fit.measures.RdThis function computes the measures of fit for the biplot. The biplot object is augmented with additional items, which can differ depending on the type of biplot. The measures provide information on the overall quality of fit and the adequacy of representation of variables.
fit.measures(bp)an object of class biplot.
An object of class biplot. The object is augmented with
additional items, depending on the type of biplot object.
the overall quality of fit.
the adequacy of representation of variables.
For an object of class PCA:
the fit measure of each individual axis.
the fit measure for each individual sample.
For an object of class CVA:
the fit measure of each individual axis.
the fit measure for each class mean.
the fit measure for each axis based on values expressed as deviations from their class means.
the fit measure for each sample expressed as deviation from its class mean.
For an object of class CA:
the fit measure for each row of the input matrix individual sample.
the fit measure for each column of the input matrix individual sample.
predicted matrix per row profile
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