R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(09
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+ ,69)
+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('Month'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('Month','Connected','Separate','Learning','Software','Happiness','Depression','Belonging'),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 = '4'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '4'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Learning Month Connected Separate Software Happiness Depression Belonging
1 13 9 41 38 12 14 12 53
2 16 9 39 32 11 18 11 86
3 19 9 30 35 15 11 14 66
4 15 9 31 33 6 12 12 67
5 14 9 34 37 13 16 21 76
6 13 9 35 29 10 18 12 78
7 19 9 39 31 12 14 22 53
8 15 9 34 36 14 14 11 80
9 14 9 36 35 12 15 10 74
10 15 9 37 38 6 15 13 76
11 16 9 38 31 10 17 10 79
12 16 9 36 34 12 19 8 54
13 16 9 38 35 12 10 15 67
14 16 9 39 38 11 16 14 54
15 17 9 33 37 15 18 10 87
16 15 9 32 33 12 14 14 58
17 15 9 36 32 10 14 14 75
18 20 9 38 38 12 17 11 88
19 18 9 39 38 11 14 10 64
20 16 9 32 32 12 16 13 57
21 16 9 32 33 11 18 7 66
22 16 9 31 31 12 11 14 68
23 19 9 39 38 13 14 12 54
24 16 9 37 39 11 12 14 56
25 17 9 39 32 9 17 11 86
26 17 9 41 32 13 9 9 80
27 16 9 36 35 10 16 11 76
28 15 9 33 37 14 14 15 69
29 16 9 33 33 12 15 14 78
30 14 9 34 33 10 11 13 67
31 15 9 31 28 12 16 9 80
32 12 9 27 32 8 13 15 54
33 14 9 37 31 10 17 10 71
34 16 9 34 37 12 15 11 84
35 14 9 34 30 12 14 13 74
36 7 9 32 33 7 16 8 71
37 10 9 29 31 6 9 20 63
38 14 9 36 33 12 15 12 71
39 16 9 29 31 10 17 10 76
40 16 9 35 33 10 13 10 69
41 16 9 37 32 10 15 9 74
42 14 9 34 33 12 16 14 75
43 20 9 38 32 15 16 8 54
44 14 9 35 33 10 12 14 52
45 14 9 38 28 10 12 11 69
46 11 9 37 35 12 11 13 68
47 14 9 38 39 13 15 9 65
48 15 9 33 34 11 15 11 75
49 16 9 36 38 11 17 15 74
50 14 9 38 32 12 13 11 75
51 16 9 32 38 14 16 10 72
52 14 9 32 30 10 14 14 67
53 12 9 32 33 12 11 18 63
54 16 9 34 38 13 12 14 62
55 9 9 32 32 5 12 11 63
56 14 9 37 32 6 15 12 76
57 16 9 39 34 12 16 13 74
58 16 9 29 34 12 15 9 67
59 15 9 37 36 11 12 10 73
60 16 9 35 34 10 12 15 70
61 12 9 30 28 7 8 20 53
62 16 9 38 34 12 13 12 77
63 16 9 34 35 14 11 12 77
64 14 10 31 35 11 14 14 52
65 16 10 34 31 12 15 13 54
66 17 10 35 37 13 10 11 80
67 18 10 36 35 14 11 17 66
68 18 10 30 27 11 12 12 73
69 12 10 39 40 12 15 13 63
70 16 10 35 37 12 15 14 69
71 10 10 38 36 8 14 13 67
72 14 10 31 38 11 16 15 54
73 18 10 34 39 14 15 13 81
74 18 10 38 41 14 15 10 69
75 16 10 34 27 12 13 11 84
76 17 10 39 30 9 12 19 80
77 16 10 37 37 13 17 13 70
78 16 10 34 31 11 13 17 69
79 13 10 28 31 12 15 13 77
80 16 10 37 27 12 13 9 54
81 16 10 33 36 12 15 11 79
82 20 10 37 38 12 16 10 30
83 16 10 35 37 12 15 9 71
84 15 10 37 33 12 16 12 73
85 15 10 32 34 11 15 12 72
86 16 10 33 31 10 14 13 77
87 14 10 38 39 9 15 13 75
88 16 10 33 34 12 14 12 69
89 16 10 29 32 12 13 15 54
90 15 10 33 33 12 7 22 70
91 12 10 31 36 9 17 13 73
92 17 10 36 32 15 13 15 54
93 16 10 35 41 12 15 13 77
94 15 10 32 28 12 14 15 82
95 13 10 29 30 12 13 10 80
96 16 10 39 36 10 16 11 80
97 16 10 37 35 13 12 16 69
98 16 10 35 31 9 14 11 78
99 16 10 37 34 12 17 11 81
100 14 10 32 36 10 15 10 76
101 16 10 38 36 14 17 10 76
102 16 10 37 35 11 12 16 73
103 20 10 36 37 15 16 12 85
104 15 10 32 28 11 11 11 66
105 16 10 33 39 11 15 16 79
106 13 10 40 32 12 9 19 68
107 17 10 38 35 12 16 11 76
108 16 10 41 39 12 15 16 71
109 16 10 36 35 11 10 15 54
110 12 10 43 42 7 10 24 46
111 16 10 30 34 12 15 14 82
112 16 10 31 33 14 11 15 74
113 17 10 32 41 11 13 11 88
114 13 10 32 33 11 14 15 38
115 12 10 37 34 10 18 12 76
116 18 10 37 32 13 16 10 86
117 14 10 33 40 13 14 14 54
