R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
> x <- array(list(2
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+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('gender'
+ ,'age'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depressionss')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('gender','age','Connected','Separate','Learning','Software','Happiness','Depressionss'),1:162))
> 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 = '8'
> 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
Depressionss gender age Connected Separate Learning Software Happiness
1 12 2 7 41 38 13 12 14
2 11 2 5 39 32 16 11 18
3 14 2 5 30 35 19 15 11
4 12 1 5 31 33 15 6 12
5 21 2 8 34 37 14 13 16
6 12 2 6 35 29 13 10 18
7 22 2 5 39 31 19 12 14
8 11 2 6 34 36 15 14 14
9 10 2 5 36 35 14 12 15
10 13 2 4 37 38 15 6 15
11 10 1 6 38 31 16 10 17
12 8 2 5 36 34 16 12 19
13 15 1 5 38 35 16 12 10
14 14 2 6 39 38 16 11 16
15 10 2 7 33 37 17 15 18
16 14 1 6 32 33 15 12 14
17 14 1 7 36 32 15 10 14
18 11 2 6 38 38 20 12 17
19 10 1 8 39 38 18 11 14
20 13 2 7 32 32 16 12 16
21 7 1 5 32 33 16 11 18
22 14 2 5 31 31 16 12 11
23 12 2 7 39 38 19 13 14
24 14 2 7 37 39 16 11 12
25 11 1 5 39 32 17 9 17
26 9 2 4 41 32 17 13 9
27 11 1 10 36 35 16 10 16
28 15 2 6 33 37 15 14 14
29 14 2 5 33 33 16 12 15
30 13 1 5 34 33 14 10 11
31 9 2 5 31 28 15 12 16
32 15 1 5 27 32 12 8 13
33 10 2 6 37 31 14 10 17
34 11 2 5 34 37 16 12 15
35 13 1 5 34 30 14 12 14
36 8 1 5 32 33 7 7 16
37 20 1 5 29 31 10 6 9
38 12 1 5 36 33 14 12 15
39 10 2 5 29 31 16 10 17
40 10 1 5 35 33 16 10 13
41 9 1 5 37 32 16 10 15
42 14 2 7 34 33 14 12 16
43 8 1 5 38 32 20 15 16
44 14 1 6 35 33 14 10 12
45 11 2 7 38 28 14 10 12
46 13 2 7 37 35 11 12 11
47 9 2 5 38 39 14 13 15
48 11 2 5 33 34 15 11 15
49 15 2 4 36 38 16 11 17
50 11 1 5 38 32 14 12 13
51 10 2 4 32 38 16 14 16
52 14 1 5 32 30 14 10 14
53 18 1 5 32 33 12 12 11
54 14 2 7 34 38 16 13 12
55 11 1 5 32 32 9 5 12
56 12 2 5 37 32 14 6 15
57 13 2 6 39 34 16 12 16
58 9 2 4 29 34 16 12 15
59 10 1 6 37 36 15 11 12
60 15 2 6 35 34 16 10 12
61 20 1 5 30 28 12 7 8
62 12 1 7 38 34 16 12 13
63 12 2 6 34 35 16 14 11
64 14 2 8 31 35 14 11 14
65 13 2 7 34 31 16 12 15
66 11 1 5 35 37 17 13 10
67 17 2 6 36 35 18 14 11
68 12 1 6 30 27 18 11 12
69 13 2 5 39 40 12 12 15
70 14 1 5 35 37 16 12 15
71 13 1 5 38 36 10 8 14
72 15 2 5 31 38 14 11 16
73 13 2 4 34 39 18 14 15
74 10 1 6 38 41 18 14 15
75 11 1 6 34 27 16 12 13
76 19 2 6 39 30 17 9 12
77 13 2 6 37 37 16 13 17
78 17 2 7 34 31 16 11 13
79 13 1 5 28 31 13 12 15
80 9 1 7 37 27 16 12 13
81 11 1 6 33 36 16 12 15
82 10 1 5 37 38 20 12 16
83 9 2 5 35 37 16 12 15
84 12 1 4 37 33 15 12 16
85 12 2 8 32 34 15 11 15
86 13 2 8 33 31 16 10 14
87 13 1 5 38 39 14 9 15
88 12 2 5 33 34 16 12 14
89 15 2 6 29 32 16 12 13
90 22 2 4 33 33 15 12 7
91 13 2 5 31 36 12 9 17
92 15 2 5 36 32 17 15 13
93 13 2 5 35 41 16 12 15
94 15 2 5 32 28 15 12 14
95 10 2 6 29 30 13 12 13
96 11 2 6 39 36 16 10 16
97 16 2 5 37 35 16 13 12
98 11 2 6 35 31 16 9 14
99 11 1 5 37 34 16 12 17
100 10 1 7 32 36 14 10 15
101 10 2 5 38 36 16 14 17
102 16 1 6 37 35 16 11 12
103 12 2 6 36 37 20 15 16
104 11 1 6 32 28 15 11 11
105 16 2 4 33 39 16 11 15
106 19 1 5 40 32 13 12 9
107 11 2 5 38 35 17 12 16
108 16 1 7 41 39 16 12 15
109 15 1 6 36 35 16 11 10
110 24 2 9 43 42 12 7 10
111 14 2 6 30 34 16 12 15
112 15 2 6 31 33 16 14 11
113 11 2 5 32 41 17 11 13
114 15 1 6 32 33 13 11 14
115 12 2 5 37 34 12 10 18
116 10 1 8 37 32 18 13 16
117 14 2 7 33 40 14 13 14
118 13 2 5 34 40 14 8 14
119 9 2 7 33 35 13 11 14
120 15 2 6 38 36 16 12 14
121 15 2 6 33 37 13 11 12
122 14 2 9 31 27 16 13 14
123 11 2 7 38 39 13 12 15
124 8 2 6 37 38 16 14 15
125 11 2 5 33 31 15 13 15
126 11 2 5 31 33 16 15 13
127 8 1 6 39 32 15 10 17
