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(1
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+ ,dim=c(10
+ ,140)
+ ,dimnames=list(c('Place'
+ ,'pageviews'
+ ,'Blogs'
+ ,'PR'
+ ,'LFM'
+ ,'KCS'
+ ,'SPR'
+ ,'CH'
+ ,'Hours'
+ ,'Month')
+ ,1:140))
> y <- array(NA,dim=c(10,140),dimnames=list(c('Place','pageviews','Blogs','PR','LFM','KCS','SPR','CH','Hours','Month'),1:140))
> 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'
> 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
Place pageviews Blogs PR LFM KCS SPR CH Hours Month
1 1 1901 61 17 56 84 4 21 51 9
2 2 2509 74 19 73 47 3 15 45 9
3 3 2114 57 18 62 63 3 17 44 9
4 4 1331 50 15 42 28 3 20 42 9
5 5 1399 48 15 59 22 2 12 38 9
6 6 7333 2 12 27 18 6 4 38 9
7 7 1170 31 20 78 27 5 11 35 9
8 8 1507 61 14 56 37 5 12 35 9
9 9 1107 36 15 59 20 5 9 34 9
10 10 2051 46 13 51 67 5 14 33 9
11 11 1290 30 17 47 28 4 11 32 9
12 12 820 49 10 35 45 3 14 31 9
13 13 1502 14 13 47 15 5 4 30 9
14 14 1451 12 12 47 23 6 7 30 9
15 15 1178 54 16 55 30 6 9 30 9
16 16 1514 44 15 54 27 2 14 29 9
17 17 883 40 15 60 43 5 13 29 9
18 18 1405 57 15 55 36 5 11 29 9
19 19 927 29 12 48 28 5 9 28 9
20 20 1352 32 13 47 28 9 8 27 9
21 21 1314 28 12 47 22 4 9 27 9
22 22 1307 40 15 52 27 4 11 27 9
23 23 1243 54 12 48 24 5 7 26 9
24 24 1232 56 12 48 52 3 15 26 9
25 25 1097 19 9 27 12 0 4 26 9
26 26 1100 67 12 12 24 5 10 26 9
27 27 1316 25 13 51 10 3 10 26 9
28 28 903 42 16 58 71 4 13 25 9
29 29 929 28 15 60 12 2 10 25 9
30 30 1049 57 13 46 24 5 10 25 9
31 31 1372 28 12 45 22 11 6 24 9
32 32 1470 35 13 42 21 5 8 24 9
33 33 821 10 12 41 13 3 7 24 9
34 34 1239 30 12 47 28 4 11 24 9
35 35 1384 23 8 32 19 5 10 24 9
36 36 820 32 15 56 29 5 11 24 9
37 37 1462 24 12 42 12 2 10 24 9
38 38 1202 42 12 41 32 6 8 23 9
39 39 1091 33 12 47 21 3 10 23 9
40 40 1228 19 14 47 19 4 5 23 9
41 41 707 17 15 49 15 8 5 23 9
42 42 868 49 15 52 14 14 5 23 9
43 43 1165 30 12 42 34 11 9 22 9
44 44 1106 3 13 55 8 8 2 22 9
45 45 1429 56 12 48 27 3 9 22 9
46 46 1671 37 13 48 31 3 13 22 9
47 47 1579 26 12 38 21 11 7 22 9
48 48 774 19 12 48 10 3 5 21 10
49 49 934 22 13 50 21 4 7 21 10
50 50 825 53 12 39 19 3 8 21 10
51 51 1375 35 12 48 27 5 8 21 10
52 52 968 12 9 36 17 6 5 21 10
53 53 1156 34 13 49 30 8 5 21 10
54 54 1374 28 13 39 19 3 10 21 10
55 55 1224 38 12 41 17 3 5 21 10
56 56 804 38 15 45 24 5 10 21 10
57 57 998 45 15 60 36 5 10 21 10
58 58 1112 15 13 45 16 3 7 21 10
59 59 1153 35 14 41 16 3 10 20 10
60 60 613 27 14 52 30 3 9 20 10
61 61 729 23 12 46 18 5 10 20 10
62 62 813 33 12 39 26 3 10 20 10
63 63 912 23 9 32 17 3 5 20 10
64 64 1178 26 14 52 28 6 8 20 10
65 65 1201 32 16 54 20 4 6 19 10
66 66 1165 35 15 51 27 3 7 19 10
67 67 705 18 13 52 13 13 6 18 10
68 68 814 18 16 57 10 5 3 17 10
69 69 1082 41 12 47 29 6 9 17 10
70 70 885 39 12 45 34 5 11 17 10
