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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> x <- array(list(11110
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+ ,13.35
+ ,2.4
+ ,3.40)
+ ,dim=c(7
+ ,154)
+ ,dimnames=list(c('y'
+ ,'x1'
+ ,'x2'
+ ,'x3'
+ ,'x4'
+ ,'x5'
+ ,'x6')
+ ,1:154))
> y <- array(NA,dim=c(7,154),dimnames=list(c('y','x1','x2','x3','x4','x5','x6'),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 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> 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
y x1 x2 x3 x4 x5 x6
1 11110 104970 2764 21046 8.40 0.85 -7.91
2 9990 105880 3043 20878 8.60 0.89 -6.31
3 10370 105160 2690 21437 11.30 0.98 -6.24
4 9530 109990 2574 21103 13.20 1.14 -6.27
5 9300 108690 2901 22227 12.60 1.56 -7.02
6 8485 106580 3250 22393 13.55 1.80 -7.85
7 8645 109370 2743 21861 13.90 2.14 -9.13
8 7450 112570 3312 22741 15.50 2.50 -9.78
9 8300 113000 3084 22657 13.20 2.91 -9.07
10 7440 116620 3270 21928 13.60 3.13 -9.98
11 6720 115100 3382 22979 13.85 3.34 -6.28
12 6555 109280 2680 22783 13.20 3.66 -4.28
13 6375 110530 2866 22412 11.50 3.68 -4.15
14 6390 112000 2739 22178 10.60 3.64 0.48
15 6030 117630 2629 21026 11.70 3.74 1.20
16 6185 115950 2846 21450 12.95 3.84 3.39
17 5715 119190 2837 20900 13.50 3.83 1.82
18 5610 119750 2625 20556 12.05 3.77 1.64
19 5195 117390 2635 20270 11.55 3.71 1.29
20 5050 113540 2720 19963 10.55 3.65 3.99
21 4830 113960 2343 19049 6.95 3.59 5.74
22 4390 115150 2118 18566 6.20 3.50 8.31
23 4260 115550 1944 19266 8.90 3.41 9.11
24 4620 117210 1984 19061 7.80 3.27 8.24
25 4510 120590 1948 18729 9.65 2.96 7.11
26 4475 119280 2174 19233 7.35 2.86 10.62
27 5720 119060 2631 20492 4.90 2.76 9.87
28 5070 115400 2729 20116 8.55 2.57 7.85
29 5190 111910 2531 20420 8.35 2.38 8.52
30 4650 106550 2686 20929 9.90 2.27 6.37
31 4680 107700 2702 21097 9.60 2.23 4.11
32 5150 107090 2898 21920 7.25 2.19 1.67
33 5450 107260 3045 22653 7.05 2.17 0.37
34 6595 106820 2722 22891 7.65 2.20 0.06
35 6660 106500 2689 23081 10.15 2.26 -0.56
36 6020 102260 3120 23452 9.00 2.42 -1.98
37 6530 100140 3351 24845 9.25 2.57 -3.03
38 5685 99810 3660 24725 10.45 2.69 -0.06
39 5865 99490 3104 23215 9.95 2.87 0.11
40 6825 97380 2580 23253 6.30 3.09 -0.47
41 5835 93310 2956 23555 9.45 3.38 -1.09
42 5890 95110 3019 23378 9.40 3.62 -1.24
43 6345 97140 3054 23421 7.50 3.82 -1.11
44 6145 98100 3157 24370 7.55 4.11 -1.42
45 6290 93200 2920 24366 7.00 4.30 -1.12
46 5865 92880 2911 24758 8.60 4.41 -1.56
47 5775 90280 3041 24844 8.40 4.44 -0.57
48 6495 87260 3252 25529 8.15 4.47 -0.97
49 7550 87420 3305 26257 7.80 4.40 -2.13
50 7505 87420 3616 27473 8.10 4.36 0.45
51 7375 87950 3576 28377 9.40 4.42 1.39
52 6715 90680 3738 27254 9.65 4.50 1.93
53 8555 88980 3988 27725 8.55 4.72 1.37
54 7310 89000 3705 26594 9.75 4.82 0.73
55 6645 90110 4380 26778 9.80 4.95 1.54
56 6930 88960 4212 27650 9.85 4.99 0.88
57 8235 87510 4964 28498 8.70 5.14 0.10
58 7440 84980 5176 28370 7.85 5.07 0.76
59 9445 81820 4913 29094 8.25 4.89 2.29
60 10375 81000 4345 28390 7.85 4.61 1.94
61 10535 83640 4820 28475 8.70 4.55 2.59
62 11915 82510 5175 30500 8.45 4.51 3.72
63 12640 84050 5540 31357 10.40 4.51 3.99
64 12515 84420 4972 30374 10.90 4.51 4.69
65 11835 87800 5197 30083 11.75 4.49 4.70
66 10465 88980 5650 30691 12.40 4.49 3.87
67 10315 89230 6057 31524 12.00 4.51 3.26
68 9775 87540 6894 31899 10.10 4.52 2.41
69 9345 89350 6624 33333 9.25 4.55 4.12
70 9665 89930 5976 32668 8.35 4.48 1.40
71 9300 91510 5732 33249 9.55 4.42 2.85
72 10710 90960 6104 34789 8.15 3.88 4.19
73 11820 88810 6792 36330 9.50 3.70 1.68
74 11180 90080 6141 35327 9.00 3.60 3.66
75 10700 89390 6663 36191 11.50 3.49 3.94
76 10720 85880 7188 37953 12.15 3.36 3.34
77 9895 84650 7129 37980 12.75 3.35 2.83
78 9950 84900 7393 38563 10.80 3.30 2.60
