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2014-05-27
lz菜鸟一枚,因为毕设用到了R,需要做几个线性回归,有一元和多元的,一元的模型如下:
建模用的是xt[,1],第一列,然后需要求xp[,1]的预测值,应该是一个10行1列的矩阵或者向量,怎么求?
> xt
             [,1]         [,2]        [,3]         [,4]
[1,] -0.07529309  1.580467967 -0.44053977  0.039589652
[2,] -0.81311151 -2.932986052 -0.91965670  0.145818141
[3,]  0.29205219 -0.155773921 -0.19321290  0.373922042
[4,] -0.58967533 -0.555759442  0.10147229 -1.914793549
[5,] -0.77835019  0.520743302 -1.38384991  0.510912872
[6,] -1.07198487  0.994278697  0.41070059  0.313505959
[7,]  0.41456312 -0.046386628  0.09873107 -0.307378076
[8,] -0.36873119 -0.827107951  0.22681848  1.074602532
[9,]  1.19689733  0.110860619 -0.28976905  0.319564293
[10,] -0.25852689 -0.166349536 -1.27124397 -0.331773017
[11,]  0.64495769  0.637691294  0.58603456  0.329858257
[12,] -1.35841260  0.498956093 -0.31468336 -1.565875714
[13,]  0.37903731 -0.115354190 -1.35472521 -0.959477996
[14,] -0.31926573 -1.982161054 -0.22664688  1.149804072
[15,]  0.85493490  0.901354226  1.45979752  0.651789988
[16,]  0.47270238 -0.398994433  1.01210483  2.712453627
[17,] -0.28095489  0.405076472  0.48968274 -0.153296744
[18,]  1.11205624  0.414071603  0.60660946 -0.322786058
[19,]  0.76966529 -2.072427675 -0.10629787 -0.745066193
[20,]  1.29285452  0.785859656  0.27397255 -0.429711462
[21,]  0.99009674 -0.030741826  0.53527233 -0.337558962
[22,] -0.85888080  1.567384136  1.84497294  0.123944759
[23,]  0.29076811  0.722183774 -0.15926964 -1.215760050
[24,] -0.15522129  0.775490257 -1.41605427 -1.278051248
[25,] -0.75798867  0.899136123 -0.41087551 -1.680157494
[26,] -1.07288776 -0.892107839  0.93908289  0.013274670
[27,]  0.24335963 -0.661441540  1.21216867 -0.327600804
[28,] -0.88897267 -0.779686798 -0.93598047 -1.196599021
[29,]  0.54971658  0.298666491  0.28220191 -0.008880562
[30,] -0.92523100  0.333245070 -0.33681012 -0.323385683
[31,]  0.11074028 -0.557917818  0.77320180  0.418749707
[32,] -0.92629055  0.236081680 -0.78158433  0.414105471
[33,]  1.58332374  0.393960592  1.15773096  1.054641490
[34,]  0.18596594 -0.135192597  0.77399798  0.193782929
[35,]  0.75887177 -0.193276617  0.50588124 -0.397430909
[36,]  1.82097446  1.219814742  1.35953600  0.171487902
[37,] -1.43414194 -0.349099742  0.15997044  0.441538935
[38,] -0.86412035 -1.032101363 -0.94255464  0.859466767
[39,]  0.88157542  0.089599313 -1.05503915 -1.317193464
[40,] -0.20155030  0.854056129  0.58590658  0.560202680
[41,]  1.21678156 -0.949921893 -0.14780668  1.358680111
[42,] -0.39319718 -0.234139643 -0.64763080 -0.522968500
[43,]  1.24392942  1.184635396  1.08663108  1.166262999
[44,]  0.13820405 -0.159906592  1.02765877 -0.068978943
[45,]  0.30942074  0.721098787  1.08068605  1.394148578
[46,]  0.58611730  0.337736362  1.33093656 -0.249645207
[47,]  1.34372642 -0.573715549 -0.09884140 -1.637964478
[48,]  0.63222322  0.356903931 -1.15845161  0.228298253
[49,]  0.13703243  0.409774103 -1.11410189  0.364445624
[50,]  0.20228217 -1.169719873 -1.23515657 -0.765270674
[51,] -0.53719604  0.766620693  0.27064330  0.204489815
[52,] -0.04437350 -1.279383765 -0.96509729 -0.114259706
[53,]  0.30736934  0.397798656  0.48847010  0.317607252
[54,] -0.38138687  0.113093644  1.31384810 -0.224746587
[55,]  0.19435562 -0.230955702  0.19487534 -0.791977915
[56,]  0.72191925  1.139998577 -0.41546691 -1.218303487
[57,]  0.28647546 -0.206762789 -2.29309077 -0.464484201
[58,]  0.58286218  0.425552681  0.96459047  0.316166037
[59,]  0.65275779 -0.452879672 -0.72885691 -1.001751340
[60,]  0.72320134 -0.395524370 -0.31497198  0.943090696
[61,] -0.30279194 -1.315876722 -0.88388305  0.018212587
[62,]  1.07283606  1.020117270  1.53653515  2.572225295
[63,]  0.05141834 -0.003753067  1.40565422 -0.728026441
[64,]  0.72512890  1.004131856  0.45173935 -0.438876903
[65,]  1.24765380 -0.330914654 -1.11146036 -0.628232857
[66,] -0.08585848 -1.702236603 -1.42987955 -1.521475929
[67,]  1.06311528 -1.012706692 -1.43097105 -1.143160660
[68,]  0.31635629  0.361029883  1.73377093 -0.213910347
[69,] -0.39008430  1.505945254  0.39639901 -1.107198412
[70,]  0.83884502  0.183906000  0.75329439 -0.229530061
[71,] -0.40659728 -0.084520368 -0.09354860  0.725955570
[72,]  0.45176323  1.149219006  2.50637368 -0.910407040
[73,] -1.61871040  0.147012717  1.00448939 -0.603258929
[74,] -1.66903296  0.936892711 -2.17055394 -1.089703954
[75,] -1.23163150  1.301289075  1.42039866  1.455100305
[76,] -1.86152980 -1.381121070 -1.07231572 -0.729858302
[77,] -0.17091313  0.830888368 -0.23405671 -0.103295607
[78,]  1.25999741  0.255009625 -0.70610488 -2.426445627
[79,]  0.28886177  1.921843058  1.56341572 -1.287861290
[80,] -0.28144059  0.561433275 -0.21344500 -0.914068233
[81,] -0.53500527 -0.178461269  0.14406987  1.258969448
[82,] -1.39555134 -0.873213925  0.54561415 -0.136823520
[83,] -0.27153102  0.986694876 -0.10143355  1.935262761
[84,]  1.77959697  2.180828532  0.46261742  0.609257137
[85,]  0.98005565  0.895714099  1.39551593  0.889506867
[86,]  1.26847491  1.263836893  0.84395935  0.859874938
[87,] -0.22777133  0.023138954  0.15471518  1.057854783
[88,]  1.11977481  1.897772283 -1.19867398 -0.218899234
[89,] -1.10754671 -0.217286224 -0.72547700  0.174526420
[90,]  1.84977643 -1.357182960  0.81873039 -0.417314183
> xp
            [,1]        [,2]        [,3]       [,4]
[1,] -0.9710420  2.21884789  2.38149900 -1.1723320
[2,]  0.6731039 -0.46184619 -1.38803847  0.4185116
[3,] -1.3552966 -1.18995928  0.29476399 -1.1290014
[4,]  0.3912002 -0.76959769 -1.74505043 -0.1761842
[5,]  1.5977703 -0.09888046 -0.76129112  0.2414394
[6,] -0.2496906 -0.09703123 -0.58484687 -1.3219854
[7,] -0.1703408 -1.45786166 -0.09276508 -1.3866091
[8,] -0.5481235 -0.53395061 -1.11274750 -0.7488708
[9,]  0.8329460 -0.34447609 -0.33229635 -1.9567128
[10,] -1.1394384 -1.41199530  0.95481775 -0.2558958
> fm

