R version 2.7.0 (2008-04-22)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1778.8
+ ,0
+ ,1264.9
+ ,0
+ ,1749.1
+ ,0
+ ,1795.6
+ ,0
+ ,1759
+ ,0
+ ,1645.1
+ ,0
+ ,1589.9
+ ,0
+ ,1712.6
+ ,0
+ ,1782.5
+ ,0
+ ,1606.6
+ ,0
+ ,1882.1
+ ,0
+ ,1846.9
+ ,0
+ ,1873.2
+ ,0
+ ,1368.3
+ ,0
+ ,1843.5
+ ,0
+ ,2074.5
+ ,0
+ ,1848.5
+ ,0
+ ,1909.3
+ ,0
+ ,1932.9
+ ,0
+ ,2119.1
+ ,0
+ ,2202
+ ,0
+ ,2260.8
+ ,0
+ ,2097.1
+ ,0
+ ,2026.2
+ ,0
+ ,2475.2
+ ,0
+ ,1732.3
+ ,0
+ ,2385.2
+ ,0
+ ,2362.2
+ ,0
+ ,2119
+ ,0
+ ,2260.3
+ ,0
+ ,2006.5
+ ,0
+ ,2073.2
+ ,0
+ ,2207.8
+ ,0
+ ,2018.9
+ ,0
+ ,2082.8
+ ,0
+ ,2314.3
+ ,0
+ ,2252.7
+ ,0
+ ,1633.1
+ ,0
+ ,2161.1
+ ,0
+ ,1987.9
+ ,0
+ ,1870.3
+ ,0
+ ,1984.6
+ ,0
+ ,1735.9
+ ,0
+ ,1910
+ ,0
+ ,2410.1
+ ,0
+ ,1994.6
+ ,0
+ ,2152.3
+ ,0
+ ,2554
+ ,0
+ ,2754.5
+ ,0
+ ,1812.3
+ ,0
+ ,2549.9
+ ,0
+ ,2558.4
+ ,0
+ ,2279.2
+ ,0
+ ,2591.8
+ ,0
+ ,2442.4
+ ,0
+ ,2607.7
+ ,0
+ ,3106.7
+ ,0
+ ,2447.5
+ ,0
+ ,3129.5
+ ,0
+ ,2606.6
+ ,0
+ ,2964.4
+ ,0
+ ,2211.6
+ ,0
+ ,3246.1
+ ,0
+ ,3141.8
+ ,0
+ ,3125.9
+ ,0
+ ,2890.5
+ ,0
+ ,2554.3
+ ,0
+ ,2771.1
+ ,0
+ ,2950
+ ,0
+ ,2512.1
+ ,0
+ ,2800
+ ,0
+ ,2877.2
+ ,0
+ ,3048.7
+ ,0
+ ,2082.7
+ ,0
+ ,2454.8
+ ,0
+ ,2807.8
+ ,0
+ ,2627.6
+ ,0
+ ,2515.9
+ ,0
+ ,2690.3
+ ,0
+ ,2770.8
+ ,0
+ ,2907.7
+ ,0
+ ,2906.3
+ ,0
+ ,3104.6
+ ,0
+ ,2862.1
+ ,0
+ ,3189.1
+ ,0
+ ,2071.8
+ ,0
+ ,2907.7
+ ,0
+ ,3194.5
+ ,0
+ ,2722.9
+ ,0
+ ,2854.8
+ ,0
+ ,2803
+ ,0
+ ,2744.9
+ ,0
+ ,2574.2
+ ,0
+ ,2740.9
+ ,0
+ ,2635.9
+ ,0
+ ,2612.7
+ ,0
+ ,3094.2
+ ,0
+ ,2029
+ ,0
+ ,2931.1
+ ,0
+ ,2952.2
+ ,0
+ ,2601.9
+ ,0
+ ,2874
+ ,0
+ ,2570.9
+ ,0
+ ,2849.8
+ ,0
+ ,3171.5
+ ,0
+ ,2843.6
+ ,0
+ ,2831.5
+ ,0
+ ,3284.4
+ ,0
+ ,3230.1
+ ,0
+ ,2412.2
+ ,0
+ ,3052.7
+ ,0
+ ,3048.9
+ ,0
+ ,2819.9
+ ,0
+ ,2962.7
+ ,0
+ ,2796.6
+ ,0
+ ,2857.2
+ ,0
+ ,3213.1
+ ,0
+ ,3116.2
+ ,0
+ ,3340.1
+ ,0
+ ,3602
+ ,0
+ ,3626.4
+ ,0
+ ,2741.6
+ ,1
+ ,3756.2
+ ,1
+ ,3140
+ ,1
+ ,3421.6
+ ,1
+ ,3243.7
+ ,1
+ ,3085.2
+ ,1
+ ,3152.8
+ ,1
+ ,3543.6
+ ,1
+ ,2959.3
+ ,1
+ ,3594.1
+ ,1
+ ,3207.9
+ ,1
+ ,3366.7
+ ,1
+ ,2658.4
+ ,1
+ ,3340.4
+ ,1
+ ,3368.4
+ ,1
+ ,3422.1
+ ,1
+ ,3268
+ ,1
+ ,3234.4
+ ,1
+ ,3365.1
+ ,1
+ ,3923.6
+ ,1
+ ,3147.3
+ ,1
+ ,3447.7
+ ,1
+ ,3719.8
+ ,1
+ ,4090.4
+ ,1
+ ,3386.7
+ ,1
+ ,3436.8
+ ,1
+ ,3744.9
+ ,1
+ ,3325.8
+ ,1
+ ,3322.1
+ ,1
+ ,3338.6
+ ,1
+ ,3464.2
+ ,1
+ ,3404.1
+ ,1
+ ,3942
+ ,1
+ ,3859.9
+ ,1
+ ,3895.4
+ ,1
+ ,4472.2
+ ,1
+ ,3025.5
+ ,1
+ ,4285.9
+ ,1)
+ ,dim=c(2
+ ,159)
+ ,dimnames=list(c('x'
+ ,'y')
+ ,1:159))
> y <- array(NA,dim=c(2,159),dimnames=list(c('x','y'),1:159))
> 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 = '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.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
x y t
1 1778.8 0 1
2 1264.9 0 2
3 1749.1 0 3
4 1795.6 0 4
5 1759.0 0 5
6 1645.1 0 6
7 1589.9 0 7
8 1712.6 0 8
9 1782.5 0 9
10 1606.6 0 10
11 1882.1 0 11
12 1846.9 0 12
13 1873.2 0 13
14 1368.3 0 14
15 1843.5 0 15
16 2074.5 0 16
17 1848.5 0 17
18 1909.3 0 18
19 1932.9 0 19
20 2119.1 0 20
21 2202.0 0 21
22 2260.8 0 22
23 2097.1 0 23
24 2026.2 0 24
25 2475.2 0 25
26 1732.3 0 26
27 2385.2 0 27
28 2362.2 0 28
29 2119.0 0 29
30 2260.3 0 30
31 2006.5 0 31
32 2073.2 0 32
33 2207.8 0 33
34 2018.9 0 34
35 2082.8 0 35
36 2314.3 0 36
37 2252.7 0 37
38 1633.1 0 38
39 2161.1 0 39
40 1987.9 0 40
41 1870.3 0 41
42 1984.6 0 42
43 1735.9 0 43
44 1910.0 0 44
45 2410.1 0 45
46 1994.6 0 46
47 2152.3 0 47
48 2554.0 0 48
49 2754.5 0 49
50 1812.3 0 50
51 2549.9 0 51
52 2558.4 0 52
53 2279.2 0 53
54 2591.8 0 54
55 2442.4 0 55
56 2607.7 0 56
57 3106.7 0 57
58 2447.5 0 58
59 3129.5 0 59
60 2606.6 0 60
61 2964.4 0 61
62 2211.6 0 62
63 3246.1 0 63
