R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(26
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+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('Organisation'
+ ,'Week'
+ ,'Concern'
+ ,'Doubts'
+ ,'Pexpect'
+ ,'Pcriticism'
+ ,'Pstandards')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('Organisation','Week','Concern','Doubts','Pexpect','Pcriticism','Pstandards'),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
Organisation Week Concern Doubts Pexpect Pcriticism Pstandards t
1 26 1 24 14 11 12 24 1
2 23 1 25 11 7 8 25 2
3 25 1 17 6 17 8 30 3
4 23 1 18 12 10 8 19 4
5 19 1 18 8 12 9 22 5
6 29 1 16 10 12 7 22 6
7 25 1 20 10 11 4 25 7
8 21 1 16 11 11 11 23 8
9 22 1 18 16 12 7 17 9
10 25 2 17 11 13 7 21 10
11 24 2 23 13 14 12 19 11
12 18 2 30 12 16 10 19 12
13 22 2 23 8 11 10 15 13
14 15 2 18 12 10 8 16 14
15 22 2 15 11 11 8 23 15
16 28 2 12 4 15 4 27 16
17 20 2 21 9 9 9 22 17
18 12 2 15 8 11 8 14 18
19 24 2 20 8 17 7 22 19
20 20 3 31 14 17 11 23 20
21 21 3 27 15 11 9 23 21
22 20 3 34 16 18 11 21 22
23 21 3 21 9 14 13 19 23
24 23 3 31 14 10 8 18 24
25 28 3 19 11 11 8 20 25
26 24 3 16 8 15 9 23 26
27 24 3 20 9 15 6 25 27
28 24 3 21 9 13 9 19 28
29 23 3 22 9 16 9 24 29
30 23 3 17 9 13 6 22 30
31 29 3 24 10 9 6 25 31
32 24 3 25 16 18 16 26 32
33 18 3 26 11 18 5 29 33
34 25 3 25 8 12 7 32 34
35 21 3 17 9 17 9 25 35
36 26 3 32 16 9 6 29 36
37 22 3 33 11 9 6 28 37
38 22 3 13 16 12 5 17 38
39 22 3 32 12 18 12 28 39
40 23 3 25 12 12 7 29 40
41 30 3 29 14 18 10 26 41
42 23 3 22 9 14 9 25 42
43 17 3 18 10 15 8 14 43
44 23 3 17 9 16 5 25 44
45 23 3 20 10 10 8 26 45
46 25 3 15 12 11 8 20 46
47 24 3 20 14 14 10 18 47
48 24 3 33 14 9 6 32 48
49 23 3 29 10 12 8 25 49
50 21 3 23 14 17 7 25 50
51 24 3 26 16 5 4 23 51
52 24 3 18 9 12 8 21 52
53 28 3 20 10 12 8 20 53
54 16 3 11 6 6 4 15 54
55 20 3 28 8 24 20 30 55
56 29 3 26 13 12 8 24 56
57 27 3 22 10 12 8 26 57
58 22 3 17 8 14 6 24 58
59 28 3 12 7 7 4 22 59
60 16 3 14 15 13 8 14 60
61 25 3 17 9 12 9 24 61
62 24 3 21 10 13 6 24 62
63 28 3 19 12 14 7 24 63
64 24 3 18 13 8 9 24 64
65 23 3 10 10 11 5 19 65
66 30 3 29 11 9 5 31 66
67 24 3 31 8 11 8 22 67
68 21 3 19 9 13 8 27 68
69 25 3 9 13 10 6 19 69
70 25 3 20 11 11 8 25 70
71 22 3 28 8 12 7 20 71
72 23 3 19 9 9 7 21 72
73 26 3 30 9 15 9 27 73
74 23 3 29 15 18 11 23 74
75 25 3 26 9 15 6 25 75
76 21 3 23 10 12 8 20 76
77 25 3 13 14 13 6 21 77
78 24 3 21 12 14 9 22 78
79 29 3 19 12 10 8 23 79
80 22 3 28 11 13 6 25 80
81 27 3 23 14 13 10 25 81
82 26 3 18 6 11 8 17 82
83 22 3 21 12 13 8 19 83
84 24 3 20 8 16 10 25 84
85 27 4 23 14 8 5 19 85
86 24 4 21 11 16 7 20 86
87 24 4 21 10 11 5 26 87
88 29 4 15 14 9 8 23 88
89 22 4 28 12 16 14 27 89
90 21 4 19 10 12 7 17 90
91 24 4 26 14 14 8 17 91
92 24 4 10 5 8 6 19 92
93 23 4 16 11 9 5 17 93
94 20 4 22 10 15 6 22 94
95 27 4 19 9 11 10 21 95
96 26 4 31 10 21 12 32 96
97 25 4 31 16 14 9 21 97
98 21 4 29 13 18 12 21 98
99 21 4 19 9 12 7 18 99
100 19 4 22 10 13 8 18 100
101 21 4 23 10 15 10 23 101
102 21 4 15 7 12 6 19 102
103 16 4 20 9 19 10 20 103
104 22 4 18 8 15 10 21 104
105 29 4 23 14 11 10 20 105
106 15 4 25 14 11 5 17 106
107 17 4 21 8 10 7 18 107
108 15 4 24 9 13 10 19 108
109 21 4 25 14 15 11 22 109
110 21 4 17 14 12 6 15 110
111 19 4 13 8 12 7 14 111
112 24 4 28 8 16 12 18 112
113 20 4 21 8 9 11 24 113
114 17 4 25 7 18 11 35 114
115 23 4 9 6 8 11 29 115
116 24 4 16 8 13 5 21 116
117 14 4 19 6 17 8 25 117
118 19 4 17 11 9 6 20 118
119 24 4 25 14 15 9 22 119
120 13 4 20 11 8 4 13 120
121 22 4 29 11 7 4 26 121
122 16 4 14 11 12 7 17 122
123 19 4 22 14 14 11 25 123