71 71 837 56 12 41 30 3 9 17 10
72 72 586 35 12 43 16 4 10 16 10
73 73 913 37 10 31 22 4 9 16 10
74 74 547 26 15 32 22 7 7 15 10
75 75 758 33 12 41 31 4 6 15 10
76 76 848 7 9 27 10 5 6 15 10
77 77 634 16 10 40 7 7 5 15 10
78 78 501 13 13 46 10 3 5 15 10
79 79 849 54 12 32 55 6 8 15 10
80 80 733 30 13 9 25 8 7 15 10
81 81 634 9 16 64 9 5 5 15 10
82 82 1010 35 15 30 31 5 10 15 10
83 83 778 0 12 46 0 0 0 15 10
84 84 480 40 12 37 24 3 10 15 10
85 85 848 22 12 22 14 5 6 15 10
86 86 714 29 12 20 11 3 6 14 10
87 87 871 25 12 21 8 8 4 14 10
88 88 776 17 14 44 9 9 3 14 10
89 89 815 32 12 24 18 9 7 14 10
90 90 811 40 12 33 14 4 5 14 10
91 91 529 24 12 45 27 2 8 13 10
92 92 642 18 13 35 10 0 0 13 10
93 93 562 15 8 31 16 3 5 13 10
94 94 626 17 16 20 13 7 5 13 10
95 95 636 28 12 13 10 5 5 13 11
96 96 935 18 11 33 16 3 5 13 11
97 97 473 16 15 58 11 3 6 12 11
98 98 836 28 13 26 8 3 5 12 11
99 99 938 17 12 36 29 7 6 12 11
100 100 656 25 13 32 12 4 4 12 11
101 101 566 2 13 34 1 0 0 12 11
102 102 765 10 12 15 26 5 8 12 11
103 103 705 9 12 40 5 5 2 11 11
104 104 558 7 12 37 5 5 2 11 11
105 105 582 27 14 26 24 6 8 11 11
106 106 608 25 12 31 19 6 3 11 11
107 107 567 16 16 47 10 5 3 11 11
108 108 434 28 8 21 6 6 3 11 11
109 109 479 7 8 21 61 0 3 11 11
110 110 488 0 5 9 25 25 1 10 11
111 111 507 16 9 28 7 2 2 10 11
112 112 394 10 11 24 10 5 2 10 11
113 113 504 0 4 15 3 3 1 9 11
114 114 368 2 8 19 1 1 2 9 11
115 115 386 5 13 35 38 5 7 9 11
116 116 451 36 13 45 13 4 4 9 11
117 117 580 10 12 20 2 0 1 9 11
118 118 565 43 13 1 8 4 6 9 11
119 119 510 14 12 29 30 10 3 9 11
120 120 495 12 12 33 11 6 2 8 11
121 121 596 15 10 32 69 23 3 8 11
122 122 412 8 12 11 2 0 2 8 11
123 123 338 39 5 10 23 6 5 7 11
124 124 446 10 13 18 8 4 4 7 11
125 125 418 0 12 41 0 0 0 7 11
126 126 335 7 6 0 2 0 0 6 11
127 127 349 10 9 10 4 2 3 6 11
128 128 308 3 12 24 4 4 2 5 11
129 129 466 8 15 28 0 0 0 5 11
130 130 228 0 11 38 9 9 1 5 11
131 131 428 8 3 4 5 5 3 5 11
132 132 242 1 8 25 0 0 0 5 11
133 133 352 0 12 40 0 0 0 5 11
134 134 244 8 0 0 13 4 4 5 11
135 135 269 3 9 23 1 0 1 5 11
136 136 242 0 4 13 0 0 0 4 11
137 137 291 0 14 6 39 0 2 4 11
138 138 213 0 9 31 10 0 0 4 11
139 139 135 0 0 0 1 0 1 3 11
140 140 210 3 1 3 3 3 3 3 11
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) pageviews Blogs PR LFM KCS
-7.982e+01 -1.116e-04 -7.824e-02 2.650e-01 -1.902e-01 1.075e-01
SPR CH Hours Month
-2.534e-01 -5.581e-02 -2.415e+00 1.995e+01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-13.4995 -5.0342 -0.7942 4.2206 28.6889
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.982e+01 1.763e+01 -4.528 1.33e-05 ***
pageviews -1.116e-04 1.239e-03 -0.090 0.9283
Blogs -7.824e-02 5.730e-02 -1.366 0.1744
PR 2.650e-01 2.712e-01 0.977 0.3304
LFM -1.902e-01 6.393e-02 -2.975 0.0035 **
KCS 1.075e-01 5.740e-02 1.873 0.0633 .
