R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(7
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+ ,dim=c(9
+ ,162)
+ ,dimnames=list(c('Age'
+ ,'connected'
+ ,'separated'
+ ,'learning'
+ ,'software'
+ ,'hapiness'
+ ,'depression'
+ ,'belonging'
+ ,'belonging_final')
+ ,1:162))
> y <- array(NA,dim=c(9,162),dimnames=list(c('Age','connected','separated','learning','software','hapiness','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 = '9'
> #'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
> 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
belonging_final Age connected separated learning software hapiness
1 32 7 41 38 13 12 14
2 51 5 39 32 16 11 18
3 42 5 30 35 19 15 11
4 41 5 31 33 15 6 12
5 46 8 34 37 14 13 16
6 47 6 35 29 13 10 18
7 37 5 39 31 19 12 14
8 49 6 34 36 15 14 14
9 45 5 36 35 14 12 15
10 47 4 37 38 15 6 15
11 49 6 38 31 16 10 17
12 33 5 36 34 16 12 19
13 42 5 38 35 16 12 10
14 33 6 39 38 16 11 16
15 53 7 33 37 17 15 18
16 36 6 32 33 15 12 14
17 45 7 36 32 15 10 14
18 54 6 38 38 20 12 17
19 41 8 39 38 18 11 14
20 36 7 32 32 16 12 16
21 41 5 32 33 16 11 18
22 44 5 31 31 16 12 11
23 33 7 39 38 19 13 14
24 37 7 37 39 16 11 12
25 52 5 39 32 17 9 17
26 47 4 41 32 17 13 9
27 43 10 36 35 16 10 16
28 44 6 33 37 15 14 14
29 45 5 33 33 16 12 15
30 44 5 34 33 14 10 11
31 49 5 31 28 15 12 16
32 33 5 27 32 12 8 13
33 43 6 37 31 14 10 17
34 54 5 34 37 16 12 15
35 42 5 34 30 14 12 14
36 44 5 32 33 7 7 16
37 37 5 29 31 10 6 9
38 43 5 36 33 14 12 15
39 46 5 29 31 16 10 17
40 42 5 35 33 16 10 13
41 45 5 37 32 16 10 15
42 44 7 34 33 14 12 16
43 33 5 38 32 20 15 16
44 31 6 35 33 14 10 12
45 42 7 38 28 14 10 12
46 40 7 37 35 11 12 11
47 43 5 38 39 14 13 15
48 46 5 33 34 15 11 15
49 42 4 36 38 16 11 17
50 45 5 38 32 14 12 13
51 44 4 32 38 16 14 16
52 40 5 32 30 14 10 14
53 37 5 32 33 12 12 11
54 46 7 34 38 16 13 12
55 36 5 32 32 9 5 12
56 47 5 37 32 14 6 15
57 45 6 39 34 16 12 16
58 42 4 29 34 16 12 15
59 43 6 37 36 15 11 12
60 43 6 35 34 16 10 12
61 32 5 30 28 12 7 8
62 45 7 38 34 16 12 13
63 45 6 34 35 16 14 11
64 31 8 31 35 14 11 14
65 33 7 34 31 16 12 15
66 49 5 35 37 17 13 10
67 42 6 36 35 18 14 11
68 41 6 30 27 18 11 12
69 38 5 39 40 12 12 15
70 42 5 35 37 16 12 15
71 44 5 38 36 10 8 14
72 33 5 31 38 14 11 16
73 48 4 34 39 18 14 15
74 40 6 38 41 18 14 15
75 50 6 34 27 16 12 13
76 49 6 39 30 17 9 12
77 43 6 37 37 16 13 17
78 44 7 34 31 16 11 13
79 47 5 28 31 13 12 15
80 33 7 37 27 16 12 13
81 46 6 33 36 16 12 15
82 0 5 37 38 20 12 16
83 45 5 35 37 16 12 15
84 43 4 37 33 15 12 16
85 44 8 32 34 15 11 15
86 47 8 33 31 16 10 14
87 45 5 38 39 14 9 15
88 42 5 33 34 16 12 14
89 33 6 29 32 16 12 13
90 43 4 33 33 15 12 7
91 46 5 31 36 12 9 17
92 33 5 36 32 17 15 13
93 46 5 35 41 16 12 15
94 48 5 32 28 15 12 14
95 47 6 29 30 13 12 13
96 47 6 39 36 16 10 16
97 43 5 37 35 16 13 12
98 46 6 35 31 16 9 14
99 48 5 37 34 16 12 17
100 46 7 32 36 14 10 15
101 45 5 38 36 16 14 17
102 45 6 37 35 16 11 12
103 52 6 36 37 20 15 16
104 42 6 32 28 15 11 11
105 47 4 33 39 16 11 15
106 41 5 40 32 13 12 9
107 47 5 38 35 17 12 16
108 43 7 41 39 16 12 15
109 33 6 36 35 16 11 10
110 30 9 43 42 12 7 10
111 49 6 30 34 16 12 15
112 44 6 31 33 16 14 11
113 55 5 32 41 17 11 13
114 11 6 32 33 13 11 14
115 47 5 37 34 12 10 18
116 53 8 37 32 18 13 16
117 33 7 33 40 14 13 14
118 44 5 34 40 14 8 14
119 42 7 33 35 13 11 14
120 55 6 38 36 16 12 14
121 33 6 33 37 13 11 12
122 46 9 31 27 16 13 14
123 54 7 38 39 13 12 15
124 47 6 37 38 16 14 15
125 45 5 33 31 15 13 15
126 47 5 31 33 16 15 13
127 55 6 39 32 15 10 17
128 44 6 44 39 17 11 17
129 53 7 33 36 15 9 19
130 44 5 35 33 12 11 15
131 42 5 32 33 16 10 13
132 40 5 28 32 10 11 9
133 46 6 40 37 16 8 15
134 40 4 27 30 12 11 15
135 46 5 37 38 14 12 15
136 53 7 32 29 15 12 16
137 33 5 28 22 13 9 11
138 42 7 34 35 15 11 14
139 35 7 30 35 11 10 11
140 40 6 35 34 12 8 15
141 41 5 31 35 8 9 13
142 33 8 32 34 16 8 15
143 51 5 30 34 15 9 16
144 53 5 30 35 17 15 14
145 46 5 31 23 16 11 15
146 55 6 40 31 10 8 16
147 47 4 32 27 18 13 16
