Calculates prediction
predict-methods.RdCalculates prediction using the results in object. The newdata argument is an optional data frame or matrix in which to look for variables with which to predict. If newdata is omitted, the scores are used.
Methods
signature(object = "Fa")generic functions - see
print,summary,predict,plot,getCenter,getEigenvalues,getFa,getLoadings,getQuan,getScores,getSdev
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()); 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
#>
#>
## If missing newdata, the scores are used
predict(faCovPcaRegMcd)
#> 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
##
## If not missing newdata, newdata should be scaled first.
##
newdata = hbk.x[1, ]
cor = FALSE # the default
newdata = {
if (cor == TRUE)
# standardized transformation
scale(newdata, center = faCovPcaRegMcd@center,
scale = sqrt(diag(faCovPcaRegMcd@covariance)))
else # cor == FALSE
# centralized transformation
scale(newdata, center = faCovPcaRegMcd@center, scale = FALSE)
}
##
## Now, prediction = predict(faCovPcaRegMcd)[1,] = faCovPcaRegMcd@scores[1,]
##
prediction = predict(faCovPcaRegMcd, newdata = newdata)
prediction
#> Factor1 Factor2
#> 1 23.37446 12.08301