79 9935 85090 7440 39149 12.15 3.34 3.01
80 10400 85020 7026 39095 12.35 3.44 1.19
81 10765 85680 6291 37010 10.30 3.60 1.26
82 10825 85110 5873 38392 11.65 3.67 0.89
83 11970 82850 6313 40879 12.85 3.62 1.28
84 12620 83430 6105 39489 11.65 3.71 -0.52
85 11765 84430 5814 39437 12.50 3.72 0.77
86 11770 83500 6179 41064 9.65 3.75 2.29
87 10925 82660 6587 40745 12.75 3.91 2.70
88 10315 81300 6571 40354 12.95 3.97 2.31
89 11190 82250 6401 40758 12.20 4.04 1.96
90 11100 81690 7068 41036 11.35 4.17 1.11
91 11430 80660 7821 42452 12.05 4.28 2.02
92 11265 80745 7404 41349 10.95 4.39 1.55
93 12865 77625 8166 44756 9.15 4.43 0.30
94 12135 76460 9453 45429 10.95 4.54 -0.07
95 12595 76170 8871 45126 10.55 4.54 3.20
96 13620 76700 9598 47608 10.75 4.47 2.50
97 13815 75285 9175 50327 10.75 4.39 1.34
98 16460 73750 10184 56565 9.30 4.33 1.46
99 12740 72165 10158 51638 11.75 4.38 3.14
100 13455 72720 11346 53623 9.95 4.38 4.47
101 13390 72950 12735 54130 11.20 4.32 4.92
102 15090 72800 14000 59598 11.45 4.41 2.56
103 13935 73420 12408 54886 12.00 4.58 3.05
104 14190 77500 11546 51647 12.10 4.57 2.17
105 13045 79360 10064 45242 11.90 4.59 2.55
106 11300 86345 6781 36956 13.00 4.60 2.48
107 11410 86705 5443 36174 8.95 4.51 4.66
108 11205 82150 4460 36306 9.45 4.44 7.06
109 11890 86460 4168 36450 8.80 4.26 8.55
110 10945 88150 4476 35245 9.85 4.16 12.19
111 11575 85895 4966 36883 9.55 4.01 15.05
112 11500 84775 5112 37155 8.70 3.82 16.40
113 13740 79430 6631 41704 8.10 3.72 19.56
114 11730 80425 6989 39876 10.15 3.58 20.40
115 12785 78450 6945 41341 9.80 3.52 18.48
116 12090 78220 6996 41502 8.05 3.43 17.71
117 12780 76860 7061 43067 8.45 3.35 17.20
118 13550 76475 7700 45269 8.30 3.29 13.80
119 14175 74935 7728 47922 4.90 3.18 11.88
120 13595 78220 7936 48442 6.30 3.09 14.08
121 13170 79650 7289 46529 6.80 2.93 16.14
122 12905 80440 7966 47832 7.25 2.77 23.51
123 13615 81291 8376 46725 6.70 2.61 29.53
124 13520 81860 8615 48111 4.40 2.41 34.12
125 13425 86588 7598 45870 4.20 2.33 37.47
126 16420 86019 7616 47137 0.85 2.21 38.54
127 17630 81739 7895 49905 -0.95 2.11 37.98
128 17680 83090 7192 49673 -0.40 2.01 36.49
129 18305 78720 7997 53612 -0.90 1.96 34.91
130 20345 77260 8143 56628 -3.20 1.81 27.21
131 20095 81190 8411 56596 -4.25 1.74 26.56
132 24050 79020 9138 62953 -9.05 1.71 26.66
133 24480 77730 9219 65080 -1.35 1.62 25.72
134 27170 76880 9697 67696 2.20 1.59 26.48
135 26415 75850 10672 67281 8.45 1.57 17.97
136 29935 72930 11393 68766 5.20 1.59 14.54
137 26460 74630 10270 65642 10.85 1.66 13.37
138 26535 74300 9542 63223 7.55 1.62 12.46
139 23955 73890 9570 64577 9.45 1.53 12.12
140 28910 74110 8881 66193 -0.20 1.47 10.05
141 22835 78550 7920 57138 5.00 1.43 13.49
142 22695 76160 9319 60428 6.20 1.27 11.08
143 23380 80205 10050 58422 8.20 1.46 9.84
144 22425 79273 9906 56300 8.80 1.53 9.48
145 21505 78811 9828 58900 11.00 1.53 9.71
146 20315 78949 10691 60032 8.70 1.57 12.07
147 18245 78919 10293 57294 13.10 1.54 12.48
148 17795 78763 10487 55815 11.05 1.52 9.70
149 16065 83083 8653 50876 12.55 1.55 8.15
150 17010 81627 8806 53966 9.40 1.62 8.76
151 16845 82635 8162 56088 11.05 1.77 6.55
152 16455 81300 9637 57691 9.05 1.96 4.45
153 17350 79994 9219 58024 12.10 2.15 8.86
154 15465 79919 8624 56388 13.35 2.40 3.40
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x1 x2 x3 x4 x5
3777.51795 -0.01977 -0.67130 0.47607 -125.85216 -789.47844
x6
-58.53632
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4014.1 -1129.0 -246.5 879.7 5270.7
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3777.51795 3436.67668 1.099 0.273488
x1 -0.01977 0.02397 -0.825 0.410970
x2 -0.67130 0.17362 -3.867 0.000165 ***
x3 0.47607 0.04339 10.973 < 2e-16 ***
x4 -125.85216 56.98019 -2.209 0.028744 *
x5 -789.47844 184.01462 -4.290 3.22e-05 ***