128 10 2 6 44 39 17 11 17
129 11 2 7 33 36 15 9 19
130 13 2 5 35 33 12 11 15
131 11 1 5 32 33 16 10 13
132 20 1 5 28 32 10 11 9
133 10 2 6 40 37 16 8 15
134 15 1 4 27 30 12 11 15
135 12 1 5 37 38 14 12 15
136 14 2 7 32 29 15 12 16
137 23 1 5 28 22 13 9 11
138 14 1 7 34 35 15 11 14
139 16 2 7 30 35 11 10 11
140 11 2 6 35 34 12 8 15
141 12 1 5 31 35 8 9 13
142 10 2 8 32 34 16 8 15
143 14 1 5 30 34 15 9 16
144 12 2 5 30 35 17 15 14
145 12 1 5 31 23 16 11 15
146 11 2 6 40 31 10 8 16
147 12 2 4 32 27 18 13 16
148 13 1 5 36 36 13 12 11
149 11 1 5 32 31 16 12 12
150 19 1 7 35 32 13 9 9
151 12 2 6 38 39 10 7 16
152 17 2 7 42 37 15 13 13
153 9 1 10 34 38 16 9 16
154 12 2 6 35 39 16 6 12
155 19 2 8 35 34 14 8 9
156 18 2 4 33 31 10 8 13
157 15 2 5 36 32 17 15 13
158 14 2 6 32 37 13 6 14
159 11 2 7 33 36 15 9 19
160 9 2 7 34 32 16 11 13
161 18 2 6 32 35 12 8 12
162 16 2 6 34 36 13 8 13
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) gender age Connected Separate Learning
25.57830 1.10354 0.09479 -0.02193 -0.01544 -0.16906
Software Happiness
-0.07670 -0.73647
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.272 -1.837 -0.026 1.501 9.518
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 25.57830 2.86896 8.916 1.32e-15 ***
gender 1.10354 0.44949 2.455 0.0152 *
age 0.09479 0.18179 0.521 0.6028
Connected -0.02193 0.06803 -0.322 0.7476
Separate -0.01544 0.06466 -0.239 0.8116
Learning -0.16906 0.11355 -1.489 0.1386
Software -0.07670 0.11714 -0.655 0.5136
Happiness -0.73647 0.09206 -8.000 2.78e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.622 on 154 degrees of freedom
Multiple R-squared: 0.3438, Adjusted R-squared: 0.314
F-statistic: 11.53 on 7 and 154 DF, p-value: 9.964e-12
> 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,] 0.7855714 0.42885721 0.21442861
[2,] 0.6878578 0.62428442 0.31214221
[3,] 0.9225829 0.15483411 0.07741705
[4,] 0.8858590 0.22828194 0.11414097
[5,] 0.8565219 0.28695629 0.14347814
[6,] 0.9113350 0.17732996 0.08866498
[7,] 0.9010915 0.19781699 0.09890850
[8,] 0.9200060 0.15998794 0.07999397
[9,] 0.9562714 0.08745712 0.04372856
[10,] 0.9560434 0.08791323 0.04395662
[11,] 0.9400200 0.11996002 0.05998001
[12,] 0.9430354 0.11392930 0.05696465
[13,] 0.9434476 0.11310479 0.05655239
[14,] 0.9256872 0.14862567 0.07431283
[15,] 0.9008159 0.19836826 0.09918413
[16,] 0.9799510 0.04009797 0.02004898
[17,] 0.9826683 0.03466332 0.01733166
[18,] 0.9799533 0.04009336 0.02004668
[19,] 0.9729548 0.05409034 0.02704517
[20,] 0.9635779 0.07284419 0.03642209
[21,] 0.9710093 0.05798141 0.02899071
[22,] 0.9658752 0.06824954 0.03412477
[23,] 0.9599941 0.08001176 0.04000588
[24,] 0.9484403 0.10311941 0.05155970
[25,] 0.9365555 0.12688893 0.06344446
[26,] 0.9406087 0.11878254 0.05939127
[27,] 0.9539668 0.09206642 0.04603321
[28,] 0.9441824 0.11163518 0.05581759
[29,] 0.9354429 0.12911414 0.06455707
[30,] 0.9319329 0.13613424 0.06806712
[31,] 0.9224296 0.15514076 0.07757038
[32,] 0.9072633 0.18547331 0.09273665
[33,] 0.8864563 0.22708747 0.11354374
[34,] 0.8593264 0.28134710 0.14067355
[35,] 0.9036277 0.19274452 0.09637226
[36,] 0.9013303 0.19733943 0.09866972
[37,] 0.9019955 0.19600897 0.09800448
[38,] 0.8853519 0.22929616 0.11464808
[39,] 0.9224361 0.15512789 0.07756394
[40,] 0.9103932 0.17921356 0.08960678
[41,] 0.8930278 0.21394446 0.10697223
[42,] 0.8802469 0.23950623 0.11975312
[43,] 0.9077434 0.18451319 0.09225660
[44,] 0.8871064 0.22578728 0.11289364
[45,] 0.9108964 0.17820729 0.08910364
[46,] 0.8940508 0.21189838 0.10594919
[47,] 0.8787703 0.24245934 0.12122967
[48,] 0.8937142 0.21257155 0.10628578
[49,] 0.9087444 0.18251115 0.09125557
[50,] 0.8880413 0.22391750 0.11195875
[51,] 0.8934260 0.21314793 0.10657397
[52,] 0.8716412 0.25671754 0.12835877
[53,] 0.8789775 0.24204500 0.12102250