64 3141.8 0 64
65 3125.9 0 65
66 2890.5 0 66
67 2554.3 0 67
68 2771.1 0 68
69 2950.0 0 69
70 2512.1 0 70
71 2800.0 0 71
72 2877.2 0 72
73 3048.7 0 73
74 2082.7 0 74
75 2454.8 0 75
76 2807.8 0 76
77 2627.6 0 77
78 2515.9 0 78
79 2690.3 0 79
80 2770.8 0 80
81 2907.7 0 81
82 2906.3 0 82
83 3104.6 0 83
84 2862.1 0 84
85 3189.1 0 85
86 2071.8 0 86
87 2907.7 0 87
88 3194.5 0 88
89 2722.9 0 89
90 2854.8 0 90
91 2803.0 0 91
92 2744.9 0 92
93 2574.2 0 93
94 2740.9 0 94
95 2635.9 0 95
96 2612.7 0 96
97 3094.2 0 97
98 2029.0 0 98
99 2931.1 0 99
100 2952.2 0 100
101 2601.9 0 101
102 2874.0 0 102
103 2570.9 0 103
104 2849.8 0 104
105 3171.5 0 105
106 2843.6 0 106
107 2831.5 0 107
108 3284.4 0 108
109 3230.1 0 109
110 2412.2 0 110
111 3052.7 0 111
112 3048.9 0 112
113 2819.9 0 113
114 2962.7 0 114
115 2796.6 0 115
116 2857.2 0 116
117 3213.1 0 117
118 3116.2 0 118
119 3340.1 0 119
120 3602.0 0 120
121 3626.4 0 121
122 2741.6 1 122
123 3756.2 1 123
124 3140.0 1 124
125 3421.6 1 125
126 3243.7 1 126
127 3085.2 1 127
128 3152.8 1 128
129 3543.6 1 129
130 2959.3 1 130
131 3594.1 1 131
132 3207.9 1 132
133 3366.7 1 133
134 2658.4 1 134
135 3340.4 1 135
136 3368.4 1 136
137 3422.1 1 137
138 3268.0 1 138
139 3234.4 1 139
140 3365.1 1 140
141 3923.6 1 141
142 3147.3 1 142
143 3447.7 1 143
144 3719.8 1 144
145 4090.4 1 145
146 3386.7 1 146
147 3436.8 1 147
148 3744.9 1 148
149 3325.8 1 149
150 3322.1 1 150
151 3338.6 1 151
152 3464.2 1 152
153 3404.1 1 153
154 3942.0 1 154
155 3859.9 1 155
156 3895.4 1 156
157 4472.2 1 157
158 3025.5 1 158
159 4285.9 1 159
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) y t
1727.1931 -0.5504 12.2663
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-900.29 -175.95 -27.35 193.47 819.74
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1727.1931 55.6138 31.057 <2e-16 ***
y -0.5504 84.8266 -0.006 0.995
t 12.2663 0.7882 15.563 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 307.5 on 156 degrees of freedom
Multiple R-squared: 0.7735, Adjusted R-squared: 0.7706
F-statistic: 266.4 on 2 and 156 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,] 0.4323539713 0.8647079426 0.5676460
[2,] 0.2951861283 0.5903722565 0.7048139
[3,] 0.1747402842 0.3494805685 0.8252597
[4,] 0.0996807374 0.1993614749 0.9003193
[5,] 0.0615078542 0.1230157085 0.9384921
[6,] 0.0405055927 0.0810111854 0.9594944
[7,] 0.0211643538 0.0423287076 0.9788356
[8,] 0.0104975100 0.0209950200 0.9895025
[9,] 0.0440625352 0.0881250704 0.9559375
[10,] 0.0275387678 0.0550775355 0.9724612
[11,] 0.0301359609 0.0602719218 0.9698640
[12,] 0.0174563684 0.0349127368 0.9825436
[13,] 0.0098916026 0.0197832051 0.9901084
[14,] 0.0054413545 0.0108827089 0.9945586
[15,] 0.0041935858 0.0083871715 0.9958064
[16,] 0.0035995952 0.0071991904 0.9964004
[17,] 0.0030651325 0.0061302651 0.9969349
[18,] 0.0016345237 0.0032690473 0.9983655
[19,] 0.0009365301 0.0018730603 0.9990635
[20,] 0.0015940557 0.0031881114 0.9984059
[21,] 0.0054952149 0.0109904298 0.9945048
[22,] 0.0046725952 0.0093451904 0.9953274
[23,] 0.0032757228 0.0065514456 0.9967243
[24,] 0.0022287821 0.0044575641 0.9977712
[25,] 0.0013006769 0.0026013537 0.9986993
[26,] 0.0013875509 0.0027751017 0.9986124
[27,] 0.0011017213 0.0022034426 0.9988983
[28,] 0.0006555447 0.0013110894 0.9993445
[29,] 0.0006622506 0.0013245012 0.9993377
[30,] 0.0005105379 0.0010210757 0.9994895
[31,] 0.0002938510 0.0005877021 0.9997061
[32,] 0.0001668014 0.0003336028 0.9998332
[33,] 0.0026942480 0.0053884960 0.9973058
[34,] 0.0018048165 0.0036096329 0.9981952
[35,] 0.0018522646 0.0037045292 0.9981477
[36,] 0.0029314172 0.0058628344 0.9970686
[37,] 0.0028346531 0.0056693063 0.9971653
