pval
# example3 Interval Principal component analysis based on min and max
#with standardisation of variables
example3bis<-gpca(xmin=List3min,xmax=List3max,nomInd=rownames(oils),
nomVar=c('Gravity','Freezing','Iodine','Saponification'),reduire=1)
# interval Principal components
example3bis$PCinterval
# Correlation between initial interval variables and principal
#components
example3bis$Correl
# print eigenvalue, % of variance, cumulative % percentage
# of PCA of the compromise
example3bis$Pval
|
Example output
Loading required package: scatterplot3d
Loading required package: sqldf
Loading required package: gsubfn
Loading required package: proto
Could not load tcltk. Will use slower R code instead.
Loading required package: RSQLite
dev.new(): using pdf(file="Rplots1.pdf")
dev.new(): using pdf(file="Rplots2.pdf")
dev.new(): using pdf(file="Rplots3.pdf")
dev.new(): using pdf(file="Rplots4.pdf")
dev.new(): using pdf(file="Rplots5.pdf")
dev.new(): using pdf(file="Rplots6.pdf")
dev.new(): using pdf(file="Rplots7.pdf")
dev.new(): using pdf(file="Rplots8.pdf")
dev.new(): using pdf(file="Rplots9.pdf")
dev.new(): using pdf(file="Rplots10.pdf")
dev.new(): using pdf(file="Rplots11.pdf")
dev.new(): using pdf(file="Rplots12.pdf")
dev.new(): using pdf(file="Rplots13.pdf")
dev.new(): using pdf(file="Rplots14.pdf")
dev.new(): using pdf(file="Rplots15.pdf")
dev.new(): using pdf(file="Rplots16.pdf")
dev.new(): using pdf(file="Rplots17.pdf")
dev.new(): using pdf(file="Rplots18.pdf")
dev.new(): using pdf(file="Rplots19.pdf")
dev.new(): using pdf(file="Rplots20.pdf")
dev.new(): using pdf(file="Rplots21.pdf")
dev.new(): using pdf(file="Rplots22.pdf")
dev.new(): using pdf(file="Rplots23.pdf")
dev.new(): using pdf(file="Rplots24.pdf")
[[1]]
PCMin.1 PCmax.1 PCMin.2 PCmax.2 PCMin.3 PCmax.3
1 -1.9043828 -1.6906276 -1.6973097 -1.46980541 -0.32840685 -0.23713980
2 0.6648543 0.7170355 0.1383440 0.18996763 -0.29651786 -0.22931097
3 1.8233001 1.9320340 0.1132950 0.14814026 0.05462642 0.09074404
4 1.9751944 1.9985675 -0.1480274 -0.05822476 0.07702845 0.07849832
5 -1.4453316 -1.3771256 0.2951176 0.53211899 -0.08157594 0.09690224
6 -1.5565107 -1.1370076 0.9751102 0.98127362 0.32916072 0.44599125
PCMin.4 PCmax.4
1 -0.322242415 0.05587024
2 -0.062027447 0.22145976
3 -0.007930257 0.10923748
4 -0.759909103 -0.66987506
5 0.431136944 0.56441518
6 0.135657847 0.30420683
[[2]]
PCMin.1 PCmax.1 PCMin.2 PCmax.2 PCMin.3 PCmax.3
1 -1.3721097 -1.2850463 -1.8447834 -1.2446613 -0.27484546 -0.09065018
2 0.1005696 0.2463789 -1.3713815 -0.9102382 -0.37663737 0.08622497
3 1.6325415 1.6735795 0.4749717 0.9530985 0.06097001 0.37048551
4 1.9974705 2.0757157 0.2746279 0.7343251 -0.01208360 0.40077155
5 -1.1616927 -0.9340642 0.7951728 0.7952078 -0.24061029 -0.05928570