118 14 10 34 40 8 14 13 70
119 13 10 33 35 11 14 9 69
120 16 10 38 36 12 14 15 90
121 13 10 33 37 11 12 15 54
122 16 10 31 27 13 14 14 76
123 13 10 38 39 12 15 11 89
124 16 10 37 38 14 15 8 76
125 15 10 33 31 13 15 11 73
126 16 10 31 33 15 13 11 79
127 15 10 39 32 10 17 8 90
128 17 10 44 39 11 17 10 74
129 15 10 33 36 9 19 11 81
130 12 10 35 33 11 15 13 72
131 16 10 32 33 10 13 11 71
132 10 10 28 32 11 9 20 66
133 16 10 40 37 8 15 10 77
134 12 10 27 30 11 15 15 65
135 14 10 37 38 12 15 12 74
136 15 10 32 29 12 16 14 82
137 13 10 28 22 9 11 23 54
138 15 10 34 35 11 14 14 63
139 11 10 30 35 10 11 16 54
140 12 10 35 34 8 15 11 64
141 8 10 31 35 9 13 12 69
142 16 10 32 34 8 15 10 54
143 15 10 30 34 9 16 14 84
144 17 10 30 35 15 14 12 86
145 16 10 31 23 11 15 12 77
146 10 10 40 31 8 16 11 89
147 18 10 32 27 13 16 12 76
148 13 10 36 36 12 11 13 60
149 16 10 32 31 12 12 11 75
150 13 10 35 32 9 9 19 73
151 10 10 38 39 7 16 12 85
152 15 10 42 37 13 13 17 79
153 16 10 34 38 9 16 9 71
154 16 10 35 39 6 12 12 72
155 14 10 35 34 8 9 19 69
156 10 9 33 31 8 13 18 78
157 17 10 36 32 15 13 15 54
158 13 10 32 37 6 14 14 69
159 15 10 33 36 9 19 11 81
160 16 10 34 32 11 13 9 84
161 12 10 32 35 8 12 18 84
162 13 10 34 36 8 13 16 69
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Month Connected Separate Software Happiness
5.719292 0.003708 0.115338 -0.023782 0.545418 0.062911
Depression Belonging
-0.076918 0.001328
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.9621 -1.1329 0.1883 1.1035 4.0576
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.719292 3.711765 1.541 0.1254
Month 0.003708 0.305395 0.012 0.9903
Connected 0.115338 0.047230 2.442 0.0157 *
Separate -0.023782 0.045119 -0.527 0.5989
Software 0.545418 0.069052 7.899 4.98e-13 ***
Happiness 0.062911 0.076446 0.823 0.4118
Depression -0.076918 0.056487 -1.362 0.1753
Belonging 0.001328 0.014562 0.091 0.9275
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.854 on 154 degrees of freedom
Multiple R-squared: 0.3539, Adjusted R-squared: 0.3246
F-statistic: 12.05 on 7 and 154 DF, p-value: 3.226e-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,] 0.44566459 0.89132918 0.55433541
[2,] 0.90251274 0.19497452 0.09748726
[3,] 0.84256894 0.31486211 0.15743106
[4,] 0.78516119 0.42967762 0.21483881
[5,] 0.79013350 0.41973301 0.20986650
[6,] 0.75724689 0.48550622 0.24275311
[7,] 0.68067299 0.63865401 0.31932701
[8,] 0.91667682 0.16664636 0.08332318
[9,] 0.92001653 0.15996693 0.07998347
[10,] 0.88860602 0.22278795 0.11139398
[11,] 0.85286344 0.29427313 0.14713656
[12,] 0.80615169 0.38769661 0.19384831
[13,] 0.82403910 0.35192180 0.17596090
[14,] 0.78111184 0.43777632 0.21888816
[15,] 0.76975901 0.46048198 0.23024099
[16,] 0.71588819 0.56822362 0.28411181
[17,] 0.66288212 0.67423576 0.33711788
[18,] 0.63532397 0.72935205 0.36467603
[19,] 0.57475702 0.85048596 0.42524298
[20,] 0.55184247 0.89631506 0.44815753
[21,] 0.48927301 0.97854603 0.51072699
[22,] 0.46758396 0.93516791 0.53241604
[23,] 0.43998626 0.87997252 0.56001374
[24,] 0.38230582 0.76461164 0.61769418
[25,] 0.36169899 0.72339797 0.63830101
[26,] 0.85706396 0.28587208 0.14293604
[27,] 0.84669917 0.30660166 0.15330083
[28,] 0.84237874 0.31524251 0.15762126
[29,] 0.86081490 0.27837020 0.13918510
[30,] 0.84558793 0.30882414 0.15441207
[31,] 0.82020360 0.35959281 0.17979640
[32,] 0.80455019 0.39089962 0.19544981
[33,] 0.81779882 0.36440236 0.18220118
[34,] 0.78535643 0.42928713 0.21464357
[35,] 0.75618726 0.48762549 0.24381274
[36,] 0.91085440 0.17829120 0.08914560
[37,] 0.92775871 0.14448259 0.07224129
[38,] 0.90844246 0.18311508 0.09155754
[39,] 0.89185942 0.21628117 0.10814058
