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(41
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+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),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 = '6'
> 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
Depression Connected Separate Learning Software Happiness Belonging
1 12 41 38 13 12 14 53
2 11 39 32 16 11 18 86
3 14 30 35 19 15 11 66
4 12 31 33 15 6 12 67
5 21 34 37 14 13 16 76
6 12 35 29 13 10 18 78
7 22 39 31 19 12 14 53
8 11 34 36 15 14 14 80
9 10 36 35 14 12 15 74
10 13 37 38 15 6 15 76
11 10 38 31 16 10 17 79
12 8 36 34 16 12 19 54
13 15 38 35 16 12 10 67
14 14 39 38 16 11 16 54
15 10 33 37 17 15 18 87
16 14 32 33 15 12 14 58
17 14 36 32 15 10 14 75
18 11 38 38 20 12 17 88
19 10 39 38 18 11 14 64
20 13 32 32 16 12 16 57
21 7 32 33 16 11 18 66
22 14 31 31 16 12 11 68
23 12 39 38 19 13 14 54
24 14 37 39 16 11 12 56
25 11 39 32 17 9 17 86
26 9 41 32 17 13 9 80
27 11 36 35 16 10 16 76
28 15 33 37 15 14 14 69
29 14 33 33 16 12 15 78
30 13 34 33 14 10 11 67
31 9 31 28 15 12 16 80
32 15 27 32 12 8 13 54
33 10 37 31 14 10 17 71
34 11 34 37 16 12 15 84
35 13 34 30 14 12 14 74
36 8 32 33 7 7 16 71
37 20 29 31 10 6 9 63
38 12 36 33 14 12 15 71
39 10 29 31 16 10 17 76
40 10 35 33 16 10 13 69
41 9 37 32 16 10 15 74
42 14 34 33 14 12 16 75
43 8 38 32 20 15 16 54
44 14 35 33 14 10 12 52
45 11 38 28 14 10 12 69
46 13 37 35 11 12 11 68
47 9 38 39 14 13 15 65
48 11 33 34 15 11 15 75
49 15 36 38 16 11 17 74
50 11 38 32 14 12 13 75
51 10 32 38 16 14 16 72
52 14 32 30 14 10 14 67
53 18 32 33 12 12 11 63
54 14 34 38 16 13 12 62
55 11 32 32 9 5 12 63
56 12 37 32 14 6 15 76
57 13 39 34 16 12 16 74
58 9 29 34 16 12 15 67
59 10 37 36 15 11 12 73
60 15 35 34 16 10 12 70
61 20 30 28 12 7 8 53
62 12 38 34 16 12 13 77
63 12 34 35 16 14 11 77
64 14 31 35 14 11 14 52
65 13 34 31 16 12 15 54
66 11 35 37 17 13 10 80
67 17 36 35 18 14 11 66
68 12 30 27 18 11 12 73
69 13 39 40 12 12 15 63
70 14 35 37 16 12 15 69
71 13 38 36 10 8 14 67
72 15 31 38 14 11 16 54
73 13 34 39 18 14 15 81
74 10 38 41 18 14 15 69
75 11 34 27 16 12 13 84
76 19 39 30 17 9 12 80
77 13 37 37 16 13 17 70
78 17 34 31 16 11 13 69
79 13 28 31 13 12 15 77
80 9 37 27 16 12 13 54
81 11 33 36 16 12 15 79
82 10 37 38 20 12 16 30
83 9 35 37 16 12 15 71
84 12 37 33 15 12 16 73
85 12 32 34 15 11 15 72
86 13 33 31 16 10 14 77
87 13 38 39 14 9 15 75
88 12 33 34 16 12 14 69
89 15 29 32 16 12 13 54
90 22 33 33 15 12 7 70
91 13 31 36 12 9 17 73
92 15 36 32 17 15 13 54
93 13 35 41 16 12 15 77
94 15 32 28 15 12 14 82
95 10 29 30 13 12 13 80
96 11 39 36 16 10 16 80
97 16 37 35 16 13 12 69
98 11 35 31 16 9 14 78
99 11 37 34 16 12 17 81
100 10 32 36 14 10 15 76
101 10 38 36 16 14 17 76
102 16 37 35 16 11 12 73
103 12 36 37 20 15 16 85
104 11 32 28 15 11 11 66
105 16 33 39 16 11 15 79
106 19 40 32 13 12 9 68
107 11 38 35 17 12 16 76
108 16 41 39 16 12 15 71
109 15 36 35 16 11 10 54
