R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
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+ ,0)
+ ,dim=c(8
+ ,154)
+ ,dimnames=list(c('Weeks'
+ ,'uselimit'
+ ,'T40'
+ ,'T20'
+ ,'used'
+ ,'CorrectAnalysis_1'
+ ,'useful'
+ ,'outcome')
+ ,1:154))
> y <- array(NA,dim=c(8,154),dimnames=list(c('Weeks','uselimit','T40','T20','used','CorrectAnalysis_1','useful','outcome'),1:154))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '6'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
CorrectAnalysis_1 Weeks uselimit T40 T20 used useful outcome t
1 0 4 1 1 0 0 0 1 1
2 0 4 0 2 0 0 0 0 2
3 0 4 0 2 0 0 0 0 3
4 0 4 0 2 0 0 0 0 4
5 0 4 0 2 0 0 0 0 5
6 0 4 1 2 0 0 1 1 6
7 0 4 0 2 0 0 0 0 7
8 0 4 0 1 0 0 0 0 8
9 0 4 0 2 0 0 0 1 9
10 0 4 1 2 0 0 0 0 10
11 0 4 1 1 0 0 0 0 11
12 0 4 0 2 0 0 0 0 12
13 0 4 0 2 0 1 1 0 13
14 0 4 1 1 0 0 0 0 14
15 0 4 0 2 0 1 1 1 15
16 0 4 0 1 0 1 1 1 16
17 1 4 1 1 0 1 1 0 17
18 0 4 1 1 0 0 0 0 18
19 0 4 0 2 0 0 0 1 19
20 1 4 0 1 0 1 1 1 20
21 0 4 1 2 0 0 1 0 21
22 0 4 1 2 0 1 1 1 22
23 0 4 0 2 0 0 1 1 23
24 0 4 1 2 0 0 1 1 24
25 0 4 0 1 0 1 0 1 25
26 0 4 0 2 0 1 1 0 26
27 0 4 1 2 0 0 0 1 27
28 0 4 0 2 0 1 0 0 28
29 0 4 0 2 0 0 0 1 29
30 0 4 0 2 0 0 1 0 30
31 0 4 0 2 0 0 0 0 31
32 0 4 1 2 0 0 0 0 32
33 0 4 1 2 0 0 1 0 33
34 0 4 0 1 0 0 0 1 34
35 0 4 0 2 0 0 0 0 35
36 0 4 0 2 0 0 0 0 36
37 0 4 1 1 0 1 1 0 37
38 0 4 0 2 0 1 0 1 38
39 0 4 0 2 0 0 1 1 39
40 0 4 0 1 0 0 1 0 40
41 1 4 0 2 0 1 1 1 41
42 0 4 0 2 0 1 0 1 42
43 0 4 1 2 0 0 1 1 43
44 0 4 1 1 0 0 0 0 44
45 0 4 0 2 0 0 1 0 45
46 0 4 0 2 0 0 1 1 46
47 0 4 0 2 0 0 0 0 47
48 0 4 0 2 0 0 0 1 48
49 0 4 0 2 0 0 1 1 49
50 0 4 0 2 0 0 0 0 50
51 0 4 0 1 0 1 0 0 51
52 1 4 1 1 0 1 1 0 52
53 0 4 0 2 0 0 0 1 53
54 1 4 0 2 0 1 0 0 54
55 0 4 0 2 0 0 0 0 55
56 0 4 0 1 0 1 0 1 56
57 0 4 0 2 0 1 1 1 57
58 0 4 0 2 0 0 0 1 58
59 0 4 0 2 0 0 0 1 59
60 1 4 1 1 0 1 1 1 60
61 0 4 1 1 0 0 0 1 61
62 0 4 0 2 0 1 1 0 62
63 0 4 0 2 0 0 0 0 63
64 0 4 1 1 0 0 0 1 64
65 0 4 0 2 0 0 0 0 65
66 0 4 0 2 0 0 0 0 66
67 1 4 0 1 0 1 1 0 67
68 0 4 1 2 0 0 0 0 68
69 0 4 0 2 0 0 0 1 69
70 0 4 0 2 0 1 0 0 70
71 0 4 0 2 0 0 0 0 71
72 0 4 0 2 0 0 0 1 72
73 0 4 0 2 0 1 0 1 73
74 0 4 1 2 0 1 0 0 74
75 0 4 0 2 0 0 0 1 75
76 0 4 0 1 0 0 1 1 76
77 0 4 0 2 0 0 0 1 77
78 0 4 0 2 0 1 1 1 78
79 1 4 0 1 0 1 0 1 79
80 0 4 0 1 0 0 1 0 80
81 0 4 0 2 0 0 0 0 81
82 0 4 1 2 0 1 0 1 82
83 0 4 0 2 0 0 0 0 83
84 1 4 0 2 0 1 0 0 84
85 0 4 0 2 0 0 1 1 85
86 0 4 1 2 0 0 0 0 86
87 0 2 1 0 2 0 0 1 87
88 0 2 1 0 1 1 0 1 88
89 0 2 0 0 2 0 0 0 89
90 0 2 0 0 2 0 0 1 90
91 0 2 0 0 2 0 1 0 91
92 0 2 1 0 1 0 0 0 92
93 0 2 1 0 2 0 1 0 93
94 0 2 0 0 2 0 0 0 94
95 0 2 0 0 1 0 0 0 95
96 0 2 0 0 2 0 0 1 96
97 0 2 1 0 1 0 0 0 97
98 0 2 0 0 2 0 0 0 98
99 0 2 1 0 2 0 0 0 99
100 0 2 0 0 2 0 0 1 100
101 0 2 1 0 2 0 0 1 101
102 0 2 0 0 2 0 0 0 102
103 0 2 0 0 2 0 0 0 103
104 0 2 0 0 2 0 0 0 104
105 0 2 0 0 1 1 0 0 105
106 0 2 0 0 2 0 0 0 106
107 0 2 0 0 2 0 0 0 107
108 0 2 1 0 1 1 0 0 108
109 0 2 0 0 2 0 0 0 109
110 0 2 1 0 2 0 0 0 110
111 0 2 1 0 1 1 1 0 111
112 0 2 0 0 1 0 0 0 112
113 0 2 0 0 2 1 0 0 113
114 0 2 1 0 1 1 0 0 114
115 0 2 1 0 2 0 0 0 115
116 0 2 0 0 2 0 0 0 116
117 0 2 1 0 2 0 0 1 117
118 0 2 1 0 2 0 0 0 118
119 0 2 0 0 2 0 0 0 119
120 0 2 0 0 2 0 0 1 120
121 0 2 1 0 2 0 0 0 121
122 0 2 0 0 2 0 0 0 122
123 0 2 1 0 1 1 0 0 123
124 0 2 0 0 2 1 1 1 124
125 0 2 0 0 2 0 0 1 125
126 0 2 0 0 1 0 0 0 126
127 0 2 0 0 2 0 1 0 127
128 0 2 0 0 2 0 0 1 128
129 0 2 0 0 2 0 0 0 129
130 0 2 0 0 2 0 0 1 130
131 0 2 1 0 2 0 0 0 131
132 0 2 1 0 2 0 0 1 132
133 0 2 1 0 2 1 0 0 133
134 0 2 0 0 2 0 0 0 134
135 0 2 0 0 2 0 0 0 135
136 0 2 0 0 2 0 0 0 136
137 0 2 1 0 2 1 1 1 137
138 0 2 1 0 1 1 1 1 138
139 0 2 0 0 1 0 0 0 139
140 0 2 0 0 2 0 0 0 140
141 1 2 0 0 2 1 0 1 141
142 0 2 0 0 1 1 0 1 142