124 25 4 15 8 6 6 20 124
125 25 4 19 20 8 7 19 125
126 23 4 20 11 17 8 21 126
127 24 4 15 8 10 4 22 127
128 26 4 20 11 11 8 24 128
129 26 4 18 10 14 9 21 129
130 25 4 33 14 11 8 26 130
131 18 4 22 11 13 11 24 131
132 21 4 16 9 12 8 16 132
133 26 4 17 9 11 5 23 133
134 23 4 16 8 9 4 18 134
135 23 4 21 10 12 8 16 135
136 22 4 26 13 20 10 26 136
137 20 4 18 13 12 6 19 137
138 13 4 18 12 13 9 21 138
139 24 4 17 8 12 9 21 139
140 15 4 22 13 12 13 22 140
141 14 4 30 14 9 9 23 141
142 22 4 30 12 15 10 29 142
143 10 4 24 14 24 20 21 143
144 24 4 21 15 7 5 21 144
145 22 4 21 13 17 11 23 145
146 24 4 29 16 11 6 27 146
147 19 4 31 9 17 9 25 147
148 20 4 20 9 11 7 21 148
149 13 4 16 9 12 9 10 149
150 20 4 22 8 14 10 20 150
151 22 4 20 7 11 9 26 151
152 24 4 28 16 16 8 24 152
153 29 4 38 11 21 7 29 153
154 12 4 22 9 14 6 19 154
155 20 4 20 11 20 13 24 155
156 21 4 17 9 13 6 19 156
157 24 4 28 14 11 8 24 157
158 22 4 22 13 15 10 22 158
159 20 4 31 16 19 16 17 159
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Week Concern Doubts Pexpect Pcriticism
16.72366 0.36602 -0.06332 0.21800 -0.13705 -0.24699
Pstandards t
0.39855 -0.02046
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.2057 -1.8989 0.2747 2.2191 7.3711
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.72366 2.45045 6.825 2.00e-10 ***
Week 0.36602 0.66201 0.553 0.5812
Concern -0.06332 0.06262 -1.011 0.3136
Doubts 0.21800 0.11108 1.963 0.0515 .
Pexpect -0.13705 0.10325 -1.327 0.1864
Pcriticism -0.24699 0.12908 -1.914 0.0576 .
Pstandards 0.39855 0.07550 5.279 4.44e-07 ***
t -0.02046 0.01178 -1.737 0.0844 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.451 on 151 degrees of freedom
Multiple R-squared: 0.2538, Adjusted R-squared: 0.2192
F-statistic: 7.336 on 7 and 151 DF, p-value: 1.424e-07
> 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,] 0.675872967 0.648254066 0.3241270
[2,] 0.532331063 0.935337874 0.4676689
[3,] 0.551748586 0.896502829 0.4482514
[4,] 0.778516224 0.442967552 0.2214838
[5,] 0.694936122 0.610127756 0.3050639
[6,] 0.692611740 0.614776520 0.3073883
[7,] 0.607519260 0.784961479 0.3924807
[8,] 0.715567632 0.568864735 0.2844324
[9,] 0.683360114 0.633279772 0.3166399
[10,] 0.641103173 0.717793654 0.3588968
[11,] 0.569117106 0.861765788 0.4308829
[12,] 0.491848027 0.983696054 0.5081520
[13,] 0.472082423 0.944164845 0.5279176
[14,] 0.529006283 0.941987433 0.4709937
[15,] 0.682543932 0.634912136 0.3174561
[16,] 0.618662684 0.762674631 0.3813373
[17,] 0.552151852 0.895696297 0.4478481
[18,] 0.547556567 0.904886867 0.4524434
[19,] 0.479314907 0.958629813 0.5206851
[20,] 0.414260511 0.828521023 0.5857395
[21,] 0.430449432 0.860898865 0.5695506
[22,] 0.368547300 0.737094600 0.6314527
[23,] 0.594739930 0.810520140 0.4052601
[24,] 0.541590103 0.916819794 0.4584099
[25,] 0.496111962 0.992223923 0.5038880
[26,] 0.445743207 0.891486414 0.5542568
[27,] 0.412175468 0.824350935 0.5878245
[28,] 0.361580262 0.723160525 0.6384197
[29,] 0.323041778 0.646083557 0.6769582
[30,] 0.291747663 0.583495327 0.7082523
[31,] 0.520381850 0.959236299 0.4796181
[32,] 0.465993388 0.931986776 0.5340066
[33,] 0.429672039 0.859344077 0.5703280
[34,] 0.381242174 0.762484347 0.6187578
[35,] 0.339058999 0.678117999 0.6609410
[36,] 0.317959174 0.635918349 0.6820408
[37,] 0.300615474 0.601230948 0.6993845
[38,] 0.292434179 0.584868359 0.7075658
[39,] 0.256888006 0.513776011 0.7431120
[40,] 0.251516085 0.503032170 0.7484839
[41,] 0.231507664 0.463015327 0.7684923
[42,] 0.204345892 0.408691785 0.7956541
[43,] 0.278724658 0.557449316 0.7212753
[44,] 0.359405013 0.718810026 0.6405950