110 24 43 42 12 7 10 46
111 14 30 34 16 12 15 82
112 15 31 33 16 14 11 74
113 11 32 41 17 11 13 88
114 15 32 33 13 11 14 38
115 12 37 34 12 10 18 76
116 10 37 32 18 13 16 86
117 14 33 40 14 13 14 54
118 13 34 40 14 8 14 70
119 9 33 35 13 11 14 69
120 15 38 36 16 12 14 90
121 15 33 37 13 11 12 54
122 14 31 27 16 13 14 76
123 11 38 39 13 12 15 89
124 8 37 38 16 14 15 76
125 11 33 31 15 13 15 73
126 11 31 33 16 15 13 79
127 8 39 32 15 10 17 90
128 10 44 39 17 11 17 74
129 11 33 36 15 9 19 81
130 13 35 33 12 11 15 72
131 11 32 33 16 10 13 71
132 20 28 32 10 11 9 66
133 10 40 37 16 8 15 77
134 15 27 30 12 11 15 65
135 12 37 38 14 12 15 74
136 14 32 29 15 12 16 82
137 23 28 22 13 9 11 54
138 14 34 35 15 11 14 63
139 16 30 35 11 10 11 54
140 11 35 34 12 8 15 64
141 12 31 35 8 9 13 69
142 10 32 34 16 8 15 54
143 14 30 34 15 9 16 84
144 12 30 35 17 15 14 86
145 12 31 23 16 11 15 77
146 11 40 31 10 8 16 89
147 12 32 27 18 13 16 76
148 13 36 36 13 12 11 60
149 11 32 31 16 12 12 75
150 19 35 32 13 9 9 73
151 12 38 39 10 7 16 85
152 17 42 37 15 13 13 79
153 9 34 38 16 9 16 71
154 12 35 39 16 6 12 72
155 19 35 34 14 8 9 69
156 18 33 31 10 8 13 78
157 15 36 32 17 15 13 54
158 14 32 37 13 6 14 69
159 11 33 36 15 9 19 81
160 9 34 32 16 11 13 84
161 18 32 35 12 8 12 84
162 16 34 36 13 8 13 69
Belonging_Final
1 32
2 51
3 42
4 41
5 46
6 47
7 37
8 49
9 45
10 47
11 49
12 33
13 42
14 33
15 53
16 36
17 45
18 54
19 41
20 36
21 41
22 44
23 33
24 37
25 52
26 47
27 43
28 44
29 45
30 44
31 49
32 33
33 43
34 54
35 42
36 44
37 37
38 43
39 46
40 42
41 45
42 44
43 33
44 31
45 42
46 40
47 43
48 46
49 42
50 45
51 44
52 40
53 37
54 46
55 36
56 47
57 45
58 42
59 43
60 43
61 32
62 45
63 45
64 31
65 33
66 49
67 42
68 41
69 38
70 42
71 44
72 33
73 48
74 40
75 50
76 49
77 43
78 44
79 47
80 33
81 46
82 0
83 45
84 43
85 44
86 47
87 45
88 42
89 33
90 43
91 46
92 33
93 46
94 48
95 47
96 47
97 43
98 46
99 48
100 46
101 45
102 45
103 52
104 42
105 47
106 41
107 47
108 43
109 33
110 30
111 49
112 44
113 55
114 11
115 47
116 53
117 33
118 44
119 42
120 55
121 33
122 46
123 54
124 47
125 45
126 47
127 55
128 44
129 53
130 44
131 42
132 40
133 46
134 40
135 46
136 53
137 33
138 42
139 35
140 40
141 41
142 33
143 51
144 53
145 46
146 55
147 47
148 38
149 46
150 46
151 53
152 47
153 41
154 44
155 43
156 51
157 33
158 43
159 53
160 51
161 50
162 46
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning
28.031925 -0.016276 0.003053 -0.144083
Software Happiness Belonging Belonging_Final
-0.046614 -0.641378 -0.121881 0.130909
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.0368 -1.8069 -0.0352 1.6838 9.5348
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 28.031925 2.991789 9.370 < 2e-16 ***
Connected -0.016276 0.067978 -0.239 0.8111
Separate 0.003053 0.063806 0.048 0.9619
Learning -0.144083 0.114073 -1.263 0.2085
Software -0.046614 0.116114 -0.401 0.6886
Happiness -0.641378 0.095854 -6.691 3.87e-10 ***
Belonging -0.121881 0.062732 -1.943 0.0539 .