143 0 2 1 0 2 0 0 0 143
144 0 2 0 0 2 0 1 1 144
145 0 2 0 0 2 0 1 0 145
146 0 2 0 0 1 0 0 1 146
147 0 2 0 0 1 1 0 0 147
148 0 2 0 0 1 0 0 0 148
149 0 2 1 0 2 0 0 0 149
150 0 2 0 0 2 0 1 1 150
151 0 2 0 0 2 0 0 1 151
152 1 2 1 0 2 1 0 0 152
153 1 2 1 0 2 1 1 0 153
154 0 2 1 0 2 1 0 0 154
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Weeks uselimit T40 T20 used
-1.157981 0.351152 -0.001993 -0.163698 0.150676 0.248752
useful outcome t
0.044040 -0.041408 0.001497
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.42910 -0.10302 -0.00974 0.04409 0.76802
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.1579815 0.3068667 -3.774 0.000234 ***
Weeks 0.3511516 0.0853216 4.116 6.45e-05 ***
uselimit -0.0019933 0.0413661 -0.048 0.961633
T40 -0.1636975 0.0581942 -2.813 0.005590 **
T20 0.1506761 0.0674098 2.235 0.026931 *
used 0.2487521 0.0452857 5.493 1.73e-07 ***
useful 0.0440401 0.0452789 0.973 0.332352
outcome -0.0414081 0.0393837 -1.051 0.294823
t 0.0014967 0.0008449 1.771 0.078586 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2313 on 145 degrees of freedom
Multiple R-squared: 0.2992, Adjusted R-squared: 0.2605
F-statistic: 7.738 on 8 and 145 DF, p-value: 1.301e-08
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.0000000000 0.000000000 1.000000000
[2,] 0.0000000000 0.000000000 1.000000000
[3,] 0.0000000000 0.000000000 1.000000000
[4,] 0.0000000000 0.000000000 1.000000000
[5,] 0.0000000000 0.000000000 1.000000000
[6,] 0.6453969236 0.709206153 0.354603076
[7,] 0.5641523425 0.871695315 0.435847657
[8,] 0.5546027813 0.890794437 0.445397219
[9,] 0.9305898745 0.138820251 0.069410126
[10,] 0.9023780835 0.195243833 0.097621916
[11,] 0.8937590022 0.212481996 0.106240998
[12,] 0.8539579739 0.292084052 0.146042026
[13,] 0.8065197531 0.386960494 0.193480247
[14,] 0.8016856651 0.396628670 0.198314335
[15,] 0.7889446898 0.422110620 0.211055310
[16,] 0.7652047788 0.469590442 0.234795221
[17,] 0.7162643459 0.567471308 0.283735654
[18,] 0.6712743134 0.657451373 0.328725687
[19,] 0.6129369998 0.774126000 0.387063000
[20,] 0.5538722376 0.892255525 0.446127762
[21,] 0.4931882057 0.986376411 0.506811794
[22,] 0.4330064178 0.866012836 0.566993582
[23,] 0.3789491565 0.757898313 0.621050844
[24,] 0.3250163676 0.650032735 0.674983632
[25,] 0.2737475796 0.547495159 0.726252420
[26,] 0.3453587229 0.690717446 0.654641277
[27,] 0.2997810411 0.599562082 0.700218959
[28,] 0.2495828504 0.499165701 0.750417150
[29,] 0.2301126874 0.460225375 0.769887313
[30,] 0.7965195367 0.406960927 0.203480463
[31,] 0.7695899656 0.460820069 0.230410034
[32,] 0.7274780897 0.545043821 0.272521910
[33,] 0.6911257345 0.617748531 0.308874266
[34,] 0.6420109252 0.715978150 0.357989075
[35,] 0.5925277585 0.814944483 0.407472241
[36,] 0.5428848892 0.914230222 0.457115111
[37,] 0.4929615972 0.985923194 0.507038403
[38,] 0.4428871861 0.885774372 0.557112814
[39,] 0.3933576130 0.786715226 0.606642387
[40,] 0.4627870779 0.925574156 0.537212922
[41,] 0.7359729414 0.528054117 0.264027059
[42,] 0.6955723221 0.608855356 0.304427678
[43,] 0.9551372423 0.089725515 0.044862758
[44,] 0.9422609874 0.115478025 0.057739013
[45,] 0.9677715596 0.064456881 0.032228440
[46,] 0.9678404821 0.064319036 0.032159518
[47,] 0.9586382346 0.082723531 0.041361765
[48,] 0.9474820727 0.105035855 0.052517927
[49,] 0.9847310756 0.030537849 0.015268924
[50,] 0.9828484613 0.034303077 0.017151539
[51,] 0.9849361032 0.030127794 0.015063897
[52,] 0.9796067244 0.040786551 0.020393276
[53,] 0.9792986150 0.041402770 0.020701385
[54,] 0.9723910124 0.055217975 0.027608988
[55,] 0.9636714946 0.072657011 0.036328505
[56,] 0.9864585046 0.027082991 0.013541495
[57,] 0.9819078320 0.036184336 0.018092168
[58,] 0.9759499688 0.048100062 0.024050031
[59,] 0.9771524853 0.045695029 0.022847515
