R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows"
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 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(13
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+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final'
+ ,'Connected'
+ ,'Separate')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('Learning','Software','Happiness','Depression','Belonging','Belonging_Final','Connected','Separate'),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 = '1'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Learning Software Happiness Depression Belonging Belonging_Final Connected
1 13 12 14 12 53 32 41
2 16 11 18 11 86 51 39
3 19 15 11 14 66 42 30
4 15 6 12 12 67 41 31
5 14 13 16 21 76 46 34
6 13 10 18 12 78 47 35
7 19 12 14 22 53 37 39
8 15 14 14 11 80 49 34
9 14 12 15 10 74 45 36
10 15 6 15 13 76 47 37
11 16 10 17 10 79 49 38
12 16 12 19 8 54 33 36
13 16 12 10 15 67 42 38
14 16 11 16 14 54 33 39
15 17 15 18 10 87 53 33
16 15 12 14 14 58 36 32
17 15 10 14 14 75 45 36
18 20 12 17 11 88 54 38
19 18 11 14 10 64 41 39
20 16 12 16 13 57 36 32
21 16 11 18 7 66 41 32
22 16 12 11 14 68 44 31
23 19 13 14 12 54 33 39
24 16 11 12 14 56 37 37
25 17 9 17 11 86 52 39
26 17 13 9 9 80 47 41
27 16 10 16 11 76 43 36
28 15 14 14 15 69 44 33
29 16 12 15 14 78 45 33
30 14 10 11 13 67 44 34
31 15 12 16 9 80 49 31
32 12 8 13 15 54 33 27
33 14 10 17 10 71 43 37
34 16 12 15 11 84 54 34
35 14 12 14 13 74 42 34
36 7 7 16 8 71 44 32
37 10 6 9 20 63 37 29
38 14 12 15 12 71 43 36
39 16 10 17 10 76 46 29
40 16 10 13 10 69 42 35
41 16 10 15 9 74 45 37
42 14 12 16 14 75 44 34
43 20 15 16 8 54 33 38
44 14 10 12 14 52 31 35
45 14 10 12 11 69 42 38
46 11 12 11 13 68 40 37
47 14 13 15 9 65 43 38
48 15 11 15 11 75 46 33
49 16 11 17 15 74 42 36
50 14 12 13 11 75 45 38
51 16 14 16 10 72 44 32
52 14 10 14 14 67 40 32
53 12 12 11 18 63 37 32
54 16 13 12 14 62 46 34
55 9 5 12 11 63 36 32
56 14 6 15 12 76 47 37
57 16 12 16 13 74 45 39
58 16 12 15 9 67 42 29
59 15 11 12 10 73 43 37
60 16 10 12 15 70 43 35
61 12 7 8 20 53 32 30
62 16 12 13 12 77 45 38
63 16 14 11 12 77 45 34
64 14 11 14 14 52 31 31
65 16 12 15 13 54 33 34
66 17 13 10 11 80 49 35
67 18 14 11 17 66 42 36
68 18 11 12 12 73 41 30
69 12 12 15 13 63 38 39
70 16 12 15 14 69 42 35
71 10 8 14 13 67 44 38
72 14 11 16 15 54 33 31
73 18 14 15 13 81 48 34
74 18 14 15 10 69 40 38
75 16 12 13 11 84 50 34
76 17 9 12 19 80 49 39
77 16 13 17 13 70 43 37
78 16 11 13 17 69 44 34
79 13 12 15 13 77 47 28
80 16 12 13 9 54 33 37
81 16 12 15 11 79 46 33
82 20 12 16 10 30 0 37
83 16 12 15 9 71 45 35
84 15 12 16 12 73 43 37
85 15 11 15 12 72 44 32
86 16 10 14 13 77 47 33
87 14 9 15 13 75 45 38
88 16 12 14 12 69 42 33
89 16 12 13 15 54 33 29
90 15 12 7 22 70 43 33
91 12 9 17 13 73 46 31
92 17 15 13 15 54 33 36
93 16 12 15 13 77 46 35
94 15 12 14 15 82 48 32
95 13 12 13 10 80 47 29
96 16 10 16 11 80 47 39
97 16 13 12 16 69 43 37
98 16 9 14 11 78 46 35
99 16 12 17 11 81 48 37
100 14 10 15 10 76 46 32
101 16 14 17 10 76 45 38
102 16 11 12 16 73 45 37
103 20 15 16 12 85 52 36
104 15 11 11 11 66 42 32
105 16 11 15 16 79 47 33
106 13 12 9 19 68 41 40
107 17 12 16 11 76 47 38
108 16 12 15 16 71 43 41
109 16 11 10 15 54 33 36
110 12 7 10 24 46 30 43
111 16 12 15 14 82 49 30
112 16 14 11 15 74 44 31
113 17 11 13 11 88 55 32
114 13 11 14 15 38 11 32
115 12 10 18 12 76 47 37
116 18 13 16 10 86 53 37
117 14 13 14 14 54 33 33
118 14 8 14 13 70 44 34
119 13 11 14 9 69 42 33
120 16 12 14 15 90 55 38
121 13 11 12 15 54 33 33
122 16 13 14 14 76 46 31
123 13 12 15 11 89 54 38
124 16 14 15 8 76 47 37
125 15 13 15 11 73 45 33
126 16 15 13 11 79 47 31
127 15 10 17 8 90 55 39
128 17 11 17 10 74 44 44
129 15 9 19 11 81 53 33
130 12 11 15 13 72 44 35
131 16 10 13 11 71 42 32
132 10 11 9 20 66 40 28
133 16 8 15 10 77 46 40
134 12 11 15 15 65 40 27
135 14 12 15 12 74 46 37
