Show/Print/Display an Object
myFaPrint.RdShow/print/display an object, including the Call, Standard deviations, Loadings, and Rotated variables (if print.x = TRUE).
Author
Ying-Ying Zhang (Robert) robertzhangyying@qq.com
Examples
data("hbk")
hbk.x = hbk[,1:3]
faCovPcaRegMcd = FaCov(x = hbk.x, factors = 2, method = "pca",
scoresMethod = "regression", cov.control = rrcov::CovControlMcd())
## you can use either object or print(object) or myFaPrint(object)
## since faCovPcaRegMcd is an object of class "Fa"
faCovPcaRegMcd
#> An object of class "FaCov"
#> Slot "call":
#> FaCov(x = hbk.x, factors = 2, cov.control = rrcov::CovControlMcd(),
#> method = "pca", scoresMethod = "regression")
#>
#> Slot "converged":
#> NULL
#>
#> Slot "loadings":
#> Factor1 Factor2
#> X1 -0.00357579 1.0407697
#> X2 1.01735884 -0.1074368
#> X3 0.55673070 0.4225046
#>
#> Slot "communality":
#> X1 X2 X3
#> 1.0832144 1.0465617 0.4884592
#>
#> Slot "uniquenesses":
#> X1 X2 X3
#> 0.1436750 0.2022401 0.6713488
#>
#> Slot "cor":
#> [1] FALSE
#>
#> Slot "covariance":
#> X1 X2 X3
#> X1 1.22688941 0.05500588 0.1271656
#> X2 0.05500588 1.24880175 0.1525276
#> X3 0.12716557 0.15252762 1.1598081
#>
#> Slot "correlation":
#> X1 X2 X3
#> X1 1.00000000 0.04443853 0.1066041
#> X2 0.04443853 1.00000000 0.1267385
#> X3 0.10660407 0.12673854 1.0000000
#>
#> Slot "usedMatrix":
#> X1 X2 X3
#> X1 1.22688941 0.05500588 0.1271656
#> X2 0.05500588 1.24880175 0.1525276
#> X3 0.12716557 0.15252762 1.1598081
#>
#> Slot "reducedCorrelation":
#> NULL
#>
#> Slot "criteria":
#> NULL
#>
#> Slot "factors":
#> [1] 2
#>
#> Slot "dof":
#> NULL
#>
#> Slot "method":
#> [1] "pca"
#>
#> Slot "scores":
#> Factor1 Factor2
#> [1,] 23.37446110 12.08300643
#> [2,] 24.34703619 11.62237670
#> [3,] 24.83571062 13.27852593
#> [4,] 26.17084132 12.61905004
#> [5,] 25.59918577 12.83553071
#> [6,] 24.28522312 12.79892797
#> [7,] 24.65518678 12.44282588
#> [8,] 23.58356104 12.06537192
#> [9,] 25.29871081 12.37449155
#> [10,] 24.28794072 11.99663283
#> [11,] 29.29010377 14.10292664
#> [12,] 29.21602329 15.67548601
#> [13,] 30.36694090 14.31639870
#> [14,] 36.61093166 12.22469826
#> [15,] 0.87846757 1.48055428
#> [16,] -0.33469848 0.81353359
#> [17,] -0.59533115 -1.67797608
#> [18,] -0.07692585 0.74960673
#> [19,] 0.88664992 -0.81140980
#> [20,] 1.32586314 1.18338691
#> [21,] 0.32355720 0.87258307
#> [22,] 1.26507774 -1.10045701
#> [23,] 0.02132206 0.03769255
#> [24,] -0.04050523 -0.62810919
#> [25,] -1.82877805 -0.54081595
#> [26,] 1.53565293 -0.52706006
#> [27,] 0.88763579 1.69714971
#> [28,] -0.65476444 0.46386279
#> [29,] -0.99571552 -0.40049510
#> [30,] -0.14326046 0.39732743
#> [31,] -0.68032580 1.14648951
#> [32,] 0.02122339 -1.36276478
#> [33,] 0.94777315 -0.29514861
#> [34,] -1.66606969 -0.94732785
#> [35,] 0.86479203 1.57748129
#> [36,] 0.74968605 -0.12896299
#> [37,] 1.40520991 -1.10997137
#> [38,] -1.02005187 -0.37734893
#> [39,] -1.60441131 0.60226525
#> [40,] 0.06134748 -1.03421632
#> [41,] 0.18615684 1.92215530
#> [42,] -0.11319178 -0.59882173