Belonging_Final 0.130909 0.090592 1.445 0.1505
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.633 on 154 degrees of freedom
Multiple R-squared: 0.3386, Adjusted R-squared: 0.3085
F-statistic: 11.26 on 7 and 154 DF, p-value: 1.769e-11
> 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,] 0.8429695 0.314060987 0.157030493
[2,] 0.9982685 0.003462990 0.001731495
[3,] 0.9960036 0.007992896 0.003996448
[4,] 0.9942481 0.011503780 0.005751890
[5,] 0.9923293 0.015341428 0.007670714
[6,] 0.9860333 0.027933306 0.013966653
[7,] 0.9850719 0.029856105 0.014928053
[8,] 0.9753405 0.049318982 0.024659491
[9,] 0.9850690 0.029862042 0.014931021
[10,] 0.9764225 0.047154930 0.023577465
[11,] 0.9855059 0.028988254 0.014494127
[12,] 0.9823493 0.035301432 0.017650716
[13,] 0.9731634 0.053673168 0.026836584
[14,] 0.9648011 0.070397724 0.035198862
[15,] 0.9495258 0.100948445 0.050474222
[16,] 0.9638277 0.072344620 0.036172310
[17,] 0.9652104 0.069579151 0.034789575
[18,] 0.9525961 0.094807887 0.047403944
[19,] 0.9642074 0.071585188 0.035792594
[20,] 0.9620004 0.075999261 0.037999631
[21,] 0.9611136 0.077772812 0.038886406
[22,] 0.9507950 0.098409921 0.049204960
[23,] 0.9379145 0.124171055 0.062085527
[24,] 0.9279386 0.144122730 0.072061365
[25,] 0.9129192 0.174161642 0.087080821
[26,] 0.9368604 0.126279283 0.063139642
[27,] 0.9656379 0.068724121 0.034362060
[28,] 0.9535170 0.092965923 0.046482962
[29,] 0.9416055 0.116788936 0.058394468
[30,] 0.9468938 0.106212394 0.053106197
[31,] 0.9464375 0.107124952 0.053562476
[32,] 0.9450534 0.109893292 0.054946646
[33,] 0.9555605 0.088879089 0.044439544
[34,] 0.9429196 0.114160850 0.057080425
[35,] 0.9415232 0.116953646 0.058476823
[36,] 0.9314911 0.137017727 0.068508864
[37,] 0.9461189 0.107762266 0.053881133
[38,] 0.9332672 0.133465553 0.066732777
[39,] 0.9525845 0.094830997 0.047415499
[40,] 0.9459995 0.108000957 0.054000479
[41,] 0.9386117 0.122776637 0.061388318
[42,] 0.9246481 0.150703704 0.075351852
[43,] 0.9289935 0.142012982 0.071006491
[44,] 0.9156465 0.168707074 0.084353537
[45,] 0.9364431 0.127113842 0.063556921
[46,] 0.9219699 0.156060264 0.078030132
[47,] 0.9126689 0.174662193 0.087331096
[48,] 0.9279433 0.144113379 0.072056690
[49,] 0.9422182 0.115563580 0.057781790
[50,] 0.9309102 0.138179587 0.069089794
[51,] 0.9317394 0.136521232 0.068260616
[52,] 0.9162765 0.167447024 0.083723512
[53,] 0.9077137 0.184572579 0.092286289
[54,] 0.8862470 0.227505912 0.113752956
[55,] 0.8615221 0.276955787 0.138477894
[56,] 0.8825301 0.234939821 0.117469911
[57,] 0.8814814 0.237037185 0.118518593
[58,] 0.8658716 0.268256877 0.134128438
[59,] 0.8400033 0.319993309 0.159996654
[60,] 0.8243124 0.351375185 0.175687593
[61,] 0.8030483 0.393903371 0.196951685
[62,] 0.7956671 0.408665785 0.204332892
[63,] 0.7782267 0.443546666 0.221773333
[64,] 0.7576778 0.484644374 0.242322187
[65,] 0.7382565 0.523486909 0.261743455
[66,] 0.8509820 0.298036047 0.149018023
[67,] 0.8436907 0.312618594 0.156309297
[68,] 0.8594681 0.281063819 0.140531909
[69,] 0.8338610 0.332277954 0.166138977
[70,] 0.9082399 0.183520136 0.091760068
[71,] 0.8882488 0.223502411 0.111751205
[72,] 0.8656182 0.268763661 0.134381830
[73,] 0.8809250 0.238149996 0.119074998
[74,] 0.8578452 0.284309624 0.142154812
[75,] 0.8299357 0.340128566 0.170064283
[76,] 0.7988317 0.402336540 0.201168270
[77,] 0.7671779 0.465644147 0.232822074
[78,] 0.7339556 0.532088820 0.266044410
[79,] 0.6974654 0.605069296 0.302534648
[80,] 0.7707010 0.458597904 0.229298952
[81,] 0.7441069 0.511786201 0.255893100
[82,] 0.7123360 0.575327981 0.287663990
[83,] 0.6842856 0.631428822 0.315714411
[84,] 0.6864534 0.627093103 0.313546552
[85,] 0.7109046 0.578190788 0.289095394
[86,] 0.6684081 0.663183790 0.331591895
[87,] 0.6431014 0.713797208 0.356898604
[88,] 0.6143586 0.771282899 0.385641449
[89,] 0.5715814 0.856837251 0.428418626
[90,] 0.5567378 0.886524348 0.443262174
[91,] 0.5084962 0.983007683 0.491503842
[92,] 0.4824684 0.964936857 0.517531572
[93,] 0.4710203 0.942040574 0.528979713