136 15 12 16 14 82 53 32
137 13 9 11 23 54 33 28
138 15 11 14 14 63 42 34
139 11 10 11 16 54 35 30
140 12 8 15 11 64 40 35
141 8 9 13 12 69 41 31
142 16 8 15 10 54 33 32
143 15 9 16 14 84 51 30
144 17 15 14 12 86 53 30
145 16 11 15 12 77 46 31
146 10 8 16 11 89 55 40
147 18 13 16 12 76 47 32
148 13 12 11 13 60 38 36
149 16 12 12 11 75 46 32
150 13 9 9 19 73 46 35
151 10 7 16 12 85 53 38
152 15 13 13 17 79 47 42
153 16 9 16 9 71 41 34
154 16 6 12 12 72 44 35
155 14 8 9 19 69 43 35
156 10 8 13 18 78 51 33
157 17 15 13 15 54 33 36
158 13 6 14 14 69 43 32
159 15 9 19 11 81 53 33
160 16 11 13 9 84 51 34
161 12 8 12 18 84 50 32
162 13 8 13 16 69 46 34
Separate
1 38
2 32
3 35
4 33
5 37
6 29
7 31
8 36
9 35
10 38
11 31
12 34
13 35
14 38
15 37
16 33
17 32
18 38
19 38
20 32
21 33
22 31
23 38
24 39
25 32
26 32
27 35
28 37
29 33
30 33
31 28
32 32
33 31
34 37
35 30
36 33
37 31
38 33
39 31
40 33
41 32
42 33
43 32
44 33
45 28
46 35
47 39
48 34
49 38
50 32
51 38
52 30
53 33
54 38
55 32
56 32
57 34
58 34
59 36
60 34
61 28
62 34
63 35
64 35
65 31
66 37
67 35
68 27
69 40
70 37
71 36
72 38
73 39
74 41
75 27
76 30
77 37
78 31
79 31
80 27
81 36
82 38
83 37
84 33
85 34
86 31
87 39
88 34
89 32
90 33
91 36
92 32
93 41
94 28
95 30
96 36
97 35
98 31
99 34
100 36
101 36
102 35
103 37
104 28
105 39
106 32
107 35
108 39
109 35
110 42
111 34
112 33
113 41
114 33
115 34
116 32
117 40
118 40
119 35
120 36
121 37
122 27
123 39
124 38
125 31
126 33
127 32
128 39
129 36
130 33
131 33
132 32
133 37
134 30
135 38
136 29
137 22
138 35
139 35
140 34
141 35
142 34
143 34
144 35
145 23
146 31
147 27
148 36
149 31
150 32
151 39
152 37
153 38
154 39
155 34
156 31
157 32
158 37
159 36
160 32
161 35
162 36
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Software Happiness Depression
5.51005 0.54256 0.05971 -0.07116
Belonging Belonging_Final Connected Separate
0.03576 -0.05223 0.11405 -0.02067
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.9026 -1.0985 0.1985 1.1037 3.9386
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.51005 2.59687 2.122 0.0355 *
Software 0.54256 0.06895 7.869 5.9e-13 ***
Happiness 0.05971 0.07638 0.782 0.4356
Depression -0.07116 0.05634 -1.263 0.2085
Belonging 0.03576 0.04453 0.803 0.4232
Belonging_Final -0.05223 0.06396 -0.817 0.4154
Connected 0.11405 0.04689 2.432 0.0161 *
Separate -0.02067 0.04481 -0.461 0.6453
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.85 on 154 degrees of freedom
Multiple R-squared: 0.3567, Adjusted R-squared: 0.3275
F-statistic: 12.2 on 7 and 154 DF, p-value: 2.357e-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,] 0.39159274 0.78318547 0.60840726
[2,] 0.62549432 0.74901135 0.37450568
[3,] 0.48670911 0.97341822 0.51329089
[4,] 0.46037903 0.92075807 0.53962097
[5,] 0.35164038 0.70328077 0.64835962
[6,] 0.26714594 0.53429188 0.73285406
[7,] 0.21797197 0.43594394 0.78202803
[8,] 0.41923109 0.83846217 0.58076891
[9,] 0.34207776 0.68415551 0.65792224
[10,] 0.26286710 0.52573420 0.73713290
[11,] 0.20332055 0.40664111 0.79667945
[12,] 0.18552385 0.37104770 0.81447615
[13,] 0.37000980 0.74001961 0.62999020
[14,] 0.40633422 0.81266845 0.59366578
[15,] 0.39534637 0.79069273 0.60465363
[16,] 0.35780658 0.71561317 0.64219342
[17,] 0.41748068 0.83496136 0.58251932
[18,] 0.42373791 0.84747582 0.57626209
[19,] 0.40499458 0.80998916 0.59500542
[20,] 0.44697981 0.89395962 0.55302019
[21,] 0.38896039 0.77792078 0.61103961
[22,] 0.34245154 0.68490307 0.65754846
[23,] 0.31917191 0.63834383 0.68082809
[24,] 0.28603986 0.57207972 0.71396014
[25,] 0.24507328 0.49014656 0.75492672
[26,] 0.82914093 0.34171814 0.17085907
[27,] 0.80277845 0.39444309 0.19722155
[28,] 0.79678649 0.40642701 0.20321351
[29,] 0.81823558 0.36352883 0.18176442
[30,] 0.79979375 0.40041250 0.20020625
[31,] 0.76696936 0.46606127 0.23303064
[32,] 0.74189201 0.51621597 0.25810799
[33,] 0.76721351 0.46557298 0.23278649