[38,] 0.0073844422 0.0147688844 0.9926156
[39,] 0.0084462835 0.0168925670 0.9915537
[40,] 0.0071496259 0.0142992518 0.9928504
[41,] 0.0070103785 0.0140207570 0.9929896
[42,] 0.0053338417 0.0106676833 0.9946662
[43,] 0.0058483799 0.0116967598 0.9941516
[44,] 0.0107956935 0.0215913870 0.9892043
[45,] 0.0244819949 0.0489639899 0.9755180
[46,] 0.0223846869 0.0447693738 0.9776153
[47,] 0.0196790420 0.0393580840 0.9803210
[48,] 0.0156446231 0.0312892462 0.9843554
[49,] 0.0135616908 0.0271233816 0.9864383
[50,] 0.0101218802 0.0202437605 0.9898781
[51,] 0.0083177034 0.0166354068 0.9916823
[52,] 0.0275137253 0.0550274506 0.9724863
[53,] 0.0211115565 0.0422231129 0.9788884
[54,] 0.0496696953 0.0993393905 0.9503303
[55,] 0.0386131115 0.0772262230 0.9613869
[56,] 0.0456454049 0.0912908098 0.9543546
[57,] 0.0550478264 0.1100956527 0.9449522
[58,] 0.1184194217 0.2368388435 0.8815806
[59,] 0.1662686479 0.3325372958 0.8337314
[60,] 0.2130916707 0.4261833414 0.7869083
[61,] 0.2006380815 0.4012761630 0.7993619
[62,] 0.1794342317 0.3588684633 0.8205658
[63,] 0.1562597479 0.3125194959 0.8437403
[64,] 0.1526463378 0.3052926756 0.8473537
[65,] 0.1435623908 0.2871247816 0.8564376
[66,] 0.1256051300 0.2512102601 0.8743949
[67,] 0.1142598261 0.2285196522 0.8857402
[68,] 0.1252197241 0.2504394483 0.8747803
[69,] 0.2507414227 0.5014828454 0.7492586
[70,] 0.2531775309 0.5063550618 0.7468225
[71,] 0.2271166317 0.4542332634 0.7728834
[72,] 0.2057454015 0.4114908030 0.7942546
[73,] 0.1990276291 0.3980552583 0.8009724
[74,] 0.1751821115 0.3503642230 0.8248179
[75,] 0.1521544704 0.3043089408 0.8478455
[76,] 0.1370917900 0.2741835801 0.8629082
[77,] 0.1234039209 0.2468078418 0.8765961
[78,] 0.1353657504 0.2707315009 0.8646342
[79,] 0.1216624204 0.2433248409 0.8783376
[80,] 0.1550854871 0.3101709741 0.8449145
[81,] 0.3310137057 0.6620274115 0.6689863
[82,] 0.3106537236 0.6213074472 0.6893463
[83,] 0.3664759934 0.7329519868 0.6335240
[84,] 0.3437454690 0.6874909380 0.6562545
[85,] 0.3221962083 0.6443924166 0.6778038
[86,] 0.2992577645 0.5985155289 0.7007422
[87,] 0.2776240901 0.5552481802 0.7223759
[88,] 0.2763682951 0.5527365902 0.7236317
[89,] 0.2532104643 0.5064209287 0.7467895
[90,] 0.2413357152 0.4826714304 0.7586643
[91,] 0.2332238845 0.4664477691 0.7667761
[92,] 0.2309291590 0.4618583179 0.7690708
[93,] 0.5079001794 0.9841996412 0.4920998
[94,] 0.4685945140 0.9371890280 0.5314055
[95,] 0.4303935971 0.8607871942 0.5696064
[96,] 0.4285737717 0.8571475435 0.5714262
[97,] 0.3861542567 0.7723085133 0.6138457
[98,] 0.4016484338 0.8032968675 0.5983516
[99,] 0.3611828798 0.7223657596 0.6388171
[100,] 0.3402517846 0.6805035692 0.6597482
[101,] 0.3046135031 0.6092270062 0.6953865
[102,] 0.2736010462 0.5472020923 0.7263990
[103,] 0.2690712375 0.5381424750 0.7309288
[104,] 0.2530271050 0.5060542100 0.7469729
[105,] 0.3748173851 0.7496347702 0.6251826
[106,] 0.3286416896 0.6572833793 0.6713583
[107,] 0.2847828000 0.5695656001 0.7152172
[108,] 0.2737339099 0.5474678199 0.7262661
[109,] 0.2435032442 0.4870064884 0.7564968
[110,] 0.2602094456 0.5204188912 0.7397906
[111,] 0.2813033203 0.5626066405 0.7186967
[112,] 0.2464741520 0.4929483039 0.7535258
[113,] 0.2362370743 0.4724741487 0.7637629
[114,] 0.2128234813 0.4256469626 0.7871765
[115,] 0.1951616498 0.3903232996 0.8048384
[116,] 0.1775493168 0.3550986336 0.8224507
[117,] 0.1812236948 0.3624473895 0.8187763
[118,] 0.3140129998 0.6280259995 0.6859870
[119,] 0.2676874175 0.5353748349 0.7323126
[120,] 0.2556528380 0.5113056759 0.7443472
[121,] 0.2173249020 0.4346498039 0.7826751
[122,] 0.1815373031 0.3630746062 0.8184627
[123,] 0.1469007056 0.2938014111 0.8530993
[124,] 0.1552824919 0.3105649838 0.8447175