[60,] 0.9696879734 0.060624053 0.030312027
[61,] 0.9605134227 0.078973155 0.039486577
[62,] 0.9598694567 0.080261087 0.040130543
[63,] 0.9631691034 0.073661793 0.036830897
[64,] 0.9523454110 0.095309178 0.047654589
[65,] 0.9528694523 0.094261095 0.047130548
[66,] 0.9398857149 0.120228570 0.060114285
[67,] 0.9442545065 0.111490987 0.055745494
[68,] 0.9863933624 0.027213275 0.013606638
[69,] 0.9838680592 0.032263882 0.016131941
[70,] 0.9792646131 0.041470774 0.020735387
[71,] 0.9842875837 0.031424833 0.015712416
[72,] 0.9828921746 0.034215651 0.017107825
[73,] 0.9986192722 0.002761456 0.001380728
[74,] 0.9979623742 0.004075252 0.002037626
[75,] 0.9969939356 0.006012129 0.003006064
[76,] 0.9956883422 0.008623316 0.004311658
[77,] 0.9937967848 0.012406430 0.006203215
[78,] 0.9912768661 0.017446268 0.008723134
[79,] 0.9880338601 0.023932280 0.011966140
[80,] 0.9840070950 0.031985810 0.015992905
[81,] 0.9816237520 0.036752496 0.018376248
[82,] 0.9759264188 0.048147162 0.024073581
[83,] 0.9679431293 0.064113741 0.032056871
[84,] 0.9644404821 0.071119036 0.035559518
[85,] 0.9544045287 0.091190943 0.045595471
[86,] 0.9520798329 0.095840334 0.047920167
[87,] 0.9385105079 0.122978984 0.061489492
[88,] 0.9224551602 0.155089680 0.077544840
[89,] 0.9046415824 0.190716835 0.095358418
[90,] 0.8860855125 0.227828975 0.113914487
[91,] 0.8605122415 0.278975517 0.139487758
[92,] 0.8312719230 0.337456154 0.168728077
[93,] 0.7983773320 0.403245336 0.201622668
[94,] 0.7659387399 0.468122520 0.234061260
[95,] 0.7257014686 0.548597063 0.274298531
[96,] 0.6823912426 0.635217515 0.317608757
[97,] 0.6380187292 0.723962542 0.361981271
[98,] 0.5891992831 0.821601434 0.410800717
[99,] 0.5395331543 0.920933691 0.460466846
[100,] 0.4951050472 0.990210094 0.504894953
[101,] 0.4976657216 0.995331443 0.502334278
[102,] 0.4891600716 0.978320143 0.510839928
[103,] 0.4349575904 0.869915181 0.565042410
[104,] 0.3834764402 0.766952880 0.616523560
[105,] 0.3305746133 0.661149227 0.669425387
[106,] 0.2896322899 0.579264580 0.710367710
[107,] 0.2472610342 0.494522068 0.752738966
[108,] 0.2034630852 0.406926170 0.796536915
[109,] 0.1674587698 0.334917540 0.832541230
[110,] 0.1375480884 0.275096177 0.862451912
[111,] 0.1072834272 0.214566854 0.892716573
[112,] 0.0819892593 0.163978519 0.918010741
[113,] 0.0780340512 0.156068102 0.921965949
[114,] 0.0576190565 0.115238113 0.942380943
[115,] 0.0576832010 0.115366402 0.942316799
[116,] 0.0427274308 0.085454862 0.957272569
[117,] 0.0298705582 0.059741116 0.970129442
[118,] 0.0201122206 0.040224441 0.979887779
[119,] 0.0131906391 0.026381278 0.986809361
[120,] 0.0091041157 0.018208231 0.990895884
[121,] 0.0071715953 0.014343191 0.992828405
[122,] 0.0068647451 0.013729490 0.993135255
[123,] 0.0039098339 0.007819668 0.996090166
[124,] 0.0021239933 0.004247987 0.997876007
[125,] 0.0011081984 0.002216397 0.998891802
[126,] 0.0015392273 0.003078455 0.998460773
[127,] 0.0014351694 0.002870339 0.998564831
[128,] 0.0009033461 0.001806692 0.999096654
[129,] 0.0003728800 0.000745760 0.999627120
[130,] 0.0108282722 0.021656544 0.989171728
[131,] 0.0047226969 0.009445394 0.995277303
> postscript(file="/var/wessaorg/rcomp/tmp/1anzw1355317761.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/252hc1355317761.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3grlt1355317762.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/47fzr1355317762.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5kqy01355317762.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 154
Frequency = 1
1 2 3 4 5
-0.0410225600 0.0777768967 0.0762802116 0.0747835265 0.0732868413
6 7 8 9 10
0.0711514065 0.0702934710 -0.0949007296 0.1087081657 0.0677967242
11 12 13 14 15
-0.0973974765 0.0628100453 -0.2314788806 -0.1018875319 -0.1930641858