[40,] 0.88983516 0.22032967 0.11016484
[41,] 0.86454441 0.27091118 0.13545559
[42,] 0.83519710 0.32960580 0.16480290
[43,] 0.85694068 0.28611864 0.14305932
[44,] 0.82823961 0.34352078 0.17176039
[45,] 0.84410586 0.31178827 0.15589414
[46,] 0.82476367 0.35047265 0.17523633
[47,] 0.79293176 0.41413648 0.20706824
[48,] 0.77080715 0.45838571 0.22919285
[49,] 0.73237750 0.53524499 0.26762250
[50,] 0.73483674 0.53032652 0.26516326
[51,] 0.70402436 0.59195129 0.29597564
[52,] 0.66969161 0.66061678 0.33030839
[53,] 0.63484198 0.73031604 0.36515802
[54,] 0.58919087 0.82161825 0.41080913
[55,] 0.54722593 0.90554815 0.45277407
[56,] 0.51087030 0.97825940 0.48912970
[57,] 0.49108291 0.98216583 0.50891709
[58,] 0.56779289 0.86441423 0.43220711
[59,] 0.75713912 0.48572175 0.24286088
[60,] 0.72057728 0.55884543 0.27942272
[61,] 0.82080583 0.35838835 0.17919417
[62,] 0.78886745 0.42226510 0.21113255
[63,] 0.77868928 0.44262144 0.22131072
[64,] 0.75401547 0.49196907 0.24598453
[65,] 0.71658300 0.56683401 0.28341700
[66,] 0.76576856 0.46846288 0.23423144
[67,] 0.73150598 0.53698803 0.26849402
[68,] 0.71233937 0.57532125 0.28766063
[69,] 0.71976462 0.56047077 0.28023538
[70,] 0.67910133 0.64179734 0.32089867
[71,] 0.63881668 0.72236663 0.36118332
[72,] 0.79877615 0.40244769 0.20122385
[73,] 0.76456775 0.47086449 0.23543225
[74,] 0.73972064 0.52055872 0.26027936
[75,] 0.70050885 0.59898231 0.29949115
[76,] 0.68869471 0.62261057 0.31130529
[77,] 0.64706075 0.70587851 0.35293925
[78,] 0.60775875 0.78448250 0.39224125
[79,] 0.58935070 0.82129859 0.41064930
[80,] 0.55362224 0.89275551 0.44637776
[81,] 0.54400559 0.91198881 0.45599441
[82,] 0.50898456 0.98203088 0.49101544
[83,] 0.46619870 0.93239740 0.53380130
[84,] 0.42206902 0.84413803 0.57793098
[85,] 0.43990047 0.87980094 0.56009953
[86,] 0.40307420 0.80614841 0.59692580
[87,] 0.36337638 0.72675277 0.63662362
[88,] 0.35868525 0.71737049 0.64131475
[89,] 0.31564304 0.63128608 0.68435696
[90,] 0.27650022 0.55300045 0.72349978
[91,] 0.25220984 0.50441967 0.74779016
[92,] 0.23351163 0.46702327 0.76648837
[93,] 0.28331297 0.56662595 0.71668703
[94,] 0.24426884 0.48853767 0.75573116
[95,] 0.23528000 0.47055999 0.76472000
[96,] 0.25280031 0.50560061 0.74719969
[97,] 0.22973207 0.45946413 0.77026793
[98,] 0.20634181 0.41268363 0.79365819
[99,] 0.19969497 0.39938994 0.80030503
[100,] 0.18608617 0.37217234 0.81391383
[101,] 0.16468358 0.32936716 0.83531642
[102,] 0.13859354 0.27718707 0.86140646
[103,] 0.15898294 0.31796587 0.84101706
[104,] 0.13946753 0.27893507 0.86053247
[105,] 0.18091293 0.36182586 0.81908707
[106,] 0.17092681 0.34185361 0.82907319
[107,] 0.14877556 0.29755111 0.85122444
[108,] 0.12856541 0.25713082 0.87143459
[109,] 0.13115767 0.26231533 0.86884233
[110,] 0.12058898 0.24117795 0.87941102
[111,] 0.10209612 0.20419224 0.89790388
[112,] 0.08204332 0.16408664 0.91795668
[113,] 0.09184521 0.18369042 0.90815479
[114,] 0.07395730 0.14791459 0.92604270
[115,] 0.06017848 0.12035697 0.93982152
[116,] 0.04588175 0.09176350 0.95411825
[117,] 0.03499971 0.06999941 0.96500029
[118,] 0.02903484 0.05806969 0.97096516
[119,] 0.02225442 0.04450885 0.97774558
[120,] 0.03043501 0.06087002 0.96956499
[121,] 0.02648556 0.05297111 0.97351444
[122,] 0.04092252 0.08184504 0.95907748
[123,] 0.04534770 0.09069539 0.95465230
[124,] 0.06013881 0.12027763 0.93986119
[125,] 0.04793535 0.09587070 0.95206465
[126,] 0.03413394 0.06826787 0.96586606
[127,] 0.02368708 0.04737417 0.97631292
[128,] 0.01578480 0.03156960 0.98421520
[129,] 0.03159172 0.06318344 0.96840828
[130,] 0.02762746 0.05525493 0.97237254
[131,] 0.64132931 0.71734139 0.35867069
[132,] 0.57775944 0.84448112 0.42224056