[125,] 0.1404959354 0.2809918708 0.8595041
[126,] 0.1567060702 0.3134121404 0.8432939
[127,] 0.1240544123 0.2481088245 0.8759456
[128,] 0.1021655215 0.2043310429 0.8978345
[129,] 0.1838465672 0.3676931343 0.8161534
[130,] 0.1445922555 0.2891845111 0.8554077
[131,] 0.1113167909 0.2226335819 0.8886832
[132,] 0.0849210499 0.1698420997 0.9150790
[133,] 0.0629631752 0.1259263505 0.9370368
[134,] 0.0480132867 0.0960265734 0.9519867
[135,] 0.0335513797 0.0671027594 0.9664486
[136,] 0.0528226900 0.1056453799 0.9471773
[137,] 0.0456295933 0.0912591865 0.9543704
[138,] 0.0303190827 0.0606381653 0.9696809
[139,] 0.0243048526 0.0486097051 0.9756951
[140,] 0.0870894694 0.1741789388 0.9129105
[141,] 0.0592293222 0.1184586445 0.9407707
[142,] 0.0388127473 0.0776254947 0.9611873
[143,] 0.0479433947 0.0958867894 0.9520566
[144,] 0.0288456994 0.0576913987 0.9711543
[145,] 0.0160130436 0.0320260873 0.9839870
[146,] 0.0086070199 0.0172140398 0.9913930
[147,] 0.0040965123 0.0081930245 0.9959035
[148,] 0.0032585526 0.0065171053 0.9967414
> postscript(file="/var/www/html/rcomp/tmp/1871m1230123040.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2kq981230123040.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3j74q1230123040.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4dv1h1230123040.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5q4gl1230123040.ps",horizontal=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 = 159
Frequency = 1
1 2 3 4 5 6
39.340592 -486.825746 -14.892083 19.341579 -29.524759 -155.691096
7 8 9 10 11 12
-223.157434 -112.723772 -55.090109 -243.256447 19.977215 -27.489123
13 14 15 16 17 18
-13.455460 -530.621798 -67.688136 151.045527 -87.220811 -38.687149
19 20 21 22 23 24
-27.353486 146.580176 217.213838 263.747501 87.781163 4.614825
25 26 27 28 29 30
441.348487 -313.817850 326.815812 291.549474 36.083137 165.116799
31 32 33 34 35 36
-100.949539 -46.515876 75.817786 -125.348552 -73.714889 145.518773
37 38 39 40 41 42
71.652435 -560.213903 -44.480240 -229.946578 -359.812916 -257.779253
43 44 45 46 47 48
-518.745591 -356.911929 130.921734 -296.844604 -151.410942 238.022721
49 50 51 52 53 54
426.256383 -528.209955 197.123707 193.357370 -98.108968 202.224694
55 56 57 58 59 60
40.558357 193.592019 680.325681 8.859344 678.593006 143.426668
61 62 63 64 65 66
488.960331 -276.106007 746.127655 629.561318 601.394980 353.728642
67 68 69 70 71 72
5.262304 209.795967 376.429629 -73.736709 201.896954 266.830616
73 74 75 76 77 78
426.064278 -552.202059 -192.368397 148.365265 -44.101072 -168.067410
79 80 81 82 83 84
-5.933748 62.299914 186.933577 173.267239 359.300901 104.534564
85 86 87 88 89 90
419.268226 -710.298112 113.335551 387.869213 -95.997125 23.636538
91 92 93 94 95 96
-40.429800 -110.796138 -293.762476 -139.328813 -256.595151 -292.061489
97 98 99 100 101 102
177.172174 -900.294164 -10.460502 -1.626839 -364.193177 -104.359515
103 104 105 106 107 108
-419.725852 -153.092190 156.341472 -183.824866 -208.191203 232.442459
109 110 111 112 113 114
165.876121 -664.290216 -36.056554 -52.122892 -293.389229 -162.855567
115 116 117 118 119 120
-341.221905 -292.888242 50.745420 -58.420918 153.212745 402.846407
121 122 123 124 125 126
414.980069 -481.535911 520.797752 -107.668586 161.665076 -28.501261
127 128 129 130 131 132
-199.267599 -143.933937 234.599726 -361.966612 260.567050 -137.899288
133 134 135 136 137 138
8.634375 -711.931963 -42.198301 -26.464638 14.969024 -151.397314
139 140 141 142 143 144
-197.263651 -78.829989 467.403673 -321.162664 -33.029002 226.804660
145 146 147 148 149 150
585.138322 -130.828015 -92.994353 202.839309 -228.527028 -244.493366
151 152 153 154 155 156