[54,] 0.8554644 0.28907121 0.14453561
[55,] 0.8276676 0.34466481 0.17233241
[56,] 0.8509277 0.29814457 0.14907229
[57,] 0.8454174 0.30916521 0.15458261
[58,] 0.8326681 0.33466388 0.16733194
[59,] 0.8088037 0.38239261 0.19119630
[60,] 0.8165608 0.36687831 0.18343916
[61,] 0.7920326 0.41593488 0.20796744
[62,] 0.8017921 0.39641575 0.19820787
[63,] 0.7768586 0.44628285 0.22314143
[64,] 0.7431019 0.51379630 0.25689815
[65,] 0.7273874 0.54522512 0.27261256
[66,] 0.7879412 0.42411757 0.21205878
[67,] 0.7822081 0.43558384 0.21779192
[68,] 0.7887233 0.42255343 0.21127671
[69,] 0.7612189 0.47756217 0.23878109
[70,] 0.8123417 0.37531653 0.18765827
[71,] 0.7798459 0.44030828 0.22015414
[72,] 0.7436099 0.51278030 0.25639015
[73,] 0.7644135 0.47117290 0.23558645
[74,] 0.7444521 0.51109572 0.25554786
[75,] 0.7150035 0.56999309 0.28499654
[76,] 0.6785603 0.64287940 0.32143970
[77,] 0.6438220 0.71235596 0.35617798
[78,] 0.6061766 0.78764683 0.39382342
[79,] 0.5692503 0.86149937 0.43074969
[80,] 0.6168746 0.76625088 0.38312544
[81,] 0.5843389 0.83132218 0.41566109
[82,] 0.5573830 0.88523391 0.44261696
[83,] 0.5178313 0.96433747 0.48216874
[84,] 0.4897027 0.97940549 0.51029725
[85,] 0.5773534 0.84529310 0.42264655
[86,] 0.5337494 0.93250114 0.46625057
[87,] 0.5035418 0.99291638 0.49645819
[88,] 0.4997233 0.99944652 0.50027674
[89,] 0.4618632 0.92372647 0.53813676
[90,] 0.4396203 0.87924061 0.56037969
[91,] 0.3934153 0.78683056 0.60658472
[92,] 0.3799049 0.75980979 0.62009511
[93,] 0.3620293 0.72405851 0.63797075
[94,] 0.4447566 0.88951318 0.55524341
[95,] 0.5428626 0.91427481 0.45713740
[96,] 0.5415570 0.91688593 0.45844296
[97,] 0.4942848 0.98856970 0.50571515
[98,] 0.6279985 0.74400310 0.37200155
[99,] 0.5809666 0.83806683 0.41903341
[100,] 0.8500256 0.29994874 0.14997437
[101,] 0.8359520 0.32809596 0.16404798
[102,] 0.8011924 0.39761521 0.19880760
[103,] 0.7853809 0.42923814 0.21461907
[104,] 0.7709309 0.45813816 0.22906908
[105,] 0.7400506 0.51989879 0.25994940
[106,] 0.6953839 0.60923224 0.30461612
[107,] 0.6789675 0.64206495 0.32103247
[108,] 0.6341389 0.73172225 0.36586113
[109,] 0.7392835 0.52143299 0.26071650
[110,] 0.7553093 0.48938148 0.24469074
[111,] 0.7094377 0.58112468 0.29056234
[112,] 0.6600607 0.67987852 0.33993926
[113,] 0.6160075 0.76798498 0.38399249
[114,] 0.6317639 0.73647226 0.36823613
[115,] 0.6001644 0.79967116 0.39983558
[116,] 0.6201277 0.75974466 0.37987233
[117,] 0.5953678 0.80926445 0.40463223
[118,] 0.5616988 0.87660233 0.43830116
[119,] 0.5382419 0.92351624 0.46175812
[120,] 0.4781732 0.95634649 0.52182675
[121,] 0.4586589 0.91731775 0.54134113
[122,] 0.4228149 0.84562979 0.57718510
[123,] 0.3816059 0.76321172 0.61839414
[124,] 0.3465895 0.69317891 0.65341054
[125,] 0.2987140 0.59742791 0.70128604
[126,] 0.2588192 0.51763832 0.74118084
[127,] 0.5274441 0.94511172 0.47255586
[128,] 0.5000800 0.99983992 0.49991996
[129,] 0.4241937 0.84838736 0.57580632
[130,] 0.4021211 0.80424213 0.59787893
[131,] 0.4411392 0.88227848 0.55886076
[132,] 0.4079413 0.81588252 0.59205874
[133,] 0.5044701 0.99105986 0.49552993
[134,] 0.4536629 0.90732573 0.54633713
[135,] 0.4110406 0.82208126 0.58895937
[136,] 0.4402990 0.88059790 0.55970105
[137,] 0.3637628 0.72752565 0.63623717
[138,] 0.3557843 0.71156855 0.64421572
[139,] 0.3270992 0.65419844 0.67290078
[140,] 0.2484951 0.49699022 0.75150489
[141,] 0.4451098 0.89021957 0.55489021
> postscript(file="/var/fisher/rcomp/tmp/1cvqt1355570351.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/2zm571355570351.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/331bj1355570351.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4q2hf1355570351.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5o0uy1355570351.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 162