6 -1.6112550 -1.3620879 0.6115381 0.7321224 0.06483170 0.07082887
PCMin.4 PCmax.4
1 0.70485335 0.82128562
2 -0.41096683 0.02181701
3 0.51543312 0.67702477
4 -0.48010353 -0.43990933
5 -1.05654835 -0.38698116
6 -0.05866723 0.09276254
[[3]]
PCMin.1 PCmax.1 PCMin.2 PCmax.2 PCMin.3 PCmax.3
1 -0.3580956 -0.1361526 -0.8274940 -0.8201524 0.59519733 0.95264381
2 0.7892446 0.8717568 -0.2017383 -0.1120540 -0.67004036 -0.66255124
3 2.6832210 2.8291702 0.1402089 0.2305543 -0.52305273 -0.38570872
4 0.1711216 0.1768957 -0.4451009 -0.2965103 0.64426819 0.69736516
5 -1.6111417 -1.3813749 0.4810522 0.5803501 -0.05201749 0.04808676
6 -2.1383522 -1.8962929 0.5154993 0.7553851 -0.35929054 -0.28490018
PCMin.4 PCmax.4
1 -0.63003738 -0.50809007
2 -0.07937279 0.09854730
3 -0.55899758 -0.43656706
4 1.02971630 1.13318317
5 -0.24486740 0.07855425
6 -0.13973020 0.25766145
PCMin.1 PCmax.1 PCMin.2 PCmax.2 PCMin.3 PCmax.3
1 -1.1402776 -1.1085272 -1.4540819 -1.1806535 0.05871343 0.14688618
2 0.5182229 0.6117237 -0.4483638 -0.3073363 -0.42532957 -0.29094805
3 2.0463542 2.1449279 0.2428252 0.4439310 -0.09003743 -0.02060773
4 1.3831869 1.4151349 -0.1061668 0.1265300 0.23689430 0.39172172
5 -1.4060553 -1.2308549 0.5568918 0.6027814 -0.12473457 0.02856776
6 -1.7687059 -1.4651295 0.7027704 0.8208726 0.03636408 0.05250986
PCMin.4 PCmax.4
1 -0.04182638 0.082372826
2 -0.18412235 0.113941355
3 0.03669898 0.062701182
4 -0.05670071 -0.005598472
5 -0.13785964 -0.066903872
6 0.08574639 0.111550691
Component 1 Component 2 Component 3 Component 4
Banana 0.9475776 0.28760035 -0.06420047 -0.123535371
Coffee -0.9788854 0.01704021 -0.20342589 -0.010529542
Thea -0.9082117 -0.40071121 0.10574003 -0.058318485
Cocoa -0.7314283 0.67698066 0.08178909 -0.004518791
eigenvalue percentage of variance cumulative percentage of variance
comp 1 4.10714018 78.9645203 78.96452
comp 2 0.98804906 18.9963859 97.96091
comp 3 0.08818825 1.6955212 99.65643
comp 4 0.01787006 0.3435726 100.00000
dev.new(): using pdf(file="Rplots25.pdf")
dev.new(): using pdf(file="Rplots26.pdf")
dev.new(): using pdf(file="Rplots27.pdf")
dev.new(): using pdf(file="Rplots28.pdf")
dev.new(): using pdf(file="Rplots29.pdf")
dev.new(): using pdf(file="Rplots30.pdf")
dev.new(): using pdf(file="Rplots31.pdf")
dev.new(): using pdf(file="Rplots32.pdf")
dev.new(): using pdf(file="Rplots33.pdf")
dev.new(): using pdf(file="Rplots34.pdf")
dev.new(): using pdf(file="Rplots35.pdf")
dev.new(): using pdf(file="Rplots36.pdf")
dev.new(): using pdf(file="Rplots37.pdf")
dev.new(): using pdf(file="Rplots38.pdf")
dev.new(): using pdf(file="Rplots39.pdf")
dev.new(): using pdf(file="Rplots40.pdf")
dev.new(): using pdf(file="Rplots41.pdf")
dev.new(): using pdf(file="Rplots42.pdf")