[45,] 0.336595681 0.673191363 0.6634043
[46,] 0.403583088 0.807166175 0.5964169
[47,] 0.379995402 0.759990803 0.6200046
[48,] 0.349115187 0.698230374 0.6508848
[49,] 0.348094410 0.696188819 0.6519056
[50,] 0.429021675 0.858043349 0.5709783
[51,] 0.384781681 0.769563362 0.6152183
[52,] 0.342932739 0.685865478 0.6570673
[53,] 0.344237342 0.688474683 0.6557627
[54,] 0.310517717 0.621035434 0.6894823
[55,] 0.271744180 0.543488359 0.7282558
[56,] 0.254076974 0.508153949 0.7459230
[57,] 0.223410806 0.446821611 0.7765892
[58,] 0.250270645 0.500541289 0.7497294
[59,] 0.216334737 0.432669474 0.7836653
[60,] 0.183655428 0.367310856 0.8163446
[61,] 0.154697045 0.309394090 0.8453030
[62,] 0.130257322 0.260514644 0.8697427
[63,] 0.111645172 0.223290345 0.8883548
[64,] 0.091480792 0.182961585 0.9085192
[65,] 0.074170779 0.148341559 0.9258292
[66,] 0.064574675 0.129149351 0.9354253
[67,] 0.052057482 0.104114964 0.9479425
[68,] 0.041024165 0.082048331 0.9589758
[69,] 0.042797283 0.085594565 0.9572027
[70,] 0.044116129 0.088232259 0.9558839
[71,] 0.035388959 0.070777918 0.9646110
[72,] 0.040816537 0.081633073 0.9591835
[73,] 0.032538978 0.065077956 0.9674610
[74,] 0.024880240 0.049760479 0.9751198
[75,] 0.023098827 0.046197654 0.9769012
[76,] 0.018290310 0.036580621 0.9817097
[77,] 0.014730229 0.029460458 0.9852698
[78,] 0.015288908 0.030577815 0.9847111
[79,] 0.012738458 0.025476916 0.9872615
[80,] 0.009562571 0.019125141 0.9904374
[81,] 0.008145342 0.016290683 0.9918547
[82,] 0.006621823 0.013243645 0.9933782
[83,] 0.004851198 0.009702395 0.9951488
[84,] 0.004593294 0.009186588 0.9954067
[85,] 0.006872635 0.013745270 0.9931274
[86,] 0.005582763 0.011165525 0.9944172
[87,] 0.004744216 0.009488432 0.9952558
[88,] 0.003625872 0.007251744 0.9963741
[89,] 0.002724004 0.005448007 0.9972760
[90,] 0.002258621 0.004517242 0.9977414
[91,] 0.001771856 0.003543712 0.9982281
[92,] 0.001294893 0.002589787 0.9987051
[93,] 0.001597367 0.003194735 0.9984026
[94,] 0.001242031 0.002484062 0.9987580
[95,] 0.005427427 0.010854853 0.9945726
[96,] 0.013297159 0.026594318 0.9867028
[97,] 0.014008234 0.028016468 0.9859918
[98,] 0.019068669 0.038137338 0.9809313
[99,] 0.014842545 0.029685089 0.9851575
[100,] 0.010749991 0.021499982 0.9892500
[101,] 0.007756585 0.015513170 0.9922434
[102,] 0.022348879 0.044697758 0.9776511
[103,] 0.022360852 0.044721704 0.9776391
[104,] 0.055763752 0.111527504 0.9442362
[105,] 0.047605142 0.095210284 0.9523949
[106,] 0.039874279 0.079748559 0.9601257
[107,] 0.104297025 0.208594050 0.8957030
[108,] 0.098256332 0.196512664 0.9017437
[109,] 0.087637535 0.175275070 0.9123625
[110,] 0.152261221 0.304522442 0.8477388
[111,] 0.149211403 0.298422805 0.8507886
[112,] 0.189016197 0.378032394 0.8109838
[113,] 0.187789385 0.375578770 0.8122106
[114,] 0.177968365 0.355936729 0.8220316
[115,] 0.154173227 0.308346454 0.8458268
[116,] 0.123796205 0.247592410 0.8762038
[117,] 0.096935752 0.193871504 0.9030642
[118,] 0.094525942 0.189051883 0.9054741
[119,] 0.132653785 0.265307570 0.8673462
[120,] 0.114738929 0.229477858 0.8852611
[121,] 0.096000949 0.192001897 0.9039991
[122,] 0.085221882 0.170443764 0.9147781
[123,] 0.083391588 0.166783177 0.9166084
[124,] 0.074196942 0.148393883 0.9258031
[125,] 0.173388639 0.346777278 0.8266114
[126,] 0.138262027 0.276524053 0.8617380
[127,] 0.115803154 0.231606308 0.8841968
[128,] 0.162832369 0.325664739 0.8371676
[129,] 0.394667676 0.789335351 0.6053323
[130,] 0.339605768 0.679211537 0.6603942
[131,] 0.481929833 0.963859666 0.5180702
[132,] 0.390634951 0.781269901 0.6093650