[34,] 0.72447243 0.55105513 0.27552757
[35,] 0.69584575 0.60830851 0.30415425
[36,] 0.86170785 0.27658431 0.13829215
[37,] 0.90959795 0.18080410 0.09040205
[38,] 0.88662233 0.22675534 0.11337767
[39,] 0.87065954 0.25868093 0.12934046
[40,] 0.86968555 0.26062891 0.13031445
[41,] 0.84142878 0.31714244 0.15857122
[42,] 0.80892453 0.38215094 0.19107547
[43,] 0.82610056 0.34779888 0.17389944
[44,] 0.81311457 0.37377086 0.18688543
[45,] 0.82701302 0.34597397 0.17298698
[46,] 0.80170663 0.39658675 0.19829337
[47,] 0.76747248 0.46505503 0.23252752
[48,] 0.74405610 0.51188780 0.25594390
[49,] 0.70627687 0.58744626 0.29372313
[50,] 0.70259845 0.59480310 0.29740155
[51,] 0.66452297 0.67095405 0.33547703
[52,] 0.62191824 0.75616353 0.37808176
[53,] 0.58218328 0.83563345 0.41781672
[54,] 0.53472523 0.93054954 0.46527477
[55,] 0.49584330 0.99168661 0.50415670
[56,] 0.46667406 0.93334812 0.53332594
[57,] 0.46372115 0.92744229 0.53627885
[58,] 0.61347894 0.77304212 0.38652106
[59,] 0.72935834 0.54128333 0.27064166
[60,] 0.69440388 0.61119224 0.30559612
[61,] 0.80262952 0.39474095 0.19737048
[62,] 0.76821289 0.46357421 0.23178711
[63,] 0.75866737 0.48266525 0.24133263
[64,] 0.73901560 0.52196879 0.26098440
[65,] 0.69957099 0.60085802 0.30042901
[66,] 0.75085640 0.49828719 0.24914360
[67,] 0.71368595 0.57262810 0.28631405
[68,] 0.70049648 0.59900705 0.29950352
[69,] 0.70634683 0.58730635 0.29365317
[70,] 0.66478008 0.67043984 0.33521992
[71,] 0.62547243 0.74905514 0.37452757
[72,] 0.74964020 0.50071961 0.25035980
[73,] 0.71257853 0.57484295 0.28742147
[74,] 0.68280185 0.63439631 0.31719815
[75,] 0.64073672 0.71852655 0.35926328
[76,] 0.63433147 0.73133706 0.36566853
[77,] 0.58950032 0.82099936 0.41049968
[78,] 0.54961569 0.90076863 0.45038431
[79,] 0.53296106 0.93407788 0.46703894
[80,] 0.49894954 0.99789908 0.50105046
[81,] 0.48002794 0.96005588 0.51997206
[82,] 0.44178065 0.88356129 0.55821935
[83,] 0.39973765 0.79947530 0.60026235
[84,] 0.35767207 0.71534414 0.64232793
[85,] 0.37383999 0.74767999 0.62616001
[86,] 0.33894948 0.67789897 0.66105052
[87,] 0.30119431 0.60238862 0.69880569
[88,] 0.29987885 0.59975770 0.70012115
[89,] 0.26002036 0.52004072 0.73997964
[90,] 0.22445621 0.44891242 0.77554379
[91,] 0.20285474 0.40570947 0.79714526
[92,] 0.18812218 0.37624436 0.81187782
[93,] 0.23142939 0.46285879 0.76857061
[94,] 0.19737412 0.39474824 0.80262588
[95,] 0.19107205 0.38214410 0.80892795
[96,] 0.20360493 0.40720987 0.79639507
[97,] 0.18369426 0.36738852 0.81630574
[98,] 0.16340521 0.32681042 0.83659479
[99,] 0.15704365 0.31408729 0.84295635
[100,] 0.14758145 0.29516290 0.85241855
[101,] 0.13003758 0.26007515 0.86996242
[102,] 0.10807782 0.21615563 0.89192218
[103,] 0.12620747 0.25241495 0.87379253
[104,] 0.11611181 0.23222362 0.88388819
[105,] 0.14804638 0.29609276 0.85195362
[106,] 0.14101835 0.28203670 0.85898165
[107,] 0.12219812 0.24439624 0.87780188
[108,] 0.10584694 0.21169388 0.89415306
[109,] 0.10860531 0.21721063 0.89139469
[110,] 0.10073982 0.20147965 0.89926018
[111,] 0.08513099 0.17026199 0.91486901
[112,] 0.06771939 0.13543877 0.93228061
[113,] 0.07688771 0.15377542 0.92311229
[114,] 0.06138552 0.12277104 0.93861448
[115,] 0.04937173 0.09874346 0.95062827
[116,] 0.03763532 0.07527063 0.96236468
[117,] 0.02818619 0.05637238 0.97181381
[118,] 0.02220529 0.04441057 0.97779471
[119,] 0.01767669 0.03535338 0.98232331
[120,] 0.02474693 0.04949386 0.97525307
[121,] 0.02052937 0.04105874 0.97947063
[122,] 0.03231400 0.06462800 0.96768600
[123,] 0.03349234 0.06698469 0.96650766
[124,] 0.04501108 0.09002216 0.95498892
[125,] 0.03571833 0.07143666 0.96428167
[126,] 0.02452083 0.04904166 0.97547917
[127,] 0.01644191 0.03288382 0.98355809
[128,] 0.01145134 0.02290268 0.98854866
[129,] 0.01871416 0.03742831 0.98128584
[130,] 0.01567360 0.03134719 0.98432640
[131,] 0.61609964 0.76780072 0.38390036
[132,] 0.55528297 0.88943406 0.44471703