148 38 5 36 36 13 12 11
149 46 5 32 31 16 12 12
150 46 7 35 32 13 9 9
151 53 6 38 39 10 7 16
152 47 7 42 37 15 13 13
153 41 10 34 38 16 9 16
154 44 6 35 39 16 6 12
155 43 8 35 34 14 8 9
156 51 4 33 31 10 8 13
157 33 5 36 32 17 15 13
158 43 6 32 37 13 6 14
159 53 7 33 36 15 9 19
160 51 7 34 32 16 11 13
161 50 6 32 35 12 8 12
162 46 6 34 36 13 8 13
depression belonging
1 12 53
2 11 86
3 14 66
4 12 67
5 21 76
6 12 78
7 22 53
8 11 80
9 10 74
10 13 76
11 10 79
12 8 54
13 15 67
14 14 54
15 10 87
16 14 58
17 14 75
18 11 88
19 10 64
20 13 57
21 7 66
22 14 68
23 12 54
24 14 56
25 11 86
26 9 80
27 11 76
28 15 69
29 14 78
30 13 67
31 9 80
32 15 54
33 10 71
34 11 84
35 13 74
36 8 71
37 20 63
38 12 71
39 10 76
40 10 69
41 9 74
42 14 75
43 8 54
44 14 52
45 11 69
46 13 68
47 9 65
48 11 75
49 15 74
50 11 75
51 10 72
52 14 67
53 18 63
54 14 62
55 11 63
56 12 76
57 13 74
58 9 67
59 10 73
60 15 70
61 20 53
62 12 77
63 12 77
64 14 52
65 13 54
66 11 80
67 17 66
68 12 73
69 13 63
70 14 69
71 13 67
72 15 54
73 13 81
74 10 69
75 11 84
76 19 80
77 13 70
78 17 69
79 13 77
80 9 54
81 11 79
82 10 30
83 9 71
84 12 73
85 12 72
86 13 77
87 13 75
88 12 69
89 15 54
90 22 70
91 13 73
92 15 54
93 13 77
94 15 82
95 10 80
96 11 80
97 16 69
98 11 78
99 11 81
100 10 76
101 10 76
102 16 73
103 12 85
104 11 66
105 16 79
106 19 68
107 11 76
108 16 71
109 15 54
110 24 46
111 14 82
112 15 74
113 11 88
114 15 38
115 12 76
116 10 86
117 14 54
118 13 70
119 9 69
120 15 90
121 15 54
122 14 76
123 11 89
124 8 76
125 11 73
126 11 79
127 8 90
128 10 74
129 11 81
130 13 72
131 11 71
132 20 66
133 10 77
134 15 65
135 12 74
136 14 82
137 23 54
138 14 63
139 16 54
140 11 64
141 12 69
142 10 54
143 14 84
144 12 86
145 12 77
146 11 89
147 12 76
148 13 60
149 11 75
150 19 73
151 12 85
152 17 79
153 9 71
154 12 72
155 19 69
156 18 78
157 15 54
158 14 69
159 11 81
160 9 84
161 18 84
162 16 69
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Age connected separated learning software
-4.623723 0.126387 -0.019123 0.053933 -0.088890 -0.002139
hapiness depression belonging
-0.058027 0.099707 0.659910
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-15.4126 -1.0306 0.0717 1.0628 8.1758
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.623723 3.354589 -1.378 0.170
Age 0.126387 0.161702 0.782 0.436
connected -0.019123 0.060509 -0.316 0.752
separated 0.053933 0.056350 0.957 0.340
learning -0.088890 0.101541 -0.875 0.383
software -0.002139 0.103282 -0.021 0.984
hapiness -0.058027 0.096267 -0.603 0.548
depression 0.099707 0.070880 1.407 0.162
belonging 0.659910 0.018189 36.281 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.329 on 153 degrees of freedom
Multiple R-squared: 0.9031, Adjusted R-squared: 0.8981
F-statistic: 178.3 on 8 and 153 DF, p-value: < 2.2e-16
> 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,] 0.1416618775 2.833238e-01 8.583381e-01
[2,] 0.0677061008 1.354122e-01 9.322939e-01
[3,] 0.0330534570 6.610691e-02 9.669465e-01
[4,] 0.0166376547 3.327531e-02 9.833623e-01
[5,] 0.0061416267 1.228325e-02 9.938584e-01
[6,] 0.0032496119 6.499224e-03 9.967504e-01
[7,] 0.0011569791 2.313958e-03 9.988430e-01
[8,] 0.0011278697 2.255739e-03 9.988721e-01
[9,] 0.0004322836 8.645671e-04 9.995677e-01
[10,] 0.0002062778 4.125556e-04 9.997937e-01
[11,] 0.0001440619 2.881237e-04 9.998559e-01
[12,] 0.0002376426 4.752851e-04 9.997624e-01
[13,] 0.0004568841 9.137682e-04 9.995431e-01
[14,] 0.0002132408 4.264816e-04 9.997868e-01
[15,] 0.0003014119 6.028237e-04 9.996986e-01
[16,] 0.0008643325 1.728665e-03 9.991357e-01
[17,] 0.0005426171 1.085234e-03 9.994574e-01
[18,] 0.0031203630 6.240726e-03 9.968796e-01
[19,] 0.0057040801 1.140816e-02 9.942959e-01
[20,] 0.0035168600 7.033720e-03 9.964831e-01
[21,] 0.0030808155 6.161631e-03 9.969192e-01
[22,] 0.0018051791 3.610358e-03 9.981948e-01
[23,] 0.0033466123 6.693225e-03 9.966534e-01
[24,] 0.0075726082 1.514522e-02 9.924274e-01
[25,] 0.0080233665 1.604673e-02 9.919766e-01
[26,] 0.0087941777 1.758836e-02 9.912058e-01
[27,] 0.0057083731 1.141675e-02 9.942916e-01