SPR -2.534e-01 1.777e-01 -1.426 0.1563
CH -5.581e-02 3.212e-01 -0.174 0.8623
Hours -2.415e+00 1.883e-01 -12.825 < 2e-16 ***
Month 1.995e+01 1.550e+00 12.873 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.924 on 130 degrees of freedom
Multiple R-squared: 0.9727, Adjusted R-squared: 0.9709
F-statistic: 515.5 on 9 and 130 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,] 0.01666899 3.333799e-02 9.833310e-01
[2,] 0.02057436 4.114872e-02 9.794256e-01
[3,] 0.02625564 5.251129e-02 9.737444e-01
[4,] 0.27713315 5.542663e-01 7.228669e-01
[5,] 0.30536023 6.107205e-01 6.946398e-01
[6,] 0.36509195 7.301839e-01 6.349081e-01
[7,] 0.48689147 9.737829e-01 5.131085e-01
[8,] 0.41067950 8.213590e-01 5.893205e-01
[9,] 0.59147085 8.170583e-01 4.085292e-01
[10,] 0.69086541 6.182692e-01 3.091346e-01
[11,] 0.74284575 5.143085e-01 2.571543e-01
[12,] 0.71905262 5.618948e-01 2.809474e-01
[13,] 0.79668201 4.066360e-01 2.033180e-01
[14,] 0.74021590 5.195682e-01 2.597841e-01
[15,] 0.93351842 1.329632e-01 6.648158e-02
[16,] 0.93445703 1.310859e-01 6.554297e-02
[17,] 0.96941767 6.116466e-02 3.058233e-02
[18,] 0.98676520 2.646961e-02 1.323480e-02
[19,] 0.99830876 3.382481e-03 1.691240e-03
[20,] 0.99930993 1.380147e-03 6.900733e-04
[21,] 0.99981830 3.634042e-04 1.817021e-04
[22,] 0.99992250 1.550085e-04 7.750426e-05
[23,] 0.99995826 8.348647e-05 4.174324e-05
[24,] 0.99998736 2.527698e-05 1.263849e-05
[25,] 0.99999476 1.047888e-05 5.239439e-06
[26,] 0.99999872 2.555407e-06 1.277703e-06
[27,] 0.99999939 1.228795e-06 6.143976e-07
[28,] 0.99999991 1.802940e-07 9.014701e-08
[29,] 0.99999998 3.213042e-08 1.606521e-08
[30,] 1.00000000 7.406207e-09 3.703104e-09
[31,] 1.00000000 9.274463e-09 4.637231e-09
[32,] 1.00000000 2.991153e-09 1.495577e-09
[33,] 1.00000000 3.679072e-10 1.839536e-10
[34,] 1.00000000 2.037432e-10 1.018716e-10
[35,] 1.00000000 1.404937e-10 7.024684e-11
[36,] 1.00000000 8.785724e-11 4.392862e-11
[37,] 1.00000000 6.381708e-11 3.190854e-11
[38,] 1.00000000 5.495598e-11 2.747799e-11
[39,] 1.00000000 8.053483e-11 4.026742e-11
[40,] 1.00000000 1.049502e-10 5.247509e-11
[41,] 1.00000000 1.645275e-10 8.226374e-11
[42,] 1.00000000 3.759662e-10 1.879831e-10
[43,] 1.00000000 5.000115e-10 2.500058e-10
[44,] 1.00000000 1.090830e-09 5.454152e-10
[45,] 1.00000000 1.758393e-09 8.791967e-10
[46,] 1.00000000 1.908231e-09 9.541156e-10
[47,] 1.00000000 4.090200e-09 2.045100e-09
[48,] 1.00000000 6.832263e-09 3.416131e-09
[49,] 1.00000000 8.536145e-09 4.268072e-09
[50,] 1.00000000 7.533680e-09 3.766840e-09
[51,] 1.00000000 3.105682e-09 1.552841e-09
[52,] 1.00000000 9.688683e-10 4.844342e-10
[53,] 1.00000000 1.259108e-09 6.295541e-10
[54,] 1.00000000 1.014497e-09 5.072483e-10
[55,] 1.00000000 2.173260e-09 1.086630e-09
[56,] 1.00000000 2.007053e-09 1.003526e-09
[57,] 1.00000000 3.493434e-09 1.746717e-09
[58,] 1.00000000 6.937449e-09 3.468725e-09
[59,] 1.00000000 9.374102e-09 4.687051e-09
[60,] 0.99999999 1.236542e-08 6.182711e-09
[61,] 0.99999999 1.437242e-08 7.186208e-09
[62,] 1.00000000 1.073978e-09 5.369891e-10
[63,] 1.00000000 6.116402e-11 3.058201e-11
[64,] 1.00000000 2.262164e-11 1.131082e-11
[65,] 1.00000000 5.886210e-12 2.943105e-12
[66,] 1.00000000 1.026150e-12 5.130749e-13
[67,] 1.00000000 7.536352e-14 3.768176e-14
[68,] 1.00000000 2.020194e-14 1.010097e-14
[69,] 1.00000000 2.378629e-14 1.189315e-14
[70,] 1.00000000 6.192981e-14 3.096491e-14
[71,] 1.00000000 2.558933e-14 1.279466e-14
[72,] 1.00000000 3.508588e-14 1.754294e-14
[73,] 1.00000000 4.305741e-14 2.152871e-14
[74,] 1.00000000 4.993943e-14 2.496971e-14