x6 -58.53632 22.61885 -2.588 0.010624 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1677 on 147 degrees of freedom
Multiple R-squared: 0.917, Adjusted R-squared: 0.9136
F-statistic: 270.7 on 6 and 147 DF, p-value: < 2.2e-16
> 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,] 3.594109e-03 7.188219e-03 0.9964059
[2,] 6.155549e-04 1.231110e-03 0.9993844
[3,] 2.148121e-04 4.296241e-04 0.9997852
[4,] 4.503357e-05 9.006715e-05 0.9999550
[5,] 8.173890e-06 1.634778e-05 0.9999918
[6,] 2.244595e-06 4.489191e-06 0.9999978
[7,] 2.962660e-05 5.925319e-05 0.9999704
[8,] 6.479765e-06 1.295953e-05 0.9999935
[9,] 3.248346e-06 6.496692e-06 0.9999968
[10,] 2.837116e-06 5.674232e-06 0.9999972
[11,] 7.272825e-07 1.454565e-06 0.9999993
[12,] 6.972110e-07 1.394422e-06 0.9999993
[13,] 1.142355e-06 2.284709e-06 0.9999989
[14,] 2.308920e-06 4.617839e-06 0.9999977
[15,] 1.616490e-06 3.232980e-06 0.9999984
[16,] 1.273109e-06 2.546217e-06 0.9999987
[17,] 8.920848e-07 1.784170e-06 0.9999991
[18,] 2.903863e-07 5.807727e-07 0.9999997
[19,] 2.180717e-07 4.361434e-07 0.9999998
[20,] 1.528116e-07 3.056231e-07 0.9999998
[21,] 4.136558e-07 8.273117e-07 0.9999996
[22,] 1.445679e-06 2.891358e-06 0.9999986
[23,] 6.283392e-06 1.256678e-05 0.9999937
[24,] 8.420126e-06 1.684025e-05 0.9999916
[25,] 3.653963e-06 7.307925e-06 0.9999963
[26,] 1.536747e-06 3.073495e-06 0.9999985
[27,] 6.990764e-07 1.398153e-06 0.9999993
[28,] 3.363914e-07 6.727829e-07 0.9999997
[29,] 1.788131e-07 3.576262e-07 0.9999998
[30,] 8.157109e-08 1.631422e-07 0.9999999
[31,] 5.735669e-08 1.147134e-07 0.9999999
[32,] 2.585265e-08 5.170529e-08 1.0000000
[33,] 1.168052e-08 2.336104e-08 1.0000000
[34,] 7.416946e-09 1.483389e-08 1.0000000
[35,] 4.541593e-09 9.083185e-09 1.0000000
[36,] 2.884443e-09 5.768886e-09 1.0000000
[37,] 1.384287e-09 2.768574e-09 1.0000000
[38,] 7.894409e-10 1.578882e-09 1.0000000
[39,] 1.042447e-09 2.084893e-09 1.0000000
[40,] 3.867695e-09 7.735389e-09 1.0000000
[41,] 4.304555e-08 8.609110e-08 1.0000000
[42,] 1.210095e-07 2.420191e-07 0.9999999
[43,] 1.370000e-07 2.740001e-07 0.9999999
[44,] 1.584670e-06 3.169340e-06 0.9999984
[45,] 1.456737e-06 2.913474e-06 0.9999985
[46,] 8.660894e-07 1.732179e-06 0.9999991
[47,] 5.040381e-07 1.008076e-06 0.9999995
[48,] 4.282388e-07 8.564776e-07 0.9999996
[49,] 2.158875e-07 4.317750e-07 0.9999998
[50,] 5.575908e-07 1.115182e-06 0.9999994
[51,] 4.640349e-06 9.280698e-06 0.9999954
[52,] 1.591669e-05 3.183338e-05 0.9999841
[53,] 1.075929e-04 2.151858e-04 0.9998924
[54,] 5.260552e-04 1.052110e-03 0.9994739
[55,] 3.151457e-03 6.302915e-03 0.9968485
[56,] 7.152359e-03 1.430472e-02 0.9928476
[57,] 9.383486e-03 1.876697e-02 0.9906165
[58,] 1.370379e-02 2.740758e-02 0.9862962
[59,] 2.623509e-02 5.247017e-02 0.9737649
[60,] 3.175071e-02 6.350143e-02 0.9682493
[61,] 2.692124e-02 5.384248e-02 0.9730788
[62,] 2.211813e-02 4.423626e-02 0.9778819
[63,] 1.760451e-02 3.520902e-02 0.9823955
[64,] 1.682541e-02 3.365082e-02 0.9831746
[65,] 1.457221e-02 2.914443e-02 0.9854278
[66,] 1.594422e-02 3.188845e-02 0.9840558
[67,] 2.061135e-02 4.122271e-02 0.9793886
[68,] 2.990879e-02 5.981759e-02 0.9700912
[69,] 3.420766e-02 6.841532e-02 0.9657923
[70,] 3.632526e-02 7.265052e-02 0.9636747
[71,] 2.981443e-02 5.962886e-02 0.9701856
[72,] 2.385037e-02 4.770074e-02 0.9761496
[73,] 1.906300e-02 3.812601e-02 0.9809370
[74,] 1.574459e-02 3.148917e-02 0.9842554
[75,] 1.619580e-02 3.239159e-02 0.9838042
[76,] 1.289428e-02 2.578855e-02 0.9871057
[77,] 1.034551e-02 2.069102e-02 0.9896545
[78,] 7.890402e-03 1.578080e-02 0.9921096
[79,] 6.901737e-03 1.380347e-02 0.9930983
[80,] 5.024016e-03 1.004803e-02 0.9949760
[81,] 3.629557e-03 7.259114e-03 0.9963704
[82,] 2.673094e-03 5.346188e-03 0.9973269
[83,] 1.858366e-03 3.716733e-03 0.9981416
[84,] 1.283505e-03 2.567011e-03 0.9987165