[28,] 0.0037434040 7.486808e-03 9.962566e-01
[29,] 0.0024113602 4.822720e-03 9.975886e-01
[30,] 0.0014794374 2.958875e-03 9.985206e-01
[31,] 0.0011224154 2.244831e-03 9.988776e-01
[32,] 0.0010699906 2.139981e-03 9.989300e-01
[33,] 0.0008018722 1.603744e-03 9.991981e-01
[34,] 0.0006436822 1.287364e-03 9.993563e-01
[35,] 0.0004517065 9.034130e-04 9.995483e-01
[36,] 0.0008322497 1.664499e-03 9.991678e-01
[37,] 0.0005067556 1.013511e-03 9.994932e-01
[38,] 0.0027772863 5.554573e-03 9.972227e-01
[39,] 0.0018148866 3.629773e-03 9.981851e-01
[40,] 0.0013051945 2.610389e-03 9.986948e-01
[41,] 0.0008823609 1.764722e-03 9.991176e-01
[42,] 0.0007843972 1.568794e-03 9.992156e-01
[43,] 0.0608096878 1.216194e-01 9.391903e-01
[44,] 0.0590417492 1.180835e-01 9.409583e-01
[45,] 0.0516228284 1.032457e-01 9.483772e-01
[46,] 0.0393823585 7.876472e-02 9.606176e-01
[47,] 0.0350560612 7.011212e-02 9.649439e-01
[48,] 0.0324508222 6.490164e-02 9.675492e-01
[49,] 0.0244584063 4.891681e-02 9.755416e-01
[50,] 0.0183233416 3.664668e-02 9.816767e-01
[51,] 0.0172735509 3.454710e-02 9.827264e-01
[52,] 0.0182892834 3.657857e-02 9.817107e-01
[53,] 0.0153902105 3.078042e-02 9.846098e-01
[54,] 0.0123968857 2.479377e-02 9.876031e-01
[55,] 0.0090697268 1.813945e-02 9.909303e-01
[56,] 0.0078098581 1.561972e-02 9.921901e-01
[57,] 0.0107005234 2.140105e-02 9.892995e-01
[58,] 0.0085775823 1.715516e-02 9.914224e-01
[59,] 0.0066137372 1.322747e-02 9.933863e-01
[60,] 0.0105506651 2.110133e-02 9.894493e-01
[61,] 0.0095336723 1.906734e-02 9.904663e-01
[62,] 0.0091560875 1.831217e-02 9.908439e-01
[63,] 0.0113393651 2.267873e-02 9.886606e-01
[64,] 0.0090228337 1.804567e-02 9.909772e-01
[65,] 0.0067811588 1.356232e-02 9.932188e-01
[66,] 0.0050923215 1.018464e-02 9.949077e-01
[67,] 0.0048963584 9.792717e-03 9.951036e-01
[68,] 0.0036109201 7.221840e-03 9.963891e-01
[69,] 0.0032140384 6.428077e-03 9.967860e-01
[70,] 0.0031330339 6.266068e-03 9.968670e-01
[71,] 0.9976733482 4.653304e-03 2.326652e-03
[72,] 0.9976606395 4.678721e-03 2.339360e-03
[73,] 0.9968840116 6.231977e-03 3.115988e-03
[74,] 0.9955390845 8.921831e-03 4.460915e-03
[75,] 0.9937400660 1.251987e-02 6.259934e-03
[76,] 0.9918808281 1.623834e-02 8.119172e-03
[77,] 0.9888483527 2.230329e-02 1.115165e-02
[78,] 0.9853039496 2.939210e-02 1.469605e-02
[79,] 0.9807762611 3.844748e-02 1.922374e-02
[80,] 0.9761985437 4.760291e-02 2.380146e-02
[81,] 0.9714664260 5.706715e-02 2.853357e-02
[82,] 0.9652515068 6.949699e-02 3.474849e-02
[83,] 0.9659492462 6.810151e-02 3.405075e-02
[84,] 0.9633962057 7.320759e-02 3.660379e-02
[85,] 0.9605452679 7.890946e-02 3.945473e-02
[86,] 0.9513599720 9.728006e-02 4.864003e-02
[87,] 0.9462554821 1.074890e-01 5.374452e-02
[88,] 0.9369543450 1.260913e-01 6.304566e-02
[89,] 0.9214566409 1.570867e-01 7.854336e-02
[90,] 0.9043805793 1.912388e-01 9.561942e-02
[91,] 0.8812357340 2.375285e-01 1.187643e-01
[92,] 0.8543365378 2.913269e-01 1.456635e-01
[93,] 0.8543970911 2.912058e-01 1.456029e-01
[94,] 0.8502281986 2.995436e-01 1.497718e-01
[95,] 0.8232743261 3.534513e-01 1.767257e-01
[96,] 0.7930181359 4.139637e-01 2.069819e-01
[97,] 0.7590517415 4.818965e-01 2.409483e-01
[98,] 0.7248450384 5.503099e-01 2.751550e-01
[99,] 0.6993315844 6.013368e-01 3.006684e-01
[100,] 0.6880384063 6.239232e-01 3.119616e-01
[101,] 0.6573709894 6.852580e-01 3.426290e-01
[102,] 0.6111765840 7.776468e-01 3.888234e-01
[103,] 0.9999751992 4.960151e-05 2.480076e-05
[104,] 0.9999590111 8.197775e-05 4.098888e-05
[105,] 0.9999291998 1.416003e-04 7.080017e-05
[106,] 0.9998915612 2.168776e-04 1.084388e-04
[107,] 0.9998117026 3.765948e-04 1.882974e-04
[108,] 0.9996650205 6.699590e-04 3.349795e-04
[109,] 0.9994370021 1.125996e-03 5.629979e-04
[110,] 0.9991025758 1.794848e-03 8.974242e-04
[111,] 0.9985781312 2.843738e-03 1.421869e-03
[112,] 0.9978820396 4.235921e-03 2.117960e-03
[113,] 0.9968390068 6.321986e-03 3.160993e-03
[114,] 0.9949867972 1.002641e-02 5.013203e-03
[115,] 0.9926400892 1.471982e-02 7.359911e-03
[116,] 0.9885005677 2.299886e-02 1.149943e-02