[94,] 0.5947369 0.810526236 0.405263118
[95,] 0.7142465 0.571506976 0.285753488
[96,] 0.7029623 0.594075454 0.297037727
[97,] 0.6579519 0.684096209 0.342048104
[98,] 0.7372016 0.525596875 0.262798438
[99,] 0.7127890 0.574422066 0.287211033
[100,] 0.8946273 0.210745352 0.105372676
[101,] 0.8964738 0.207052476 0.103526238
[102,] 0.8723905 0.255219027 0.127609514
[103,] 0.8511604 0.297679264 0.148839632
[104,] 0.8819857 0.236028669 0.118014335
[105,] 0.8664385 0.267122959 0.133561480
[106,] 0.8358811 0.328237711 0.164118855
[107,] 0.8305923 0.338815464 0.169407732
[108,] 0.7961744 0.407651255 0.203825628
[109,] 0.8446049 0.310790138 0.155395069
[110,] 0.8683857 0.263228523 0.131614261
[111,] 0.8371806 0.325638866 0.162819433
[112,] 0.8067545 0.386490966 0.193245483
[113,] 0.7668956 0.466208787 0.233104393
[114,] 0.7774376 0.445124797 0.222562399
[115,] 0.7450423 0.509915406 0.254957703
[116,] 0.7169580 0.566083901 0.283041950
[117,] 0.7156906 0.568618867 0.284309433
[118,] 0.6982627 0.603474698 0.301737349
[119,] 0.6543831 0.691233854 0.345616927
[120,] 0.5946558 0.810688397 0.405344198
[121,] 0.5724743 0.855051431 0.427525716
[122,] 0.5346530 0.930693973 0.465346987
[123,] 0.4758667 0.951733448 0.524133276
[124,] 0.4321976 0.864395245 0.567802377
[125,] 0.3696321 0.739264185 0.630367908
[126,] 0.3190934 0.638186877 0.680906561
[127,] 0.6230210 0.753958067 0.376979034
[128,] 0.5487709 0.902458116 0.451229058
[129,] 0.4731250 0.946250091 0.526874955
[130,] 0.4178878 0.835775598 0.582112201
[131,] 0.4062899 0.812579736 0.593710132
[132,] 0.3592433 0.718486538 0.640756731
[133,] 0.3687994 0.737598845 0.631200578
[134,] 0.2867187 0.573437314 0.713281343
[135,] 0.2143192 0.428638477 0.785680762
[136,] 0.3105507 0.621101445 0.689449278
[137,] 0.2256918 0.451383552 0.774308224
[138,] 0.4501917 0.900383483 0.549808259
[139,] 0.5280081 0.943983863 0.471991932
[140,] 0.3891580 0.778316079 0.610841961
[141,] 0.5446222 0.910755524 0.455377762
> postscript(file="/var/wessaorg/rcomp/tmp/1x5jp1356212668.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/wessaorg/rcomp/tmp/268hf1356212668.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3ewyt1356212668.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4wvs21356212668.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5zzqv1356212668.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-1.79827545 1.67342169 -0.61259807 -2.69190297 9.53481943 1.68720253
7 8 9 10 11 12
8.40049381 -1.45939306 -2.22736592 0.62609693 -0.61913991 -2.23733401
13 14 15 16 17 18
-0.57397443 1.82853596 0.75109839 0.44443233 1.31315094 1.47143045
19 20 21 22 23 24
-2.99452241 0.75244438 -3.57208615 -1.17425870 -0.92874355 -1.05245933
25 26 27 28 29 30
0.95199021 -6.03676625 0.11453219 1.83513719 2.50559953 -2.63480919
31 32 33 34 35 36
-2.29427100 0.01122554 -1.11317212 -0.93723554 0.50669475 -5.12137188
37 38 39 40 41 42
2.67321275 -0.32508366 -0.73854218 -3.54202917 -3.00699159 2.64035501
43 44 45 46 47 48
-3.40663647 -1.10354235 -3.40748095 -2.28558981 -3.99551774 -1.18470158
49 50 51 52 53 54
4.68051385 -2.34653064 -1.39171000 0.78956868 2.56654136 -1.45190954
55 56 57 58 59 60
-4.41666304 -0.49966992 1.75406132 -3.51051852 -3.90086921 0.80451103
61 62 63 64 65 66
1.82778959 -0.82070808 -2.07839635 0.15461637 0.17375796 -3.77012946
67 68 69 70 71 72
2.29436400 -1.29326107 0.09371144 1.82174357 -1.32428697 2.41015732
73 74 75 76 77 78
1.85786714 -1.49842534 -1.66582705 5.41264821 2.17463803 3.23259456
79 80 81 82 83 84
0.61437302 -5.04795820 -0.51258744 -0.18619604 -3.32722329 0.72041587
85 86 87 88 89 90
-0.30480131 0.39340010 0.77501611 -0.84302949 0.80656610 4.51726294
91 92 93 94 95 96
1.29015807 1.20442555 1.26094043 2.81391964 -3.28341232 0.12419402
97 98 99 100 101 102
1.85197123 -1.36787036 0.82332323 -2.27589978 -0.28995388 1.98444806
103 104 105 106 107 108
1.83656929 -4.32146341 4.30073135 2.64689855 -0.13924203 4.02614900
109 110 111 112 113 114
-1.05940521 6.68805617 2.41760241 0.64414702 -1.81067304 1.94477362