16 17 18 19 20
-0.3582583865 0.6008301720 -0.1078742725 0.0937413143 0.6357548729
21 22 23 24 25
0.0072930643 -0.2015476732 0.0437144504 0.0442110739 -0.3276884295
26 27 28 29 30
-0.2509357874 0.0837611417 -0.2098890344 0.0787744628 -0.0081704106
31 32 33 34 35
0.0343730275 0.0348696510 -0.0106671574 -0.0924064784 0.0283862869
36 37 38 39 40
0.0268896018 -0.4291035309 -0.1834478209 0.0197674881 -0.1868347776
41 42 43 44 45
0.7680220004 -0.1894345615 0.0157740561 -0.1467880863 -0.0306206878
46 47 48 49 50
0.0092906921 0.0104260652 0.0503374451 0.0048006366 0.0059360098
51 52 53 54 55
-0.4080103083 0.5484461919 0.0428540193 0.7511971518 -0.0015474160
56 57 58 59 60
-0.3740856690 -0.2559249620 0.0353705936 0.0338739085 0.5778807757
61 62 63 64 65
-0.1308236687 -0.3048164527 -0.0135208971 -0.1353137242 -0.0165142674
66 67 68 69 70
-0.0180109526 0.5240026061 -0.0190110142 0.0189070570 -0.2727498106
71 72 73 74 75
-0.0254943783 0.0144170016 -0.2358318010 -0.2767432425 0.0099269461
76 77 78 79 80
-0.1993073778 0.0069335758 -0.2873553500 0.5914905726 -0.2467021834
81 82 83 84 85
-0.0404612298 -0.2473086587 -0.0434546000 0.7062965974 -0.0490800286
86 87 88 89 90
-0.0459513468 0.0675158825 -0.0320567951 0.0211211385 0.0610325184
91 92 93 94 95
-0.0259123551 0.1693005167 -0.0269124167 0.0136377128 0.1628171526
96 97 98 99 100
0.0520524075 0.1618170910 0.0076509722 0.0081475957 0.0460656669
101 102 103 104 105
0.0465622904 0.0016642316 0.0001675465 -0.0013291387 -0.1009018162
106 107 108 109 110
-0.0043225089 -0.0058191941 -0.1033985630 -0.0088125644 -0.0083159409
111 112 113 114 115
-0.1519287418 0.1373735052 -0.2635514224 -0.1123786739 -0.0157993666
116 117 118 119 120
-0.0192893604 0.0226153281 -0.0202894221 -0.0237794158 0.0161319640
121 122 123 124 125
-0.0247794775 -0.0282694713 -0.1258488402 -0.2826470173 0.0086485383
126 127 128 129 130
0.1164199131 -0.0797930203 0.0041584829 -0.0387462673 0.0011651126
131 132 133 134 135
-0.0397463290 0.0001650509 -0.2914918167 -0.0462296930 -0.0477263782
136 137 138 139 140
-0.0492230633 -0.3001106155 -0.1509311757 0.0969630062 -0.0552098039
141 142 143 144 145
0.7359494586 -0.1148711016 -0.0577065507 -0.0638286028 -0.1067333529
146 147 148 149 150
0.1278942752 -0.1637625923 0.0834928399 -0.0666866616 -0.0728087136
151 152 153 154
-0.0302652755 0.6800711656 0.6345343571 -0.3229222047
> postscript(file="/var/wessaorg/rcomp/tmp/6z1kr1355317762.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 154
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.0410225600 NA
1 0.0777768967 -0.0410225600
2 0.0762802116 0.0777768967
3 0.0747835265 0.0762802116
4 0.0732868413 0.0747835265
5 0.0711514065 0.0732868413
6 0.0702934710 0.0711514065
7 -0.0949007296 0.0702934710
8 0.1087081657 -0.0949007296
9 0.0677967242 0.1087081657
10 -0.0973974765 0.0677967242
11 0.0628100453 -0.0973974765
12 -0.2314788806 0.0628100453
13 -0.1018875319 -0.2314788806
14 -0.1930641858 -0.1018875319
15 -0.3582583865 -0.1930641858
16 0.6008301720 -0.3582583865
17 -0.1078742725 0.6008301720
18 0.0937413143 -0.1078742725
19 0.6357548729 0.0937413143
20 0.0072930643 0.6357548729
21 -0.2015476732 0.0072930643
22 0.0437144504 -0.2015476732
23 0.0442110739 0.0437144504
24 -0.3276884295 0.0442110739
25 -0.2509357874 -0.3276884295
26 0.0837611417 -0.2509357874
27 -0.2098890344 0.0837611417
28 0.0787744628 -0.2098890344
29 -0.0081704106 0.0787744628
30 0.0343730275 -0.0081704106
31 0.0348696510 0.0343730275
32 -0.0106671574 0.0348696510
33 -0.0924064784 -0.0106671574
34 0.0283862869 -0.0924064784
35 0.0268896018 0.0283862869
36 -0.4291035309 0.0268896018
37 -0.1834478209 -0.4291035309
38 0.0197674881 -0.1834478209
39 -0.1868347776 0.0197674881
40 0.7680220004 -0.1868347776