[117,] 0.9873361887 2.532762e-02 1.266381e-02
[118,] 0.9877406755 2.451865e-02 1.225932e-02
[119,] 0.9824344433 3.513111e-02 1.756556e-02
[120,] 0.9761886815 4.762264e-02 2.381132e-02
[121,] 0.9692890181 6.142196e-02 3.071098e-02
[122,] 0.9605649158 7.887017e-02 3.943508e-02
[123,] 0.9488984317 1.022031e-01 5.110157e-02
[124,] 0.9263963561 1.472073e-01 7.360364e-02
[125,] 0.9395948045 1.208104e-01 6.040520e-02
[126,] 0.9359810895 1.280378e-01 6.401891e-02
[127,] 0.9558580783 8.828384e-02 4.414192e-02
[128,] 0.9445297341 1.109405e-01 5.547027e-02
[129,] 0.9150447638 1.699105e-01 8.495524e-02
[130,] 0.9330143027 1.339714e-01 6.698570e-02
[131,] 0.8975738768 2.048522e-01 1.024261e-01
[132,] 0.8795693971 2.408612e-01 1.204306e-01
[133,] 0.8224143121 3.551714e-01 1.775857e-01
[134,] 0.8162409710 3.675181e-01 1.837590e-01
[135,] 0.7810160636 4.379679e-01 2.189839e-01
[136,] 0.7458158460 5.083683e-01 2.541842e-01
[137,] 0.8045494415 3.909011e-01 1.954506e-01
[138,] 0.7168310665 5.663379e-01 2.831689e-01
[139,] 0.5564759441 8.870481e-01 4.435241e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1pahq1321534609.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2sf5n1321534609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3pxra1321534609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4bye51321534609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/53fbg1321534609.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
0.295503886 -1.347066107 2.087152571 0.436856564 -1.779023371
6 7 8 9 10
-0.477379489 5.423835875 -0.139917530 0.102675028 0.543499247
11 12 13 14 15
1.220268811 1.964114954 1.149404858 0.904905702 -0.446998637
16 17 18 19 20
1.198263329 -1.020452944 0.163677629 2.513580059 2.090369961
21 22 23 24 25
2.062172090 2.729098606 1.132825894 3.134410848 -0.320482343
26 27 28 29 30
-1.452633229 -3.717259466 1.647213937 -2.707515849 3.256160103
31 32 33 34 35
0.671751693 0.489648499 0.302648631 2.435533332 -3.023053196
36 37 38 39 40
0.738293713 -2.270064778 -0.009141311 0.154279708 0.548415785
41 42 43 44 45
0.556803815 -2.081188509 2.298123129 0.005871239 0.287161926
46 47 48 49 50
-1.969411449 3.966228263 0.426372410 -3.139574779 -0.572951312
51 52 53 54 55
0.550677675 -0.545912532 -1.814477851 8.175791839 -2.286218158
56 57 58 59 60
0.751526167 0.024277085 2.045992145 -1.485942486 0.151625988
61 62 63 64 65
-0.368148089 -2.175338608 -2.291151686 -0.313066931 1.104252677
66 67 68 69 70
-0.104808576 1.685354782 -3.067151226 -0.327506099 0.054191405
71 72 73 74 75
2.985096560 0.600822573 -1.583568909 -1.649673525 -1.267579110
76 77 78 79 80
-0.467329221 0.524040234 1.688578449 -0.202317737 1.660125030
81 82 83 84 85
-2.356492297 -15.412581250 2.232903304 -1.036535001 -0.091887255
86 87 88 89 90
-0.281500408 -1.040258711 0.319129940 0.765624631 -0.652604878
91 92 93 94 95
1.245774044 1.121180934 -1.341116032 -2.343239091 -2.052314459
96 97 98 99 100
-1.847916449 0.828948388 -1.453117130 -1.249513947 -0.604623483
101 102 103 104 105
-0.934719707 0.058641186 0.007780262 2.311544039 -1.766188862
106 107 108 109 110
-1.033983326 1.046090431 -0.710943979 0.561466308 0.956442919
111 112 113 114 115
-1.584845982 -1.560043541 0.512610764 -10.883156169 0.648519972
116 117 118 119 120
0.401237718 0.266360077 1.068701356 0.042733621 -0.976743719
121 122 123 124 125
0.245616290 -0.663779700 -1.414835394 0.895263351 0.912270603
126 127 128 129 130
-1.216188387 0.036968784 -0.705875349 3.334100158 0.032199783
131 132 133 134 135
-0.928480340 -1.312154175 -0.865594946 0.587360526 0.760586404
136 137 138 139 140
2.565814655 0.225423358 3.700565560 1.832073322 1.324156889
141 142 143 144 145
-1.548612924 1.068382606 -0.815560729 0.084661114 -0.349249279
146 147 148 149 150
-0.035939616 1.480525212 1.667368773 0.485996065 0.311657640
151 152 153 154 155
0.032146613 -2.165748661 -2.420479051 -0.147297409 -0.196203587
156 157 158 159 160
2.469959989 1.121180934 0.532901984 3.334100158 -0.466356985
161 162
-2.857377862 3.371919740