Frequency = 1
1 2 3 4 5 6
-1.534443837 0.895049542 -0.597305633 -2.132816701 8.920543586 1.082346638
7 8 9 10 11 12
9.517618389 -2.132488456 -2.595272479 0.276600391 0.053256469 -1.326690149
13 14 15 16 17 18
0.207885318 2.419924132 0.126947417 1.727480854 1.551562981 0.887425878
19 20 21 22 23 24
-1.800938879 1.155727550 -2.139477199 -1.374416461 -0.487215326 -0.649178072
25 26 27 28 29 30
1.277771865 -7.272086499 -0.044495654 1.861018334 1.646200090 -1.665757990
31 32 33 34 35 36
-2.907423046 1.146721418 -1.410335411 -1.270131391 0.650759297 -4.440806049
37 38 39 40 41 42
2.737720448 0.477393497 -1.152843239 -2.832757268 -2.331390845 1.876891647
43 44 45 46 47 48
-1.513222435 -0.002148086 -4.211865518 -3.216007031 -3.412973158 -1.584131132
49 50 51 52 53 54
4.280193465 -1.967129963 -1.313884503 1.453497693 3.105663018 -0.576992889
55 56 57 58 59 60
-4.217408546 -1.079864573 1.434886415 -3.331287096 -3.666220073 0.247878384
61 62 63 64 65 66
2.391701102 -0.787714009 -3.188277177 0.197538644 0.447676561 -3.581263090
67 68 69 70 71 72
2.193706875 -1.451442270 0.209560137 2.855332981 -0.151982382 3.001161288
73 74 75 76 77 78
1.347062140 -0.620400227 -1.888681690 4.366212449 2.250510058 2.898029089
79 80 81 82 83 84
1.102033445 -3.917687713 -0.298749464 0.327352074 -3.248203119 1.499648833
85 86 87 88 89 90
-0.890431920 -0.558920081 1.383752821 -1.074837319 0.975316160 3.680147407
91 92 93 94 95 96
1.215231067 1.622776246 0.813537128 1.641560284 -4.562745226 -0.687648521
97 98 99 100 101 102
1.632068419 -2.402184389 1.325829281 -1.907001143 -0.571503363 2.487408619
103 104 105 106 107 108
1.321769392 -3.635814398 3.756898770 2.961772943 -0.307752202 4.828191058
109 110 111 112 113 114
-0.007464594 6.883104988 1.501059498 -0.284932116 -2.632831405 2.312650816
115 116 117 118 119 120
1.129105575 -0.311055792 0.566766396 -0.605236305 -4.832877727 1.970883325
121 122 123 124 125 126
-0.180161707 0.470799880 -1.848321120 -4.130297292 -1.477031257 -2.640493805
127 128 129 130 131 132
-2.078443952 -0.549463221 1.049642138 -0.062900913 -1.898542084 3.114739888
133 134 135 136 137 138
-2.540162718 2.913694665 0.576497078 1.940358610 7.787120415 1.630714153
139 140 141 142 143 144
-0.522909994 -2.372364280 -2.318812796 -2.951475756 3.036687563 -0.726015728
145 146 147 148 149 150
0.474826498 -1.910683143 0.777754796 -1.591254955 -2.512479618 2.432442316
151 152 153 154 155 156
-0.907761723 3.150406940 -2.118749542 -2.981756427 1.357346725 2.915983218
157 158 159 160 161 162
1.622776246 -0.112657687 1.049642138 -5.086535849 2.367868554 1.332696384
> postscript(file="/var/fisher/rcomp/tmp/6so6y1355570351.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 = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.534443837 NA
1 0.895049542 -1.534443837
2 -0.597305633 0.895049542
3 -2.132816701 -0.597305633
4 8.920543586 -2.132816701
5 1.082346638 8.920543586
6 9.517618389 1.082346638
7 -2.132488456 9.517618389
8 -2.595272479 -2.132488456
9 0.276600391 -2.595272479
10 0.053256469 0.276600391
11 -1.326690149 0.053256469
12 0.207885318 -1.326690149
13 2.419924132 0.207885318
14 0.126947417 2.419924132
15 1.727480854 0.126947417
16 1.551562981 1.727480854
17 0.887425878 1.551562981
18 -1.800938879 0.887425878
19 1.155727550 -1.800938879
20 -2.139477199 1.155727550
21 -1.374416461 -2.139477199
22 -0.487215326 -1.374416461
23 -0.649178072 -0.487215326
24 1.277771865 -0.649178072
25 -7.272086499 1.277771865
26 -0.044495654 -7.272086499
27 1.861018334 -0.044495654
28 1.646200090 1.861018334
29 -1.665757990 1.646200090
30 -2.907423046 -1.665757990
31 1.146721418 -2.907423046
32 -1.410335411 1.146721418
33 -1.270131391 -1.410335411
34 0.650759297 -1.270131391
35 -4.440806049 0.650759297