[133,] 0.47194886 0.94389773 0.52805114
[134,] 0.39183591 0.78367181 0.60816409
[135,] 0.30519025 0.61038051 0.69480975
[136,] 0.35044474 0.70088948 0.64955526
[137,] 0.30988329 0.61976659 0.69011671
[138,] 0.72321234 0.55357531 0.27678766
[139,] 0.68231323 0.63537353 0.31768677
[140,] 0.54094773 0.91810455 0.45905227
[141,] 0.74596135 0.50807731 0.25403865
> postscript(file="/var/fisher/rcomp/tmp/10ebr1351956152.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/2baeu1351956152.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3i9ka1351956152.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4evfd1351956152.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5ekh61351956152.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-3.117601011 0.031409542 2.826208692 3.263872656 -1.452637097 -1.883489323
7 8 9 10 11 12
3.938638684 -1.594411390 -1.883298629 2.566461096 0.801769700 -0.196710791
13 14 15 16 17 18
0.636573306 0.692451452 -0.353645490 -0.021982012 0.448428914 3.871783141
19 20 21 22 23 24
2.587482548 0.802533083 0.758692721 1.290129671 2.584417168 1.317476987
25 26 27 28 29 30
2.228481186 0.118860325 1.037292647 -1.042840712 0.559138014 0.039024333
31 32 33 34 35 36
-0.594186433 -0.184903151 -1.111490636 0.569799207 -1.642024303 -5.902573456
37 38 39 40 41 42
-0.866817165 -1.779490672 1.778844077 1.416075093 0.954616002 -1.559576607
43 44 45 46 47 48
2.085282872 -0.206202870 -0.898546595 -4.591634126 -2.425089799 0.068399177
49 50 51 52 53 54
0.800952617 -2.018586087 -0.490631866 -0.111764506 -2.684778008 0.809386322
55 56 57 58 59 60
-2.517571084 1.371304878 -0.092351762 0.916882834 -0.323702883 1.868732899
61 62 63 64 65 66
0.570716868 0.022385148 -0.466440278 -0.370634890 0.564048311 1.093600353
67 68 69 70 71 72
1.897930322 3.326545364 -3.880914238 0.578839151 -3.449196578 -0.323948286
73 74 75 76 77 78
1.462205539 0.845093066 0.273624586 3.112856240 -0.365942088 1.548836272
79 80 81 82 83 84
-1.842852459 -0.026008619 0.424121949 3.227893717 0.308208809 -1.024776775
85 86 87 88 89 90
0.256431299 1.731711306 -0.223327636 0.662336718 1.416730228 0.787728045
91 92 93 94 95 96
-1.482603395 -0.009346032 0.513187104 -0.285606992 -2.178923984 0.781685537
97 98 99 100 101 102
0.140519351 1.815846911 -0.159901088 -0.340573484 -1.366811481 1.187071465
103 104 105 106 107 108
2.585220688 0.410210238 1.436727372 -2.397171216 0.932990453 0.058877693
109 110 111 112 113 114
1.402035548 -0.311586001 0.987859590 0.102928012 2.451743724 -1.998883265
115 116 117 118 119 120
-2.936748200 1.327114786 -1.547598713 0.982402073 -1.987919366 0.274939017
121 122 123 124 125 126
-1.333886989 0.304154405 -3.023893971 -1.129863105 -1.059438245 -0.865806131
127 128 129 130 131 132
-0.513911570 0.657837937 1.107093822 -3.035236727 1.757879269 -3.395499358
133 134 135 136 137 138
1.817021409 -2.001079808 -1.740798301 -0.194351716 0.640535211 0.468402482
139 140 141 142 143 144
-2.255150678 -1.452049433 -5.453720060 2.810925984 1.588789011 0.364109135
145 146 147 148 149 150
1.068825869 -4.254547241 1.980591286 -2.275292865 0.757020012 -0.116840102
151 152 153 154 155 156
-3.208809380 -1.525645289 1.801977048 3.909544202 1.453398005 -2.594466136
157 158 159 160 161 162
-0.009346032 1.288332196 1.107093822 0.829427294 -0.604839935 0.313163501
> postscript(file="/var/fisher/rcomp/tmp/6zotq1351956152.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 -3.117601011 NA
1 0.031409542 -3.117601011
2 2.826208692 0.031409542
3 3.263872656 2.826208692
4 -1.452637097 3.263872656
5 -1.883489323 -1.452637097
6 3.938638684 -1.883489323
7 -1.594411390 3.938638684
8 -1.883298629 -1.594411390
9 2.566461096 -1.883298629
10 0.801769700 2.566461096
11 -0.196710791 0.801769700
12 0.636573306 -0.196710791
13 0.692451452 0.636573306
14 -0.353645490 0.692451452
15 -0.021982012 -0.353645490
16 0.448428914 -0.021982012