> postscript(file="/var/wessaorg/rcomp/tmp/6lz331321534609.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 0.295503886 NA
1 -1.347066107 0.295503886
2 2.087152571 -1.347066107
3 0.436856564 2.087152571
4 -1.779023371 0.436856564
5 -0.477379489 -1.779023371
6 5.423835875 -0.477379489
7 -0.139917530 5.423835875
8 0.102675028 -0.139917530
9 0.543499247 0.102675028
10 1.220268811 0.543499247
11 1.964114954 1.220268811
12 1.149404858 1.964114954
13 0.904905702 1.149404858
14 -0.446998637 0.904905702
15 1.198263329 -0.446998637
16 -1.020452944 1.198263329
17 0.163677629 -1.020452944
18 2.513580059 0.163677629
19 2.090369961 2.513580059
20 2.062172090 2.090369961
21 2.729098606 2.062172090
22 1.132825894 2.729098606
23 3.134410848 1.132825894
24 -0.320482343 3.134410848
25 -1.452633229 -0.320482343
26 -3.717259466 -1.452633229
27 1.647213937 -3.717259466
28 -2.707515849 1.647213937
29 3.256160103 -2.707515849
30 0.671751693 3.256160103
31 0.489648499 0.671751693
32 0.302648631 0.489648499
33 2.435533332 0.302648631
34 -3.023053196 2.435533332
35 0.738293713 -3.023053196
36 -2.270064778 0.738293713
37 -0.009141311 -2.270064778
38 0.154279708 -0.009141311
39 0.548415785 0.154279708
40 0.556803815 0.548415785
41 -2.081188509 0.556803815
42 2.298123129 -2.081188509
43 0.005871239 2.298123129
44 0.287161926 0.005871239
45 -1.969411449 0.287161926
46 3.966228263 -1.969411449
47 0.426372410 3.966228263
48 -3.139574779 0.426372410
49 -0.572951312 -3.139574779
50 0.550677675 -0.572951312
51 -0.545912532 0.550677675
52 -1.814477851 -0.545912532
53 8.175791839 -1.814477851
54 -2.286218158 8.175791839
55 0.751526167 -2.286218158
56 0.024277085 0.751526167
57 2.045992145 0.024277085
58 -1.485942486 2.045992145
59 0.151625988 -1.485942486
60 -0.368148089 0.151625988
61 -2.175338608 -0.368148089
62 -2.291151686 -2.175338608
63 -0.313066931 -2.291151686
64 1.104252677 -0.313066931
65 -0.104808576 1.104252677
66 1.685354782 -0.104808576
67 -3.067151226 1.685354782
68 -0.327506099 -3.067151226
69 0.054191405 -0.327506099
70 2.985096560 0.054191405
71 0.600822573 2.985096560
72 -1.583568909 0.600822573
73 -1.649673525 -1.583568909
74 -1.267579110 -1.649673525
75 -0.467329221 -1.267579110
76 0.524040234 -0.467329221
77 1.688578449 0.524040234
78 -0.202317737 1.688578449
79 1.660125030 -0.202317737
80 -2.356492297 1.660125030
81 -15.412581250 -2.356492297
82 2.232903304 -15.412581250
83 -1.036535001 2.232903304
84 -0.091887255 -1.036535001
85 -0.281500408 -0.091887255
86 -1.040258711 -0.281500408
87 0.319129940 -1.040258711
88 0.765624631 0.319129940
89 -0.652604878 0.765624631
90 1.245774044 -0.652604878
91 1.121180934 1.245774044
92 -1.341116032 1.121180934
93 -2.343239091 -1.341116032
94 -2.052314459 -2.343239091
95 -1.847916449 -2.052314459
96 0.828948388 -1.847916449
97 -1.453117130 0.828948388
98 -1.249513947 -1.453117130
99 -0.604623483 -1.249513947
100 -0.934719707 -0.604623483
101 0.058641186 -0.934719707
102 0.007780262 0.058641186
103 2.311544039 0.007780262
104 -1.766188862 2.311544039
105 -1.033983326 -1.766188862
106 1.046090431 -1.033983326
107 -0.710943979 1.046090431
108 0.561466308 -0.710943979
109 0.956442919 0.561466308
110 -1.584845982 0.956442919
111 -1.560043541 -1.584845982
112 0.512610764 -1.560043541
113 -10.883156169 0.512610764
114 0.648519972 -10.883156169
115 0.401237718 0.648519972
116 0.266360077 0.401237718
117 1.068701356 0.266360077
118 0.042733621 1.068701356
119 -0.976743719 0.042733621
120 0.245616290 -0.976743719
121 -0.663779700 0.245616290
122 -1.414835394 -0.663779700
123 0.895263351 -1.414835394
124 0.912270603 0.895263351
125 -1.216188387 0.912270603
126 0.036968784 -1.216188387
127 -0.705875349 0.036968784
128 3.334100158 -0.705875349
129 0.032199783 3.334100158
130 -0.928480340 0.032199783
131 -1.312154175 -0.928480340
132 -0.865594946 -1.312154175
133 0.587360526 -0.865594946
134 0.760586404 0.587360526
135 2.565814655 0.760586404
136 0.225423358 2.565814655
137 3.700565560 0.225423358
138 1.832073322 3.700565560
139 1.324156889 1.832073322