36 2.737720448 -4.440806049
37 0.477393497 2.737720448
38 -1.152843239 0.477393497
39 -2.832757268 -1.152843239
40 -2.331390845 -2.832757268
41 1.876891647 -2.331390845
42 -1.513222435 1.876891647
43 -0.002148086 -1.513222435
44 -4.211865518 -0.002148086
45 -3.216007031 -4.211865518
46 -3.412973158 -3.216007031
47 -1.584131132 -3.412973158
48 4.280193465 -1.584131132
49 -1.967129963 4.280193465
50 -1.313884503 -1.967129963
51 1.453497693 -1.313884503
52 3.105663018 1.453497693
53 -0.576992889 3.105663018
54 -4.217408546 -0.576992889
55 -1.079864573 -4.217408546
56 1.434886415 -1.079864573
57 -3.331287096 1.434886415
58 -3.666220073 -3.331287096
59 0.247878384 -3.666220073
60 2.391701102 0.247878384
61 -0.787714009 2.391701102
62 -3.188277177 -0.787714009
63 0.197538644 -3.188277177
64 0.447676561 0.197538644
65 -3.581263090 0.447676561
66 2.193706875 -3.581263090
67 -1.451442270 2.193706875
68 0.209560137 -1.451442270
69 2.855332981 0.209560137
70 -0.151982382 2.855332981
71 3.001161288 -0.151982382
72 1.347062140 3.001161288
73 -0.620400227 1.347062140
74 -1.888681690 -0.620400227
75 4.366212449 -1.888681690
76 2.250510058 4.366212449
77 2.898029089 2.250510058
78 1.102033445 2.898029089
79 -3.917687713 1.102033445
80 -0.298749464 -3.917687713
81 0.327352074 -0.298749464
82 -3.248203119 0.327352074
83 1.499648833 -3.248203119
84 -0.890431920 1.499648833
85 -0.558920081 -0.890431920
86 1.383752821 -0.558920081
87 -1.074837319 1.383752821
88 0.975316160 -1.074837319
89 3.680147407 0.975316160
90 1.215231067 3.680147407
91 1.622776246 1.215231067
92 0.813537128 1.622776246
93 1.641560284 0.813537128
94 -4.562745226 1.641560284
95 -0.687648521 -4.562745226
96 1.632068419 -0.687648521
97 -2.402184389 1.632068419
98 1.325829281 -2.402184389
99 -1.907001143 1.325829281
100 -0.571503363 -1.907001143
101 2.487408619 -0.571503363
102 1.321769392 2.487408619
103 -3.635814398 1.321769392
104 3.756898770 -3.635814398
105 2.961772943 3.756898770
106 -0.307752202 2.961772943
107 4.828191058 -0.307752202
108 -0.007464594 4.828191058
109 6.883104988 -0.007464594
110 1.501059498 6.883104988
111 -0.284932116 1.501059498
112 -2.632831405 -0.284932116
113 2.312650816 -2.632831405
114 1.129105575 2.312650816
115 -0.311055792 1.129105575
116 0.566766396 -0.311055792
117 -0.605236305 0.566766396
118 -4.832877727 -0.605236305
119 1.970883325 -4.832877727
120 -0.180161707 1.970883325
121 0.470799880 -0.180161707
122 -1.848321120 0.470799880
123 -4.130297292 -1.848321120
124 -1.477031257 -4.130297292
125 -2.640493805 -1.477031257
126 -2.078443952 -2.640493805
127 -0.549463221 -2.078443952
128 1.049642138 -0.549463221
129 -0.062900913 1.049642138
130 -1.898542084 -0.062900913
131 3.114739888 -1.898542084
132 -2.540162718 3.114739888
133 2.913694665 -2.540162718
134 0.576497078 2.913694665
135 1.940358610 0.576497078
136 7.787120415 1.940358610
137 1.630714153 7.787120415
138 -0.522909994 1.630714153
139 -2.372364280 -0.522909994
140 -2.318812796 -2.372364280
141 -2.951475756 -2.318812796
142 3.036687563 -2.951475756
143 -0.726015728 3.036687563
144 0.474826498 -0.726015728
145 -1.910683143 0.474826498
146 0.777754796 -1.910683143
147 -1.591254955 0.777754796
148 -2.512479618 -1.591254955
149 2.432442316 -2.512479618
150 -0.907761723 2.432442316
151 3.150406940 -0.907761723
152 -2.118749542 3.150406940
153 -2.981756427 -2.118749542
154 1.357346725 -2.981756427
155 2.915983218 1.357346725
156 1.622776246 2.915983218
157 -0.112657687 1.622776246
158 1.049642138 -0.112657687
159 -5.086535849 1.049642138
160 2.367868554 -5.086535849
161 1.332696384 2.367868554
162 NA 1.332696384
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.895049542 -1.534443837
[2,] -0.597305633 0.895049542
[3,] -2.132816701 -0.597305633