[133,] 0.552332757 0.895334487 0.4476672
[134,] 0.498813860 0.997627720 0.5011861
[135,] 0.402455716 0.804911431 0.5975443
[136,] 0.298069267 0.596138534 0.7019307
[137,] 0.289509793 0.579019587 0.7104902
[138,] 0.172331531 0.344663062 0.8276685
> postscript(file="/var/www/html/rcomp/tmp/11f3e1291127967.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2u6lh1291127967.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3u6lh1291127967.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4u6lh1291127967.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55gk21291127967.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 = 159
Frequency = 1
1 2 3 4 5 6
2.30454807 -1.89236518 0.08929717 0.28976565 -3.49232041 5.47151209
7 8 9 10 11 12
-0.32843480 -2.25319699 -0.65576022 1.56823665 2.70169092 -2.83652745
13 14 15 16 17 18
2.52170512 -6.67601389 -2.28029537 3.04226144 -3.05202060 -7.97796446
19 20 21 22 23 24
1.74596395 -2.62172213 -3.38879000 -1.89272104 0.57356145 0.75256269
25 26 27 28 29 30
5.00718953 1.09124157 -0.39111543 2.55084079 0.05300795 -0.59813034
31 32 33 34 35 36
3.90370881 0.98424566 -7.75452448 -1.66730171 -2.40232081 -1.38968842
37 38 39 40 41 42
-3.81734206 -1.60504595 -1.34237844 -3.22091754 6.37569493 -0.35366212
43 44 45 46 47 48
-2.53038140 -1.34320465 -1.83064457 1.96556398 2.56881457 -3.84049965
49 50 51 52 53 54
-0.50632302 -3.29954386 -2.11357101 1.67077738 5.99841326 -5.49645419
55 56 57 58 59 60
-0.39516054 5.19147869 2.81558898 -1.46732049 3.79835674 -4.79995235
61 62 63 64 65 66
1.84294027 0.29472647 4.13658284 -0.45256545 0.13128766 3.08007099
67 68 69 70 71 72
2.48318500 -3.19280711 1.60574246 0.99842546 1.06222389 0.48514850
73 74 75 76 77 78
3.12704979 1.27548377 1.97082310 -0.34107668 1.41871463 1.86118655
79 80 81 82 83 84
5.56129012 -1.51034838 3.52748824 6.39571697 0.77509100 2.11808041
85 86 87 88 89 90
3.71440891 2.45404937 -0.87799309 4.55308598 -0.32025719 0.27472335
91 92 93 94 95 96
3.38746127 2.24354663 1.02302629 -2.28209246 5.60476127 2.64741152
97 98 99 100 101 102
3.04358545 0.88058590 0.27831175 -1.34524690 -0.48613954 -0.12310791
103 104 105 106 107 108
-3.67333664 1.49176417 7.37114450 -6.52108148 -3.48746881 -4.74149987
109 110 111 112 113 114
-0.42230970 0.23536249 -0.04387445 6.11527663 -1.90508108 -7.56398191
115 116 117 118 119 120
-1.31773699 2.10158749 -7.55703150 -3.35083130 2.28829673 -6.96115340
121 122 123 124 125 126
-2.68903019 -4.60516704 -3.65852124 2.88816872 1.46547175 2.19459085
127 128 129 130 131 132
1.20664465 3.21759002 5.18319721 1.63050678 -3.57933084 1.80760780
133 134 135 136 137 138
3.22351703 1.87032532 3.96756153 0.25544818 -1.52511397 -8.20572423
139 140 141 142 143 144
3.48639116 -5.67716888 -8.16584167 -1.03140153 -6.93511973 0.64272220
145 146 147 148 149 150
1.15450368 -0.62395374 -1.59048250 -0.98856315 -3.20629956 0.94765553
151 152 153 154 155 156
0.01006452 1.81034483 6.99947718 -7.77793445 0.23836152 0.80935799
157 158 159
1.66342257 1.36125467 3.32042861
> postscript(file="/var/www/html/rcomp/tmp/65gk21291127967.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 2.30454807 NA
1 -1.89236518 2.30454807
2 0.08929717 -1.89236518
3 0.28976565 0.08929717
4 -3.49232041 0.28976565
5 5.47151209 -3.49232041
6 -0.32843480 5.47151209
7 -2.25319699 -0.32843480
8 -0.65576022 -2.25319699
9 1.56823665 -0.65576022
10 2.70169092 1.56823665
11 -2.83652745 2.70169092
12 2.52170512 -2.83652745
13 -6.67601389 2.52170512
14 -2.28029537 -6.67601389
15 3.04226144 -2.28029537
16 -3.05202060 3.04226144
17 -7.97796446 -3.05202060
18 1.74596395 -7.97796446
