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(12
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+ ,16)
+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('perceived_competence'
+ ,'gender'
+ ,'age'
+ ,'connected'
+ ,'separate'
+ ,'learning'
+ ,'happiness'
+ ,'depression')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('perceived_competence','gender','age','connected','separate','learning','happiness','depression'),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'
> #'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
perceived_competence gender age connected separate learning happiness
1 12 2 7 41 38 13 14
2 11 2 5 39 32 16 18
3 15 2 5 30 35 19 11
4 6 1 5 31 33 15 12
5 13 2 8 34 37 14 16
6 10 2 6 35 29 13 18
7 12 2 5 39 31 19 14
8 14 2 6 34 36 15 14
9 12 2 5 36 35 14 15
10 6 2 4 37 38 15 15
11 10 1 6 38 31 16 17
12 12 2 5 36 34 16 19
13 12 1 5 38 35 16 10
14 11 2 6 39 38 16 16
15 15 2 7 33 37 17 18
16 12 1 6 32 33 15 14
17 10 1 7 36 32 15 14
18 12 2 6 38 38 20 17
19 11 1 8 39 38 18 14
20 12 2 7 32 32 16 16
21 11 1 5 32 33 16 18
22 12 2 5 31 31 16 11
23 13 2 7 39 38 19 14
24 11 2 7 37 39 16 12
25 9 1 5 39 32 17 17
26 13 2 4 41 32 17 9
27 10 1 10 36 35 16 16
28 14 2 6 33 37 15 14
29 12 2 5 33 33 16 15
30 10 1 5 34 33 14 11
31 12 2 5 31 28 15 16
32 8 1 5 27 32 12 13
33 10 2 6 37 31 14 17
34 12 2 5 34 37 16 15
35 12 1 5 34 30 14 14
36 7 1 5 32 33 7 16
37 6 1 5 29 31 10 9
38 12 1 5 36 33 14 15
39 10 2 5 29 31 16 17
40 10 1 5 35 33 16 13
41 10 1 5 37 32 16 15
42 12 2 7 34 33 14 16
43 15 1 5 38 32 20 16
44 10 1 6 35 33 14 12
45 10 2 7 38 28 14 12
46 12 2 7 37 35 11 11
47 13 2 5 38 39 14 15
48 11 2 5 33 34 15 15
49 11 2 4 36 38 16 17
50 12 1 5 38 32 14 13
51 14 2 4 32 38 16 16
52 10 1 5 32 30 14 14
53 12 1 5 32 33 12 11
54 13 2 7 34 38 16 12
55 5 1 5 32 32 9 12
56 6 2 5 37 32 14 15
57 12 2 6 39 34 16 16
58 12 2 4 29 34 16 15
59 11 1 6 37 36 15 12
60 10 2 6 35 34 16 12
61 7 1 5 30 28 12 8
62 12 1 7 38 34 16 13
63 14 2 6 34 35 16 11
64 11 2 8 31 35 14 14
65 12 2 7 34 31 16 15
66 13 1 5 35 37 17 10
67 14 2 6 36 35 18 11
68 11 1 6 30 27 18 12
69 12 2 5 39 40 12 15
70 12 1 5 35 37 16 15
71 8 1 5 38 36 10 14
72 11 2 5 31 38 14 16
73 14 2 4 34 39 18 15
74 14 1 6 38 41 18 15
75 12 1 6 34 27 16 13
76 9 2 6 39 30 17 12
77 13 2 6 37 37 16 17
78 11 2 7 34 31 16 13
79 12 1 5 28 31 13 15
80 12 1 7 37 27 16 13
81 12 1 6 33 36 16 15
82 12 1 5 37 38 20 16
83 12 2 5 35 37 16 15
84 12 1 4 37 33 15 16
85 11 2 8 32 34 15 15
86 10 2 8 33 31 16 14
87 9 1 5 38 39 14 15
88 12 2 5 33 34 16 14
89 12 2 6 29 32 16 13
90 12 2 4 33 33 15 7
91 9 2 5 31 36 12 17
92 15 2 5 36 32 17 13
93 12 2 5 35 41 16 15
94 12 2 5 32 28 15 14
95 12 2 6 29 30 13 13
96 10 2 6 39 36 16 16
97 13 2 5 37 35 16 12
98 9 2 6 35 31 16 14
99 12 1 5 37 34 16 17
100 10 1 7 32 36 14 15
101 14 2 5 38 36 16 17
102 11 1 6 37 35 16 12
103 15 2 6 36 37 20 16
104 11 1 6 32 28 15 11
105 11 2 4 33 39 16 15
106 12 1 5 40 32 13 9
107 12 2 5 38 35 17 16
108 12 1 7 41 39 16 15
109 11 1 6 36 35 16 10
110 7 2 9 43 42 12 10
111 12 2 6 30 34 16 15
112 14 2 6 31 33 16 11
113 11 2 5 32 41 17 13
114 11 1 6 32 33 13 14
115 10 2 5 37 34 12 18
116 13 1 8 37 32 18 16
117 13 2 7 33 40 14 14
118 8 2 5 34 40 14 14
119 11 2 7 33 35 13 14
120 12 2 6 38 36 16 14
121 11 2 6 33 37 13 12
122 13 2 9 31 27 16 14
123 12 2 7 38 39 13 15
124 14 2 6 37 38 16 15
125 13 2 5 33 31 15 15
126 15 2 5 31 33 16 13