[4,] 8.920543586 -2.132816701
[5,] 1.082346638 8.920543586
[6,] 9.517618389 1.082346638
[7,] -2.132488456 9.517618389
[8,] -2.595272479 -2.132488456
[9,] 0.276600391 -2.595272479
[10,] 0.053256469 0.276600391
[11,] -1.326690149 0.053256469
[12,] 0.207885318 -1.326690149
[13,] 2.419924132 0.207885318
[14,] 0.126947417 2.419924132
[15,] 1.727480854 0.126947417
[16,] 1.551562981 1.727480854
[17,] 0.887425878 1.551562981
[18,] -1.800938879 0.887425878
[19,] 1.155727550 -1.800938879
[20,] -2.139477199 1.155727550
[21,] -1.374416461 -2.139477199
[22,] -0.487215326 -1.374416461
[23,] -0.649178072 -0.487215326
[24,] 1.277771865 -0.649178072
[25,] -7.272086499 1.277771865
[26,] -0.044495654 -7.272086499
[27,] 1.861018334 -0.044495654
[28,] 1.646200090 1.861018334
[29,] -1.665757990 1.646200090
[30,] -2.907423046 -1.665757990
[31,] 1.146721418 -2.907423046
[32,] -1.410335411 1.146721418
[33,] -1.270131391 -1.410335411
[34,] 0.650759297 -1.270131391
[35,] -4.440806049 0.650759297
[36,] 2.737720448 -4.440806049
[37,] 0.477393497 2.737720448
[38,] -1.152843239 0.477393497
[39,] -2.832757268 -1.152843239
[40,] -2.331390845 -2.832757268
[41,] 1.876891647 -2.331390845
[42,] -1.513222435 1.876891647
[43,] -0.002148086 -1.513222435
[44,] -4.211865518 -0.002148086
[45,] -3.216007031 -4.211865518
[46,] -3.412973158 -3.216007031
[47,] -1.584131132 -3.412973158
[48,] 4.280193465 -1.584131132
[49,] -1.967129963 4.280193465
[50,] -1.313884503 -1.967129963
[51,] 1.453497693 -1.313884503
[52,] 3.105663018 1.453497693
[53,] -0.576992889 3.105663018
[54,] -4.217408546 -0.576992889
[55,] -1.079864573 -4.217408546
[56,] 1.434886415 -1.079864573
[57,] -3.331287096 1.434886415
[58,] -3.666220073 -3.331287096
[59,] 0.247878384 -3.666220073
[60,] 2.391701102 0.247878384
[61,] -0.787714009 2.391701102
[62,] -3.188277177 -0.787714009
[63,] 0.197538644 -3.188277177
[64,] 0.447676561 0.197538644
[65,] -3.581263090 0.447676561
[66,] 2.193706875 -3.581263090
[67,] -1.451442270 2.193706875
[68,] 0.209560137 -1.451442270
[69,] 2.855332981 0.209560137
[70,] -0.151982382 2.855332981
[71,] 3.001161288 -0.151982382
[72,] 1.347062140 3.001161288
[73,] -0.620400227 1.347062140
[74,] -1.888681690 -0.620400227
[75,] 4.366212449 -1.888681690
[76,] 2.250510058 4.366212449
[77,] 2.898029089 2.250510058
[78,] 1.102033445 2.898029089
[79,] -3.917687713 1.102033445
[80,] -0.298749464 -3.917687713
[81,] 0.327352074 -0.298749464
[82,] -3.248203119 0.327352074
[83,] 1.499648833 -3.248203119
[84,] -0.890431920 1.499648833
[85,] -0.558920081 -0.890431920
[86,] 1.383752821 -0.558920081
[87,] -1.074837319 1.383752821
[88,] 0.975316160 -1.074837319
[89,] 3.680147407 0.975316160
[90,] 1.215231067 3.680147407
[91,] 1.622776246 1.215231067
[92,] 0.813537128 1.622776246
[93,] 1.641560284 0.813537128
[94,] -4.562745226 1.641560284
[95,] -0.687648521 -4.562745226
[96,] 1.632068419 -0.687648521
[97,] -2.402184389 1.632068419
[98,] 1.325829281 -2.402184389
[99,] -1.907001143 1.325829281
[100,] -0.571503363 -1.907001143
[101,] 2.487408619 -0.571503363
[102,] 1.321769392 2.487408619
[103,] -3.635814398 1.321769392
[104,] 3.756898770 -3.635814398
[105,] 2.961772943 3.756898770
[106,] -0.307752202 2.961772943
[107,] 4.828191058 -0.307752202
[108,] -0.007464594 4.828191058
[109,] 6.883104988 -0.007464594
[110,] 1.501059498 6.883104988
[111,] -0.284932116 1.501059498
[112,] -2.632831405 -0.284932116
[113,] 2.312650816 -2.632831405
[114,] 1.129105575 2.312650816
[115,] -0.311055792 1.129105575
[116,] 0.566766396 -0.311055792
[117,] -0.605236305 0.566766396
[118,] -4.832877727 -0.605236305
[119,] 1.970883325 -4.832877727
[120,] -0.180161707 1.970883325
[121,] 0.470799880 -0.180161707
[122,] -1.848321120 0.470799880
[123,] -4.130297292 -1.848321120
[124,] -1.477031257 -4.130297292