41 -0.1894345615 0.7680220004
42 0.0157740561 -0.1894345615
43 -0.1467880863 0.0157740561
44 -0.0306206878 -0.1467880863
45 0.0092906921 -0.0306206878
46 0.0104260652 0.0092906921
47 0.0503374451 0.0104260652
48 0.0048006366 0.0503374451
49 0.0059360098 0.0048006366
50 -0.4080103083 0.0059360098
51 0.5484461919 -0.4080103083
52 0.0428540193 0.5484461919
53 0.7511971518 0.0428540193
54 -0.0015474160 0.7511971518
55 -0.3740856690 -0.0015474160
56 -0.2559249620 -0.3740856690
57 0.0353705936 -0.2559249620
58 0.0338739085 0.0353705936
59 0.5778807757 0.0338739085
60 -0.1308236687 0.5778807757
61 -0.3048164527 -0.1308236687
62 -0.0135208971 -0.3048164527
63 -0.1353137242 -0.0135208971
64 -0.0165142674 -0.1353137242
65 -0.0180109526 -0.0165142674
66 0.5240026061 -0.0180109526
67 -0.0190110142 0.5240026061
68 0.0189070570 -0.0190110142
69 -0.2727498106 0.0189070570
70 -0.0254943783 -0.2727498106
71 0.0144170016 -0.0254943783
72 -0.2358318010 0.0144170016
73 -0.2767432425 -0.2358318010
74 0.0099269461 -0.2767432425
75 -0.1993073778 0.0099269461
76 0.0069335758 -0.1993073778
77 -0.2873553500 0.0069335758
78 0.5914905726 -0.2873553500
79 -0.2467021834 0.5914905726
80 -0.0404612298 -0.2467021834
81 -0.2473086587 -0.0404612298
82 -0.0434546000 -0.2473086587
83 0.7062965974 -0.0434546000
84 -0.0490800286 0.7062965974
85 -0.0459513468 -0.0490800286
86 0.0675158825 -0.0459513468
87 -0.0320567951 0.0675158825
88 0.0211211385 -0.0320567951
89 0.0610325184 0.0211211385
90 -0.0259123551 0.0610325184
91 0.1693005167 -0.0259123551
92 -0.0269124167 0.1693005167
93 0.0136377128 -0.0269124167
94 0.1628171526 0.0136377128
95 0.0520524075 0.1628171526
96 0.1618170910 0.0520524075
97 0.0076509722 0.1618170910
98 0.0081475957 0.0076509722
99 0.0460656669 0.0081475957
100 0.0465622904 0.0460656669
101 0.0016642316 0.0465622904
102 0.0001675465 0.0016642316
103 -0.0013291387 0.0001675465
104 -0.1009018162 -0.0013291387
105 -0.0043225089 -0.1009018162
106 -0.0058191941 -0.0043225089
107 -0.1033985630 -0.0058191941
108 -0.0088125644 -0.1033985630
109 -0.0083159409 -0.0088125644
110 -0.1519287418 -0.0083159409
111 0.1373735052 -0.1519287418
112 -0.2635514224 0.1373735052
113 -0.1123786739 -0.2635514224
114 -0.0157993666 -0.1123786739
115 -0.0192893604 -0.0157993666
116 0.0226153281 -0.0192893604
117 -0.0202894221 0.0226153281
118 -0.0237794158 -0.0202894221
119 0.0161319640 -0.0237794158
120 -0.0247794775 0.0161319640
121 -0.0282694713 -0.0247794775
122 -0.1258488402 -0.0282694713
123 -0.2826470173 -0.1258488402
124 0.0086485383 -0.2826470173
125 0.1164199131 0.0086485383
126 -0.0797930203 0.1164199131
127 0.0041584829 -0.0797930203
128 -0.0387462673 0.0041584829
129 0.0011651126 -0.0387462673
130 -0.0397463290 0.0011651126
131 0.0001650509 -0.0397463290
132 -0.2914918167 0.0001650509
133 -0.0462296930 -0.2914918167
134 -0.0477263782 -0.0462296930
135 -0.0492230633 -0.0477263782
136 -0.3001106155 -0.0492230633
137 -0.1509311757 -0.3001106155
138 0.0969630062 -0.1509311757
139 -0.0552098039 0.0969630062
140 0.7359494586 -0.0552098039
141 -0.1148711016 0.7359494586
142 -0.0577065507 -0.1148711016
143 -0.0638286028 -0.0577065507
144 -0.1067333529 -0.0638286028
145 0.1278942752 -0.1067333529
146 -0.1637625923 0.1278942752
147 0.0834928399 -0.1637625923
148 -0.0666866616 0.0834928399
149 -0.0728087136 -0.0666866616
150 -0.0302652755 -0.0728087136
151 0.6800711656 -0.0302652755
152 0.6345343571 0.6800711656
153 -0.3229222047 0.6345343571
154 NA -0.3229222047
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.0777768967 -0.0410225600
[2,] 0.0762802116 0.0777768967
[3,] 0.0747835265 0.0762802116
[4,] 0.0732868413 0.0747835265
[5,] 0.0711514065 0.0732868413
[6,] 0.0702934710 0.0711514065
[7,] -0.0949007296 0.0702934710
[8,] 0.1087081657 -0.0949007296