17 3.871783141 0.448428914
18 2.587482548 3.871783141
19 0.802533083 2.587482548
20 0.758692721 0.802533083
21 1.290129671 0.758692721
22 2.584417168 1.290129671
23 1.317476987 2.584417168
24 2.228481186 1.317476987
25 0.118860325 2.228481186
26 1.037292647 0.118860325
27 -1.042840712 1.037292647
28 0.559138014 -1.042840712
29 0.039024333 0.559138014
30 -0.594186433 0.039024333
31 -0.184903151 -0.594186433
32 -1.111490636 -0.184903151
33 0.569799207 -1.111490636
34 -1.642024303 0.569799207
35 -5.902573456 -1.642024303
36 -0.866817165 -5.902573456
37 -1.779490672 -0.866817165
38 1.778844077 -1.779490672
39 1.416075093 1.778844077
40 0.954616002 1.416075093
41 -1.559576607 0.954616002
42 2.085282872 -1.559576607
43 -0.206202870 2.085282872
44 -0.898546595 -0.206202870
45 -4.591634126 -0.898546595
46 -2.425089799 -4.591634126
47 0.068399177 -2.425089799
48 0.800952617 0.068399177
49 -2.018586087 0.800952617
50 -0.490631866 -2.018586087
51 -0.111764506 -0.490631866
52 -2.684778008 -0.111764506
53 0.809386322 -2.684778008
54 -2.517571084 0.809386322
55 1.371304878 -2.517571084
56 -0.092351762 1.371304878
57 0.916882834 -0.092351762
58 -0.323702883 0.916882834
59 1.868732899 -0.323702883
60 0.570716868 1.868732899
61 0.022385148 0.570716868
62 -0.466440278 0.022385148
63 -0.370634890 -0.466440278
64 0.564048311 -0.370634890
65 1.093600353 0.564048311
66 1.897930322 1.093600353
67 3.326545364 1.897930322
68 -3.880914238 3.326545364
69 0.578839151 -3.880914238
70 -3.449196578 0.578839151
71 -0.323948286 -3.449196578
72 1.462205539 -0.323948286
73 0.845093066 1.462205539
74 0.273624586 0.845093066
75 3.112856240 0.273624586
76 -0.365942088 3.112856240
77 1.548836272 -0.365942088
78 -1.842852459 1.548836272
79 -0.026008619 -1.842852459
80 0.424121949 -0.026008619
81 3.227893717 0.424121949
82 0.308208809 3.227893717
83 -1.024776775 0.308208809
84 0.256431299 -1.024776775
85 1.731711306 0.256431299
86 -0.223327636 1.731711306
87 0.662336718 -0.223327636
88 1.416730228 0.662336718
89 0.787728045 1.416730228
90 -1.482603395 0.787728045
91 -0.009346032 -1.482603395
92 0.513187104 -0.009346032
93 -0.285606992 0.513187104
94 -2.178923984 -0.285606992
95 0.781685537 -2.178923984
96 0.140519351 0.781685537
97 1.815846911 0.140519351
98 -0.159901088 1.815846911
99 -0.340573484 -0.159901088
100 -1.366811481 -0.340573484
101 1.187071465 -1.366811481
102 2.585220688 1.187071465
103 0.410210238 2.585220688
104 1.436727372 0.410210238
105 -2.397171216 1.436727372
106 0.932990453 -2.397171216
107 0.058877693 0.932990453
108 1.402035548 0.058877693
109 -0.311586001 1.402035548
110 0.987859590 -0.311586001
111 0.102928012 0.987859590
112 2.451743724 0.102928012
113 -1.998883265 2.451743724
114 -2.936748200 -1.998883265
115 1.327114786 -2.936748200
116 -1.547598713 1.327114786
117 0.982402073 -1.547598713
118 -1.987919366 0.982402073
119 0.274939017 -1.987919366
120 -1.333886989 0.274939017
121 0.304154405 -1.333886989
122 -3.023893971 0.304154405
123 -1.129863105 -3.023893971
124 -1.059438245 -1.129863105
125 -0.865806131 -1.059438245
126 -0.513911570 -0.865806131
127 0.657837937 -0.513911570
128 1.107093822 0.657837937
129 -3.035236727 1.107093822
130 1.757879269 -3.035236727
131 -3.395499358 1.757879269
132 1.817021409 -3.395499358
133 -2.001079808 1.817021409
134 -1.740798301 -2.001079808
135 -0.194351716 -1.740798301
136 0.640535211 -0.194351716
137 0.468402482 0.640535211
138 -2.255150678 0.468402482
139 -1.452049433 -2.255150678
140 -5.453720060 -1.452049433
141 2.810925984 -5.453720060
142 1.588789011 2.810925984
143 0.364109135 1.588789011
144 1.068825869 0.364109135
145 -4.254547241 1.068825869
146 1.980591286 -4.254547241
147 -2.275292865 1.980591286
148 0.757020012 -2.275292865
149 -0.116840102 0.757020012
150 -3.208809380 -0.116840102
151 -1.525645289 -3.208809380
152 1.801977048 -1.525645289
153 3.909544202 1.801977048