[133,] 0.49704773 0.99409546 0.50295227
[134,] 0.42928352 0.85856703 0.57071648
[135,] 0.34258214 0.68516428 0.65741786
[136,] 0.42795724 0.85591447 0.57204276
[137,] 0.35129202 0.70258404 0.64870798
[138,] 0.70313265 0.59373469 0.29686735
[139,] 0.62276424 0.75447152 0.37723576
[140,] 0.47709844 0.95419687 0.52290156
[141,] 0.82949704 0.34100592 0.17050296
> postscript(file="/var/fisher/rcomp/tmp/16i1n1352140704.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/25nb81352140704.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/33scl1352140704.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4606v1352140704.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5h9j91352140704.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
-3.15091700 0.11009654 2.73550241 3.26328703 -1.37685456 -1.86693937
7 8 9 10 11 12
3.68246312 -1.59472731 -1.89021036 2.56640623 0.74235855 -0.29291259
13 14 15 16 17 18
0.58755494 0.65186348 -0.33888248 -0.08459252 0.49853387 3.88296605
19 20 21 22 23 24
2.45673242 0.69021317 0.66013102 1.15863284 2.53301261 1.15530891
25 26 27 28 29 30
2.26384349 0.20891368 1.21197715 -1.13332631 0.71059893 -0.12456974
31 32 33 34 35 36
-0.62779556 -0.20487258 -1.13167966 0.45166851 -1.48478175 -5.96209686
37 38 39 40 41 42
-0.74421236 -1.77995498 1.78438305 1.40086024 0.93702229 -1.46366574
43 44 45 46 47 48
1.98132502 -0.20598029 -0.92423669 -4.51518241 -2.63614039 0.05302891
49 50 51 52 53 54
0.98532445 -1.99082167 -0.50860253 -0.07705580 -2.59482731 0.37873582
55 56 57 58 59 60
-2.40202143 1.34679313 -0.09216262 0.82574977 -0.24628720 1.87081647
61 62 63 64 65 66
0.59987504 0.13100550 -0.34887518 -0.37201191 0.49894158 1.10707044
67 68 69 70 71 72
1.81593978 3.49716340 -3.87565718 0.58329692 -3.61617818 -0.35222390
73 74 75 76 77 78
1.56250947 0.93390448 0.33595705 3.15043781 -0.39686463 1.45793409
79 80 81 82 83 84
-1.83957530 -0.12405298 0.54615560 4.05761198 0.19605001 -0.96456786
85 86 87 88 89 90
0.24556068 1.73748248 -0.16378356 0.65169958 1.37907124 0.83614666
91 92 93 94 95 96
-1.55093182 -0.06454781 0.59088472 -0.16216688 -2.08761252 0.88072591
97 98 99 100 101 102
0.10220703 1.89706051 -0.09123834 -0.32060463 -1.32012554 1.18773119
103 104 105 106 107 108
2.59370978 0.28555882 1.54751205 -2.34892086 0.88675711 0.09001526
109 110 111 112 113 114
1.37720437 -0.37912317 1.07137470 0.18060366 2.43969361 -1.43940461
115 116 117 118 119 120
-2.97975486 1.29513128 -1.57726728 0.93631912 -2.00985442 0.32544204
121 122 123 124 125 126
-1.35503878 0.31502018 -2.97246607 -1.18523747 -1.11020693 -0.80494876
127 128 129 130 131 132
-0.52134935 0.69810510 0.92811054 -3.04731713 1.81742770 -3.33987460
133 134 135 136 137 138
1.86998332 -2.03282957 -1.78407261 -0.34112422 0.63400553 0.26736664
139 140 141 142 143 144
-2.37134318 -1.53049285 -5.39467725 2.75188242 1.64206210 0.36266545
145 146 147 148 149 150
1.09265119 -4.27464017 1.92002434 -2.36914636 0.73662543 -0.14261746
151 152 153 154 155 156
-3.22605638 -1.42618975 1.90851371 3.93428239 1.45567756 -2.72179940
157 158 159 160 161 162
-0.06454781 1.26472965 0.92811054 0.84645118 -0.46009701 0.13618254
> postscript(file="/var/fisher/rcomp/tmp/6yh2g1352140704.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 -3.15091700 NA
1 0.11009654 -3.15091700
2 2.73550241 0.11009654
3 3.26328703 2.73550241
4 -1.37685456 3.26328703
5 -1.86693937 -1.37685456
6 3.68246312 -1.86693937
7 -1.59472731 3.68246312
8 -1.89021036 -1.59472731
9 2.56640623 -1.89021036
10 0.74235855 2.56640623
11 -0.29291259 0.74235855
12 0.58755494 -0.29291259
13 0.65186348 0.58755494
14 -0.33888248 0.65186348
15 -0.08459252 -0.33888248
16 0.49853387 -0.08459252
