inst/extdata/crossdata.for1.R

In forsearch: Diagnostic Analysis Using Forward Search Procedure for Various Models

crossdata.for1 <-
list(`Rows in stage` = list(NULL, NULL, NULL, c(7, 9, 16, 12), 
    c(15, 14, 6, 13, 16), c(6, 14, 15, 13, 12, 8), c(14, 6, 13, 
    12, 8, 7, 15), c(14, 13, 8, 6, 12, 7, 15, 16), c(8, 14, 6, 
    7, 13, 15, 12, 16, 11), c(6, 14, 13, 15, 12, 8, 7, 16, 11, 
    9), c(14, 13, 15, 6, 12, 11, 9, 8, 16, 7, 5), c(14, 13, 15, 
    12, 9, 11, 6, 16, 5, 8, 10, 7), c(14, 13, 9, 11, 15, 12, 
    5, 6, 16, 10, 8, 7, 3), c(3, 14, 15, 13, 1, 11, 12, 9, 6, 
    5, 16, 4, 10, 8), c(1, 11, 3, 9, 14, 15, 13, 12, 5, 4, 10, 
    16, 2, 6, 8), c(1, 11, 9, 14, 4, 15, 13, 12, 3, 5, 2, 10, 
    16, 6, 8, 7), c(1, 14, 4, 15, 13, 11, 12, 3, 9, 2, 6, 5, 
    16, 10, 8, 7, 17), c(17, 4, 1, 15, 14, 12, 13, 3, 11, 6, 
    2, 9, 5, 16, 18, 8, 10, 7), c(4, 1, 15, 14, 12, 13, 17, 18, 
    3, 11, 6, 2, 9, 5, 16, 8, 10, 7, 19)), `Standardized residuals` = structure(c(0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14.055736261687786, 
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0.52864015649780483, 1.1750244975236415, 0.97406448802392831, 
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-57.238110978360538, -58.326700023263186, 13.772496394444527, 
14.717502130067093, 13.451762227886155, 14.551741506052409, -1.6110897707192966, 
0.2437393101487273, 0.89472127321814643, 0.6930226940608365, 
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0.35132164029044483, 0.94945317917589356, -56.155233275872767, 
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14.177249964905505, 12.912054967823604, 14.002357267680935, -2.1448889978260719, 
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0.49187285057650099, 1.1407506937959677, 0.93941845195868845, 
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0.46396667917364637, 1.0631643558038257, -56.043554015975609, 
-56.985067106128483, -58.075738101215016, 14.237404779657034, 
15.182360330472688, 13.915844723661358, 15.022012256527084, -1.1507148795712432, 
0.7102533540213426, 1.3564841617530785, 1.1555902201623214, -0.63891375219504887, 
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-0.38216002683646755, -0.21302942448107864, 0.46465110069026377, 
1.0650647135594888, -56.044276149478542, -56.984017113847337, 
-58.072527524045917, 14.485599861665881, 15.430430167418058, 
14.163588622827691, 15.273671830927039, -0.90534420202218924, 
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0.40901571121654651, -0.89620085717649789, 0.82570572128651565, 
1.5976643706361033, 1.408237689812899, -0.5844990220659515, -1.2528224670544259, 
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0.13850824065213996, -0.84724311815033559, 0.69425567004986222, 
1.5981280225849148, 1.3725614766035883, -0.77866403184504884, 
-1.482596743413521, -0.65564210634125597, -0.47729974587136259, 
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0.94914822090862927, -0.010500691488686053, -1.2309804582082067, 
-2.55035747972267, -0.28996461039367755, 0.73687124272200777, 
-0.63825915931126953, 0.12868581140751192, -0.845465407810016, 
0.68948257145565495, 1.5981448583064854, 1.371266033341946, -0.78571438082933476, 
-1.4909401047523396, -0.66015450277422449, -0.47520500686021444, 
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0.94675362620997772, 0.60324852941082874, -0.62118618018984706, 
-1.9454474714155572, -0.29368477549043187, 0.73392739642048788, 
-0.64113435615054715, 0.1183173461350958, -0.84358887325127319, 
0.68444413281859817, 1.5981626299381597, 1.3698985754416526, 
-0.7931566638716927, -1.4997472796519957, -0.66491774662616698, 
-0.47299381977486632, -0.50785908936017932, -0.45997995089753768, 
0.40017436365333758, 0.94422591424018665, 1.2511165185213471, 
0.022507007871304264, -1.3069100731922529), .Dim = c(19L, 19L
)), `Number of model parameters` = 4L, Sigma = 7.6655101370121663, 