[75,] 1.00000000 9.806204e-14 4.903102e-14
[76,] 1.00000000 1.661541e-13 8.307705e-14
[77,] 1.00000000 3.466473e-13 1.733237e-13
[78,] 1.00000000 2.906788e-13 1.453394e-13
[79,] 1.00000000 3.558995e-13 1.779498e-13
[80,] 1.00000000 3.601703e-13 1.800851e-13
[81,] 1.00000000 3.931654e-13 1.965827e-13
[82,] 1.00000000 1.352700e-12 6.763500e-13
[83,] 1.00000000 1.369353e-12 6.846763e-13
[84,] 1.00000000 4.373033e-12 2.186517e-12
[85,] 1.00000000 2.474694e-12 1.237347e-12
[86,] 1.00000000 3.572035e-12 1.786017e-12
[87,] 1.00000000 1.047548e-11 5.237739e-12
[88,] 1.00000000 1.945911e-11 9.729557e-12
[89,] 1.00000000 6.139951e-11 3.069975e-11
[90,] 1.00000000 1.841061e-10 9.205305e-11
[91,] 1.00000000 3.009759e-10 1.504880e-10
[92,] 1.00000000 5.116769e-10 2.558384e-10
[93,] 1.00000000 8.810700e-10 4.405350e-10
[94,] 1.00000000 1.898518e-09 9.492591e-10
[95,] 1.00000000 6.515120e-09 3.257560e-09
[96,] 0.99999999 2.009616e-08 1.004808e-08
[97,] 0.99999997 6.218702e-08 3.109351e-08
[98,] 0.99999990 2.078240e-07 1.039120e-07
[99,] 0.99999973 5.454140e-07 2.727070e-07
[100,] 0.99999914 1.721378e-06 8.606889e-07
[101,] 0.99999907 1.865138e-06 9.325689e-07
[102,] 0.99999869 2.629517e-06 1.314759e-06
[103,] 0.99999969 6.287344e-07 3.143672e-07
[104,] 0.99999950 1.009887e-06 5.049437e-07
[105,] 0.99999825 3.506702e-06 1.753351e-06
[106,] 0.99999967 6.695826e-07 3.347913e-07
[107,] 0.99999867 2.653164e-06 1.326582e-06
[108,] 0.99999432 1.136829e-05 5.684147e-06
[109,] 0.99998305 3.389961e-05 1.694980e-05
[110,] 0.99995963 8.074377e-05 4.037188e-05
[111,] 0.99990432 1.913658e-04 9.568289e-05
[112,] 0.99962357 7.528540e-04 3.764270e-04
[113,] 0.99862303 2.753948e-03 1.376974e-03
[114,] 0.99346065 1.307869e-02 6.539346e-03
[115,] 0.97143714 5.712572e-02 2.856286e-02
> postscript(file="/var/fisher/rcomp/tmp/16spz1355667838.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/2z9zo1355667838.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3i5731355667838.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/44pcl1355667838.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5x1sc1355667838.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 140
Frequency = 1
1 2 3 4 5 6
28.68893225 22.37887531 16.15465937 12.61119818 6.98243748 0.75184458
7 8 9 10 11 12
2.83887292 2.61025642 1.16057370 -5.13094714 -5.93171033 -8.25296412
13 14 15 16 17 18
-7.66882921 -7.00509102 -2.92871100 -5.42562756 -4.68324003 -2.60500601
19 20 21 22 23 24
-6.05173223 -6.68135356 -6.30001018 -4.63176513 -4.57120262 -6.48620538
25 26 27 28 29 30
-8.66978712 -7.24968835 -1.36099603 -7.09146157 -0.87070550 -0.25067279
31 32 33 34 35 36
-2.31094459 -2.88945885 -3.54588290 -0.92895082 -1.08832372 2.24347745
37 38 39 40 41 42
1.83272555 0.35955110 2.31788765 1.89720549 4.24170441 9.96213829
43 44 45 46 47 48
3.30019881 6.03251969 7.23002289 6.29872093 7.55847586 -13.49953963
49 50 51 52 53 54
-12.94893249 -11.34544457 -10.33367195 -11.50505839 -8.24073848 -9.39355733
55 56 57 58 59 60
-7.04655563 -6.09418362 -2.96181740 -5.14370330 -5.84723384 -5.00201176
61 62 63 64 65 66
-3.06058375 -3.96701260 -3.58634567 -0.09766111 -0.94836937 -0.97335179
67 68 69 70 71 72
0.93437019 -2.18453340 -1.65113878 -1.88930602 -0.51388325 -1.40497143
73 74 75 76 77 78
-2.66511711 -5.46727367 -3.17285346 -3.55400991 1.10738667 0.86774221
79 80 81 82 83 84
-1.19275543 -3.04710755 6.81255412 1.60128301 5.18210099 6.30525606
85 86 87 88 89 90
4.44365153 2.99701152 5.36985897 9.66799269 7.82781131 10.21620118
91 92 93 94 95 96