-240.259704 -126.926041 -199.292379 326.341283 231.974946 255.208608
157 158 159
819.742270 -639.224067 608.909595
> postscript(file="/var/www/html/rcomp/tmp/6onfw1230123040.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 39.340592 NA
1 -486.825746 39.340592
2 -14.892083 -486.825746
3 19.341579 -14.892083
4 -29.524759 19.341579
5 -155.691096 -29.524759
6 -223.157434 -155.691096
7 -112.723772 -223.157434
8 -55.090109 -112.723772
9 -243.256447 -55.090109
10 19.977215 -243.256447
11 -27.489123 19.977215
12 -13.455460 -27.489123
13 -530.621798 -13.455460
14 -67.688136 -530.621798
15 151.045527 -67.688136
16 -87.220811 151.045527
17 -38.687149 -87.220811
18 -27.353486 -38.687149
19 146.580176 -27.353486
20 217.213838 146.580176
21 263.747501 217.213838
22 87.781163 263.747501
23 4.614825 87.781163
24 441.348487 4.614825
25 -313.817850 441.348487
26 326.815812 -313.817850
27 291.549474 326.815812
28 36.083137 291.549474
29 165.116799 36.083137
30 -100.949539 165.116799
31 -46.515876 -100.949539
32 75.817786 -46.515876
33 -125.348552 75.817786
34 -73.714889 -125.348552
35 145.518773 -73.714889
36 71.652435 145.518773
37 -560.213903 71.652435
38 -44.480240 -560.213903
39 -229.946578 -44.480240
40 -359.812916 -229.946578
41 -257.779253 -359.812916
42 -518.745591 -257.779253
43 -356.911929 -518.745591
44 130.921734 -356.911929
45 -296.844604 130.921734
46 -151.410942 -296.844604
47 238.022721 -151.410942
48 426.256383 238.022721
49 -528.209955 426.256383
50 197.123707 -528.209955
51 193.357370 197.123707
52 -98.108968 193.357370
53 202.224694 -98.108968
54 40.558357 202.224694
55 193.592019 40.558357
56 680.325681 193.592019
57 8.859344 680.325681
58 678.593006 8.859344
59 143.426668 678.593006
60 488.960331 143.426668
61 -276.106007 488.960331
62 746.127655 -276.106007
63 629.561318 746.127655
64 601.394980 629.561318
65 353.728642 601.394980
66 5.262304 353.728642
67 209.795967 5.262304
68 376.429629 209.795967
69 -73.736709 376.429629
70 201.896954 -73.736709
71 266.830616 201.896954
72 426.064278 266.830616
73 -552.202059 426.064278
74 -192.368397 -552.202059
75 148.365265 -192.368397
76 -44.101072 148.365265
77 -168.067410 -44.101072
78 -5.933748 -168.067410
79 62.299914 -5.933748
80 186.933577 62.299914
81 173.267239 186.933577
82 359.300901 173.267239
83 104.534564 359.300901
84 419.268226 104.534564
85 -710.298112 419.268226
86 113.335551 -710.298112
87 387.869213 113.335551
88 -95.997125 387.869213
89 23.636538 -95.997125
90 -40.429800 23.636538
91 -110.796138 -40.429800
92 -293.762476 -110.796138
93 -139.328813 -293.762476
94 -256.595151 -139.328813
95 -292.061489 -256.595151
96 177.172174 -292.061489
97 -900.294164 177.172174
98 -10.460502 -900.294164
99 -1.626839 -10.460502
100 -364.193177 -1.626839
101 -104.359515 -364.193177
102 -419.725852 -104.359515
103 -153.092190 -419.725852
104 156.341472 -153.092190
105 -183.824866 156.341472
106 -208.191203 -183.824866
107 232.442459 -208.191203
108 165.876121 232.442459
109 -664.290216 165.876121
110 -36.056554 -664.290216
111 -52.122892 -36.056554
112 -293.389229 -52.122892
113 -162.855567 -293.389229
114 -341.221905 -162.855567
115 -292.888242 -341.221905
116 50.745420 -292.888242
117 -58.420918 50.745420
118 153.212745 -58.420918
119 402.846407 153.212745
120 414.980069 402.846407
121 -481.535911 414.980069
122 520.797752 -481.535911
123 -107.668586 520.797752
124 161.665076 -107.668586
125 -28.501261 161.665076
126 -199.267599 -28.501261
127 -143.933937 -199.267599
128 234.599726 -143.933937
129 -361.966612 234.599726
130 260.567050 -361.966612
131 -137.899288 260.567050
132 8.634375 -137.899288
133 -711.931963 8.634375