    `Fixed parameter estimates` = structure(list(m = 1:19, `(Intercept)` = c(0, 
    0, 0, -514.37458500000037, -505.77147500000035, -492.52552500000081, 
    -488.09913999999992, -491.76376199999913, -502.60962949999958, 
    -509.69795599999952, -514.64614200000085, -520.29370914285778, 
    -305.54955771428592, -301.90376866666634, -297.99585033333324, 
    -295.91903271428549, -5.7520334999998459, 4.7842552499996192, 
    15.906263999999908), x1 = c(0, 0, 0, 341.67328000000015, 
    338.44298250000014, 333.13058000000024, 331.65511833333329, 
    333.48742933333313, 337.10271849999987, 339.46549399999986, 
    341.11488933333356, 342.99741171428576, 164.04395219047643, 
    157.89056822222204, 154.63396961111104, 156.01851469047605, 
    10.935015083333273, 5.6668707083334731, 0.10586633333336332
    ), x2 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35.790691904761843, 
    37.436732222222268, 38.088051944444459, 37.395779404761932, 
    5.155001714285655, 3.9843029642858041, 2.7485242142857595
    ), `x1:x2` = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 16.120388845238114, 16.705738220238068, 17.323627595238076
    )), class = "data.frame", row.names = c(NA, -19L)), `s^2` = c(0, 
    0, 0, 226.85678965450012, 43.351867564637438, 16.017682378050544, 
    15.278742478240884, 25.652448385476411, 47.517671838465951, 
    56.680027471257333, 59.796917658433124, 69.690971432056728, 
    62.72187428884903, 42.140013993701764, 42.446302585893797, 
    53.116063906751229, 49.32205934198344, 48.957731254121015, 
    55.060767764448094), Leverage = structure(c(0, 4, 4, 4, 4, 
    5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 8, 
    8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 
    10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 
    11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 
    13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 
    14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 
    15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 
    16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 
    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 
    18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 
    18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 
    19, 19, 19, 19, 19, 19, 19, 0, 7, 9, 16, 12, 15, 14, 6, 13, 
    16, 6, 14, 15, 13, 12, 8, 14, 6, 13, 12, 8, 7, 15, 14, 13, 
    8, 6, 12, 7, 15, 16, 8, 14, 6, 7, 13, 15, 12, 16, 11, 6, 
    14, 13, 15, 12, 8, 7, 16, 11, 9, 14, 13, 15, 6, 12, 11, 9, 
    8, 16, 7, 5, 14, 13, 15, 12, 9, 11, 6, 16, 5, 8, 10, 7, 14, 
    13, 9, 11, 15, 12, 5, 6, 16, 10, 8, 7, 3, 3, 14, 15, 13, 
    1, 11, 12, 9, 6, 5, 16, 4, 10, 8, 1, 11, 3, 9, 14, 15, 13, 
    12, 5, 4, 10, 16, 2, 6, 8, 1, 11, 9, 14, 4, 15, 13, 12, 3, 
    5, 2, 10, 16, 6, 8, 7, 1, 14, 4, 15, 13, 11, 12, 3, 9, 2, 
    6, 5, 16, 10, 8, 7, 17, 17, 4, 1, 15, 14, 12, 13, 3, 11, 
    6, 2, 9, 5, 16, 18, 8, 10, 7, 4, 1, 15, 14, 12, 13, 17, 18, 
    3, 11, 6, 2, 9, 5, 16, 8, 10, 7, 19, 0, 1.0000000006609753, 
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    0.15000733732202653, 0.13802267848620631, 0.32841568094347634
    ), .Dim = c(185L, 3L), .Dimnames = list(c("", "", "", "", 
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    "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
    "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
    ""), c("m", "Observation", "leverage"))), `Modified Cook distance` = c(0, 
    0, 0, 0, 0.053786968441285173, 0.66675661934134889, 0.10845919881742937, 
    0.16263853554158156, 0.27978366488781048, 0.12575764855652108, 
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    0.29854567734017373, 0.091001450531546249, 0.14325771271139862, 
    85.165544897518998, 0.22470134942093992, 0.33140685178367563
    ), Call = quote(forsearch_lm(formula = y ~ x1 * x2, data = crossdata, 
        initial.sample = 100)))