17 3.88296605 0.49853387
18 2.45673242 3.88296605
19 0.69021317 2.45673242
20 0.66013102 0.69021317
21 1.15863284 0.66013102
22 2.53301261 1.15863284
23 1.15530891 2.53301261
24 2.26384349 1.15530891
25 0.20891368 2.26384349
26 1.21197715 0.20891368
27 -1.13332631 1.21197715
28 0.71059893 -1.13332631
29 -0.12456974 0.71059893
30 -0.62779556 -0.12456974
31 -0.20487258 -0.62779556
32 -1.13167966 -0.20487258
33 0.45166851 -1.13167966
34 -1.48478175 0.45166851
35 -5.96209686 -1.48478175
36 -0.74421236 -5.96209686
37 -1.77995498 -0.74421236
38 1.78438305 -1.77995498
39 1.40086024 1.78438305
40 0.93702229 1.40086024
41 -1.46366574 0.93702229
42 1.98132502 -1.46366574
43 -0.20598029 1.98132502
44 -0.92423669 -0.20598029
45 -4.51518241 -0.92423669
46 -2.63614039 -4.51518241
47 0.05302891 -2.63614039
48 0.98532445 0.05302891
49 -1.99082167 0.98532445
50 -0.50860253 -1.99082167
51 -0.07705580 -0.50860253
52 -2.59482731 -0.07705580
53 0.37873582 -2.59482731
54 -2.40202143 0.37873582
55 1.34679313 -2.40202143
56 -0.09216262 1.34679313
57 0.82574977 -0.09216262
58 -0.24628720 0.82574977
59 1.87081647 -0.24628720
60 0.59987504 1.87081647
61 0.13100550 0.59987504
62 -0.34887518 0.13100550
63 -0.37201191 -0.34887518
64 0.49894158 -0.37201191
65 1.10707044 0.49894158
66 1.81593978 1.10707044
67 3.49716340 1.81593978
68 -3.87565718 3.49716340
69 0.58329692 -3.87565718
70 -3.61617818 0.58329692
71 -0.35222390 -3.61617818
72 1.56250947 -0.35222390
73 0.93390448 1.56250947
74 0.33595705 0.93390448
75 3.15043781 0.33595705
76 -0.39686463 3.15043781
77 1.45793409 -0.39686463
78 -1.83957530 1.45793409
79 -0.12405298 -1.83957530
80 0.54615560 -0.12405298
81 4.05761198 0.54615560
82 0.19605001 4.05761198
83 -0.96456786 0.19605001
84 0.24556068 -0.96456786
85 1.73748248 0.24556068
86 -0.16378356 1.73748248
87 0.65169958 -0.16378356
88 1.37907124 0.65169958
89 0.83614666 1.37907124
90 -1.55093182 0.83614666
91 -0.06454781 -1.55093182
92 0.59088472 -0.06454781
93 -0.16216688 0.59088472
94 -2.08761252 -0.16216688
95 0.88072591 -2.08761252
96 0.10220703 0.88072591
97 1.89706051 0.10220703
98 -0.09123834 1.89706051
99 -0.32060463 -0.09123834
100 -1.32012554 -0.32060463
101 1.18773119 -1.32012554
102 2.59370978 1.18773119
103 0.28555882 2.59370978
104 1.54751205 0.28555882
105 -2.34892086 1.54751205
106 0.88675711 -2.34892086
107 0.09001526 0.88675711
108 1.37720437 0.09001526
109 -0.37912317 1.37720437
110 1.07137470 -0.37912317
111 0.18060366 1.07137470
112 2.43969361 0.18060366
113 -1.43940461 2.43969361
114 -2.97975486 -1.43940461
115 1.29513128 -2.97975486
116 -1.57726728 1.29513128
117 0.93631912 -1.57726728
118 -2.00985442 0.93631912
119 0.32544204 -2.00985442
120 -1.35503878 0.32544204
121 0.31502018 -1.35503878
122 -2.97246607 0.31502018
123 -1.18523747 -2.97246607
124 -1.11020693 -1.18523747
125 -0.80494876 -1.11020693
126 -0.52134935 -0.80494876
127 0.69810510 -0.52134935
128 0.92811054 0.69810510
129 -3.04731713 0.92811054
130 1.81742770 -3.04731713
131 -3.33987460 1.81742770
132 1.86998332 -3.33987460
133 -2.03282957 1.86998332
134 -1.78407261 -2.03282957
135 -0.34112422 -1.78407261
136 0.63400553 -0.34112422
137 0.26736664 0.63400553
138 -2.37134318 0.26736664
139 -1.53049285 -2.37134318
140 -5.39467725 -1.53049285
141 2.75188242 -5.39467725
142 1.64206210 2.75188242
143 0.36266545 1.64206210
144 1.09265119 0.36266545
145 -4.27464017 1.09265119
146 1.92002434 -4.27464017
147 -2.36914636 1.92002434
148 0.73662543 -2.36914636
149 -0.14261746 0.73662543
150 -3.22605638 -0.14261746
151 -1.42618975 -3.22605638
152 1.90851371 -1.42618975
153 3.93428239 1.90851371
154 1.45567756 3.93428239
155 -2.72179940 1.45567756