8.06376761 7.31401969 9.02897708 7.31671671 -11.22842573 -8.06037620
97 98 99 100 101 102
-5.39480389 -8.70500624 -7.57509541 -6.05098575 -6.53475695 -9.20901499
103 104 105 106 107 108
-4.03108067 -3.77456414 -5.28331799 -2.69748849 0.29092128 0.07325654
109 110 111 112 113 114
-7.99761604 -1.35195857 -0.38275227 -0.71774592 -2.56946331 -1.96346259
115 116 117 118 119 120
-1.69303694 5.90813809 0.39959235 0.74898871 4.05273455 4.21359945
121 122 123 124 125 126
3.92910158 1.15382584 3.25196994 2.44634315 6.92327059 -0.37593678
127 128 129 130 131 132
2.42664551 2.77865286 3.45787362 8.13669086 4.96606293 7.16957436
133 134 135 136 137 138
9.89657136 6.92213948 9.63199359 7.45439909 0.39786288 10.47476990
139 140
6.56363623 8.76928466
> postscript(file="/var/fisher/rcomp/tmp/68niv1355667838.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 = 140
Frequency = 1
lag(myerror, k = 1) myerror
0 28.68893225 NA
1 22.37887531 28.68893225
2 16.15465937 22.37887531
3 12.61119818 16.15465937
4 6.98243748 12.61119818
5 0.75184458 6.98243748
6 2.83887292 0.75184458
7 2.61025642 2.83887292
8 1.16057370 2.61025642
9 -5.13094714 1.16057370
10 -5.93171033 -5.13094714
11 -8.25296412 -5.93171033
12 -7.66882921 -8.25296412
13 -7.00509102 -7.66882921
14 -2.92871100 -7.00509102
15 -5.42562756 -2.92871100
16 -4.68324003 -5.42562756
17 -2.60500601 -4.68324003
18 -6.05173223 -2.60500601
19 -6.68135356 -6.05173223
20 -6.30001018 -6.68135356
21 -4.63176513 -6.30001018
22 -4.57120262 -4.63176513
23 -6.48620538 -4.57120262
24 -8.66978712 -6.48620538
25 -7.24968835 -8.66978712
26 -1.36099603 -7.24968835
27 -7.09146157 -1.36099603
28 -0.87070550 -7.09146157
29 -0.25067279 -0.87070550
30 -2.31094459 -0.25067279
31 -2.88945885 -2.31094459
32 -3.54588290 -2.88945885
33 -0.92895082 -3.54588290
34 -1.08832372 -0.92895082
35 2.24347745 -1.08832372
36 1.83272555 2.24347745
37 0.35955110 1.83272555
38 2.31788765 0.35955110
39 1.89720549 2.31788765
40 4.24170441 1.89720549
41 9.96213829 4.24170441
42 3.30019881 9.96213829
43 6.03251969 3.30019881
44 7.23002289 6.03251969
45 6.29872093 7.23002289
46 7.55847586 6.29872093
47 -13.49953963 7.55847586
48 -12.94893249 -13.49953963
49 -11.34544457 -12.94893249
50 -10.33367195 -11.34544457
51 -11.50505839 -10.33367195
52 -8.24073848 -11.50505839
53 -9.39355733 -8.24073848
54 -7.04655563 -9.39355733
55 -6.09418362 -7.04655563
56 -2.96181740 -6.09418362
57 -5.14370330 -2.96181740
58 -5.84723384 -5.14370330
59 -5.00201176 -5.84723384
60 -3.06058375 -5.00201176
61 -3.96701260 -3.06058375
62 -3.58634567 -3.96701260
63 -0.09766111 -3.58634567
64 -0.94836937 -0.09766111
65 -0.97335179 -0.94836937
66 0.93437019 -0.97335179
67 -2.18453340 0.93437019
68 -1.65113878 -2.18453340
69 -1.88930602 -1.65113878
70 -0.51388325 -1.88930602
71 -1.40497143 -0.51388325
72 -2.66511711 -1.40497143
73 -5.46727367 -2.66511711
74 -3.17285346 -5.46727367
75 -3.55400991 -3.17285346
76 1.10738667 -3.55400991
77 0.86774221 1.10738667
78 -1.19275543 0.86774221
79 -3.04710755 -1.19275543
80 6.81255412 -3.04710755
81 1.60128301 6.81255412
82 5.18210099 1.60128301
83 6.30525606 5.18210099
84 4.44365153 6.30525606
85 2.99701152 4.44365153
86 5.36985897 2.99701152
87 9.66799269 5.36985897
88 7.82781131 9.66799269
89 10.21620118 7.82781131
90 8.06376761 10.21620118
91 7.31401969 8.06376761
92 9.02897708 7.31401969
93 7.31671671 9.02897708
94 -11.22842573 7.31671671
95 -8.06037620 -11.22842573
96 -5.39480389 -8.06037620
97 -8.70500624 -5.39480389
98 -7.57509541 -8.70500624
99 -6.05098575 -7.57509541
100 -6.53475695 -6.05098575
101 -9.20901499 -6.53475695