134 -42.198301 -711.931963
135 -26.464638 -42.198301
136 14.969024 -26.464638
137 -151.397314 14.969024
138 -197.263651 -151.397314
139 -78.829989 -197.263651
140 467.403673 -78.829989
141 -321.162664 467.403673
142 -33.029002 -321.162664
143 226.804660 -33.029002
144 585.138322 226.804660
145 -130.828015 585.138322
146 -92.994353 -130.828015
147 202.839309 -92.994353
148 -228.527028 202.839309
149 -244.493366 -228.527028
150 -240.259704 -244.493366
151 -126.926041 -240.259704
152 -199.292379 -126.926041
153 326.341283 -199.292379
154 231.974946 326.341283
155 255.208608 231.974946
156 819.742270 255.208608
157 -639.224067 819.742270
158 608.909595 -639.224067
159 NA 608.909595
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -486.825746 39.340592
[2,] -14.892083 -486.825746
[3,] 19.341579 -14.892083
[4,] -29.524759 19.341579
[5,] -155.691096 -29.524759
[6,] -223.157434 -155.691096
[7,] -112.723772 -223.157434
[8,] -55.090109 -112.723772
[9,] -243.256447 -55.090109
[10,] 19.977215 -243.256447
[11,] -27.489123 19.977215
[12,] -13.455460 -27.489123
[13,] -530.621798 -13.455460
[14,] -67.688136 -530.621798
[15,] 151.045527 -67.688136
[16,] -87.220811 151.045527
[17,] -38.687149 -87.220811
[18,] -27.353486 -38.687149
[19,] 146.580176 -27.353486
[20,] 217.213838 146.580176
[21,] 263.747501 217.213838
[22,] 87.781163 263.747501
[23,] 4.614825 87.781163
[24,] 441.348487 4.614825
[25,] -313.817850 441.348487
[26,] 326.815812 -313.817850
[27,] 291.549474 326.815812
[28,] 36.083137 291.549474
[29,] 165.116799 36.083137
[30,] -100.949539 165.116799
[31,] -46.515876 -100.949539
[32,] 75.817786 -46.515876
[33,] -125.348552 75.817786
[34,] -73.714889 -125.348552
[35,] 145.518773 -73.714889
[36,] 71.652435 145.518773
[37,] -560.213903 71.652435
[38,] -44.480240 -560.213903
[39,] -229.946578 -44.480240
[40,] -359.812916 -229.946578
[41,] -257.779253 -359.812916
[42,] -518.745591 -257.779253
[43,] -356.911929 -518.745591
[44,] 130.921734 -356.911929
[45,] -296.844604 130.921734
[46,] -151.410942 -296.844604
[47,] 238.022721 -151.410942
[48,] 426.256383 238.022721
[49,] -528.209955 426.256383
[50,] 197.123707 -528.209955
[51,] 193.357370 197.123707
[52,] -98.108968 193.357370
[53,] 202.224694 -98.108968
[54,] 40.558357 202.224694
[55,] 193.592019 40.558357
[56,] 680.325681 193.592019
[57,] 8.859344 680.325681
[58,] 678.593006 8.859344
[59,] 143.426668 678.593006
[60,] 488.960331 143.426668
[61,] -276.106007 488.960331
[62,] 746.127655 -276.106007
[63,] 629.561318 746.127655
[64,] 601.394980 629.561318
[65,] 353.728642 601.394980
[66,] 5.262304 353.728642
[67,] 209.795967 5.262304
[68,] 376.429629 209.795967
[69,] -73.736709 376.429629
[70,] 201.896954 -73.736709
[71,] 266.830616 201.896954
[72,] 426.064278 266.830616
[73,] -552.202059 426.064278
[74,] -192.368397 -552.202059
[75,] 148.365265 -192.368397
[76,] -44.101072 148.365265
[77,] -168.067410 -44.101072
[78,] -5.933748 -168.067410
[79,] 62.299914 -5.933748
[80,] 186.933577 62.299914
[81,] 173.267239 186.933577
[82,] 359.300901 173.267239
[83,] 104.534564 359.300901
[84,] 419.268226 104.534564
[85,] -710.298112 419.268226
[86,] 113.335551 -710.298112
[87,] 387.869213 113.335551
[88,] -95.997125 387.869213
[89,] 23.636538 -95.997125
[90,] -40.429800 23.636538
[91,] -110.796138 -40.429800
[92,] -293.762476 -110.796138
[93,] -139.328813 -293.762476
[94,] -256.595151 -139.328813
[95,] -292.061489 -256.595151
[96,] 177.172174 -292.061489
[97,] -900.294164 177.172174
[98,] -10.460502 -900.294164
[99,] -1.626839 -10.460502
[100,] -364.193177 -1.626839
[101,] -104.359515 -364.193177
[102,] -419.725852 -104.359515
[103,] -153.092190 -419.725852
[104,] 156.341472 -153.092190