[85,] 1.087171e-03 2.174343e-03 0.9989128
[86,] 7.448305e-04 1.489661e-03 0.9992552
[87,] 5.222731e-04 1.044546e-03 0.9994777
[88,] 3.777745e-04 7.555491e-04 0.9996222
[89,] 4.342116e-04 8.684231e-04 0.9995658
[90,] 6.453916e-04 1.290783e-03 0.9993546
[91,] 7.222675e-04 1.444535e-03 0.9992777
[92,] 7.734472e-04 1.546894e-03 0.9992266
[93,] 7.663762e-04 1.532752e-03 0.9992336
[94,] 8.961667e-04 1.792333e-03 0.9991038
[95,] 6.996983e-04 1.399397e-03 0.9993003
[96,] 4.551222e-04 9.102444e-04 0.9995449
[97,] 5.364735e-04 1.072947e-03 0.9994635
[98,] 8.840631e-04 1.768126e-03 0.9991159
[99,] 7.375137e-04 1.475027e-03 0.9992625
[100,] 1.103435e-03 2.206870e-03 0.9988966
[101,] 2.161826e-03 4.323652e-03 0.9978382
[102,] 6.147602e-03 1.229520e-02 0.9938524
[103,] 2.479540e-02 4.959081e-02 0.9752046
[104,] 5.421496e-02 1.084299e-01 0.9457850
[105,] 7.615986e-02 1.523197e-01 0.9238401
[106,] 1.017299e-01 2.034598e-01 0.8982701
[107,] 1.023868e-01 2.047735e-01 0.8976132
[108,] 9.299719e-02 1.859944e-01 0.9070028
[109,] 9.099813e-02 1.819963e-01 0.9090019
[110,] 7.244644e-02 1.448929e-01 0.9275536
[111,] 5.828577e-02 1.165715e-01 0.9417142
[112,] 5.636974e-02 1.127395e-01 0.9436303
[113,] 4.271096e-02 8.542192e-02 0.9572890
[114,] 3.516239e-02 7.032478e-02 0.9648376
[115,] 2.697518e-02 5.395036e-02 0.9730248
[116,] 2.095958e-02 4.191916e-02 0.9790404
[117,] 7.591516e-02 1.518303e-01 0.9240848
[118,] 1.096949e-01 2.193898e-01 0.8903051
[119,] 2.540493e-01 5.080986e-01 0.7459507
[120,] 2.338802e-01 4.677603e-01 0.7661198
[121,] 2.103885e-01 4.207770e-01 0.7896115
[122,] 1.808534e-01 3.617068e-01 0.8191466
[123,] 1.666289e-01 3.332578e-01 0.8333711
[124,] 1.954815e-01 3.909630e-01 0.8045185
[125,] 2.333064e-01 4.666127e-01 0.7666936
[126,] 2.278838e-01 4.557675e-01 0.7721162
[127,] 2.661479e-01 5.322958e-01 0.7338521
[128,] 3.009437e-01 6.018875e-01 0.6990563
[129,] 3.798703e-01 7.597406e-01 0.6201297
[130,] 2.887892e-01 5.775783e-01 0.7112108
[131,] 2.258695e-01 4.517390e-01 0.7741305
[132,] 1.567968e-01 3.135936e-01 0.8432032
[133,] 2.875863e-01 5.751726e-01 0.7124137
[134,] 4.626691e-01 9.253381e-01 0.5373309
[135,] 7.120884e-01 5.758231e-01 0.2879116
> postscript(file="/var/fisher/rcomp/tmp/1n23t1354174667.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2lg4v1354174667.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3ydxv1354174667.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4xmf01354174667.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/518su1354174667.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 6
2508.65751 1824.32547 2101.95309 1802.23701 1443.12515 1002.12646
7 8 9 10 11 12
1367.73430 646.54466 1467.76323 1321.98974 560.62238 190.54054
13 14 15 16 17 18
146.18227 342.55936 827.95908 1258.03037 1077.28915 764.41792
19 20 21 22 23 24
314.85647 281.79888 -145.85318 -498.43840 -755.00970 -537.64326
25 26 27 28 29 30
-525.00502 -837.06696 -320.18655 -606.62410 -969.20181 -1771.03795
31 32 33 34 35 36
-1989.17263 -2261.61827 -2325.59125 -1438.37433 -1166.59876 -1879.22988
37 38 39 40 41 42
-1830.80295 -1998.15156 -1389.72830 -1160.91456 -1533.63302 -1142.09654
43 44 45 46 47 48
-717.56082 -1064.13452 -1074.83895 -1436.37425 -1474.97281 -1030.32009
49 50 51 52 53 54
-450.36968 -708.29223 -1019.03317 -855.46598 896.99780 193.35384
55 56 57 58 59 60
72.16722 -194.23604 1111.25241 345.89420 1765.00326 2340.76352
61 62 63 64 65 66
2929.00346 3564.04768 4417.73961 4490.62820 4258.78613 2959.98029
67 68 69 70 71 72
2621.31973 2150.29169 908.94153 794.23627 208.60415 600.24106
73 74 75 76 77 78
1276.84959 676.45701 366.09486 -225.62999 -1089.63979 -1428.37047
79 80 81 82 83 84
-1461.55995 -1252.57380 -502.91856 -1189.21065 -843.11033 155.12189
85 86 87 88 89 90
-660.32537 -1449.26290 -1344.65020 -1756.42013 -1228.70909 -1068.46273