[9,] 0.0677967242 0.1087081657
[10,] -0.0973974765 0.0677967242
[11,] 0.0628100453 -0.0973974765
[12,] -0.2314788806 0.0628100453
[13,] -0.1018875319 -0.2314788806
[14,] -0.1930641858 -0.1018875319
[15,] -0.3582583865 -0.1930641858
[16,] 0.6008301720 -0.3582583865
[17,] -0.1078742725 0.6008301720
[18,] 0.0937413143 -0.1078742725
[19,] 0.6357548729 0.0937413143
[20,] 0.0072930643 0.6357548729
[21,] -0.2015476732 0.0072930643
[22,] 0.0437144504 -0.2015476732
[23,] 0.0442110739 0.0437144504
[24,] -0.3276884295 0.0442110739
[25,] -0.2509357874 -0.3276884295
[26,] 0.0837611417 -0.2509357874
[27,] -0.2098890344 0.0837611417
[28,] 0.0787744628 -0.2098890344
[29,] -0.0081704106 0.0787744628
[30,] 0.0343730275 -0.0081704106
[31,] 0.0348696510 0.0343730275
[32,] -0.0106671574 0.0348696510
[33,] -0.0924064784 -0.0106671574
[34,] 0.0283862869 -0.0924064784
[35,] 0.0268896018 0.0283862869
[36,] -0.4291035309 0.0268896018
[37,] -0.1834478209 -0.4291035309
[38,] 0.0197674881 -0.1834478209
[39,] -0.1868347776 0.0197674881
[40,] 0.7680220004 -0.1868347776
[41,] -0.1894345615 0.7680220004
[42,] 0.0157740561 -0.1894345615
[43,] -0.1467880863 0.0157740561
[44,] -0.0306206878 -0.1467880863
[45,] 0.0092906921 -0.0306206878
[46,] 0.0104260652 0.0092906921
[47,] 0.0503374451 0.0104260652
[48,] 0.0048006366 0.0503374451
[49,] 0.0059360098 0.0048006366
[50,] -0.4080103083 0.0059360098
[51,] 0.5484461919 -0.4080103083
[52,] 0.0428540193 0.5484461919
[53,] 0.7511971518 0.0428540193
[54,] -0.0015474160 0.7511971518
[55,] -0.3740856690 -0.0015474160
[56,] -0.2559249620 -0.3740856690
[57,] 0.0353705936 -0.2559249620
[58,] 0.0338739085 0.0353705936
[59,] 0.5778807757 0.0338739085
[60,] -0.1308236687 0.5778807757
[61,] -0.3048164527 -0.1308236687
[62,] -0.0135208971 -0.3048164527
[63,] -0.1353137242 -0.0135208971
[64,] -0.0165142674 -0.1353137242
[65,] -0.0180109526 -0.0165142674
[66,] 0.5240026061 -0.0180109526
[67,] -0.0190110142 0.5240026061
[68,] 0.0189070570 -0.0190110142
[69,] -0.2727498106 0.0189070570
[70,] -0.0254943783 -0.2727498106
[71,] 0.0144170016 -0.0254943783
[72,] -0.2358318010 0.0144170016
[73,] -0.2767432425 -0.2358318010
[74,] 0.0099269461 -0.2767432425
[75,] -0.1993073778 0.0099269461
[76,] 0.0069335758 -0.1993073778
[77,] -0.2873553500 0.0069335758
[78,] 0.5914905726 -0.2873553500
[79,] -0.2467021834 0.5914905726
[80,] -0.0404612298 -0.2467021834
[81,] -0.2473086587 -0.0404612298
[82,] -0.0434546000 -0.2473086587
[83,] 0.7062965974 -0.0434546000
[84,] -0.0490800286 0.7062965974
[85,] -0.0459513468 -0.0490800286
[86,] 0.0675158825 -0.0459513468
[87,] -0.0320567951 0.0675158825
[88,] 0.0211211385 -0.0320567951
[89,] 0.0610325184 0.0211211385
[90,] -0.0259123551 0.0610325184
[91,] 0.1693005167 -0.0259123551
[92,] -0.0269124167 0.1693005167
[93,] 0.0136377128 -0.0269124167
[94,] 0.1628171526 0.0136377128
[95,] 0.0520524075 0.1628171526
[96,] 0.1618170910 0.0520524075
[97,] 0.0076509722 0.1618170910
[98,] 0.0081475957 0.0076509722
[99,] 0.0460656669 0.0081475957
[100,] 0.0465622904 0.0460656669
[101,] 0.0016642316 0.0465622904
[102,] 0.0001675465 0.0016642316
[103,] -0.0013291387 0.0001675465
[104,] -0.1009018162 -0.0013291387
[105,] -0.0043225089 -0.1009018162
[106,] -0.0058191941 -0.0043225089
[107,] -0.1033985630 -0.0058191941
[108,] -0.0088125644 -0.1033985630
[109,] -0.0083159409 -0.0088125644
[110,] -0.1519287418 -0.0083159409
[111,] 0.1373735052 -0.1519287418
[112,] -0.2635514224 0.1373735052
[113,] -0.1123786739 -0.2635514224
[114,] -0.0157993666 -0.1123786739
[115,] -0.0192893604 -0.0157993666
[116,] 0.0226153281 -0.0192893604
[117,] -0.0202894221 0.0226153281
[118,] -0.0237794158 -0.0202894221
[119,] 0.0161319640 -0.0237794158
[120,] -0.0247794775 0.0161319640
[121,] -0.0282694713 -0.0247794775