115 116 117 118 119 120
1.31664706 -0.52231297 0.24707527 -0.45962851 -4.32494582 3.08991957
121 122 123 124 125 126
-0.26383357 1.52192748 -0.70108166 -3.85691134 -1.19516820 -1.80980759
127 128 129 130 131 132
-2.19476841 -0.31006243 1.09639736 0.31483089 -2.34709728 2.85986508
133 134 135 136 137 138
-1.83192030 1.86425002 -0.35115708 2.43907648 6.96596978 0.24821324
139 140 141 142 143 144
-0.54453493 -2.27947036 -2.68161153 -3.05440747 2.75699929 0.02098043
145 146 147 148 149 150
0.20417039 -0.75234200 0.97821790 -2.72997905 -2.92525790 2.38053181
151 152 153 154 155 156
-0.08163446 4.11971241 -2.32137721 -2.28435495 2.37710159 3.39253767
157 158 159 160 161 162
1.20442555 0.28869503 1.09639736 -3.85861376 3.87303207 1.38342131
> postscript(file="/var/wessaorg/rcomp/tmp/6ihbf1356212668.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 -1.79827545 NA
1 1.67342169 -1.79827545
2 -0.61259807 1.67342169
3 -2.69190297 -0.61259807
4 9.53481943 -2.69190297
5 1.68720253 9.53481943
6 8.40049381 1.68720253
7 -1.45939306 8.40049381
8 -2.22736592 -1.45939306
9 0.62609693 -2.22736592
10 -0.61913991 0.62609693
11 -2.23733401 -0.61913991
12 -0.57397443 -2.23733401
13 1.82853596 -0.57397443
14 0.75109839 1.82853596
15 0.44443233 0.75109839
16 1.31315094 0.44443233
17 1.47143045 1.31315094
18 -2.99452241 1.47143045
19 0.75244438 -2.99452241
20 -3.57208615 0.75244438
21 -1.17425870 -3.57208615
22 -0.92874355 -1.17425870
23 -1.05245933 -0.92874355
24 0.95199021 -1.05245933
25 -6.03676625 0.95199021
26 0.11453219 -6.03676625
27 1.83513719 0.11453219
28 2.50559953 1.83513719
29 -2.63480919 2.50559953
30 -2.29427100 -2.63480919
31 0.01122554 -2.29427100
32 -1.11317212 0.01122554
33 -0.93723554 -1.11317212
34 0.50669475 -0.93723554
35 -5.12137188 0.50669475
36 2.67321275 -5.12137188
37 -0.32508366 2.67321275
38 -0.73854218 -0.32508366
39 -3.54202917 -0.73854218
40 -3.00699159 -3.54202917
41 2.64035501 -3.00699159
42 -3.40663647 2.64035501
43 -1.10354235 -3.40663647
44 -3.40748095 -1.10354235
45 -2.28558981 -3.40748095
46 -3.99551774 -2.28558981
47 -1.18470158 -3.99551774
48 4.68051385 -1.18470158
49 -2.34653064 4.68051385
50 -1.39171000 -2.34653064
51 0.78956868 -1.39171000
52 2.56654136 0.78956868
53 -1.45190954 2.56654136
54 -4.41666304 -1.45190954
55 -0.49966992 -4.41666304
56 1.75406132 -0.49966992
57 -3.51051852 1.75406132
58 -3.90086921 -3.51051852
59 0.80451103 -3.90086921
60 1.82778959 0.80451103
61 -0.82070808 1.82778959
62 -2.07839635 -0.82070808
63 0.15461637 -2.07839635
64 0.17375796 0.15461637
65 -3.77012946 0.17375796
66 2.29436400 -3.77012946
67 -1.29326107 2.29436400
68 0.09371144 -1.29326107
69 1.82174357 0.09371144
70 -1.32428697 1.82174357
71 2.41015732 -1.32428697
72 1.85786714 2.41015732
73 -1.49842534 1.85786714
74 -1.66582705 -1.49842534
75 5.41264821 -1.66582705
76 2.17463803 5.41264821
77 3.23259456 2.17463803
78 0.61437302 3.23259456
79 -5.04795820 0.61437302
80 -0.51258744 -5.04795820
81 -0.18619604 -0.51258744
82 -3.32722329 -0.18619604
83 0.72041587 -3.32722329
84 -0.30480131 0.72041587
85 0.39340010 -0.30480131
86 0.77501611 0.39340010
87 -0.84302949 0.77501611
88 0.80656610 -0.84302949
89 4.51726294 0.80656610
90 1.29015807 4.51726294
91 1.20442555 1.29015807
92 1.26094043 1.20442555
93 2.81391964 1.26094043
94 -3.28341232 2.81391964
95 0.12419402 -3.28341232
96 1.85197123 0.12419402
97 -1.36787036 1.85197123
98 0.82332323 -1.36787036
99 -2.27589978 0.82332323
100 -0.28995388 -2.27589978
101 1.98444806 -0.28995388
102 1.83656929 1.98444806
103 -4.32146341 1.83656929
104 4.30073135 -4.32146341
105 2.64689855 4.30073135
106 -0.13924203 2.64689855
107 4.02614900 -0.13924203
108 -1.05940521 4.02614900
109 6.68805617 -1.05940521
110 2.41760241 6.68805617
111 0.64414702 2.41760241
112 -1.81067304 0.64414702
113 1.94477362 -1.81067304
114 1.31664706 1.94477362
115 -0.52231297 1.31664706
116 0.24707527 -0.52231297
117 -0.45962851 0.24707527
118 -4.32494582 -0.45962851
119 3.08991957 -4.32494582
120 -0.26383357 3.08991957