19 -2.62172213 1.74596395
20 -3.38879000 -2.62172213
21 -1.89272104 -3.38879000
22 0.57356145 -1.89272104
23 0.75256269 0.57356145
24 5.00718953 0.75256269
25 1.09124157 5.00718953
26 -0.39111543 1.09124157
27 2.55084079 -0.39111543
28 0.05300795 2.55084079
29 -0.59813034 0.05300795
30 3.90370881 -0.59813034
31 0.98424566 3.90370881
32 -7.75452448 0.98424566
33 -1.66730171 -7.75452448
34 -2.40232081 -1.66730171
35 -1.38968842 -2.40232081
36 -3.81734206 -1.38968842
37 -1.60504595 -3.81734206
38 -1.34237844 -1.60504595
39 -3.22091754 -1.34237844
40 6.37569493 -3.22091754
41 -0.35366212 6.37569493
42 -2.53038140 -0.35366212
43 -1.34320465 -2.53038140
44 -1.83064457 -1.34320465
45 1.96556398 -1.83064457
46 2.56881457 1.96556398
47 -3.84049965 2.56881457
48 -0.50632302 -3.84049965
49 -3.29954386 -0.50632302
50 -2.11357101 -3.29954386
51 1.67077738 -2.11357101
52 5.99841326 1.67077738
53 -5.49645419 5.99841326
54 -0.39516054 -5.49645419
55 5.19147869 -0.39516054
56 2.81558898 5.19147869
57 -1.46732049 2.81558898
58 3.79835674 -1.46732049
59 -4.79995235 3.79835674
60 1.84294027 -4.79995235
61 0.29472647 1.84294027
62 4.13658284 0.29472647
63 -0.45256545 4.13658284
64 0.13128766 -0.45256545
65 3.08007099 0.13128766
66 2.48318500 3.08007099
67 -3.19280711 2.48318500
68 1.60574246 -3.19280711
69 0.99842546 1.60574246
70 1.06222389 0.99842546
71 0.48514850 1.06222389
72 3.12704979 0.48514850
73 1.27548377 3.12704979
74 1.97082310 1.27548377
75 -0.34107668 1.97082310
76 1.41871463 -0.34107668
77 1.86118655 1.41871463
78 5.56129012 1.86118655
79 -1.51034838 5.56129012
80 3.52748824 -1.51034838
81 6.39571697 3.52748824
82 0.77509100 6.39571697
83 2.11808041 0.77509100
84 3.71440891 2.11808041
85 2.45404937 3.71440891
86 -0.87799309 2.45404937
87 4.55308598 -0.87799309
88 -0.32025719 4.55308598
89 0.27472335 -0.32025719
90 3.38746127 0.27472335
91 2.24354663 3.38746127
92 1.02302629 2.24354663
93 -2.28209246 1.02302629
94 5.60476127 -2.28209246
95 2.64741152 5.60476127
96 3.04358545 2.64741152
97 0.88058590 3.04358545
98 0.27831175 0.88058590
99 -1.34524690 0.27831175
100 -0.48613954 -1.34524690
101 -0.12310791 -0.48613954
102 -3.67333664 -0.12310791
103 1.49176417 -3.67333664
104 7.37114450 1.49176417
105 -6.52108148 7.37114450
106 -3.48746881 -6.52108148
107 -4.74149987 -3.48746881
108 -0.42230970 -4.74149987
109 0.23536249 -0.42230970
110 -0.04387445 0.23536249
111 6.11527663 -0.04387445
112 -1.90508108 6.11527663
113 -7.56398191 -1.90508108
114 -1.31773699 -7.56398191
115 2.10158749 -1.31773699
116 -7.55703150 2.10158749
117 -3.35083130 -7.55703150
118 2.28829673 -3.35083130
119 -6.96115340 2.28829673
120 -2.68903019 -6.96115340
121 -4.60516704 -2.68903019
122 -3.65852124 -4.60516704
123 2.88816872 -3.65852124
124 1.46547175 2.88816872
125 2.19459085 1.46547175
126 1.20664465 2.19459085
127 3.21759002 1.20664465
128 5.18319721 3.21759002
129 1.63050678 5.18319721
130 -3.57933084 1.63050678
131 1.80760780 -3.57933084
132 3.22351703 1.80760780
133 1.87032532 3.22351703
134 3.96756153 1.87032532
135 0.25544818 3.96756153
136 -1.52511397 0.25544818
137 -8.20572423 -1.52511397
138 3.48639116 -8.20572423
139 -5.67716888 3.48639116
140 -8.16584167 -5.67716888
141 -1.03140153 -8.16584167
142 -6.93511973 -1.03140153
143 0.64272220 -6.93511973
144 1.15450368 0.64272220
145 -0.62395374 1.15450368
146 -1.59048250 -0.62395374
147 -0.98856315 -1.59048250
148 -3.20629956 -0.98856315
149 0.94765553 -3.20629956
150 0.01006452 0.94765553
151 1.81034483 0.01006452
152 6.99947718 1.81034483
153 -7.77793445 6.99947718