#> [43,] 0.97827831 -1.65842318
#> [44,] -0.42141261 0.86497778
#> [45,] -1.74410700 0.24465420
#> [46,] -0.49884452 0.80604306
#> [47,] -1.75203599 1.21093123
#> [48,] 0.24815304 1.70416567
#> [49,] 0.51599641 1.23750475
#> [50,] 1.21199639 0.65981510
#> [51,] -0.77346099 0.29396447
#> [52,] -1.23505304 1.49707994
#> [53,] -0.34337530 -0.32700427
#> [54,] 0.43714215 -0.96286408
#> [55,] 0.25351912 -1.18596060
#> [56,] 0.76927705 -0.24033134
#> [57,] -1.23515172 0.09662262
#> [58,] 1.10917203 0.39971572
#> [59,] 0.74995363 0.38770297
#> [60,] 0.50450331 -0.50119529
#> [61,] 1.84511298 -0.80798712
#> [62,] -1.78265404 -0.66860453
#> [63,] 0.16718657 -0.94482480
#> [64,] 1.31183557 1.20554365
#> [65,] -0.47635280 0.85094120
#> [66,] -0.22439584 -1.36787167
#> [67,] -0.02401342 -0.12687401
#> [68,] -1.48792563 -1.07691539
#> [69,] -1.48261557 0.15956004
#> [70,] 0.83487804 -0.15251392
#> [71,] 0.74070096 0.61300322
#> [72,] 0.16973526 -0.86332857
#> [73,] 0.23278757 -0.85700738
#> [74,] 0.40917146 -1.36044628
#> [75,] -0.61438698 -0.53327581
#>
#> Slot "scoresMethod":
#> [1] "regression"
#>
#> Slot "scoringCoef":
#> X1 X2 X3
#> Factor1 -0.07760082 0.7707987 0.3871595
#> Factor2 0.82485664 -0.1583555 0.2946736
#>
#> Slot "meanF":
#> Factor1 Factor2
#> 4.958958 2.405817
#>
#> Slot "corF":
#> Factor1 Factor2
#> Factor1 1.0000000 0.9730208
#> Factor2 0.9730208 1.0000000
#>
#> Slot "STATISTIC":
#> NULL
#>
#> Slot "PVAL":
#> NULL
#>
#> Slot "n.obs":
#> [1] 75
#>
#> Slot "center":
#> X1 X2 X3
#> 1.537705 1.780328 1.686885
#>
#> Slot "eigenvalues":
#> [1] 1.436470 1.181766 1.017264
#>
#> Slot "cov.control":
#> An object of class "CovControlMcd"
#> Slot "alpha":
#> [1] 0.5
#>
#> Slot "nsamp":
#> [1] 500
#>
#> Slot "scalefn":
#> NULL
#>
#> Slot "maxcsteps":
#> [1] 200
#>
#> Slot "seed":
#> NULL
#>
#> Slot "use.correction":
#> [1] TRUE
#>
#> Slot "trace":
#> [1] FALSE
#>
#> Slot "tolSolve":
#> [1] 1e-14
#>
#>
print(faCovPcaRegMcd)
#> [1] "Call:\n FaCov(x = hbk.x, factors = 2, cov.control = rrcov::CovControlMcd(), \n"
#> [2] "Call:\n method = \"pca\", scoresMethod = \"regression\") \n"
#> [1] "Standard deviations:\n 1.19852810026492"
#> [2] "Standard deviations:\n 1.08709048278114"
#> [3] "Standard deviations:\n 1.00859500489464"
#> [1] "Loadings:\n -0.00357579016818161" "Loadings:\n 1.01735883737835"
#> [3] "Loadings:\n 0.556730699513955" "Loadings:\n 1.04076973563145"
#> [5] "Loadings:\n -0.107436757583636" "Loadings:\n 0.422504631149865"
myFaPrint(faCovPcaRegMcd)
#> [1] "Call:\n FaCov(x = hbk.x, factors = 2, cov.control = rrcov::CovControlMcd(), \n"
#> [2] "Call:\n method = \"pca\", scoresMethod = \"regression\") \n"
#> [1] "Standard deviations:\n 1.19852810026492"
#> [2] "Standard deviations:\n 1.08709048278114"
#> [3] "Standard deviations:\n 1.00859500489464"
#> [1] "Loadings:\n -0.00357579016818161" "Loadings:\n 1.01735883737835"
#> [3] "Loadings:\n 0.556730699513955" "Loadings:\n 1.04076973563145"
#> [5] "Loadings:\n -0.107436757583636" "Loadings:\n 0.422504631149865"