121 1.52192748 -0.26383357
122 -0.70108166 1.52192748
123 -3.85691134 -0.70108166
124 -1.19516820 -3.85691134
125 -1.80980759 -1.19516820
126 -2.19476841 -1.80980759
127 -0.31006243 -2.19476841
128 1.09639736 -0.31006243
129 0.31483089 1.09639736
130 -2.34709728 0.31483089
131 2.85986508 -2.34709728
132 -1.83192030 2.85986508
133 1.86425002 -1.83192030
134 -0.35115708 1.86425002
135 2.43907648 -0.35115708
136 6.96596978 2.43907648
137 0.24821324 6.96596978
138 -0.54453493 0.24821324
139 -2.27947036 -0.54453493
140 -2.68161153 -2.27947036
141 -3.05440747 -2.68161153
142 2.75699929 -3.05440747
143 0.02098043 2.75699929
144 0.20417039 0.02098043
145 -0.75234200 0.20417039
146 0.97821790 -0.75234200
147 -2.72997905 0.97821790
148 -2.92525790 -2.72997905
149 2.38053181 -2.92525790
150 -0.08163446 2.38053181
151 4.11971241 -0.08163446
152 -2.32137721 4.11971241
153 -2.28435495 -2.32137721
154 2.37710159 -2.28435495
155 3.39253767 2.37710159
156 1.20442555 3.39253767
157 0.28869503 1.20442555
158 1.09639736 0.28869503
159 -3.85861376 1.09639736
160 3.87303207 -3.85861376
161 1.38342131 3.87303207
162 NA 1.38342131
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.67342169 -1.79827545
[2,] -0.61259807 1.67342169
[3,] -2.69190297 -0.61259807
[4,] 9.53481943 -2.69190297
[5,] 1.68720253 9.53481943
[6,] 8.40049381 1.68720253
[7,] -1.45939306 8.40049381
[8,] -2.22736592 -1.45939306
[9,] 0.62609693 -2.22736592
[10,] -0.61913991 0.62609693
[11,] -2.23733401 -0.61913991
[12,] -0.57397443 -2.23733401
[13,] 1.82853596 -0.57397443
[14,] 0.75109839 1.82853596
[15,] 0.44443233 0.75109839
[16,] 1.31315094 0.44443233
[17,] 1.47143045 1.31315094
[18,] -2.99452241 1.47143045
[19,] 0.75244438 -2.99452241
[20,] -3.57208615 0.75244438
[21,] -1.17425870 -3.57208615
[22,] -0.92874355 -1.17425870
[23,] -1.05245933 -0.92874355
[24,] 0.95199021 -1.05245933
[25,] -6.03676625 0.95199021
[26,] 0.11453219 -6.03676625
[27,] 1.83513719 0.11453219
[28,] 2.50559953 1.83513719
[29,] -2.63480919 2.50559953
[30,] -2.29427100 -2.63480919
[31,] 0.01122554 -2.29427100
[32,] -1.11317212 0.01122554
[33,] -0.93723554 -1.11317212
[34,] 0.50669475 -0.93723554
[35,] -5.12137188 0.50669475
[36,] 2.67321275 -5.12137188
[37,] -0.32508366 2.67321275
[38,] -0.73854218 -0.32508366
[39,] -3.54202917 -0.73854218
[40,] -3.00699159 -3.54202917
[41,] 2.64035501 -3.00699159
[42,] -3.40663647 2.64035501
[43,] -1.10354235 -3.40663647
[44,] -3.40748095 -1.10354235
[45,] -2.28558981 -3.40748095
[46,] -3.99551774 -2.28558981
[47,] -1.18470158 -3.99551774
[48,] 4.68051385 -1.18470158
[49,] -2.34653064 4.68051385
[50,] -1.39171000 -2.34653064
[51,] 0.78956868 -1.39171000
[52,] 2.56654136 0.78956868
[53,] -1.45190954 2.56654136
[54,] -4.41666304 -1.45190954
[55,] -0.49966992 -4.41666304
[56,] 1.75406132 -0.49966992
[57,] -3.51051852 1.75406132
[58,] -3.90086921 -3.51051852
[59,] 0.80451103 -3.90086921
[60,] 1.82778959 0.80451103
[61,] -0.82070808 1.82778959
[62,] -2.07839635 -0.82070808
[63,] 0.15461637 -2.07839635
[64,] 0.17375796 0.15461637
[65,] -3.77012946 0.17375796
[66,] 2.29436400 -3.77012946
[67,] -1.29326107 2.29436400
[68,] 0.09371144 -1.29326107
[69,] 1.82174357 0.09371144
[70,] -1.32428697 1.82174357
[71,] 2.41015732 -1.32428697
[72,] 1.85786714 2.41015732
[73,] -1.49842534 1.85786714
[74,] -1.66582705 -1.49842534
[75,] 5.41264821 -1.66582705
[76,] 2.17463803 5.41264821
[77,] 3.23259456 2.17463803
[78,] 0.61437302 3.23259456
[79,] -5.04795820 0.61437302
[80,] -0.51258744 -5.04795820
[81,] -0.18619604 -0.51258744
[82,] -3.32722329 -0.18619604
[83,] 0.72041587 -3.32722329
[84,] -0.30480131 0.72041587
[85,] 0.39340010 -0.30480131
[86,] 0.77501611 0.39340010
[87,] -0.84302949 0.77501611
[88,] 0.80656610 -0.84302949
[89,] 4.51726294 0.80656610
[90,] 1.29015807 4.51726294
[91,] 1.20442555 1.29015807
[92,] 1.26094043 1.20442555
[93,] 2.81391964 1.26094043
[94,] -3.28341232 2.81391964
[95,] 0.12419402 -3.28341232
[96,] 1.85197123 0.12419402
[97,] -1.36787036 1.85197123
[98,] 0.82332323 -1.36787036