[105,] -183.824866 156.341472
[106,] -208.191203 -183.824866
[107,] 232.442459 -208.191203
[108,] 165.876121 232.442459
[109,] -664.290216 165.876121
[110,] -36.056554 -664.290216
[111,] -52.122892 -36.056554
[112,] -293.389229 -52.122892
[113,] -162.855567 -293.389229
[114,] -341.221905 -162.855567
[115,] -292.888242 -341.221905
[116,] 50.745420 -292.888242
[117,] -58.420918 50.745420
[118,] 153.212745 -58.420918
[119,] 402.846407 153.212745
[120,] 414.980069 402.846407
[121,] -481.535911 414.980069
[122,] 520.797752 -481.535911
[123,] -107.668586 520.797752
[124,] 161.665076 -107.668586
[125,] -28.501261 161.665076
[126,] -199.267599 -28.501261
[127,] -143.933937 -199.267599
[128,] 234.599726 -143.933937
[129,] -361.966612 234.599726
[130,] 260.567050 -361.966612
[131,] -137.899288 260.567050
[132,] 8.634375 -137.899288
[133,] -711.931963 8.634375
[134,] -42.198301 -711.931963
[135,] -26.464638 -42.198301
[136,] 14.969024 -26.464638
[137,] -151.397314 14.969024
[138,] -197.263651 -151.397314
[139,] -78.829989 -197.263651
[140,] 467.403673 -78.829989
[141,] -321.162664 467.403673
[142,] -33.029002 -321.162664
[143,] 226.804660 -33.029002
[144,] 585.138322 226.804660
[145,] -130.828015 585.138322
[146,] -92.994353 -130.828015
[147,] 202.839309 -92.994353
[148,] -228.527028 202.839309
[149,] -244.493366 -228.527028
[150,] -240.259704 -244.493366
[151,] -126.926041 -240.259704
[152,] -199.292379 -126.926041
[153,] 326.341283 -199.292379
[154,] 231.974946 326.341283
[155,] 255.208608 231.974946
[156,] 819.742270 255.208608
[157,] -639.224067 819.742270
[158,] 608.909595 -639.224067
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -486.825746 39.340592
2 -14.892083 -486.825746
3 19.341579 -14.892083
4 -29.524759 19.341579
5 -155.691096 -29.524759
6 -223.157434 -155.691096
7 -112.723772 -223.157434
8 -55.090109 -112.723772
9 -243.256447 -55.090109
10 19.977215 -243.256447
11 -27.489123 19.977215
12 -13.455460 -27.489123
13 -530.621798 -13.455460
14 -67.688136 -530.621798
15 151.045527 -67.688136
16 -87.220811 151.045527
17 -38.687149 -87.220811
18 -27.353486 -38.687149
19 146.580176 -27.353486
20 217.213838 146.580176
21 263.747501 217.213838
22 87.781163 263.747501
23 4.614825 87.781163
24 441.348487 4.614825
25 -313.817850 441.348487
26 326.815812 -313.817850
27 291.549474 326.815812
28 36.083137 291.549474
29 165.116799 36.083137
30 -100.949539 165.116799
31 -46.515876 -100.949539
32 75.817786 -46.515876
33 -125.348552 75.817786
34 -73.714889 -125.348552
35 145.518773 -73.714889
36 71.652435 145.518773
37 -560.213903 71.652435
38 -44.480240 -560.213903
39 -229.946578 -44.480240
40 -359.812916 -229.946578
41 -257.779253 -359.812916
42 -518.745591 -257.779253
43 -356.911929 -518.745591
44 130.921734 -356.911929
45 -296.844604 130.921734
46 -151.410942 -296.844604
47 238.022721 -151.410942
48 426.256383 238.022721
49 -528.209955 426.256383
50 197.123707 -528.209955
51 193.357370 197.123707
52 -98.108968 193.357370
53 202.224694 -98.108968
54 40.558357 202.224694
55 193.592019 40.558357
56 680.325681 193.592019
57 8.859344 680.325681
58 678.593006 8.859344
59 143.426668 678.593006
60 488.960331 143.426668
61 -276.106007 488.960331
62 746.127655 -276.106007
63 629.561318 746.127655
64 601.394980 629.561318
65 353.728642 601.394980
66 5.262304 353.728642
67 209.795967 5.262304
68 376.429629 209.795967
69 -73.736709 376.429629
70 201.896954 -73.736709
71 266.830616 201.896954
72 426.064278 266.830616
73 -552.202059 426.064278
74 -192.368397 -552.202059
75 148.365265 -192.368397
76 -44.101072 148.365265
77 -168.067410 -44.101072
78 -5.933748 -168.067410
79 62.299914 -5.933748
80 186.933577 62.299914