91 92 93 94 95 96
-699.23411 -696.49199 -536.71564 -454.44953 -105.55938 165.28606
97 98 99 100 101 102
-1377.13324 -1277.66865 -2254.71252 -1824.90794 -1057.99839 -1150.53157
103 104 105 106 107 108
-886.64910 365.49323 1324.46180 1600.56617 738.61638 -131.00056
109 110 111 112 113 114
237.92465 373.02159 518.82619 212.25725 1231.20344 548.08991
115 116 117 118 119 120
633.26890 -445.05494 -526.01878 -648.23644 -1925.01697 -2314.09022
121 122 123 124 125 126
-2177.24697 -2230.74556 -544.82946 -1306.64485 -816.27264 1122.67786
127 128 129 130 131 132
779.35619 397.63224 -593.46294 -778.74260 -981.37592 -229.51369
133 134 135 136 137 138
59.75218 2276.01975 2625.39327 5270.71729 3260.52085 3491.73720
139 140 141 142 143 144
425.99758 2770.47944 1273.12737 342.41235 2882.20102 2932.02904
145 146 147 148 149 150
1003.09723 -263.48318 -743.71661 -798.98016 -1201.73388 -1959.31660
151 152 153 154
-3350.21610 -3764.19578 -2542.15067 -4014.13585
> postscript(file="/var/fisher/rcomp/tmp/6rkvq1354174667.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 2508.65751 NA
1 1824.32547 2508.65751
2 2101.95309 1824.32547
3 1802.23701 2101.95309
4 1443.12515 1802.23701
5 1002.12646 1443.12515
6 1367.73430 1002.12646
7 646.54466 1367.73430
8 1467.76323 646.54466
9 1321.98974 1467.76323
10 560.62238 1321.98974
11 190.54054 560.62238
12 146.18227 190.54054
13 342.55936 146.18227
14 827.95908 342.55936
15 1258.03037 827.95908
16 1077.28915 1258.03037
17 764.41792 1077.28915
18 314.85647 764.41792
19 281.79888 314.85647
20 -145.85318 281.79888
21 -498.43840 -145.85318
22 -755.00970 -498.43840
23 -537.64326 -755.00970
24 -525.00502 -537.64326
25 -837.06696 -525.00502
26 -320.18655 -837.06696
27 -606.62410 -320.18655
28 -969.20181 -606.62410
29 -1771.03795 -969.20181
30 -1989.17263 -1771.03795
31 -2261.61827 -1989.17263
32 -2325.59125 -2261.61827
33 -1438.37433 -2325.59125
34 -1166.59876 -1438.37433
35 -1879.22988 -1166.59876
36 -1830.80295 -1879.22988
37 -1998.15156 -1830.80295
38 -1389.72830 -1998.15156
39 -1160.91456 -1389.72830
40 -1533.63302 -1160.91456
41 -1142.09654 -1533.63302
42 -717.56082 -1142.09654
43 -1064.13452 -717.56082
44 -1074.83895 -1064.13452
45 -1436.37425 -1074.83895
46 -1474.97281 -1436.37425
47 -1030.32009 -1474.97281
48 -450.36968 -1030.32009
49 -708.29223 -450.36968
50 -1019.03317 -708.29223
51 -855.46598 -1019.03317
52 896.99780 -855.46598
53 193.35384 896.99780
54 72.16722 193.35384
55 -194.23604 72.16722
56 1111.25241 -194.23604
57 345.89420 1111.25241
58 1765.00326 345.89420
59 2340.76352 1765.00326
60 2929.00346 2340.76352
61 3564.04768 2929.00346
62 4417.73961 3564.04768
63 4490.62820 4417.73961
64 4258.78613 4490.62820
65 2959.98029 4258.78613
66 2621.31973 2959.98029
67 2150.29169 2621.31973
68 908.94153 2150.29169
69 794.23627 908.94153
70 208.60415 794.23627
71 600.24106 208.60415
72 1276.84959 600.24106
73 676.45701 1276.84959
74 366.09486 676.45701
75 -225.62999 366.09486
76 -1089.63979 -225.62999
77 -1428.37047 -1089.63979
78 -1461.55995 -1428.37047
79 -1252.57380 -1461.55995
80 -502.91856 -1252.57380
81 -1189.21065 -502.91856
82 -843.11033 -1189.21065
83 155.12189 -843.11033
84 -660.32537 155.12189
85 -1449.26290 -660.32537
86 -1344.65020 -1449.26290
87 -1756.42013 -1344.65020
88 -1228.70909 -1756.42013
89 -1068.46273 -1228.70909
90 -699.23411 -1068.46273
91 -696.49199 -699.23411
92 -536.71564 -696.49199
93 -454.44953 -536.71564
94 -105.55938 -454.44953
95 165.28606 -105.55938
96 -1377.13324 165.28606
97 -1277.66865 -1377.13324
98 -2254.71252 -1277.66865
99 -1824.90794 -2254.71252
100 -1057.99839 -1824.90794
101 -1150.53157 -1057.99839
102 -886.64910 -1150.53157
103 365.49323 -886.64910
104 1324.46180 365.49323
105 1600.56617 1324.46180
106 738.61638 1600.56617
107 -131.00056 738.61638