102 -4.03108067 -9.20901499
103 -3.77456414 -4.03108067
104 -5.28331799 -3.77456414
105 -2.69748849 -5.28331799
106 0.29092128 -2.69748849
107 0.07325654 0.29092128
108 -7.99761604 0.07325654
109 -1.35195857 -7.99761604
110 -0.38275227 -1.35195857
111 -0.71774592 -0.38275227
112 -2.56946331 -0.71774592
113 -1.96346259 -2.56946331
114 -1.69303694 -1.96346259
115 5.90813809 -1.69303694
116 0.39959235 5.90813809
117 0.74898871 0.39959235
118 4.05273455 0.74898871
119 4.21359945 4.05273455
120 3.92910158 4.21359945
121 1.15382584 3.92910158
122 3.25196994 1.15382584
123 2.44634315 3.25196994
124 6.92327059 2.44634315
125 -0.37593678 6.92327059
126 2.42664551 -0.37593678
127 2.77865286 2.42664551
128 3.45787362 2.77865286
129 8.13669086 3.45787362
130 4.96606293 8.13669086
131 7.16957436 4.96606293
132 9.89657136 7.16957436
133 6.92213948 9.89657136
134 9.63199359 6.92213948
135 7.45439909 9.63199359
136 0.39786288 7.45439909
137 10.47476990 0.39786288
138 6.56363623 10.47476990
139 8.76928466 6.56363623
140 NA 8.76928466
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 22.37887531 28.68893225
[2,] 16.15465937 22.37887531
[3,] 12.61119818 16.15465937
[4,] 6.98243748 12.61119818
[5,] 0.75184458 6.98243748
[6,] 2.83887292 0.75184458
[7,] 2.61025642 2.83887292
[8,] 1.16057370 2.61025642
[9,] -5.13094714 1.16057370
[10,] -5.93171033 -5.13094714
[11,] -8.25296412 -5.93171033
[12,] -7.66882921 -8.25296412
[13,] -7.00509102 -7.66882921
[14,] -2.92871100 -7.00509102
[15,] -5.42562756 -2.92871100
[16,] -4.68324003 -5.42562756
[17,] -2.60500601 -4.68324003
[18,] -6.05173223 -2.60500601
[19,] -6.68135356 -6.05173223
[20,] -6.30001018 -6.68135356
[21,] -4.63176513 -6.30001018
[22,] -4.57120262 -4.63176513
[23,] -6.48620538 -4.57120262
[24,] -8.66978712 -6.48620538
[25,] -7.24968835 -8.66978712
[26,] -1.36099603 -7.24968835
[27,] -7.09146157 -1.36099603
[28,] -0.87070550 -7.09146157
[29,] -0.25067279 -0.87070550
[30,] -2.31094459 -0.25067279
[31,] -2.88945885 -2.31094459
[32,] -3.54588290 -2.88945885
[33,] -0.92895082 -3.54588290
[34,] -1.08832372 -0.92895082
[35,] 2.24347745 -1.08832372
[36,] 1.83272555 2.24347745
[37,] 0.35955110 1.83272555
[38,] 2.31788765 0.35955110
[39,] 1.89720549 2.31788765
[40,] 4.24170441 1.89720549
[41,] 9.96213829 4.24170441
[42,] 3.30019881 9.96213829
[43,] 6.03251969 3.30019881
[44,] 7.23002289 6.03251969
[45,] 6.29872093 7.23002289
[46,] 7.55847586 6.29872093
[47,] -13.49953963 7.55847586
[48,] -12.94893249 -13.49953963
[49,] -11.34544457 -12.94893249
[50,] -10.33367195 -11.34544457
[51,] -11.50505839 -10.33367195
[52,] -8.24073848 -11.50505839
[53,] -9.39355733 -8.24073848
[54,] -7.04655563 -9.39355733
[55,] -6.09418362 -7.04655563
[56,] -2.96181740 -6.09418362
[57,] -5.14370330 -2.96181740
[58,] -5.84723384 -5.14370330
[59,] -5.00201176 -5.84723384
[60,] -3.06058375 -5.00201176
[61,] -3.96701260 -3.06058375
[62,] -3.58634567 -3.96701260
[63,] -0.09766111 -3.58634567
[64,] -0.94836937 -0.09766111
[65,] -0.97335179 -0.94836937
[66,] 0.93437019 -0.97335179
[67,] -2.18453340 0.93437019
[68,] -1.65113878 -2.18453340
[69,] -1.88930602 -1.65113878
[70,] -0.51388325 -1.88930602
[71,] -1.40497143 -0.51388325
[72,] -2.66511711 -1.40497143
[73,] -5.46727367 -2.66511711
[74,] -3.17285346 -5.46727367
[75,] -3.55400991 -3.17285346
[76,] 1.10738667 -3.55400991
[77,] 0.86774221 1.10738667
[78,] -1.19275543 0.86774221
[79,] -3.04710755 -1.19275543
[80,] 6.81255412 -3.04710755
[81,] 1.60128301 6.81255412
[82,] 5.18210099 1.60128301
[83,] 6.30525606 5.18210099
[84,] 4.44365153 6.30525606