140 -1.548612924 1.324156889
141 1.068382606 -1.548612924
142 -0.815560729 1.068382606
143 0.084661114 -0.815560729
144 -0.349249279 0.084661114
145 -0.035939616 -0.349249279
146 1.480525212 -0.035939616
147 1.667368773 1.480525212
148 0.485996065 1.667368773
149 0.311657640 0.485996065
150 0.032146613 0.311657640
151 -2.165748661 0.032146613
152 -2.420479051 -2.165748661
153 -0.147297409 -2.420479051
154 -0.196203587 -0.147297409
155 2.469959989 -0.196203587
156 1.121180934 2.469959989
157 0.532901984 1.121180934
158 3.334100158 0.532901984
159 -0.466356985 3.334100158
160 -2.857377862 -0.466356985
161 3.371919740 -2.857377862
162 NA 3.371919740
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.347066107 0.295503886
[2,] 2.087152571 -1.347066107
[3,] 0.436856564 2.087152571
[4,] -1.779023371 0.436856564
[5,] -0.477379489 -1.779023371
[6,] 5.423835875 -0.477379489
[7,] -0.139917530 5.423835875
[8,] 0.102675028 -0.139917530
[9,] 0.543499247 0.102675028
[10,] 1.220268811 0.543499247
[11,] 1.964114954 1.220268811
[12,] 1.149404858 1.964114954
[13,] 0.904905702 1.149404858
[14,] -0.446998637 0.904905702
[15,] 1.198263329 -0.446998637
[16,] -1.020452944 1.198263329
[17,] 0.163677629 -1.020452944
[18,] 2.513580059 0.163677629
[19,] 2.090369961 2.513580059
[20,] 2.062172090 2.090369961
[21,] 2.729098606 2.062172090
[22,] 1.132825894 2.729098606
[23,] 3.134410848 1.132825894
[24,] -0.320482343 3.134410848
[25,] -1.452633229 -0.320482343
[26,] -3.717259466 -1.452633229
[27,] 1.647213937 -3.717259466
[28,] -2.707515849 1.647213937
[29,] 3.256160103 -2.707515849
[30,] 0.671751693 3.256160103
[31,] 0.489648499 0.671751693
[32,] 0.302648631 0.489648499
[33,] 2.435533332 0.302648631
[34,] -3.023053196 2.435533332
[35,] 0.738293713 -3.023053196
[36,] -2.270064778 0.738293713
[37,] -0.009141311 -2.270064778
[38,] 0.154279708 -0.009141311
[39,] 0.548415785 0.154279708
[40,] 0.556803815 0.548415785
[41,] -2.081188509 0.556803815
[42,] 2.298123129 -2.081188509
[43,] 0.005871239 2.298123129
[44,] 0.287161926 0.005871239
[45,] -1.969411449 0.287161926
[46,] 3.966228263 -1.969411449
[47,] 0.426372410 3.966228263
[48,] -3.139574779 0.426372410
[49,] -0.572951312 -3.139574779
[50,] 0.550677675 -0.572951312
[51,] -0.545912532 0.550677675
[52,] -1.814477851 -0.545912532
[53,] 8.175791839 -1.814477851
[54,] -2.286218158 8.175791839
[55,] 0.751526167 -2.286218158
[56,] 0.024277085 0.751526167
[57,] 2.045992145 0.024277085
[58,] -1.485942486 2.045992145
[59,] 0.151625988 -1.485942486
[60,] -0.368148089 0.151625988
[61,] -2.175338608 -0.368148089
[62,] -2.291151686 -2.175338608
[63,] -0.313066931 -2.291151686
[64,] 1.104252677 -0.313066931
[65,] -0.104808576 1.104252677
[66,] 1.685354782 -0.104808576
[67,] -3.067151226 1.685354782
[68,] -0.327506099 -3.067151226
[69,] 0.054191405 -0.327506099
[70,] 2.985096560 0.054191405
[71,] 0.600822573 2.985096560
[72,] -1.583568909 0.600822573
[73,] -1.649673525 -1.583568909
[74,] -1.267579110 -1.649673525
[75,] -0.467329221 -1.267579110
[76,] 0.524040234 -0.467329221
[77,] 1.688578449 0.524040234
[78,] -0.202317737 1.688578449
[79,] 1.660125030 -0.202317737
[80,] -2.356492297 1.660125030
[81,] -15.412581250 -2.356492297
[82,] 2.232903304 -15.412581250
[83,] -1.036535001 2.232903304
[84,] -0.091887255 -1.036535001
[85,] -0.281500408 -0.091887255
[86,] -1.040258711 -0.281500408
[87,] 0.319129940 -1.040258711
[88,] 0.765624631 0.319129940
[89,] -0.652604878 0.765624631
[90,] 1.245774044 -0.652604878
[91,] 1.121180934 1.245774044
[92,] -1.341116032 1.121180934
[93,] -2.343239091 -1.341116032
[94,] -2.052314459 -2.343239091
[95,] -1.847916449 -2.052314459
[96,] 0.828948388 -1.847916449
[97,] -1.453117130 0.828948388
[98,] -1.249513947 -1.453117130
[99,] -0.604623483 -1.249513947
[100,] -0.934719707 -0.604623483
[101,] 0.058641186 -0.934719707
[102,] 0.007780262 0.058641186
[103,] 2.311544039 0.007780262
[104,] -1.766188862 2.311544039
[105,] -1.033983326 -1.766188862
[106,] 1.046090431 -1.033983326
[107,] -0.710943979 1.046090431