154 0.23836152 -7.77793445
155 0.80935799 0.23836152
156 1.66342257 0.80935799
157 1.36125467 1.66342257
158 3.32042861 1.36125467
159 NA 3.32042861
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.89236518 2.30454807
[2,] 0.08929717 -1.89236518
[3,] 0.28976565 0.08929717
[4,] -3.49232041 0.28976565
[5,] 5.47151209 -3.49232041
[6,] -0.32843480 5.47151209
[7,] -2.25319699 -0.32843480
[8,] -0.65576022 -2.25319699
[9,] 1.56823665 -0.65576022
[10,] 2.70169092 1.56823665
[11,] -2.83652745 2.70169092
[12,] 2.52170512 -2.83652745
[13,] -6.67601389 2.52170512
[14,] -2.28029537 -6.67601389
[15,] 3.04226144 -2.28029537
[16,] -3.05202060 3.04226144
[17,] -7.97796446 -3.05202060
[18,] 1.74596395 -7.97796446
[19,] -2.62172213 1.74596395
[20,] -3.38879000 -2.62172213
[21,] -1.89272104 -3.38879000
[22,] 0.57356145 -1.89272104
[23,] 0.75256269 0.57356145
[24,] 5.00718953 0.75256269
[25,] 1.09124157 5.00718953
[26,] -0.39111543 1.09124157
[27,] 2.55084079 -0.39111543
[28,] 0.05300795 2.55084079
[29,] -0.59813034 0.05300795
[30,] 3.90370881 -0.59813034
[31,] 0.98424566 3.90370881
[32,] -7.75452448 0.98424566
[33,] -1.66730171 -7.75452448
[34,] -2.40232081 -1.66730171
[35,] -1.38968842 -2.40232081
[36,] -3.81734206 -1.38968842
[37,] -1.60504595 -3.81734206
[38,] -1.34237844 -1.60504595
[39,] -3.22091754 -1.34237844
[40,] 6.37569493 -3.22091754
[41,] -0.35366212 6.37569493
[42,] -2.53038140 -0.35366212
[43,] -1.34320465 -2.53038140
[44,] -1.83064457 -1.34320465
[45,] 1.96556398 -1.83064457
[46,] 2.56881457 1.96556398
[47,] -3.84049965 2.56881457
[48,] -0.50632302 -3.84049965
[49,] -3.29954386 -0.50632302
[50,] -2.11357101 -3.29954386
[51,] 1.67077738 -2.11357101
[52,] 5.99841326 1.67077738
[53,] -5.49645419 5.99841326
[54,] -0.39516054 -5.49645419
[55,] 5.19147869 -0.39516054
[56,] 2.81558898 5.19147869
[57,] -1.46732049 2.81558898
[58,] 3.79835674 -1.46732049
[59,] -4.79995235 3.79835674
[60,] 1.84294027 -4.79995235
[61,] 0.29472647 1.84294027
[62,] 4.13658284 0.29472647
[63,] -0.45256545 4.13658284
[64,] 0.13128766 -0.45256545
[65,] 3.08007099 0.13128766
[66,] 2.48318500 3.08007099
[67,] -3.19280711 2.48318500
[68,] 1.60574246 -3.19280711
[69,] 0.99842546 1.60574246
[70,] 1.06222389 0.99842546
[71,] 0.48514850 1.06222389
[72,] 3.12704979 0.48514850
[73,] 1.27548377 3.12704979
[74,] 1.97082310 1.27548377
[75,] -0.34107668 1.97082310
[76,] 1.41871463 -0.34107668
[77,] 1.86118655 1.41871463
[78,] 5.56129012 1.86118655
[79,] -1.51034838 5.56129012
[80,] 3.52748824 -1.51034838
[81,] 6.39571697 3.52748824
[82,] 0.77509100 6.39571697
[83,] 2.11808041 0.77509100
[84,] 3.71440891 2.11808041
[85,] 2.45404937 3.71440891
[86,] -0.87799309 2.45404937
[87,] 4.55308598 -0.87799309
[88,] -0.32025719 4.55308598
[89,] 0.27472335 -0.32025719
[90,] 3.38746127 0.27472335
[91,] 2.24354663 3.38746127
[92,] 1.02302629 2.24354663
[93,] -2.28209246 1.02302629
[94,] 5.60476127 -2.28209246
[95,] 2.64741152 5.60476127
[96,] 3.04358545 2.64741152
[97,] 0.88058590 3.04358545
[98,] 0.27831175 0.88058590
[99,] -1.34524690 0.27831175
[100,] -0.48613954 -1.34524690
[101,] -0.12310791 -0.48613954
[102,] -3.67333664 -0.12310791
[103,] 1.49176417 -3.67333664
[104,] 7.37114450 1.49176417
[105,] -6.52108148 7.37114450
[106,] -3.48746881 -6.52108148
[107,] -4.74149987 -3.48746881
[108,] -0.42230970 -4.74149987
[109,] 0.23536249 -0.42230970
[110,] -0.04387445 0.23536249
[111,] 6.11527663 -0.04387445
[112,] -1.90508108 6.11527663
[113,] -7.56398191 -1.90508108
[114,] -1.31773699 -7.56398191
[115,] 2.10158749 -1.31773699
[116,] -7.55703150 2.10158749
[117,] -3.35083130 -7.55703150