[125,] -2.640493805 -1.477031257
[126,] -2.078443952 -2.640493805
[127,] -0.549463221 -2.078443952
[128,] 1.049642138 -0.549463221
[129,] -0.062900913 1.049642138
[130,] -1.898542084 -0.062900913
[131,] 3.114739888 -1.898542084
[132,] -2.540162718 3.114739888
[133,] 2.913694665 -2.540162718
[134,] 0.576497078 2.913694665
[135,] 1.940358610 0.576497078
[136,] 7.787120415 1.940358610
[137,] 1.630714153 7.787120415
[138,] -0.522909994 1.630714153
[139,] -2.372364280 -0.522909994
[140,] -2.318812796 -2.372364280
[141,] -2.951475756 -2.318812796
[142,] 3.036687563 -2.951475756
[143,] -0.726015728 3.036687563
[144,] 0.474826498 -0.726015728
[145,] -1.910683143 0.474826498
[146,] 0.777754796 -1.910683143
[147,] -1.591254955 0.777754796
[148,] -2.512479618 -1.591254955
[149,] 2.432442316 -2.512479618
[150,] -0.907761723 2.432442316
[151,] 3.150406940 -0.907761723
[152,] -2.118749542 3.150406940
[153,] -2.981756427 -2.118749542
[154,] 1.357346725 -2.981756427
[155,] 2.915983218 1.357346725
[156,] 1.622776246 2.915983218
[157,] -0.112657687 1.622776246
[158,] 1.049642138 -0.112657687
[159,] -5.086535849 1.049642138
[160,] 2.367868554 -5.086535849
[161,] 1.332696384 2.367868554
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.895049542 -1.534443837
2 -0.597305633 0.895049542
3 -2.132816701 -0.597305633
4 8.920543586 -2.132816701
5 1.082346638 8.920543586
6 9.517618389 1.082346638
7 -2.132488456 9.517618389
8 -2.595272479 -2.132488456
9 0.276600391 -2.595272479
10 0.053256469 0.276600391
11 -1.326690149 0.053256469
12 0.207885318 -1.326690149
13 2.419924132 0.207885318
14 0.126947417 2.419924132
15 1.727480854 0.126947417
16 1.551562981 1.727480854
17 0.887425878 1.551562981
18 -1.800938879 0.887425878
19 1.155727550 -1.800938879
20 -2.139477199 1.155727550
21 -1.374416461 -2.139477199
22 -0.487215326 -1.374416461
23 -0.649178072 -0.487215326
24 1.277771865 -0.649178072
25 -7.272086499 1.277771865
26 -0.044495654 -7.272086499
27 1.861018334 -0.044495654
28 1.646200090 1.861018334
29 -1.665757990 1.646200090
30 -2.907423046 -1.665757990
31 1.146721418 -2.907423046
32 -1.410335411 1.146721418
33 -1.270131391 -1.410335411
34 0.650759297 -1.270131391
35 -4.440806049 0.650759297
36 2.737720448 -4.440806049
37 0.477393497 2.737720448
38 -1.152843239 0.477393497
39 -2.832757268 -1.152843239
40 -2.331390845 -2.832757268
41 1.876891647 -2.331390845
42 -1.513222435 1.876891647
43 -0.002148086 -1.513222435
44 -4.211865518 -0.002148086
45 -3.216007031 -4.211865518
46 -3.412973158 -3.216007031
47 -1.584131132 -3.412973158
48 4.280193465 -1.584131132
49 -1.967129963 4.280193465
50 -1.313884503 -1.967129963
51 1.453497693 -1.313884503
52 3.105663018 1.453497693
53 -0.576992889 3.105663018
54 -4.217408546 -0.576992889
55 -1.079864573 -4.217408546
56 1.434886415 -1.079864573
57 -3.331287096 1.434886415
58 -3.666220073 -3.331287096
59 0.247878384 -3.666220073
60 2.391701102 0.247878384
61 -0.787714009 2.391701102
62 -3.188277177 -0.787714009
63 0.197538644 -3.188277177
64 0.447676561 0.197538644
65 -3.581263090 0.447676561
66 2.193706875 -3.581263090
67 -1.451442270 2.193706875
68 0.209560137 -1.451442270
69 2.855332981 0.209560137
70 -0.151982382 2.855332981
71 3.001161288 -0.151982382
72 1.347062140 3.001161288
73 -0.620400227 1.347062140
74 -1.888681690 -0.620400227
75 4.366212449 -1.888681690
76 2.250510058 4.366212449
77 2.898029089 2.250510058
78 1.102033445 2.898029089
79 -3.917687713 1.102033445
80 -0.298749464 -3.917687713
81 0.327352074 -0.298749464
82 -3.248203119 0.327352074
83 1.499648833 -3.248203119
84 -0.890431920 1.499648833
85 -0.558920081 -0.890431920
86 1.383752821 -0.558920081
87 -1.074837319 1.383752821
88 0.975316160 -1.074837319
89 3.680147407 0.975316160
90 1.215231067 3.680147407