[108,] 0.561466308 -0.710943979
[109,] 0.956442919 0.561466308
[110,] -1.584845982 0.956442919
[111,] -1.560043541 -1.584845982
[112,] 0.512610764 -1.560043541
[113,] -10.883156169 0.512610764
[114,] 0.648519972 -10.883156169
[115,] 0.401237718 0.648519972
[116,] 0.266360077 0.401237718
[117,] 1.068701356 0.266360077
[118,] 0.042733621 1.068701356
[119,] -0.976743719 0.042733621
[120,] 0.245616290 -0.976743719
[121,] -0.663779700 0.245616290
[122,] -1.414835394 -0.663779700
[123,] 0.895263351 -1.414835394
[124,] 0.912270603 0.895263351
[125,] -1.216188387 0.912270603
[126,] 0.036968784 -1.216188387
[127,] -0.705875349 0.036968784
[128,] 3.334100158 -0.705875349
[129,] 0.032199783 3.334100158
[130,] -0.928480340 0.032199783
[131,] -1.312154175 -0.928480340
[132,] -0.865594946 -1.312154175
[133,] 0.587360526 -0.865594946
[134,] 0.760586404 0.587360526
[135,] 2.565814655 0.760586404
[136,] 0.225423358 2.565814655
[137,] 3.700565560 0.225423358
[138,] 1.832073322 3.700565560
[139,] 1.324156889 1.832073322
[140,] -1.548612924 1.324156889
[141,] 1.068382606 -1.548612924
[142,] -0.815560729 1.068382606
[143,] 0.084661114 -0.815560729
[144,] -0.349249279 0.084661114
[145,] -0.035939616 -0.349249279
[146,] 1.480525212 -0.035939616
[147,] 1.667368773 1.480525212
[148,] 0.485996065 1.667368773
[149,] 0.311657640 0.485996065
[150,] 0.032146613 0.311657640
[151,] -2.165748661 0.032146613
[152,] -2.420479051 -2.165748661
[153,] -0.147297409 -2.420479051
[154,] -0.196203587 -0.147297409
[155,] 2.469959989 -0.196203587
[156,] 1.121180934 2.469959989
[157,] 0.532901984 1.121180934
[158,] 3.334100158 0.532901984
[159,] -0.466356985 3.334100158
[160,] -2.857377862 -0.466356985
[161,] 3.371919740 -2.857377862
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.347066107 0.295503886
2 2.087152571 -1.347066107
3 0.436856564 2.087152571
4 -1.779023371 0.436856564
5 -0.477379489 -1.779023371
6 5.423835875 -0.477379489
7 -0.139917530 5.423835875
8 0.102675028 -0.139917530
9 0.543499247 0.102675028
10 1.220268811 0.543499247
11 1.964114954 1.220268811
12 1.149404858 1.964114954
13 0.904905702 1.149404858
14 -0.446998637 0.904905702
15 1.198263329 -0.446998637
16 -1.020452944 1.198263329
17 0.163677629 -1.020452944
18 2.513580059 0.163677629
19 2.090369961 2.513580059
20 2.062172090 2.090369961
21 2.729098606 2.062172090
22 1.132825894 2.729098606
23 3.134410848 1.132825894
24 -0.320482343 3.134410848
25 -1.452633229 -0.320482343
26 -3.717259466 -1.452633229
27 1.647213937 -3.717259466
28 -2.707515849 1.647213937
29 3.256160103 -2.707515849
30 0.671751693 3.256160103
31 0.489648499 0.671751693
32 0.302648631 0.489648499
33 2.435533332 0.302648631
34 -3.023053196 2.435533332
35 0.738293713 -3.023053196
36 -2.270064778 0.738293713
37 -0.009141311 -2.270064778
38 0.154279708 -0.009141311
39 0.548415785 0.154279708
40 0.556803815 0.548415785
41 -2.081188509 0.556803815
42 2.298123129 -2.081188509
43 0.005871239 2.298123129
44 0.287161926 0.005871239
45 -1.969411449 0.287161926
46 3.966228263 -1.969411449
47 0.426372410 3.966228263
48 -3.139574779 0.426372410
49 -0.572951312 -3.139574779
50 0.550677675 -0.572951312
51 -0.545912532 0.550677675
52 -1.814477851 -0.545912532
53 8.175791839 -1.814477851
54 -2.286218158 8.175791839
55 0.751526167 -2.286218158
56 0.024277085 0.751526167
57 2.045992145 0.024277085
58 -1.485942486 2.045992145
59 0.151625988 -1.485942486
60 -0.368148089 0.151625988
61 -2.175338608 -0.368148089
62 -2.291151686 -2.175338608
63 -0.313066931 -2.291151686
64 1.104252677 -0.313066931
65 -0.104808576 1.104252677
66 1.685354782 -0.104808576
67 -3.067151226 1.685354782
68 -0.327506099 -3.067151226
69 0.054191405 -0.327506099
70 2.985096560 0.054191405
71 0.600822573 2.985096560
72 -1.583568909 0.600822573
73 -1.649673525 -1.583568909
74 -1.267579110 -1.649673525
75 -0.467329221 -1.267579110
76 0.524040234 -0.467329221
77 1.688578449 0.524040234
78 -0.202317737 1.688578449
79 1.660125030 -0.202317737
80 -2.356492297 1.660125030
81 -15.412581250 -2.356492297