108 237.92465 -131.00056
109 373.02159 237.92465
110 518.82619 373.02159
111 212.25725 518.82619
112 1231.20344 212.25725
113 548.08991 1231.20344
114 633.26890 548.08991
115 -445.05494 633.26890
116 -526.01878 -445.05494
117 -648.23644 -526.01878
118 -1925.01697 -648.23644
119 -2314.09022 -1925.01697
120 -2177.24697 -2314.09022
121 -2230.74556 -2177.24697
122 -544.82946 -2230.74556
123 -1306.64485 -544.82946
124 -816.27264 -1306.64485
125 1122.67786 -816.27264
126 779.35619 1122.67786
127 397.63224 779.35619
128 -593.46294 397.63224
129 -778.74260 -593.46294
130 -981.37592 -778.74260
131 -229.51369 -981.37592
132 59.75218 -229.51369
133 2276.01975 59.75218
134 2625.39327 2276.01975
135 5270.71729 2625.39327
136 3260.52085 5270.71729
137 3491.73720 3260.52085
138 425.99758 3491.73720
139 2770.47944 425.99758
140 1273.12737 2770.47944
141 342.41235 1273.12737
142 2882.20102 342.41235
143 2932.02904 2882.20102
144 1003.09723 2932.02904
145 -263.48318 1003.09723
146 -743.71661 -263.48318
147 -798.98016 -743.71661
148 -1201.73388 -798.98016
149 -1959.31660 -1201.73388
150 -3350.21610 -1959.31660
151 -3764.19578 -3350.21610
152 -2542.15067 -3764.19578
153 -4014.13585 -2542.15067
154 NA -4014.13585
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1824.32547 2508.65751
[2,] 2101.95309 1824.32547
[3,] 1802.23701 2101.95309
[4,] 1443.12515 1802.23701
[5,] 1002.12646 1443.12515
[6,] 1367.73430 1002.12646
[7,] 646.54466 1367.73430
[8,] 1467.76323 646.54466
[9,] 1321.98974 1467.76323
[10,] 560.62238 1321.98974
[11,] 190.54054 560.62238
[12,] 146.18227 190.54054
[13,] 342.55936 146.18227
[14,] 827.95908 342.55936
[15,] 1258.03037 827.95908
[16,] 1077.28915 1258.03037
[17,] 764.41792 1077.28915
[18,] 314.85647 764.41792
[19,] 281.79888 314.85647
[20,] -145.85318 281.79888
[21,] -498.43840 -145.85318
[22,] -755.00970 -498.43840
[23,] -537.64326 -755.00970
[24,] -525.00502 -537.64326
[25,] -837.06696 -525.00502
[26,] -320.18655 -837.06696
[27,] -606.62410 -320.18655
[28,] -969.20181 -606.62410
[29,] -1771.03795 -969.20181
[30,] -1989.17263 -1771.03795
[31,] -2261.61827 -1989.17263
[32,] -2325.59125 -2261.61827
[33,] -1438.37433 -2325.59125
[34,] -1166.59876 -1438.37433
[35,] -1879.22988 -1166.59876
[36,] -1830.80295 -1879.22988
[37,] -1998.15156 -1830.80295
[38,] -1389.72830 -1998.15156
[39,] -1160.91456 -1389.72830
[40,] -1533.63302 -1160.91456
[41,] -1142.09654 -1533.63302
[42,] -717.56082 -1142.09654
[43,] -1064.13452 -717.56082
[44,] -1074.83895 -1064.13452
[45,] -1436.37425 -1074.83895
[46,] -1474.97281 -1436.37425
[47,] -1030.32009 -1474.97281
[48,] -450.36968 -1030.32009
[49,] -708.29223 -450.36968
[50,] -1019.03317 -708.29223
[51,] -855.46598 -1019.03317
[52,] 896.99780 -855.46598
[53,] 193.35384 896.99780
[54,] 72.16722 193.35384
[55,] -194.23604 72.16722
[56,] 1111.25241 -194.23604
[57,] 345.89420 1111.25241
[58,] 1765.00326 345.89420
[59,] 2340.76352 1765.00326
[60,] 2929.00346 2340.76352
[61,] 3564.04768 2929.00346
[62,] 4417.73961 3564.04768
[63,] 4490.62820 4417.73961
[64,] 4258.78613 4490.62820
[65,] 2959.98029 4258.78613
[66,] 2621.31973 2959.98029
[67,] 2150.29169 2621.31973
[68,] 908.94153 2150.29169
[69,] 794.23627 908.94153
[70,] 208.60415 794.23627
[71,] 600.24106 208.60415
[72,] 1276.84959 600.24106
[73,] 676.45701 1276.84959
[74,] 366.09486 676.45701
[75,] -225.62999 366.09486
[76,] -1089.63979 -225.62999
[77,] -1428.37047 -1089.63979
[78,] -1461.55995 -1428.37047
[79,] -1252.57380 -1461.55995
[80,] -502.91856 -1252.57380
[81,] -1189.21065 -502.91856
[82,] -843.11033 -1189.21065
[83,] 155.12189 -843.11033
[84,] -660.32537 155.12189
[85,] -1449.26290 -660.32537
[86,] -1344.65020 -1449.26290
[87,] -1756.42013 -1344.65020
[88,] -1228.70909 -1756.42013
[89,] -1068.46273 -1228.70909
[90,] -699.23411 -1068.46273