PCMin.1 PCmax.1 PCMin.2 PCmax.2 PCMin.3 PCmax.3
1 -2.777942976 -1.86800934 -0.72797797 1.1742529 -0.9088893 -0.1684641
2 -1.427877361 -1.19250193 -1.37224148 -1.0972948 -0.5752133 -0.4181994
3 -0.118956988 -0.07267297 -0.31996162 -0.3167105 0.4211059 0.4495401
4 -1.033084069 -0.27195335 -0.33313137 0.9257008 -0.1160384 0.3729293
5 -0.875403208 -0.46821222 -0.04131050 1.3592194 0.4381686 1.1656965
6 -0.001737265 0.22466975 -0.04997672 0.3970405 0.2423624 0.4398678
7 2.449889906 2.56662173 0.23544143 0.5165745 -0.4774781 -0.3122823
8 2.155007810 2.71216248 -0.77216778 0.4225431 -0.4380188 -0.1150868
PCMin.4 PCmax.4
1 0.2160371 0.342281865
2 -0.1358097 -0.098052444
3 -0.1196108 -0.005565342
4 -0.2648157 -0.207579595
5 0.1884373 0.406702409
6 -0.3927452 -0.346653211
7 -0.1547665 -0.084390324
8 0.2713041 0.385226101
Component 1 Component 2 Component 3 Component 4
Gravity -0.9483807 -0.1604614 0.1845905 -0.20187270
Freezing 0.9362286 -0.0627428 -0.2991362 -0.17336920
Iodine -0.8118563 -0.4627703 -0.3511809 0.05835227
Saponification 0.7251872 -0.6469225 0.2344409 0.02514482
eigenvalue percentage of variance cumulative percentage of variance
comp 1 5.1816748 74.023926 74.02393
comp 2 1.1591118 16.558740 90.58267
comp 3 0.5282316 7.546166 98.12883
comp 4 0.1309818 1.871168 100.00000
dev.new(): using pdf(file="Rplots43.pdf")
dev.new(): using pdf(file="Rplots44.pdf")
PCMin.1 PCmax.1 PCMin.2 PCmax.2 PCMin.3 PCmax.3
1 -88.902038 -79.261269 -8.314733 46.032639 -6.962809 25.248600
2 -89.251536 -86.082264 -27.061680 -17.939659 3.369509 4.002519
3 2.986508 3.269241 -4.370036 3.002492 -8.975449 -5.408637
4 -1.799106 -1.267630 -4.021274 7.897671 -7.189329 -4.439322
5 16.974239 28.792078 10.964405 22.444283 -26.267866 -17.159775
6 23.693967 26.537784 -1.223200 6.328128 -5.379904 -1.374109
7 68.817983 75.029000 -10.800809 -3.631286 12.000982 17.238101
8 46.075834 54.387210 -9.786896 -9.520046 7.193457 14.104032
PCMin.4 PCmax.4
1 -0.013877666 -0.001677701
2 0.001647901 0.004104825
3 0.002363158 0.005346489
4 0.007669855 0.008832632
5 -0.010859778 -0.006642694
6 0.013436084 0.015013543
7 -0.003321378 0.005846788
8 -0.015861174 -0.012020886
Component 1 Component 2 Component 3 Component 4
Gravity -0.7986670 0.23710871 -0.43018703 3.476343e-01
Freezing 0.6916349 -0.48693247 0.53342098 5.077643e-07
Iodine -0.9966081 -0.07716975 0.02858404 -3.144872e-08
Saponification 0.4228250 -0.78155566 -0.45868261 -1.040742e-07
eigenvalue percentage of variance cumulative percentage of variance
comp 1 5.926919e+03 8.724434e+01 87.24434
comp 2 5.413405e+02 7.968542e+00 95.21289
comp 3 3.252110e+02 4.787111e+00 100.00000
comp 4 1.744089e-04 2.567302e-06 100.00000
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