[118,] 2.28829673 -3.35083130
[119,] -6.96115340 2.28829673
[120,] -2.68903019 -6.96115340
[121,] -4.60516704 -2.68903019
[122,] -3.65852124 -4.60516704
[123,] 2.88816872 -3.65852124
[124,] 1.46547175 2.88816872
[125,] 2.19459085 1.46547175
[126,] 1.20664465 2.19459085
[127,] 3.21759002 1.20664465
[128,] 5.18319721 3.21759002
[129,] 1.63050678 5.18319721
[130,] -3.57933084 1.63050678
[131,] 1.80760780 -3.57933084
[132,] 3.22351703 1.80760780
[133,] 1.87032532 3.22351703
[134,] 3.96756153 1.87032532
[135,] 0.25544818 3.96756153
[136,] -1.52511397 0.25544818
[137,] -8.20572423 -1.52511397
[138,] 3.48639116 -8.20572423
[139,] -5.67716888 3.48639116
[140,] -8.16584167 -5.67716888
[141,] -1.03140153 -8.16584167
[142,] -6.93511973 -1.03140153
[143,] 0.64272220 -6.93511973
[144,] 1.15450368 0.64272220
[145,] -0.62395374 1.15450368
[146,] -1.59048250 -0.62395374
[147,] -0.98856315 -1.59048250
[148,] -3.20629956 -0.98856315
[149,] 0.94765553 -3.20629956
[150,] 0.01006452 0.94765553
[151,] 1.81034483 0.01006452
[152,] 6.99947718 1.81034483
[153,] -7.77793445 6.99947718
[154,] 0.23836152 -7.77793445
[155,] 0.80935799 0.23836152
[156,] 1.66342257 0.80935799
[157,] 1.36125467 1.66342257
[158,] 3.32042861 1.36125467
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.89236518 2.30454807
2 0.08929717 -1.89236518
3 0.28976565 0.08929717
4 -3.49232041 0.28976565
5 5.47151209 -3.49232041
6 -0.32843480 5.47151209
7 -2.25319699 -0.32843480
8 -0.65576022 -2.25319699
9 1.56823665 -0.65576022
10 2.70169092 1.56823665
11 -2.83652745 2.70169092
12 2.52170512 -2.83652745
13 -6.67601389 2.52170512
14 -2.28029537 -6.67601389
15 3.04226144 -2.28029537
16 -3.05202060 3.04226144
17 -7.97796446 -3.05202060
18 1.74596395 -7.97796446
19 -2.62172213 1.74596395
20 -3.38879000 -2.62172213
21 -1.89272104 -3.38879000
22 0.57356145 -1.89272104
23 0.75256269 0.57356145
24 5.00718953 0.75256269
25 1.09124157 5.00718953
26 -0.39111543 1.09124157
27 2.55084079 -0.39111543
28 0.05300795 2.55084079
29 -0.59813034 0.05300795
30 3.90370881 -0.59813034
31 0.98424566 3.90370881
32 -7.75452448 0.98424566
33 -1.66730171 -7.75452448
34 -2.40232081 -1.66730171
35 -1.38968842 -2.40232081
36 -3.81734206 -1.38968842
37 -1.60504595 -3.81734206
38 -1.34237844 -1.60504595
39 -3.22091754 -1.34237844
40 6.37569493 -3.22091754
41 -0.35366212 6.37569493
42 -2.53038140 -0.35366212
43 -1.34320465 -2.53038140
44 -1.83064457 -1.34320465
45 1.96556398 -1.83064457
46 2.56881457 1.96556398
47 -3.84049965 2.56881457
48 -0.50632302 -3.84049965
49 -3.29954386 -0.50632302
50 -2.11357101 -3.29954386
51 1.67077738 -2.11357101
52 5.99841326 1.67077738
53 -5.49645419 5.99841326
54 -0.39516054 -5.49645419
55 5.19147869 -0.39516054
56 2.81558898 5.19147869
57 -1.46732049 2.81558898
58 3.79835674 -1.46732049
59 -4.79995235 3.79835674
60 1.84294027 -4.79995235
61 0.29472647 1.84294027
62 4.13658284 0.29472647
63 -0.45256545 4.13658284
64 0.13128766 -0.45256545
65 3.08007099 0.13128766
66 2.48318500 3.08007099
67 -3.19280711 2.48318500
68 1.60574246 -3.19280711
69 0.99842546 1.60574246
70 1.06222389 0.99842546
71 0.48514850 1.06222389
72 3.12704979 0.48514850
73 1.27548377 3.12704979
74 1.97082310 1.27548377
75 -0.34107668 1.97082310
76 1.41871463 -0.34107668
77 1.86118655 1.41871463
78 5.56129012 1.86118655
79 -1.51034838 5.56129012
80 3.52748824 -1.51034838
81 6.39571697 3.52748824
82 0.77509100 6.39571697
83 2.11808041 0.77509100
84 3.71440891 2.11808041
85 2.45404937 3.71440891
86 -0.87799309 2.45404937
87 4.55308598 -0.87799309
88 -0.32025719 4.55308598
89 0.27472335 -0.32025719
