summary.biplot.RdThis function is used to print summary output of the biplot. These summary outputs are related to measures of fit.
# S3 method for biplot
summary(
object,
adequacy = TRUE,
axis.predictivity = TRUE,
sample.predictivity = TRUE,
class.predictivity = TRUE,
within.class.axis.predictivity = TRUE,
within.class.sample.predictivity = TRUE,
...
)an object of class biplot.
a logical value indicating whether variable adequacies should be reported, with default TRUE.
a logical value indicating whether axis predictivities should be reported, with default TRUE.
a logical value indicating whether sample predictivities should be reported, with default TRUE.
a logical value indicating whether class predictivities should be reported, with default TRUE
(only applicable to objects of class CVA).
a logical value indicating whether within class axis predictivity
should be reported, with default TRUE (only applicable to objects
of class CVA).
a logical value indicating whether within class sample predictivity
should be reported, with default TRUE (only applicable to objects
of class CVA).
additional arguments.
This function will not produce a return value, it is called for side effects.
out <- biplot (iris[,1:4]) |> PCA() |> fit.measures()
summary(out)
#> Object of class biplot, based on 150 samples and 4 variables.
#> 4 numeric variables.
#>
#> Quality of fit in 2 dimension(s) = 97.8%
#> Adequacy of variables in 2 dimension(s):
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 0.5617091 0.5402798 0.7639426 0.1340685
#> Axis predictivity in 2 dimension(s):
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 0.9579017 0.8400028 0.9980931 0.9365937
#> Sample predictivity in 2 dimension(s):
#> 1 2 3 4 5 6 7 8
#> 0.9998927 0.9927400 0.9999141 0.9991226 0.9984312 0.9949770 0.9914313 0.9996346
#> 9 10 11 12 13 14 15 16
#> 0.9998677 0.9941340 0.9991205 0.9949153 0.9945491 0.9996034 0.9942676 0.9897890
#> 17 18 19 20 21 22 23 24
#> 0.9937752 0.9990534 0.9972926 0.9928624 0.9896250 0.9932656 0.9918132 0.9955885
#> 25 26 27 28 29 30 31 32
#> 0.9812917 0.9897303 0.9979903 0.9990514 0.9963870 0.9975607 0.9985741 0.9876345
#> 33 34 35 36 37 38 39 40
#> 0.9833383 0.9957412 0.9970200 0.9935405 0.9859750 0.9953399 0.9994047 0.9990244
#> 41 42 43 44 45 46 47 48
#> 0.9980903 0.9756895 0.9953372 0.9830035 0.9763861 0.9959863 0.9905695 0.9987006
#> 49 50 51 52 53 54 55 56
#> 0.9996383 0.9987482 0.9275369 0.9996655 0.9544488 0.9460515 0.9172857 0.9061058
#> 57 58 59 60 61 62 63 64
#> 0.9727694 0.9996996 0.8677939 0.8686502 0.9613130 0.9328852 0.4345132 0.9679973
#> 65 66 67 68 69 70 71 72
#> 0.7995848 0.9083037 0.7968614 0.5835260 0.7900027 0.8575646 0.8524748 0.6615410
#> 73 74 75 76 77 78 79 80
#> 0.9367709 0.8661203 0.8350955 0.8929908 0.8702600 0.9873164 0.9969031 0.6815512
#> 81 82 83 84 85 86 87 88
#> 0.8937189 0.8409681 0.7829405 0.9848354 0.6901625 0.8073582 0.9666041 0.6665514
#> 89 90 91 92 93 94 95 96
#> 0.6993846 0.9909923 0.9008345 0.9710941 0.8037223 0.9913632 0.9744493 0.7089660
#> 97 98 99 100 101 102 103 104
#> 0.9071738 0.9064541 0.9625371 0.9872279 0.9171603 0.9636413 0.9976224 0.9829885
#> 105 106 107 108 109 110 111 112
#> 0.9854704 0.9888092 0.8464463 0.9729353 0.9771293 0.9794313 0.9746239 0.9977302
#> 113 114 115 116 117 118 119 120
#> 0.9941859 0.9605563 0.8476794 0.9289985 0.9929982 0.9916850 0.9818957 0.9493751
#> 121 122 123 124 125 126 127 128
#> 0.9865358 0.8716778 0.9728177 0.9846364 0.9840890 0.9861783 0.9854516 0.9691512
#> 129 130 131 132 133 134 135 136
#> 0.9942007 0.9585884 0.9705389 0.9937852 0.9874192 0.9723192 0.9230503 0.9794405
#> 137 138 139 140 141 142 143 144
#> 0.8947527 0.9797055 0.9458421 0.9902488 0.9674660 0.9350646 0.9636413 0.9867931
#> 145 146 147 148 149 150
#> 0.9500265 0.9470544 0.9688318 0.9886543 0.8735433 0.9281727