Print/Display an Object

Print/display an object, including the Call, Standard deviations, Loadings.

Usage

print(x, ...)

Arguments

x: an object of class "Fa" or "SummaryFa" or of a class derived from "Fa" or of class or "SummaryFa".
...: additional arguments, e.g., print.x=TRUE

Methods

x = "Fa": generic functions - see print, summary, predict, plot, getCenter, getEigenvalues, getFa, getLoadings, getQuan, getScores, getSdev
x = "SummaryFa": generic functions - see print, summary, predict, plot, getCenter, getEigenvalues, getFa, getLoadings, getQuan, getScores, getSdev

Value

An invisible argument x.

References

Zhang, Y. Y. (2013), An Object Oriented Solution for Robust Factor Analysis.

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"