[85,] 2.99701152 4.44365153
[86,] 5.36985897 2.99701152
[87,] 9.66799269 5.36985897
[88,] 7.82781131 9.66799269
[89,] 10.21620118 7.82781131
[90,] 8.06376761 10.21620118
[91,] 7.31401969 8.06376761
[92,] 9.02897708 7.31401969
[93,] 7.31671671 9.02897708
[94,] -11.22842573 7.31671671
[95,] -8.06037620 -11.22842573
[96,] -5.39480389 -8.06037620
[97,] -8.70500624 -5.39480389
[98,] -7.57509541 -8.70500624
[99,] -6.05098575 -7.57509541
[100,] -6.53475695 -6.05098575
[101,] -9.20901499 -6.53475695
[102,] -4.03108067 -9.20901499
[103,] -3.77456414 -4.03108067
[104,] -5.28331799 -3.77456414
[105,] -2.69748849 -5.28331799
[106,] 0.29092128 -2.69748849
[107,] 0.07325654 0.29092128
[108,] -7.99761604 0.07325654
[109,] -1.35195857 -7.99761604
[110,] -0.38275227 -1.35195857
[111,] -0.71774592 -0.38275227
[112,] -2.56946331 -0.71774592
[113,] -1.96346259 -2.56946331
[114,] -1.69303694 -1.96346259
[115,] 5.90813809 -1.69303694
[116,] 0.39959235 5.90813809
[117,] 0.74898871 0.39959235
[118,] 4.05273455 0.74898871
[119,] 4.21359945 4.05273455
[120,] 3.92910158 4.21359945
[121,] 1.15382584 3.92910158
[122,] 3.25196994 1.15382584
[123,] 2.44634315 3.25196994
[124,] 6.92327059 2.44634315
[125,] -0.37593678 6.92327059
[126,] 2.42664551 -0.37593678
[127,] 2.77865286 2.42664551
[128,] 3.45787362 2.77865286
[129,] 8.13669086 3.45787362
[130,] 4.96606293 8.13669086
[131,] 7.16957436 4.96606293
[132,] 9.89657136 7.16957436
[133,] 6.92213948 9.89657136
[134,] 9.63199359 6.92213948
[135,] 7.45439909 9.63199359
[136,] 0.39786288 7.45439909
[137,] 10.47476990 0.39786288
[138,] 6.56363623 10.47476990
[139,] 8.76928466 6.56363623
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 22.37887531 28.68893225
2 16.15465937 22.37887531
3 12.61119818 16.15465937
4 6.98243748 12.61119818
5 0.75184458 6.98243748
6 2.83887292 0.75184458
7 2.61025642 2.83887292
8 1.16057370 2.61025642
9 -5.13094714 1.16057370
10 -5.93171033 -5.13094714
11 -8.25296412 -5.93171033
12 -7.66882921 -8.25296412
13 -7.00509102 -7.66882921
14 -2.92871100 -7.00509102
15 -5.42562756 -2.92871100
16 -4.68324003 -5.42562756
17 -2.60500601 -4.68324003
18 -6.05173223 -2.60500601
19 -6.68135356 -6.05173223
20 -6.30001018 -6.68135356
21 -4.63176513 -6.30001018
22 -4.57120262 -4.63176513
23 -6.48620538 -4.57120262
24 -8.66978712 -6.48620538
25 -7.24968835 -8.66978712
26 -1.36099603 -7.24968835
27 -7.09146157 -1.36099603
28 -0.87070550 -7.09146157
29 -0.25067279 -0.87070550
30 -2.31094459 -0.25067279
31 -2.88945885 -2.31094459
32 -3.54588290 -2.88945885
33 -0.92895082 -3.54588290
34 -1.08832372 -0.92895082
35 2.24347745 -1.08832372
36 1.83272555 2.24347745
37 0.35955110 1.83272555
38 2.31788765 0.35955110
39 1.89720549 2.31788765
40 4.24170441 1.89720549
41 9.96213829 4.24170441
42 3.30019881 9.96213829
43 6.03251969 3.30019881
44 7.23002289 6.03251969
45 6.29872093 7.23002289
46 7.55847586 6.29872093
47 -13.49953963 7.55847586
48 -12.94893249 -13.49953963
49 -11.34544457 -12.94893249
50 -10.33367195 -11.34544457
51 -11.50505839 -10.33367195
52 -8.24073848 -11.50505839
53 -9.39355733 -8.24073848
54 -7.04655563 -9.39355733
55 -6.09418362 -7.04655563
56 -2.96181740 -6.09418362
57 -5.14370330 -2.96181740
58 -5.84723384 -5.14370330
59 -5.00201176 -5.84723384
60 -3.06058375 -5.00201176
61 -3.96701260 -3.06058375
62 -3.58634567 -3.96701260
63 -0.09766111 -3.58634567
64 -0.94836937 -0.09766111
65 -0.97335179 -0.94836937
66 0.93437019 -0.97335179
67 -2.18453340 0.93437019
68 -1.65113878 -2.18453340
69 -1.88930602 -1.65113878
70 -0.51388325 -1.88930602