91 1.622776246 1.215231067
92 0.813537128 1.622776246
93 1.641560284 0.813537128
94 -4.562745226 1.641560284
95 -0.687648521 -4.562745226
96 1.632068419 -0.687648521
97 -2.402184389 1.632068419
98 1.325829281 -2.402184389
99 -1.907001143 1.325829281
100 -0.571503363 -1.907001143
101 2.487408619 -0.571503363
102 1.321769392 2.487408619
103 -3.635814398 1.321769392
104 3.756898770 -3.635814398
105 2.961772943 3.756898770
106 -0.307752202 2.961772943
107 4.828191058 -0.307752202
108 -0.007464594 4.828191058
109 6.883104988 -0.007464594
110 1.501059498 6.883104988
111 -0.284932116 1.501059498
112 -2.632831405 -0.284932116
113 2.312650816 -2.632831405
114 1.129105575 2.312650816
115 -0.311055792 1.129105575
116 0.566766396 -0.311055792
117 -0.605236305 0.566766396
118 -4.832877727 -0.605236305
119 1.970883325 -4.832877727
120 -0.180161707 1.970883325
121 0.470799880 -0.180161707
122 -1.848321120 0.470799880
123 -4.130297292 -1.848321120
124 -1.477031257 -4.130297292
125 -2.640493805 -1.477031257
126 -2.078443952 -2.640493805
127 -0.549463221 -2.078443952
128 1.049642138 -0.549463221
129 -0.062900913 1.049642138
130 -1.898542084 -0.062900913
131 3.114739888 -1.898542084
132 -2.540162718 3.114739888
133 2.913694665 -2.540162718
134 0.576497078 2.913694665
135 1.940358610 0.576497078
136 7.787120415 1.940358610
137 1.630714153 7.787120415
138 -0.522909994 1.630714153
139 -2.372364280 -0.522909994
140 -2.318812796 -2.372364280
141 -2.951475756 -2.318812796
142 3.036687563 -2.951475756
143 -0.726015728 3.036687563
144 0.474826498 -0.726015728
145 -1.910683143 0.474826498
146 0.777754796 -1.910683143
147 -1.591254955 0.777754796
148 -2.512479618 -1.591254955
149 2.432442316 -2.512479618
150 -0.907761723 2.432442316
151 3.150406940 -0.907761723
152 -2.118749542 3.150406940
153 -2.981756427 -2.118749542
154 1.357346725 -2.981756427
155 2.915983218 1.357346725
156 1.622776246 2.915983218
157 -0.112657687 1.622776246
158 1.049642138 -0.112657687
159 -5.086535849 1.049642138
160 2.367868554 -5.086535849
161 1.332696384 2.367868554
> 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/7rdb61355570351.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/8p50q1355570351.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/9ao9c1355570351.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/10op2w1355570351.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11ngdx1355570351.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12x6tv1355570351.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13ue921355570351.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14atzj1355570351.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/15fl7n1355570351.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/16ztxy1355570351.tab")
+ }
>
> try(system("convert tmp/1cvqt1355570351.ps tmp/1cvqt1355570351.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zm571355570351.ps tmp/2zm571355570351.png",intern=TRUE))
character(0)
> try(system("convert tmp/331bj1355570351.ps tmp/331bj1355570351.png",intern=TRUE))
character(0)
> try(system("convert tmp/4q2hf1355570351.ps tmp/4q2hf1355570351.png",intern=TRUE))
character(0)
> try(system("convert tmp/5o0uy1355570351.ps tmp/5o0uy1355570351.png",intern=TRUE))
character(0)
> try(system("convert tmp/6so6y1355570351.ps tmp/6so6y1355570351.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rdb61355570351.ps tmp/7rdb61355570351.png",intern=TRUE))
character(0)
> try(system("convert tmp/8p50q1355570351.ps tmp/8p50q1355570351.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ao9c1355570351.ps tmp/9ao9c1355570351.png",intern=TRUE))
character(0)
> try(system("convert tmp/10op2w1355570351.ps tmp/10op2w1355570351.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.090 1.596 9.684