81 173.267239 186.933577
82 359.300901 173.267239
83 104.534564 359.300901
84 419.268226 104.534564
85 -710.298112 419.268226
86 113.335551 -710.298112
87 387.869213 113.335551
88 -95.997125 387.869213
89 23.636538 -95.997125
90 -40.429800 23.636538
91 -110.796138 -40.429800
92 -293.762476 -110.796138
93 -139.328813 -293.762476
94 -256.595151 -139.328813
95 -292.061489 -256.595151
96 177.172174 -292.061489
97 -900.294164 177.172174
98 -10.460502 -900.294164
99 -1.626839 -10.460502
100 -364.193177 -1.626839
101 -104.359515 -364.193177
102 -419.725852 -104.359515
103 -153.092190 -419.725852
104 156.341472 -153.092190
105 -183.824866 156.341472
106 -208.191203 -183.824866
107 232.442459 -208.191203
108 165.876121 232.442459
109 -664.290216 165.876121
110 -36.056554 -664.290216
111 -52.122892 -36.056554
112 -293.389229 -52.122892
113 -162.855567 -293.389229
114 -341.221905 -162.855567
115 -292.888242 -341.221905
116 50.745420 -292.888242
117 -58.420918 50.745420
118 153.212745 -58.420918
119 402.846407 153.212745
120 414.980069 402.846407
121 -481.535911 414.980069
122 520.797752 -481.535911
123 -107.668586 520.797752
124 161.665076 -107.668586
125 -28.501261 161.665076
126 -199.267599 -28.501261
127 -143.933937 -199.267599
128 234.599726 -143.933937
129 -361.966612 234.599726
130 260.567050 -361.966612
131 -137.899288 260.567050
132 8.634375 -137.899288
133 -711.931963 8.634375
134 -42.198301 -711.931963
135 -26.464638 -42.198301
136 14.969024 -26.464638
137 -151.397314 14.969024
138 -197.263651 -151.397314
139 -78.829989 -197.263651
140 467.403673 -78.829989
141 -321.162664 467.403673
142 -33.029002 -321.162664
143 226.804660 -33.029002
144 585.138322 226.804660
145 -130.828015 585.138322
146 -92.994353 -130.828015
147 202.839309 -92.994353
148 -228.527028 202.839309
149 -244.493366 -228.527028
150 -240.259704 -244.493366
151 -126.926041 -240.259704
152 -199.292379 -126.926041
153 326.341283 -199.292379
154 231.974946 326.341283
155 255.208608 231.974946
156 819.742270 255.208608
157 -639.224067 819.742270
158 608.909595 -639.224067
> 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/www/html/rcomp/tmp/7u4ll1230123040.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/81kw81230123040.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9qywx1230123040.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10k6r41230123040.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/www/html/rcomp/tmp/11bx0b1230123040.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/www/html/rcomp/tmp/12oouo1230123040.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/www/html/rcomp/tmp/13zzrr1230123041.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/www/html/rcomp/tmp/14iqyh1230123041.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/www/html/rcomp/tmp/15nuh61230123041.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/www/html/rcomp/tmp/16vo3h1230123041.tab")
+ }
>
> system("convert tmp/1871m1230123040.ps tmp/1871m1230123040.png")
> system("convert tmp/2kq981230123040.ps tmp/2kq981230123040.png")
> system("convert tmp/3j74q1230123040.ps tmp/3j74q1230123040.png")
> system("convert tmp/4dv1h1230123040.ps tmp/4dv1h1230123040.png")
> system("convert tmp/5q4gl1230123040.ps tmp/5q4gl1230123040.png")
> system("convert tmp/6onfw1230123040.ps tmp/6onfw1230123040.png")
> system("convert tmp/7u4ll1230123040.ps tmp/7u4ll1230123040.png")
> system("convert tmp/81kw81230123040.ps tmp/81kw81230123040.png")
> system("convert tmp/9qywx1230123040.ps tmp/9qywx1230123040.png")
> system("convert tmp/10k6r41230123040.ps tmp/10k6r41230123040.png")
>
>
> proc.time()
user system elapsed
7.254 2.898 7.651