82 2.232903304 -15.412581250
83 -1.036535001 2.232903304
84 -0.091887255 -1.036535001
85 -0.281500408 -0.091887255
86 -1.040258711 -0.281500408
87 0.319129940 -1.040258711
88 0.765624631 0.319129940
89 -0.652604878 0.765624631
90 1.245774044 -0.652604878
91 1.121180934 1.245774044
92 -1.341116032 1.121180934
93 -2.343239091 -1.341116032
94 -2.052314459 -2.343239091
95 -1.847916449 -2.052314459
96 0.828948388 -1.847916449
97 -1.453117130 0.828948388
98 -1.249513947 -1.453117130
99 -0.604623483 -1.249513947
100 -0.934719707 -0.604623483
101 0.058641186 -0.934719707
102 0.007780262 0.058641186
103 2.311544039 0.007780262
104 -1.766188862 2.311544039
105 -1.033983326 -1.766188862
106 1.046090431 -1.033983326
107 -0.710943979 1.046090431
108 0.561466308 -0.710943979
109 0.956442919 0.561466308
110 -1.584845982 0.956442919
111 -1.560043541 -1.584845982
112 0.512610764 -1.560043541
113 -10.883156169 0.512610764
114 0.648519972 -10.883156169
115 0.401237718 0.648519972
116 0.266360077 0.401237718
117 1.068701356 0.266360077
118 0.042733621 1.068701356
119 -0.976743719 0.042733621
120 0.245616290 -0.976743719
121 -0.663779700 0.245616290
122 -1.414835394 -0.663779700
123 0.895263351 -1.414835394
124 0.912270603 0.895263351
125 -1.216188387 0.912270603
126 0.036968784 -1.216188387
127 -0.705875349 0.036968784
128 3.334100158 -0.705875349
129 0.032199783 3.334100158
130 -0.928480340 0.032199783
131 -1.312154175 -0.928480340
132 -0.865594946 -1.312154175
133 0.587360526 -0.865594946
134 0.760586404 0.587360526
135 2.565814655 0.760586404
136 0.225423358 2.565814655
137 3.700565560 0.225423358
138 1.832073322 3.700565560
139 1.324156889 1.832073322
140 -1.548612924 1.324156889
141 1.068382606 -1.548612924
142 -0.815560729 1.068382606
143 0.084661114 -0.815560729
144 -0.349249279 0.084661114
145 -0.035939616 -0.349249279
146 1.480525212 -0.035939616
147 1.667368773 1.480525212
148 0.485996065 1.667368773
149 0.311657640 0.485996065
150 0.032146613 0.311657640
151 -2.165748661 0.032146613
152 -2.420479051 -2.165748661
153 -0.147297409 -2.420479051
154 -0.196203587 -0.147297409
155 2.469959989 -0.196203587
156 1.121180934 2.469959989
157 0.532901984 1.121180934
158 3.334100158 0.532901984
159 -0.466356985 3.334100158
160 -2.857377862 -0.466356985
161 3.371919740 -2.857377862
> 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/78i371321534609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8daq31321534609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9l7te1321534609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10nz1c1321534609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11xsm91321534609.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/12r1ev1321534609.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/13gqb21321534609.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/14pe5y1321534609.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/15w95a1321534609.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/16kwd51321534609.tab")
+ }
>
> try(system("convert tmp/1pahq1321534609.ps tmp/1pahq1321534609.png",intern=TRUE))
character(0)
> try(system("convert tmp/2sf5n1321534609.ps tmp/2sf5n1321534609.png",intern=TRUE))
character(0)
> try(system("convert tmp/3pxra1321534609.ps tmp/3pxra1321534609.png",intern=TRUE))
character(0)
> try(system("convert tmp/4bye51321534609.ps tmp/4bye51321534609.png",intern=TRUE))
character(0)
> try(system("convert tmp/53fbg1321534609.ps tmp/53fbg1321534609.png",intern=TRUE))
character(0)
> try(system("convert tmp/6lz331321534609.ps tmp/6lz331321534609.png",intern=TRUE))
character(0)
> try(system("convert tmp/78i371321534609.ps tmp/78i371321534609.png",intern=TRUE))
character(0)
> try(system("convert tmp/8daq31321534609.ps tmp/8daq31321534609.png",intern=TRUE))
character(0)
> try(system("convert tmp/9l7te1321534609.ps tmp/9l7te1321534609.png",intern=TRUE))
character(0)
> try(system("convert tmp/10nz1c1321534609.ps tmp/10nz1c1321534609.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.228 0.542 5.858