156 -0.06454781 -2.72179940
157 1.26472965 -0.06454781
158 0.92811054 1.26472965
159 0.84645118 0.92811054
160 -0.46009701 0.84645118
161 0.13618254 -0.46009701
162 NA 0.13618254
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.11009654 -3.15091700
[2,] 2.73550241 0.11009654
[3,] 3.26328703 2.73550241
[4,] -1.37685456 3.26328703
[5,] -1.86693937 -1.37685456
[6,] 3.68246312 -1.86693937
[7,] -1.59472731 3.68246312
[8,] -1.89021036 -1.59472731
[9,] 2.56640623 -1.89021036
[10,] 0.74235855 2.56640623
[11,] -0.29291259 0.74235855
[12,] 0.58755494 -0.29291259
[13,] 0.65186348 0.58755494
[14,] -0.33888248 0.65186348
[15,] -0.08459252 -0.33888248
[16,] 0.49853387 -0.08459252
[17,] 3.88296605 0.49853387
[18,] 2.45673242 3.88296605
[19,] 0.69021317 2.45673242
[20,] 0.66013102 0.69021317
[21,] 1.15863284 0.66013102
[22,] 2.53301261 1.15863284
[23,] 1.15530891 2.53301261
[24,] 2.26384349 1.15530891
[25,] 0.20891368 2.26384349
[26,] 1.21197715 0.20891368
[27,] -1.13332631 1.21197715
[28,] 0.71059893 -1.13332631
[29,] -0.12456974 0.71059893
[30,] -0.62779556 -0.12456974
[31,] -0.20487258 -0.62779556
[32,] -1.13167966 -0.20487258
[33,] 0.45166851 -1.13167966
[34,] -1.48478175 0.45166851
[35,] -5.96209686 -1.48478175
[36,] -0.74421236 -5.96209686
[37,] -1.77995498 -0.74421236
[38,] 1.78438305 -1.77995498
[39,] 1.40086024 1.78438305
[40,] 0.93702229 1.40086024
[41,] -1.46366574 0.93702229
[42,] 1.98132502 -1.46366574
[43,] -0.20598029 1.98132502
[44,] -0.92423669 -0.20598029
[45,] -4.51518241 -0.92423669
[46,] -2.63614039 -4.51518241
[47,] 0.05302891 -2.63614039
[48,] 0.98532445 0.05302891
[49,] -1.99082167 0.98532445
[50,] -0.50860253 -1.99082167
[51,] -0.07705580 -0.50860253
[52,] -2.59482731 -0.07705580
[53,] 0.37873582 -2.59482731
[54,] -2.40202143 0.37873582
[55,] 1.34679313 -2.40202143
[56,] -0.09216262 1.34679313
[57,] 0.82574977 -0.09216262
[58,] -0.24628720 0.82574977
[59,] 1.87081647 -0.24628720
[60,] 0.59987504 1.87081647
[61,] 0.13100550 0.59987504
[62,] -0.34887518 0.13100550
[63,] -0.37201191 -0.34887518
[64,] 0.49894158 -0.37201191
[65,] 1.10707044 0.49894158
[66,] 1.81593978 1.10707044
[67,] 3.49716340 1.81593978
[68,] -3.87565718 3.49716340
[69,] 0.58329692 -3.87565718
[70,] -3.61617818 0.58329692
[71,] -0.35222390 -3.61617818
[72,] 1.56250947 -0.35222390
[73,] 0.93390448 1.56250947
[74,] 0.33595705 0.93390448
[75,] 3.15043781 0.33595705
[76,] -0.39686463 3.15043781
[77,] 1.45793409 -0.39686463
[78,] -1.83957530 1.45793409
[79,] -0.12405298 -1.83957530
[80,] 0.54615560 -0.12405298
[81,] 4.05761198 0.54615560
[82,] 0.19605001 4.05761198
[83,] -0.96456786 0.19605001
[84,] 0.24556068 -0.96456786
[85,] 1.73748248 0.24556068
[86,] -0.16378356 1.73748248
[87,] 0.65169958 -0.16378356
[88,] 1.37907124 0.65169958
[89,] 0.83614666 1.37907124
[90,] -1.55093182 0.83614666
[91,] -0.06454781 -1.55093182
[92,] 0.59088472 -0.06454781
[93,] -0.16216688 0.59088472
[94,] -2.08761252 -0.16216688
[95,] 0.88072591 -2.08761252
[96,] 0.10220703 0.88072591
[97,] 1.89706051 0.10220703
[98,] -0.09123834 1.89706051
[99,] -0.32060463 -0.09123834
[100,] -1.32012554 -0.32060463
[101,] 1.18773119 -1.32012554
[102,] 2.59370978 1.18773119
[103,] 0.28555882 2.59370978
[104,] 1.54751205 0.28555882
[105,] -2.34892086 1.54751205
[106,] 0.88675711 -2.34892086
[107,] 0.09001526 0.88675711
[108,] 1.37720437 0.09001526
[109,] -0.37912317 1.37720437
[110,] 1.07137470 -0.37912317
[111,] 0.18060366 1.07137470
[112,] 2.43969361 0.18060366
[113,] -1.43940461 2.43969361
[114,] -2.97975486 -1.43940461
[115,] 1.29513128 -2.97975486
[116,] -1.57726728 1.29513128
[117,] 0.93631912 -1.57726728
[118,] -2.00985442 0.93631912