71 -1.40497143 -0.51388325
72 -2.66511711 -1.40497143
73 -5.46727367 -2.66511711
74 -3.17285346 -5.46727367
75 -3.55400991 -3.17285346
76 1.10738667 -3.55400991
77 0.86774221 1.10738667
78 -1.19275543 0.86774221
79 -3.04710755 -1.19275543
80 6.81255412 -3.04710755
81 1.60128301 6.81255412
82 5.18210099 1.60128301
83 6.30525606 5.18210099
84 4.44365153 6.30525606
85 2.99701152 4.44365153
86 5.36985897 2.99701152
87 9.66799269 5.36985897
88 7.82781131 9.66799269
89 10.21620118 7.82781131
90 8.06376761 10.21620118
91 7.31401969 8.06376761
92 9.02897708 7.31401969
93 7.31671671 9.02897708
94 -11.22842573 7.31671671
95 -8.06037620 -11.22842573
96 -5.39480389 -8.06037620
97 -8.70500624 -5.39480389
98 -7.57509541 -8.70500624
99 -6.05098575 -7.57509541
100 -6.53475695 -6.05098575
101 -9.20901499 -6.53475695
102 -4.03108067 -9.20901499
103 -3.77456414 -4.03108067
104 -5.28331799 -3.77456414
105 -2.69748849 -5.28331799
106 0.29092128 -2.69748849
107 0.07325654 0.29092128
108 -7.99761604 0.07325654
109 -1.35195857 -7.99761604
110 -0.38275227 -1.35195857
111 -0.71774592 -0.38275227
112 -2.56946331 -0.71774592
113 -1.96346259 -2.56946331
114 -1.69303694 -1.96346259
115 5.90813809 -1.69303694
116 0.39959235 5.90813809
117 0.74898871 0.39959235
118 4.05273455 0.74898871
119 4.21359945 4.05273455
120 3.92910158 4.21359945
121 1.15382584 3.92910158
122 3.25196994 1.15382584
123 2.44634315 3.25196994
124 6.92327059 2.44634315
125 -0.37593678 6.92327059
126 2.42664551 -0.37593678
127 2.77865286 2.42664551
128 3.45787362 2.77865286
129 8.13669086 3.45787362
130 4.96606293 8.13669086
131 7.16957436 4.96606293
132 9.89657136 7.16957436
133 6.92213948 9.89657136
134 9.63199359 6.92213948
135 7.45439909 9.63199359
136 0.39786288 7.45439909
137 10.47476990 0.39786288
138 6.56363623 10.47476990
139 8.76928466 6.56363623
> 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/7a0031355667838.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/8fj7z1355667838.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/9tftp1355667838.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/10iesb1355667838.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11mrvc1355667838.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/120vw91355667838.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/138id11355667838.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14v29l1355667838.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/15c5vo1355667838.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/163u2l1355667838.tab")
+ }
>
> try(system("convert tmp/16spz1355667838.ps tmp/16spz1355667838.png",intern=TRUE))
character(0)
> try(system("convert tmp/2z9zo1355667838.ps tmp/2z9zo1355667838.png",intern=TRUE))
character(0)
> try(system("convert tmp/3i5731355667838.ps tmp/3i5731355667838.png",intern=TRUE))
character(0)
> try(system("convert tmp/44pcl1355667838.ps tmp/44pcl1355667838.png",intern=TRUE))
character(0)
> try(system("convert tmp/5x1sc1355667838.ps tmp/5x1sc1355667838.png",intern=TRUE))
character(0)
> try(system("convert tmp/68niv1355667838.ps tmp/68niv1355667838.png",intern=TRUE))
character(0)
> try(system("convert tmp/7a0031355667838.ps tmp/7a0031355667838.png",intern=TRUE))
character(0)
> try(system("convert tmp/8fj7z1355667838.ps tmp/8fj7z1355667838.png",intern=TRUE))
character(0)
> try(system("convert tmp/9tftp1355667838.ps tmp/9tftp1355667838.png",intern=TRUE))
character(0)
> try(system("convert tmp/10iesb1355667838.ps tmp/10iesb1355667838.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.068 1.775 9.868