[91,] -696.49199 -699.23411
[92,] -536.71564 -696.49199
[93,] -454.44953 -536.71564
[94,] -105.55938 -454.44953
[95,] 165.28606 -105.55938
[96,] -1377.13324 165.28606
[97,] -1277.66865 -1377.13324
[98,] -2254.71252 -1277.66865
[99,] -1824.90794 -2254.71252
[100,] -1057.99839 -1824.90794
[101,] -1150.53157 -1057.99839
[102,] -886.64910 -1150.53157
[103,] 365.49323 -886.64910
[104,] 1324.46180 365.49323
[105,] 1600.56617 1324.46180
[106,] 738.61638 1600.56617
[107,] -131.00056 738.61638
[108,] 237.92465 -131.00056
[109,] 373.02159 237.92465
[110,] 518.82619 373.02159
[111,] 212.25725 518.82619
[112,] 1231.20344 212.25725
[113,] 548.08991 1231.20344
[114,] 633.26890 548.08991
[115,] -445.05494 633.26890
[116,] -526.01878 -445.05494
[117,] -648.23644 -526.01878
[118,] -1925.01697 -648.23644
[119,] -2314.09022 -1925.01697
[120,] -2177.24697 -2314.09022
[121,] -2230.74556 -2177.24697
[122,] -544.82946 -2230.74556
[123,] -1306.64485 -544.82946
[124,] -816.27264 -1306.64485
[125,] 1122.67786 -816.27264
[126,] 779.35619 1122.67786
[127,] 397.63224 779.35619
[128,] -593.46294 397.63224
[129,] -778.74260 -593.46294
[130,] -981.37592 -778.74260
[131,] -229.51369 -981.37592
[132,] 59.75218 -229.51369
[133,] 2276.01975 59.75218
[134,] 2625.39327 2276.01975
[135,] 5270.71729 2625.39327
[136,] 3260.52085 5270.71729
[137,] 3491.73720 3260.52085
[138,] 425.99758 3491.73720
[139,] 2770.47944 425.99758
[140,] 1273.12737 2770.47944
[141,] 342.41235 1273.12737
[142,] 2882.20102 342.41235
[143,] 2932.02904 2882.20102
[144,] 1003.09723 2932.02904
[145,] -263.48318 1003.09723
[146,] -743.71661 -263.48318
[147,] -798.98016 -743.71661
[148,] -1201.73388 -798.98016
[149,] -1959.31660 -1201.73388
[150,] -3350.21610 -1959.31660
[151,] -3764.19578 -3350.21610
[152,] -2542.15067 -3764.19578
[153,] -4014.13585 -2542.15067
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1824.32547 2508.65751
2 2101.95309 1824.32547
3 1802.23701 2101.95309
4 1443.12515 1802.23701
5 1002.12646 1443.12515
6 1367.73430 1002.12646
7 646.54466 1367.73430
8 1467.76323 646.54466
9 1321.98974 1467.76323
10 560.62238 1321.98974
11 190.54054 560.62238
12 146.18227 190.54054
13 342.55936 146.18227
14 827.95908 342.55936
15 1258.03037 827.95908
16 1077.28915 1258.03037
17 764.41792 1077.28915
18 314.85647 764.41792
19 281.79888 314.85647
20 -145.85318 281.79888
21 -498.43840 -145.85318
22 -755.00970 -498.43840
23 -537.64326 -755.00970
24 -525.00502 -537.64326
25 -837.06696 -525.00502
26 -320.18655 -837.06696
27 -606.62410 -320.18655
28 -969.20181 -606.62410
29 -1771.03795 -969.20181
30 -1989.17263 -1771.03795
31 -2261.61827 -1989.17263
32 -2325.59125 -2261.61827
33 -1438.37433 -2325.59125
34 -1166.59876 -1438.37433
35 -1879.22988 -1166.59876
36 -1830.80295 -1879.22988
37 -1998.15156 -1830.80295
38 -1389.72830 -1998.15156
39 -1160.91456 -1389.72830
40 -1533.63302 -1160.91456
41 -1142.09654 -1533.63302
42 -717.56082 -1142.09654
43 -1064.13452 -717.56082
44 -1074.83895 -1064.13452
45 -1436.37425 -1074.83895
46 -1474.97281 -1436.37425
47 -1030.32009 -1474.97281
48 -450.36968 -1030.32009
49 -708.29223 -450.36968
50 -1019.03317 -708.29223
51 -855.46598 -1019.03317
52 896.99780 -855.46598
53 193.35384 896.99780
54 72.16722 193.35384
55 -194.23604 72.16722
56 1111.25241 -194.23604
57 345.89420 1111.25241
58 1765.00326 345.89420
59 2340.76352 1765.00326
60 2929.00346 2340.76352
61 3564.04768 2929.00346
62 4417.73961 3564.04768
63 4490.62820 4417.73961
64 4258.78613 4490.62820
65 2959.98029 4258.78613
66 2621.31973 2959.98029
67 2150.29169 2621.31973
68 908.94153 2150.29169
69 794.23627 908.94153
70 208.60415 794.23627
71 600.24106 208.60415
72 1276.84959 600.24106
73 676.45701 1276.84959
74 366.09486 676.45701
75 -225.62999 366.09486
76 -1089.63979 -225.62999
77 -1428.37047 -1089.63979