[119,] 0.32544204 -2.00985442
[120,] -1.35503878 0.32544204
[121,] 0.31502018 -1.35503878
[122,] -2.97246607 0.31502018
[123,] -1.18523747 -2.97246607
[124,] -1.11020693 -1.18523747
[125,] -0.80494876 -1.11020693
[126,] -0.52134935 -0.80494876
[127,] 0.69810510 -0.52134935
[128,] 0.92811054 0.69810510
[129,] -3.04731713 0.92811054
[130,] 1.81742770 -3.04731713
[131,] -3.33987460 1.81742770
[132,] 1.86998332 -3.33987460
[133,] -2.03282957 1.86998332
[134,] -1.78407261 -2.03282957
[135,] -0.34112422 -1.78407261
[136,] 0.63400553 -0.34112422
[137,] 0.26736664 0.63400553
[138,] -2.37134318 0.26736664
[139,] -1.53049285 -2.37134318
[140,] -5.39467725 -1.53049285
[141,] 2.75188242 -5.39467725
[142,] 1.64206210 2.75188242
[143,] 0.36266545 1.64206210
[144,] 1.09265119 0.36266545
[145,] -4.27464017 1.09265119
[146,] 1.92002434 -4.27464017
[147,] -2.36914636 1.92002434
[148,] 0.73662543 -2.36914636
[149,] -0.14261746 0.73662543
[150,] -3.22605638 -0.14261746
[151,] -1.42618975 -3.22605638
[152,] 1.90851371 -1.42618975
[153,] 3.93428239 1.90851371
[154,] 1.45567756 3.93428239
[155,] -2.72179940 1.45567756
[156,] -0.06454781 -2.72179940
[157,] 1.26472965 -0.06454781
[158,] 0.92811054 1.26472965
[159,] 0.84645118 0.92811054
[160,] -0.46009701 0.84645118
[161,] 0.13618254 -0.46009701
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.11009654 -3.15091700
2 2.73550241 0.11009654
3 3.26328703 2.73550241
4 -1.37685456 3.26328703
5 -1.86693937 -1.37685456
6 3.68246312 -1.86693937
7 -1.59472731 3.68246312
8 -1.89021036 -1.59472731
9 2.56640623 -1.89021036
10 0.74235855 2.56640623
11 -0.29291259 0.74235855
12 0.58755494 -0.29291259
13 0.65186348 0.58755494
14 -0.33888248 0.65186348
15 -0.08459252 -0.33888248
16 0.49853387 -0.08459252
17 3.88296605 0.49853387
18 2.45673242 3.88296605
19 0.69021317 2.45673242
20 0.66013102 0.69021317
21 1.15863284 0.66013102
22 2.53301261 1.15863284
23 1.15530891 2.53301261
24 2.26384349 1.15530891
25 0.20891368 2.26384349
26 1.21197715 0.20891368
27 -1.13332631 1.21197715
28 0.71059893 -1.13332631
29 -0.12456974 0.71059893
30 -0.62779556 -0.12456974
31 -0.20487258 -0.62779556
32 -1.13167966 -0.20487258
33 0.45166851 -1.13167966
34 -1.48478175 0.45166851
35 -5.96209686 -1.48478175
36 -0.74421236 -5.96209686
37 -1.77995498 -0.74421236
38 1.78438305 -1.77995498
39 1.40086024 1.78438305
40 0.93702229 1.40086024
41 -1.46366574 0.93702229
42 1.98132502 -1.46366574
43 -0.20598029 1.98132502
44 -0.92423669 -0.20598029
45 -4.51518241 -0.92423669
46 -2.63614039 -4.51518241
47 0.05302891 -2.63614039
48 0.98532445 0.05302891
49 -1.99082167 0.98532445
50 -0.50860253 -1.99082167
51 -0.07705580 -0.50860253
52 -2.59482731 -0.07705580
53 0.37873582 -2.59482731
54 -2.40202143 0.37873582
55 1.34679313 -2.40202143
56 -0.09216262 1.34679313
57 0.82574977 -0.09216262
58 -0.24628720 0.82574977
59 1.87081647 -0.24628720
60 0.59987504 1.87081647
61 0.13100550 0.59987504
62 -0.34887518 0.13100550
63 -0.37201191 -0.34887518
64 0.49894158 -0.37201191
65 1.10707044 0.49894158
66 1.81593978 1.10707044
67 3.49716340 1.81593978
68 -3.87565718 3.49716340
69 0.58329692 -3.87565718
70 -3.61617818 0.58329692
71 -0.35222390 -3.61617818
72 1.56250947 -0.35222390
73 0.93390448 1.56250947
74 0.33595705 0.93390448
75 3.15043781 0.33595705
76 -0.39686463 3.15043781
77 1.45793409 -0.39686463
78 -1.83957530 1.45793409
79 -0.12405298 -1.83957530
80 0.54615560 -0.12405298
81 4.05761198 0.54615560
82 0.19605001 4.05761198
83 -0.96456786 0.19605001
84 0.24556068 -0.96456786
85 1.73748248 0.24556068
86 -0.16378356 1.73748248
87 0.65169958 -0.16378356
88 1.37907124 0.65169958
89 0.83614666 1.37907124