Call:
lm(formula = yt ~ xt[, 1])

Coefficients:
(Intercept)      xt[, 1]  
   -0.03302      1.31891  



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2014-5-27 10:10:31
自己先顶一下,跪求大神帮忙啊
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2014-5-27 12:24:15
cbind(1, xp[, 1])%*%fm$coefficients
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2014-5-27 14:23:55
ntsean 发表于 2014-5-27 12:24
cbind(1, xp[, 1])%*%fm$coefficients
我已经用别的方法,就是把xt中每一列分别命名为x1~x4,然后建模,就解决了,但是仍然感谢
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2014-5-27 15:02:05
ntsean 发表于 2014-5-27 12:24
cbind(1, xp[, 1])%*%fm$coefficients
再问一个问题可以吗,我要计算得到模型预测之后的MSE和R方,MSE=SSE/n-p,R^2怎么得到啊
> summary(lm.1)

Call:
lm(formula = yt ~ x1)

Residuals:
     Min       1Q   Median       3Q      Max
-1.88149 -0.72921 -0.03311  0.70375  2.91875

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept) -0.03302    0.10509  -0.314    0.754   
x1           1.31891    0.12044  10.950   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9861 on 88 degrees of freedom
Multiple R-squared:  0.5767,    Adjusted R-squared:  0.5719
F-statistic: 119.9 on 1 and 88 DF,  p-value: < 2.2e-16

我不知道用summary(lm.1)$后面加什么可以得到p什么的,n和p是什么东西啊?谢谢
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2014-5-27 15:27:44
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