90 3.38746127 0.27472335
91 2.24354663 3.38746127
92 1.02302629 2.24354663
93 -2.28209246 1.02302629
94 5.60476127 -2.28209246
95 2.64741152 5.60476127
96 3.04358545 2.64741152
97 0.88058590 3.04358545
98 0.27831175 0.88058590
99 -1.34524690 0.27831175
100 -0.48613954 -1.34524690
101 -0.12310791 -0.48613954
102 -3.67333664 -0.12310791
103 1.49176417 -3.67333664
104 7.37114450 1.49176417
105 -6.52108148 7.37114450
106 -3.48746881 -6.52108148
107 -4.74149987 -3.48746881
108 -0.42230970 -4.74149987
109 0.23536249 -0.42230970
110 -0.04387445 0.23536249
111 6.11527663 -0.04387445
112 -1.90508108 6.11527663
113 -7.56398191 -1.90508108
114 -1.31773699 -7.56398191
115 2.10158749 -1.31773699
116 -7.55703150 2.10158749
117 -3.35083130 -7.55703150
118 2.28829673 -3.35083130
119 -6.96115340 2.28829673
120 -2.68903019 -6.96115340
121 -4.60516704 -2.68903019
122 -3.65852124 -4.60516704
123 2.88816872 -3.65852124
124 1.46547175 2.88816872
125 2.19459085 1.46547175
126 1.20664465 2.19459085
127 3.21759002 1.20664465
128 5.18319721 3.21759002
129 1.63050678 5.18319721
130 -3.57933084 1.63050678
131 1.80760780 -3.57933084
132 3.22351703 1.80760780
133 1.87032532 3.22351703
134 3.96756153 1.87032532
135 0.25544818 3.96756153
136 -1.52511397 0.25544818
137 -8.20572423 -1.52511397
138 3.48639116 -8.20572423
139 -5.67716888 3.48639116
140 -8.16584167 -5.67716888
141 -1.03140153 -8.16584167
142 -6.93511973 -1.03140153
143 0.64272220 -6.93511973
144 1.15450368 0.64272220
145 -0.62395374 1.15450368
146 -1.59048250 -0.62395374
147 -0.98856315 -1.59048250
148 -3.20629956 -0.98856315
149 0.94765553 -3.20629956
150 0.01006452 0.94765553
151 1.81034483 0.01006452
152 6.99947718 1.81034483
153 -7.77793445 6.99947718
154 0.23836152 -7.77793445
155 0.80935799 0.23836152
156 1.66342257 0.80935799
157 1.36125467 1.66342257
158 3.32042861 1.36125467
> 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/7gpjn1291127967.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8gpjn1291127967.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9qy081291127967.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10qy081291127967.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/114qgg1291127967.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/12qqf41291127967.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/13m0ud1291127967.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/14p1t11291127967.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/15tjrp1291127967.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/16ek8d1291127967.tab")
+ }
>
> try(system("convert tmp/11f3e1291127967.ps tmp/11f3e1291127967.png",intern=TRUE))
character(0)
> try(system("convert tmp/2u6lh1291127967.ps tmp/2u6lh1291127967.png",intern=TRUE))
character(0)
> try(system("convert tmp/3u6lh1291127967.ps tmp/3u6lh1291127967.png",intern=TRUE))
character(0)
> try(system("convert tmp/4u6lh1291127967.ps tmp/4u6lh1291127967.png",intern=TRUE))
character(0)
> try(system("convert tmp/55gk21291127967.ps tmp/55gk21291127967.png",intern=TRUE))
character(0)
> try(system("convert tmp/65gk21291127967.ps tmp/65gk21291127967.png",intern=TRUE))
character(0)
> try(system("convert tmp/7gpjn1291127967.ps tmp/7gpjn1291127967.png",intern=TRUE))
character(0)
> try(system("convert tmp/8gpjn1291127967.ps tmp/8gpjn1291127967.png",intern=TRUE))
character(0)
> try(system("convert tmp/9qy081291127967.ps tmp/9qy081291127967.png",intern=TRUE))
character(0)
> try(system("convert tmp/10qy081291127967.ps tmp/10qy081291127967.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.183 1.854 10.882