[99,] -2.27589978 0.82332323
[100,] -0.28995388 -2.27589978
[101,] 1.98444806 -0.28995388
[102,] 1.83656929 1.98444806
[103,] -4.32146341 1.83656929
[104,] 4.30073135 -4.32146341
[105,] 2.64689855 4.30073135
[106,] -0.13924203 2.64689855
[107,] 4.02614900 -0.13924203
[108,] -1.05940521 4.02614900
[109,] 6.68805617 -1.05940521
[110,] 2.41760241 6.68805617
[111,] 0.64414702 2.41760241
[112,] -1.81067304 0.64414702
[113,] 1.94477362 -1.81067304
[114,] 1.31664706 1.94477362
[115,] -0.52231297 1.31664706
[116,] 0.24707527 -0.52231297
[117,] -0.45962851 0.24707527
[118,] -4.32494582 -0.45962851
[119,] 3.08991957 -4.32494582
[120,] -0.26383357 3.08991957
[121,] 1.52192748 -0.26383357
[122,] -0.70108166 1.52192748
[123,] -3.85691134 -0.70108166
[124,] -1.19516820 -3.85691134
[125,] -1.80980759 -1.19516820
[126,] -2.19476841 -1.80980759
[127,] -0.31006243 -2.19476841
[128,] 1.09639736 -0.31006243
[129,] 0.31483089 1.09639736
[130,] -2.34709728 0.31483089
[131,] 2.85986508 -2.34709728
[132,] -1.83192030 2.85986508
[133,] 1.86425002 -1.83192030
[134,] -0.35115708 1.86425002
[135,] 2.43907648 -0.35115708
[136,] 6.96596978 2.43907648
[137,] 0.24821324 6.96596978
[138,] -0.54453493 0.24821324
[139,] -2.27947036 -0.54453493
[140,] -2.68161153 -2.27947036
[141,] -3.05440747 -2.68161153
[142,] 2.75699929 -3.05440747
[143,] 0.02098043 2.75699929
[144,] 0.20417039 0.02098043
[145,] -0.75234200 0.20417039
[146,] 0.97821790 -0.75234200
[147,] -2.72997905 0.97821790
[148,] -2.92525790 -2.72997905
[149,] 2.38053181 -2.92525790
[150,] -0.08163446 2.38053181
[151,] 4.11971241 -0.08163446
[152,] -2.32137721 4.11971241
[153,] -2.28435495 -2.32137721
[154,] 2.37710159 -2.28435495
[155,] 3.39253767 2.37710159
[156,] 1.20442555 3.39253767
[157,] 0.28869503 1.20442555
[158,] 1.09639736 0.28869503
[159,] -3.85861376 1.09639736
[160,] 3.87303207 -3.85861376
[161,] 1.38342131 3.87303207
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.67342169 -1.79827545
2 -0.61259807 1.67342169
3 -2.69190297 -0.61259807
4 9.53481943 -2.69190297
5 1.68720253 9.53481943
6 8.40049381 1.68720253
7 -1.45939306 8.40049381
8 -2.22736592 -1.45939306
9 0.62609693 -2.22736592
10 -0.61913991 0.62609693
11 -2.23733401 -0.61913991
12 -0.57397443 -2.23733401
13 1.82853596 -0.57397443
14 0.75109839 1.82853596
15 0.44443233 0.75109839
16 1.31315094 0.44443233
17 1.47143045 1.31315094
18 -2.99452241 1.47143045
19 0.75244438 -2.99452241
20 -3.57208615 0.75244438
21 -1.17425870 -3.57208615
22 -0.92874355 -1.17425870
23 -1.05245933 -0.92874355
24 0.95199021 -1.05245933
25 -6.03676625 0.95199021
26 0.11453219 -6.03676625
27 1.83513719 0.11453219
28 2.50559953 1.83513719
29 -2.63480919 2.50559953
30 -2.29427100 -2.63480919
31 0.01122554 -2.29427100
32 -1.11317212 0.01122554
33 -0.93723554 -1.11317212
34 0.50669475 -0.93723554
35 -5.12137188 0.50669475
36 2.67321275 -5.12137188
37 -0.32508366 2.67321275
38 -0.73854218 -0.32508366
39 -3.54202917 -0.73854218
40 -3.00699159 -3.54202917
41 2.64035501 -3.00699159
42 -3.40663647 2.64035501
43 -1.10354235 -3.40663647
44 -3.40748095 -1.10354235
45 -2.28558981 -3.40748095
46 -3.99551774 -2.28558981
47 -1.18470158 -3.99551774
48 4.68051385 -1.18470158
49 -2.34653064 4.68051385
50 -1.39171000 -2.34653064
51 0.78956868 -1.39171000
52 2.56654136 0.78956868
53 -1.45190954 2.56654136
54 -4.41666304 -1.45190954
55 -0.49966992 -4.41666304
56 1.75406132 -0.49966992
57 -3.51051852 1.75406132
58 -3.90086921 -3.51051852
59 0.80451103 -3.90086921
60 1.82778959 0.80451103
61 -0.82070808 1.82778959
62 -2.07839635 -0.82070808
63 0.15461637 -2.07839635
64 0.17375796 0.15461637
65 -3.77012946 0.17375796
66 2.29436400 -3.77012946
67 -1.29326107 2.29436400
68 0.09371144 -1.29326107
69 1.82174357 0.09371144
70 -1.32428697 1.82174357
71 2.41015732 -1.32428697
72 1.85786714 2.41015732
73 -1.49842534 1.85786714
74 -1.66582705 -1.49842534
75 5.41264821 -1.66582705
76 2.17463803 5.41264821
77 3.23259456 2.17463803
78 0.61437302 3.23259456
79 -5.04795820 0.61437302