90 -1.55093182 0.83614666
91 -0.06454781 -1.55093182
92 0.59088472 -0.06454781
93 -0.16216688 0.59088472
94 -2.08761252 -0.16216688
95 0.88072591 -2.08761252
96 0.10220703 0.88072591
97 1.89706051 0.10220703
98 -0.09123834 1.89706051
99 -0.32060463 -0.09123834
100 -1.32012554 -0.32060463
101 1.18773119 -1.32012554
102 2.59370978 1.18773119
103 0.28555882 2.59370978
104 1.54751205 0.28555882
105 -2.34892086 1.54751205
106 0.88675711 -2.34892086
107 0.09001526 0.88675711
108 1.37720437 0.09001526
109 -0.37912317 1.37720437
110 1.07137470 -0.37912317
111 0.18060366 1.07137470
112 2.43969361 0.18060366
113 -1.43940461 2.43969361
114 -2.97975486 -1.43940461
115 1.29513128 -2.97975486
116 -1.57726728 1.29513128
117 0.93631912 -1.57726728
118 -2.00985442 0.93631912
119 0.32544204 -2.00985442
120 -1.35503878 0.32544204
121 0.31502018 -1.35503878
122 -2.97246607 0.31502018
123 -1.18523747 -2.97246607
124 -1.11020693 -1.18523747
125 -0.80494876 -1.11020693
126 -0.52134935 -0.80494876
127 0.69810510 -0.52134935
128 0.92811054 0.69810510
129 -3.04731713 0.92811054
130 1.81742770 -3.04731713
131 -3.33987460 1.81742770
132 1.86998332 -3.33987460
133 -2.03282957 1.86998332
134 -1.78407261 -2.03282957
135 -0.34112422 -1.78407261
136 0.63400553 -0.34112422
137 0.26736664 0.63400553
138 -2.37134318 0.26736664
139 -1.53049285 -2.37134318
140 -5.39467725 -1.53049285
141 2.75188242 -5.39467725
142 1.64206210 2.75188242
143 0.36266545 1.64206210
144 1.09265119 0.36266545
145 -4.27464017 1.09265119
146 1.92002434 -4.27464017
147 -2.36914636 1.92002434
148 0.73662543 -2.36914636
149 -0.14261746 0.73662543
150 -3.22605638 -0.14261746
151 -1.42618975 -3.22605638
152 1.90851371 -1.42618975
153 3.93428239 1.90851371
154 1.45567756 3.93428239
155 -2.72179940 1.45567756
156 -0.06454781 -2.72179940
157 1.26472965 -0.06454781
158 0.92811054 1.26472965
159 0.84645118 0.92811054
160 -0.46009701 0.84645118
161 0.13618254 -0.46009701
> 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/7wbe71352140704.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/82m4i1352140704.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9x3id1352140704.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/103l8z1352140704.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11wovn1352140704.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/12niao1352140704.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/13f9pf1352140704.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/14g4k31352140704.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/15vt871352140704.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/1667zi1352140705.tab")
+ }
>
> try(system("convert tmp/16i1n1352140704.ps tmp/16i1n1352140704.png",intern=TRUE))
character(0)
> try(system("convert tmp/25nb81352140704.ps tmp/25nb81352140704.png",intern=TRUE))
character(0)
> try(system("convert tmp/33scl1352140704.ps tmp/33scl1352140704.png",intern=TRUE))
character(0)
> try(system("convert tmp/4606v1352140704.ps tmp/4606v1352140704.png",intern=TRUE))
character(0)
> try(system("convert tmp/5h9j91352140704.ps tmp/5h9j91352140704.png",intern=TRUE))
character(0)
> try(system("convert tmp/6yh2g1352140704.ps tmp/6yh2g1352140704.png",intern=TRUE))
character(0)
> try(system("convert tmp/7wbe71352140704.ps tmp/7wbe71352140704.png",intern=TRUE))
character(0)
> try(system("convert tmp/82m4i1352140704.ps tmp/82m4i1352140704.png",intern=TRUE))
character(0)
> try(system("convert tmp/9x3id1352140704.ps tmp/9x3id1352140704.png",intern=TRUE))
character(0)
> try(system("convert tmp/103l8z1352140704.ps tmp/103l8z1352140704.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.158 1.142 9.308