154 1.453398005 3.909544202
155 -2.594466136 1.453398005
156 -0.009346032 -2.594466136
157 1.288332196 -0.009346032
158 1.107093822 1.288332196
159 0.829427294 1.107093822
160 -0.604839935 0.829427294
161 0.313163501 -0.604839935
162 NA 0.313163501
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.031409542 -3.117601011
[2,] 2.826208692 0.031409542
[3,] 3.263872656 2.826208692
[4,] -1.452637097 3.263872656
[5,] -1.883489323 -1.452637097
[6,] 3.938638684 -1.883489323
[7,] -1.594411390 3.938638684
[8,] -1.883298629 -1.594411390
[9,] 2.566461096 -1.883298629
[10,] 0.801769700 2.566461096
[11,] -0.196710791 0.801769700
[12,] 0.636573306 -0.196710791
[13,] 0.692451452 0.636573306
[14,] -0.353645490 0.692451452
[15,] -0.021982012 -0.353645490
[16,] 0.448428914 -0.021982012
[17,] 3.871783141 0.448428914
[18,] 2.587482548 3.871783141
[19,] 0.802533083 2.587482548
[20,] 0.758692721 0.802533083
[21,] 1.290129671 0.758692721
[22,] 2.584417168 1.290129671
[23,] 1.317476987 2.584417168
[24,] 2.228481186 1.317476987
[25,] 0.118860325 2.228481186
[26,] 1.037292647 0.118860325
[27,] -1.042840712 1.037292647
[28,] 0.559138014 -1.042840712
[29,] 0.039024333 0.559138014
[30,] -0.594186433 0.039024333
[31,] -0.184903151 -0.594186433
[32,] -1.111490636 -0.184903151
[33,] 0.569799207 -1.111490636
[34,] -1.642024303 0.569799207
[35,] -5.902573456 -1.642024303
[36,] -0.866817165 -5.902573456
[37,] -1.779490672 -0.866817165
[38,] 1.778844077 -1.779490672
[39,] 1.416075093 1.778844077
[40,] 0.954616002 1.416075093
[41,] -1.559576607 0.954616002
[42,] 2.085282872 -1.559576607
[43,] -0.206202870 2.085282872
[44,] -0.898546595 -0.206202870
[45,] -4.591634126 -0.898546595
[46,] -2.425089799 -4.591634126
[47,] 0.068399177 -2.425089799
[48,] 0.800952617 0.068399177
[49,] -2.018586087 0.800952617
[50,] -0.490631866 -2.018586087
[51,] -0.111764506 -0.490631866
[52,] -2.684778008 -0.111764506
[53,] 0.809386322 -2.684778008
[54,] -2.517571084 0.809386322
[55,] 1.371304878 -2.517571084
[56,] -0.092351762 1.371304878
[57,] 0.916882834 -0.092351762
[58,] -0.323702883 0.916882834
[59,] 1.868732899 -0.323702883
[60,] 0.570716868 1.868732899
[61,] 0.022385148 0.570716868
[62,] -0.466440278 0.022385148
[63,] -0.370634890 -0.466440278
[64,] 0.564048311 -0.370634890
[65,] 1.093600353 0.564048311
[66,] 1.897930322 1.093600353
[67,] 3.326545364 1.897930322
[68,] -3.880914238 3.326545364
[69,] 0.578839151 -3.880914238
[70,] -3.449196578 0.578839151
[71,] -0.323948286 -3.449196578
[72,] 1.462205539 -0.323948286
[73,] 0.845093066 1.462205539
[74,] 0.273624586 0.845093066
[75,] 3.112856240 0.273624586
[76,] -0.365942088 3.112856240
[77,] 1.548836272 -0.365942088
[78,] -1.842852459 1.548836272
[79,] -0.026008619 -1.842852459
[80,] 0.424121949 -0.026008619
[81,] 3.227893717 0.424121949
[82,] 0.308208809 3.227893717
[83,] -1.024776775 0.308208809
[84,] 0.256431299 -1.024776775
[85,] 1.731711306 0.256431299
[86,] -0.223327636 1.731711306
[87,] 0.662336718 -0.223327636
[88,] 1.416730228 0.662336718
[89,] 0.787728045 1.416730228
[90,] -1.482603395 0.787728045
[91,] -0.009346032 -1.482603395
[92,] 0.513187104 -0.009346032
[93,] -0.285606992 0.513187104
[94,] -2.178923984 -0.285606992
[95,] 0.781685537 -2.178923984
[96,] 0.140519351 0.781685537
[97,] 1.815846911 0.140519351
[98,] -0.159901088 1.815846911
[99,] -0.340573484 -0.159901088
[100,] -1.366811481 -0.340573484
[101,] 1.187071465 -1.366811481
[102,] 2.585220688 1.187071465
[103,] 0.410210238 2.585220688
[104,] 1.436727372 0.410210238
[105,] -2.397171216 1.436727372
[106,] 0.932990453 -2.397171216
[107,] 0.058877693 0.932990453
[108,] 1.402035548 0.058877693
[109,] -0.311586001 1.402035548
[110,] 0.987859590 -0.311586001
[111,] 0.102928012 0.987859590
[112,] 2.451743724 0.102928012
[113,] -1.998883265 2.451743724
[114,] -2.936748200 -1.998883265
[115,] 1.327114786 -2.936748200
[116,] -1.547598713 1.327114786