78 -1461.55995 -1428.37047
79 -1252.57380 -1461.55995
80 -502.91856 -1252.57380
81 -1189.21065 -502.91856
82 -843.11033 -1189.21065
83 155.12189 -843.11033
84 -660.32537 155.12189
85 -1449.26290 -660.32537
86 -1344.65020 -1449.26290
87 -1756.42013 -1344.65020
88 -1228.70909 -1756.42013
89 -1068.46273 -1228.70909
90 -699.23411 -1068.46273
91 -696.49199 -699.23411
92 -536.71564 -696.49199
93 -454.44953 -536.71564
94 -105.55938 -454.44953
95 165.28606 -105.55938
96 -1377.13324 165.28606
97 -1277.66865 -1377.13324
98 -2254.71252 -1277.66865
99 -1824.90794 -2254.71252
100 -1057.99839 -1824.90794
101 -1150.53157 -1057.99839
102 -886.64910 -1150.53157
103 365.49323 -886.64910
104 1324.46180 365.49323
105 1600.56617 1324.46180
106 738.61638 1600.56617
107 -131.00056 738.61638
108 237.92465 -131.00056
109 373.02159 237.92465
110 518.82619 373.02159
111 212.25725 518.82619
112 1231.20344 212.25725
113 548.08991 1231.20344
114 633.26890 548.08991
115 -445.05494 633.26890
116 -526.01878 -445.05494
117 -648.23644 -526.01878
118 -1925.01697 -648.23644
119 -2314.09022 -1925.01697
120 -2177.24697 -2314.09022
121 -2230.74556 -2177.24697
122 -544.82946 -2230.74556
123 -1306.64485 -544.82946
124 -816.27264 -1306.64485
125 1122.67786 -816.27264
126 779.35619 1122.67786
127 397.63224 779.35619
128 -593.46294 397.63224
129 -778.74260 -593.46294
130 -981.37592 -778.74260
131 -229.51369 -981.37592
132 59.75218 -229.51369
133 2276.01975 59.75218
134 2625.39327 2276.01975
135 5270.71729 2625.39327
136 3260.52085 5270.71729
137 3491.73720 3260.52085
138 425.99758 3491.73720
139 2770.47944 425.99758
140 1273.12737 2770.47944
141 342.41235 1273.12737
142 2882.20102 342.41235
143 2932.02904 2882.20102
144 1003.09723 2932.02904
145 -263.48318 1003.09723
146 -743.71661 -263.48318
147 -798.98016 -743.71661
148 -1201.73388 -798.98016
149 -1959.31660 -1201.73388
150 -3350.21610 -1959.31660
151 -3764.19578 -3350.21610
152 -2542.15067 -3764.19578
153 -4014.13585 -2542.15067
> 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/fisher/rcomp/tmp/7zwig1354174667.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8cx6y1354174667.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9paoo1354174667.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10whf01354174667.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/11kpfr1354174667.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/fisher/rcomp/tmp/122oeh1354174667.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/fisher/rcomp/tmp/136xeu1354174667.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/fisher/rcomp/tmp/14541k1354174667.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/fisher/rcomp/tmp/15kgi41354174668.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/fisher/rcomp/tmp/16v61a1354174668.tab")
+ }
>
> try(system("convert tmp/1n23t1354174667.ps tmp/1n23t1354174667.png",intern=TRUE))
character(0)
> try(system("convert tmp/2lg4v1354174667.ps tmp/2lg4v1354174667.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ydxv1354174667.ps tmp/3ydxv1354174667.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xmf01354174667.ps tmp/4xmf01354174667.png",intern=TRUE))
character(0)
> try(system("convert tmp/518su1354174667.ps tmp/518su1354174667.png",intern=TRUE))
character(0)
> try(system("convert tmp/6rkvq1354174667.ps tmp/6rkvq1354174667.png",intern=TRUE))
character(0)
> try(system("convert tmp/7zwig1354174667.ps tmp/7zwig1354174667.png",intern=TRUE))
character(0)
> try(system("convert tmp/8cx6y1354174667.ps tmp/8cx6y1354174667.png",intern=TRUE))
character(0)
> try(system("convert tmp/9paoo1354174667.ps tmp/9paoo1354174667.png",intern=TRUE))
character(0)
> try(system("convert tmp/10whf01354174667.ps tmp/10whf01354174667.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.621 1.414 9.085