80 -0.51258744 -5.04795820
81 -0.18619604 -0.51258744
82 -3.32722329 -0.18619604
83 0.72041587 -3.32722329
84 -0.30480131 0.72041587
85 0.39340010 -0.30480131
86 0.77501611 0.39340010
87 -0.84302949 0.77501611
88 0.80656610 -0.84302949
89 4.51726294 0.80656610
90 1.29015807 4.51726294
91 1.20442555 1.29015807
92 1.26094043 1.20442555
93 2.81391964 1.26094043
94 -3.28341232 2.81391964
95 0.12419402 -3.28341232
96 1.85197123 0.12419402
97 -1.36787036 1.85197123
98 0.82332323 -1.36787036
99 -2.27589978 0.82332323
100 -0.28995388 -2.27589978
101 1.98444806 -0.28995388
102 1.83656929 1.98444806
103 -4.32146341 1.83656929
104 4.30073135 -4.32146341
105 2.64689855 4.30073135
106 -0.13924203 2.64689855
107 4.02614900 -0.13924203
108 -1.05940521 4.02614900
109 6.68805617 -1.05940521
110 2.41760241 6.68805617
111 0.64414702 2.41760241
112 -1.81067304 0.64414702
113 1.94477362 -1.81067304
114 1.31664706 1.94477362
115 -0.52231297 1.31664706
116 0.24707527 -0.52231297
117 -0.45962851 0.24707527
118 -4.32494582 -0.45962851
119 3.08991957 -4.32494582
120 -0.26383357 3.08991957
121 1.52192748 -0.26383357
122 -0.70108166 1.52192748
123 -3.85691134 -0.70108166
124 -1.19516820 -3.85691134
125 -1.80980759 -1.19516820
126 -2.19476841 -1.80980759
127 -0.31006243 -2.19476841
128 1.09639736 -0.31006243
129 0.31483089 1.09639736
130 -2.34709728 0.31483089
131 2.85986508 -2.34709728
132 -1.83192030 2.85986508
133 1.86425002 -1.83192030
134 -0.35115708 1.86425002
135 2.43907648 -0.35115708
136 6.96596978 2.43907648
137 0.24821324 6.96596978
138 -0.54453493 0.24821324
139 -2.27947036 -0.54453493
140 -2.68161153 -2.27947036
141 -3.05440747 -2.68161153
142 2.75699929 -3.05440747
143 0.02098043 2.75699929
144 0.20417039 0.02098043
145 -0.75234200 0.20417039
146 0.97821790 -0.75234200
147 -2.72997905 0.97821790
148 -2.92525790 -2.72997905
149 2.38053181 -2.92525790
150 -0.08163446 2.38053181
151 4.11971241 -0.08163446
152 -2.32137721 4.11971241
153 -2.28435495 -2.32137721
154 2.37710159 -2.28435495
155 3.39253767 2.37710159
156 1.20442555 3.39253767
157 0.28869503 1.20442555
158 1.09639736 0.28869503
159 -3.85861376 1.09639736
160 3.87303207 -3.85861376
161 1.38342131 3.87303207
> 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/wessaorg/rcomp/tmp/77pry1356212668.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/wessaorg/rcomp/tmp/8f2ai1356212668.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/wessaorg/rcomp/tmp/9edyg1356212668.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/wessaorg/rcomp/tmp/10zkkt1356212668.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11yufo1356212668.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1236oc1356212668.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/137gjn1356212668.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14390c1356212668.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/wessaorg/rcomp/tmp/15d2z31356212668.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/wessaorg/rcomp/tmp/1670wl1356212668.tab")
+ }
>
> try(system("convert tmp/1x5jp1356212668.ps tmp/1x5jp1356212668.png",intern=TRUE))
character(0)
> try(system("convert tmp/268hf1356212668.ps tmp/268hf1356212668.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ewyt1356212668.ps tmp/3ewyt1356212668.png",intern=TRUE))
character(0)
> try(system("convert tmp/4wvs21356212668.ps tmp/4wvs21356212668.png",intern=TRUE))
character(0)
> try(system("convert tmp/5zzqv1356212668.ps tmp/5zzqv1356212668.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ihbf1356212668.ps tmp/6ihbf1356212668.png",intern=TRUE))
character(0)
> try(system("convert tmp/77pry1356212668.ps tmp/77pry1356212668.png",intern=TRUE))
character(0)
> try(system("convert tmp/8f2ai1356212668.ps tmp/8f2ai1356212668.png",intern=TRUE))
character(0)
> try(system("convert tmp/9edyg1356212668.ps tmp/9edyg1356212668.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zkkt1356212668.ps tmp/10zkkt1356212668.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.583 0.845 8.589