[117,] 0.982402073 -1.547598713
[118,] -1.987919366 0.982402073
[119,] 0.274939017 -1.987919366
[120,] -1.333886989 0.274939017
[121,] 0.304154405 -1.333886989
[122,] -3.023893971 0.304154405
[123,] -1.129863105 -3.023893971
[124,] -1.059438245 -1.129863105
[125,] -0.865806131 -1.059438245
[126,] -0.513911570 -0.865806131
[127,] 0.657837937 -0.513911570
[128,] 1.107093822 0.657837937
[129,] -3.035236727 1.107093822
[130,] 1.757879269 -3.035236727
[131,] -3.395499358 1.757879269
[132,] 1.817021409 -3.395499358
[133,] -2.001079808 1.817021409
[134,] -1.740798301 -2.001079808
[135,] -0.194351716 -1.740798301
[136,] 0.640535211 -0.194351716
[137,] 0.468402482 0.640535211
[138,] -2.255150678 0.468402482
[139,] -1.452049433 -2.255150678
[140,] -5.453720060 -1.452049433
[141,] 2.810925984 -5.453720060
[142,] 1.588789011 2.810925984
[143,] 0.364109135 1.588789011
[144,] 1.068825869 0.364109135
[145,] -4.254547241 1.068825869
[146,] 1.980591286 -4.254547241
[147,] -2.275292865 1.980591286
[148,] 0.757020012 -2.275292865
[149,] -0.116840102 0.757020012
[150,] -3.208809380 -0.116840102
[151,] -1.525645289 -3.208809380
[152,] 1.801977048 -1.525645289
[153,] 3.909544202 1.801977048
[154,] 1.453398005 3.909544202
[155,] -2.594466136 1.453398005
[156,] -0.009346032 -2.594466136
[157,] 1.288332196 -0.009346032
[158,] 1.107093822 1.288332196
[159,] 0.829427294 1.107093822
[160,] -0.604839935 0.829427294
[161,] 0.313163501 -0.604839935
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.031409542 -3.117601011
2 2.826208692 0.031409542
3 3.263872656 2.826208692
4 -1.452637097 3.263872656
5 -1.883489323 -1.452637097
6 3.938638684 -1.883489323
7 -1.594411390 3.938638684
8 -1.883298629 -1.594411390
9 2.566461096 -1.883298629
10 0.801769700 2.566461096
11 -0.196710791 0.801769700
12 0.636573306 -0.196710791
13 0.692451452 0.636573306
14 -0.353645490 0.692451452
15 -0.021982012 -0.353645490
16 0.448428914 -0.021982012
17 3.871783141 0.448428914
18 2.587482548 3.871783141
19 0.802533083 2.587482548
20 0.758692721 0.802533083
21 1.290129671 0.758692721
22 2.584417168 1.290129671
23 1.317476987 2.584417168
24 2.228481186 1.317476987
25 0.118860325 2.228481186
26 1.037292647 0.118860325
27 -1.042840712 1.037292647
28 0.559138014 -1.042840712
29 0.039024333 0.559138014
30 -0.594186433 0.039024333
31 -0.184903151 -0.594186433
32 -1.111490636 -0.184903151
33 0.569799207 -1.111490636
34 -1.642024303 0.569799207
35 -5.902573456 -1.642024303
36 -0.866817165 -5.902573456
37 -1.779490672 -0.866817165
38 1.778844077 -1.779490672
39 1.416075093 1.778844077
40 0.954616002 1.416075093
41 -1.559576607 0.954616002
42 2.085282872 -1.559576607
43 -0.206202870 2.085282872
44 -0.898546595 -0.206202870
45 -4.591634126 -0.898546595
46 -2.425089799 -4.591634126
47 0.068399177 -2.425089799
48 0.800952617 0.068399177
49 -2.018586087 0.800952617
50 -0.490631866 -2.018586087
51 -0.111764506 -0.490631866
52 -2.684778008 -0.111764506
53 0.809386322 -2.684778008
54 -2.517571084 0.809386322
55 1.371304878 -2.517571084
56 -0.092351762 1.371304878
57 0.916882834 -0.092351762
58 -0.323702883 0.916882834
59 1.868732899 -0.323702883
60 0.570716868 1.868732899
61 0.022385148 0.570716868
62 -0.466440278 0.022385148
63 -0.370634890 -0.466440278
64 0.564048311 -0.370634890
65 1.093600353 0.564048311
66 1.897930322 1.093600353
67 3.326545364 1.897930322
68 -3.880914238 3.326545364
69 0.578839151 -3.880914238
70 -3.449196578 0.578839151
71 -0.323948286 -3.449196578
72 1.462205539 -0.323948286
73 0.845093066 1.462205539
74 0.273624586 0.845093066
75 3.112856240 0.273624586
76 -0.365942088 3.112856240
77 1.548836272 -0.365942088
78 -1.842852459 1.548836272
79 -0.026008619 -1.842852459
80 0.424121949 -0.026008619
81 3.227893717 0.424121949
82 0.308208809 3.227893717
83 -1.024776775 0.308208809
84 0.256431299 -1.024776775
85 1.731711306 0.256431299
86 -0.223327636 1.731711306