[122,] -0.1258488402 -0.0282694713
[123,] -0.2826470173 -0.1258488402
[124,] 0.0086485383 -0.2826470173
[125,] 0.1164199131 0.0086485383
[126,] -0.0797930203 0.1164199131
[127,] 0.0041584829 -0.0797930203
[128,] -0.0387462673 0.0041584829
[129,] 0.0011651126 -0.0387462673
[130,] -0.0397463290 0.0011651126
[131,] 0.0001650509 -0.0397463290
[132,] -0.2914918167 0.0001650509
[133,] -0.0462296930 -0.2914918167
[134,] -0.0477263782 -0.0462296930
[135,] -0.0492230633 -0.0477263782
[136,] -0.3001106155 -0.0492230633
[137,] -0.1509311757 -0.3001106155
[138,] 0.0969630062 -0.1509311757
[139,] -0.0552098039 0.0969630062
[140,] 0.7359494586 -0.0552098039
[141,] -0.1148711016 0.7359494586
[142,] -0.0577065507 -0.1148711016
[143,] -0.0638286028 -0.0577065507
[144,] -0.1067333529 -0.0638286028
[145,] 0.1278942752 -0.1067333529
[146,] -0.1637625923 0.1278942752
[147,] 0.0834928399 -0.1637625923
[148,] -0.0666866616 0.0834928399
[149,] -0.0728087136 -0.0666866616
[150,] -0.0302652755 -0.0728087136
[151,] 0.6800711656 -0.0302652755
[152,] 0.6345343571 0.6800711656
[153,] -0.3229222047 0.6345343571
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.0777768967 -0.0410225600
2 0.0762802116 0.0777768967
3 0.0747835265 0.0762802116
4 0.0732868413 0.0747835265
5 0.0711514065 0.0732868413
6 0.0702934710 0.0711514065
7 -0.0949007296 0.0702934710
8 0.1087081657 -0.0949007296
9 0.0677967242 0.1087081657
10 -0.0973974765 0.0677967242
11 0.0628100453 -0.0973974765
12 -0.2314788806 0.0628100453
13 -0.1018875319 -0.2314788806
14 -0.1930641858 -0.1018875319
15 -0.3582583865 -0.1930641858
16 0.6008301720 -0.3582583865
17 -0.1078742725 0.6008301720
18 0.0937413143 -0.1078742725
19 0.6357548729 0.0937413143
20 0.0072930643 0.6357548729
21 -0.2015476732 0.0072930643
22 0.0437144504 -0.2015476732
23 0.0442110739 0.0437144504
24 -0.3276884295 0.0442110739
25 -0.2509357874 -0.3276884295
26 0.0837611417 -0.2509357874
27 -0.2098890344 0.0837611417
28 0.0787744628 -0.2098890344
29 -0.0081704106 0.0787744628
30 0.0343730275 -0.0081704106
31 0.0348696510 0.0343730275
32 -0.0106671574 0.0348696510
33 -0.0924064784 -0.0106671574
34 0.0283862869 -0.0924064784
35 0.0268896018 0.0283862869
36 -0.4291035309 0.0268896018
37 -0.1834478209 -0.4291035309
38 0.0197674881 -0.1834478209
39 -0.1868347776 0.0197674881
40 0.7680220004 -0.1868347776
41 -0.1894345615 0.7680220004
42 0.0157740561 -0.1894345615
43 -0.1467880863 0.0157740561
44 -0.0306206878 -0.1467880863
45 0.0092906921 -0.0306206878
46 0.0104260652 0.0092906921
47 0.0503374451 0.0104260652
48 0.0048006366 0.0503374451
49 0.0059360098 0.0048006366
50 -0.4080103083 0.0059360098
51 0.5484461919 -0.4080103083
52 0.0428540193 0.5484461919
53 0.7511971518 0.0428540193
54 -0.0015474160 0.7511971518
55 -0.3740856690 -0.0015474160
56 -0.2559249620 -0.3740856690
57 0.0353705936 -0.2559249620
58 0.0338739085 0.0353705936
59 0.5778807757 0.0338739085
60 -0.1308236687 0.5778807757
61 -0.3048164527 -0.1308236687
62 -0.0135208971 -0.3048164527
63 -0.1353137242 -0.0135208971
64 -0.0165142674 -0.1353137242
65 -0.0180109526 -0.0165142674
66 0.5240026061 -0.0180109526
67 -0.0190110142 0.5240026061
68 0.0189070570 -0.0190110142
69 -0.2727498106 0.0189070570
70 -0.0254943783 -0.2727498106
71 0.0144170016 -0.0254943783
72 -0.2358318010 0.0144170016
73 -0.2767432425 -0.2358318010
74 0.0099269461 -0.2767432425
75 -0.1993073778 0.0099269461
76 0.0069335758 -0.1993073778
77 -0.2873553500 0.0069335758
78 0.5914905726 -0.2873553500
79 -0.2467021834 0.5914905726
80 -0.0404612298 -0.2467021834
81 -0.2473086587 -0.0404612298
82 -0.0434546000 -0.2473086587
83 0.7062965974 -0.0434546000
84 -0.0490800286 0.7062965974
85 -0.0459513468 -0.0490800286
86 0.0675158825 -0.0459513468
87 -0.0320567951 0.0675158825