87 0.662336718 -0.223327636
88 1.416730228 0.662336718
89 0.787728045 1.416730228
90 -1.482603395 0.787728045
91 -0.009346032 -1.482603395
92 0.513187104 -0.009346032
93 -0.285606992 0.513187104
94 -2.178923984 -0.285606992
95 0.781685537 -2.178923984
96 0.140519351 0.781685537
97 1.815846911 0.140519351
98 -0.159901088 1.815846911
99 -0.340573484 -0.159901088
100 -1.366811481 -0.340573484
101 1.187071465 -1.366811481
102 2.585220688 1.187071465
103 0.410210238 2.585220688
104 1.436727372 0.410210238
105 -2.397171216 1.436727372
106 0.932990453 -2.397171216
107 0.058877693 0.932990453
108 1.402035548 0.058877693
109 -0.311586001 1.402035548
110 0.987859590 -0.311586001
111 0.102928012 0.987859590
112 2.451743724 0.102928012
113 -1.998883265 2.451743724
114 -2.936748200 -1.998883265
115 1.327114786 -2.936748200
116 -1.547598713 1.327114786
117 0.982402073 -1.547598713
118 -1.987919366 0.982402073
119 0.274939017 -1.987919366
120 -1.333886989 0.274939017
121 0.304154405 -1.333886989
122 -3.023893971 0.304154405
123 -1.129863105 -3.023893971
124 -1.059438245 -1.129863105
125 -0.865806131 -1.059438245
126 -0.513911570 -0.865806131
127 0.657837937 -0.513911570
128 1.107093822 0.657837937
129 -3.035236727 1.107093822
130 1.757879269 -3.035236727
131 -3.395499358 1.757879269
132 1.817021409 -3.395499358
133 -2.001079808 1.817021409
134 -1.740798301 -2.001079808
135 -0.194351716 -1.740798301
136 0.640535211 -0.194351716
137 0.468402482 0.640535211
138 -2.255150678 0.468402482
139 -1.452049433 -2.255150678
140 -5.453720060 -1.452049433
141 2.810925984 -5.453720060
142 1.588789011 2.810925984
143 0.364109135 1.588789011
144 1.068825869 0.364109135
145 -4.254547241 1.068825869
146 1.980591286 -4.254547241
147 -2.275292865 1.980591286
148 0.757020012 -2.275292865
149 -0.116840102 0.757020012
150 -3.208809380 -0.116840102
151 -1.525645289 -3.208809380
152 1.801977048 -1.525645289
153 3.909544202 1.801977048
154 1.453398005 3.909544202
155 -2.594466136 1.453398005
156 -0.009346032 -2.594466136
157 1.288332196 -0.009346032
158 1.107093822 1.288332196
159 0.829427294 1.107093822
160 -0.604839935 0.829427294
161 0.313163501 -0.604839935
> 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/7dqgu1351956152.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/88jph1351956152.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/9b6og1351956152.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/10lz0j1351956152.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11o7sd1351956152.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/1265311351956152.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13s7741351956152.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14f35q1351956152.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/15hpeu1351956152.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/16kcsn1351956152.tab")
+ }
>
> try(system("convert tmp/10ebr1351956152.ps tmp/10ebr1351956152.png",intern=TRUE))
character(0)
> try(system("convert tmp/2baeu1351956152.ps tmp/2baeu1351956152.png",intern=TRUE))
character(0)
> try(system("convert tmp/3i9ka1351956152.ps tmp/3i9ka1351956152.png",intern=TRUE))
character(0)
> try(system("convert tmp/4evfd1351956152.ps tmp/4evfd1351956152.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ekh61351956152.ps tmp/5ekh61351956152.png",intern=TRUE))
character(0)
> try(system("convert tmp/6zotq1351956152.ps tmp/6zotq1351956152.png",intern=TRUE))
character(0)
> try(system("convert tmp/7dqgu1351956152.ps tmp/7dqgu1351956152.png",intern=TRUE))
character(0)
> try(system("convert tmp/88jph1351956152.ps tmp/88jph1351956152.png",intern=TRUE))
character(0)
> try(system("convert tmp/9b6og1351956152.ps tmp/9b6og1351956152.png",intern=TRUE))
character(0)
> try(system("convert tmp/10lz0j1351956152.ps tmp/10lz0j1351956152.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.094 1.129 9.221