88 0.0211211385 -0.0320567951
89 0.0610325184 0.0211211385
90 -0.0259123551 0.0610325184
91 0.1693005167 -0.0259123551
92 -0.0269124167 0.1693005167
93 0.0136377128 -0.0269124167
94 0.1628171526 0.0136377128
95 0.0520524075 0.1628171526
96 0.1618170910 0.0520524075
97 0.0076509722 0.1618170910
98 0.0081475957 0.0076509722
99 0.0460656669 0.0081475957
100 0.0465622904 0.0460656669
101 0.0016642316 0.0465622904
102 0.0001675465 0.0016642316
103 -0.0013291387 0.0001675465
104 -0.1009018162 -0.0013291387
105 -0.0043225089 -0.1009018162
106 -0.0058191941 -0.0043225089
107 -0.1033985630 -0.0058191941
108 -0.0088125644 -0.1033985630
109 -0.0083159409 -0.0088125644
110 -0.1519287418 -0.0083159409
111 0.1373735052 -0.1519287418
112 -0.2635514224 0.1373735052
113 -0.1123786739 -0.2635514224
114 -0.0157993666 -0.1123786739
115 -0.0192893604 -0.0157993666
116 0.0226153281 -0.0192893604
117 -0.0202894221 0.0226153281
118 -0.0237794158 -0.0202894221
119 0.0161319640 -0.0237794158
120 -0.0247794775 0.0161319640
121 -0.0282694713 -0.0247794775
122 -0.1258488402 -0.0282694713
123 -0.2826470173 -0.1258488402
124 0.0086485383 -0.2826470173
125 0.1164199131 0.0086485383
126 -0.0797930203 0.1164199131
127 0.0041584829 -0.0797930203
128 -0.0387462673 0.0041584829
129 0.0011651126 -0.0387462673
130 -0.0397463290 0.0011651126
131 0.0001650509 -0.0397463290
132 -0.2914918167 0.0001650509
133 -0.0462296930 -0.2914918167
134 -0.0477263782 -0.0462296930
135 -0.0492230633 -0.0477263782
136 -0.3001106155 -0.0492230633
137 -0.1509311757 -0.3001106155
138 0.0969630062 -0.1509311757
139 -0.0552098039 0.0969630062
140 0.7359494586 -0.0552098039
141 -0.1148711016 0.7359494586
142 -0.0577065507 -0.1148711016
143 -0.0638286028 -0.0577065507
144 -0.1067333529 -0.0638286028
145 0.1278942752 -0.1067333529
146 -0.1637625923 0.1278942752
147 0.0834928399 -0.1637625923
148 -0.0666866616 0.0834928399
149 -0.0728087136 -0.0666866616
150 -0.0302652755 -0.0728087136
151 0.6800711656 -0.0302652755
152 0.6345343571 0.6800711656
153 -0.3229222047 0.6345343571
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7aqab1355317762.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/863nz1355317762.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9uh6e1355317762.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10kpiv1355317762.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11bxp61355317762.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12t5rg1355317762.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13sfxh1355317762.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14sv5l1355317762.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/1505py1355317762.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16a3wb1355317762.tab")
+ }
>
> try(system("convert tmp/1anzw1355317761.ps tmp/1anzw1355317761.png",intern=TRUE))
character(0)
> try(system("convert tmp/252hc1355317761.ps tmp/252hc1355317761.png",intern=TRUE))
character(0)
> try(system("convert tmp/3grlt1355317762.ps tmp/3grlt1355317762.png",intern=TRUE))
character(0)
> try(system("convert tmp/47fzr1355317762.ps tmp/47fzr1355317762.png",intern=TRUE))
character(0)
> try(system("convert tmp/5kqy01355317762.ps tmp/5kqy01355317762.png",intern=TRUE))
character(0)
> try(system("convert tmp/6z1kr1355317762.ps tmp/6z1kr1355317762.png",intern=TRUE))
character(0)
> try(system("convert tmp/7aqab1355317762.ps tmp/7aqab1355317762.png",intern=TRUE))
character(0)
> try(system("convert tmp/863nz1355317762.ps tmp/863nz1355317762.png",intern=TRUE))
character(0)
> try(system("convert tmp/9uh6e1355317762.ps tmp/9uh6e1355317762.png",intern=TRUE))
character(0)
> try(system("convert tmp/10kpiv1355317762.ps tmp/10kpiv1355317762.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.216 1.011 9.235