127 10 1 6 39 32 15 17
128 11 2 6 44 39 17 17
129 9 2 7 33 36 15 19
130 11 2 5 35 33 12 15
131 10 1 5 32 33 16 13
132 11 1 5 28 32 10 9
133 8 2 6 40 37 16 15
134 11 1 4 27 30 12 15
135 12 1 5 37 38 14 15
136 12 2 7 32 29 15 16
137 9 1 5 28 22 13 11
138 11 1 7 34 35 15 14
139 10 2 7 30 35 11 11
140 8 2 6 35 34 12 15
141 9 1 5 31 35 8 13
142 8 2 8 32 34 16 15
143 9 1 5 30 34 15 16
144 15 2 5 30 35 17 14
145 11 1 5 31 23 16 15
146 8 2 6 40 31 10 16
147 13 2 4 32 27 18 16
148 12 1 5 36 36 13 11
149 12 1 5 32 31 16 12
150 9 1 7 35 32 13 9
151 7 2 6 38 39 10 16
152 13 2 7 42 37 15 13
153 9 1 10 34 38 16 16
154 6 2 6 35 39 16 12
155 8 2 8 35 34 14 9
156 8 2 4 33 31 10 13
157 15 2 5 36 32 17 13
158 6 2 6 32 37 13 14
159 9 2 7 33 36 15 19
160 11 2 7 34 32 16 13
161 8 2 6 32 35 12 12
162 8 2 6 34 36 13 13
depression
1 12
2 11
3 14
4 12
5 21
6 12
7 22
8 11
9 10
10 13
11 10
12 8
13 15
14 14
15 10
16 14
17 14
18 11
19 10
20 13
21 7
22 14
23 12
24 14
25 11
26 9
27 11
28 15
29 14
30 13
31 9
32 15
33 10
34 11
35 13
36 8
37 20
38 12
39 10
40 10
41 9
42 14
43 8
44 14
45 11
46 13
47 9
48 11
49 15
50 11
51 10
52 14
53 18
54 14
55 11
56 12
57 13
58 9
59 10
60 15
61 20
62 12
63 12
64 14
65 13
66 11
67 17
68 12
69 13
70 14
71 13
72 15
73 13
74 10
75 11
76 19
77 13
78 17
79 13
80 9
81 11
82 10
83 9
84 12
85 12
86 13
87 13
88 12
89 15
90 22
91 13
92 15
93 13
94 15
95 10
96 11
97 16
98 11
99 11
100 10
101 10
102 16
103 12
104 11
105 16
106 19
107 11
108 16
109 15
110 24
111 14
112 15
113 11
114 15
115 12
116 10
117 14
118 13
119 9
120 15
121 15
122 14
123 11
124 8
125 11
126 11
127 8
128 10
129 11
130 13
131 11
132 20
133 10
134 15
135 12
136 14
137 23
138 14
139 16
140 11
141 12
142 10
143 14
144 12
145 12
146 11
147 12
148 13
149 11
150 19
151 12
152 17
153 9
154 12
155 19
156 18
157 15
158 14
159 11
160 9
161 18
162 16
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) gender age connected separate learning
5.37902 0.51370 -0.16489 -0.03653 0.02044 0.51875
happiness depression
-0.06574 -0.03620
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.0125 -0.9423 0.0958 1.2489 2.9479
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.37902 2.38776 2.253 0.0257 *
gender 0.51370 0.31204 1.646 0.1017
age -0.16489 0.12429 -1.327 0.1866
connected -0.03653 0.04666 -0.783 0.4348
separate 0.02044 0.04440 0.460 0.6458
learning 0.51875 0.06652 7.798 8.8e-13 ***
happiness -0.06574 0.07506 -0.876 0.3825
depression -0.03620 0.05528 -0.655 0.5136
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.802 on 154 degrees of freedom
Multiple R-squared: 0.3232, Adjusted R-squared: 0.2924
F-statistic: 10.51 on 7 and 154 DF, p-value: 9.268e-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,] 0.50607609 0.98784783 0.4939239
[2,] 0.33889711 0.67779423 0.6611029
[3,] 0.79242873 0.41514254 0.2075713
[4,] 0.73826922 0.52346157 0.2617308
[5,] 0.65431803 0.69136394 0.3456820
[6,] 0.75134804 0.49730392 0.2486520
[7,] 0.75720676 0.48558649 0.2427932
[8,] 0.81492347 0.37015306 0.1850765
[9,] 0.84187833 0.31624335 0.1581217
[10,] 0.86450642 0.27098717 0.1354936
[11,] 0.86682065 0.26635870 0.1331793
[12,] 0.82879287 0.34241427 0.1712071
[13,] 0.78899304 0.42201393 0.2110070
[14,] 0.77820347 0.44359306 0.2217965
[15,] 0.74156682 0.51686635 0.2584332
[16,] 0.70801734 0.58396533 0.2919827
[17,] 0.74089130 0.51821741 0.2591087
[18,] 0.76424657 0.47150686 0.2357534
[19,] 0.70989677 0.58020645 0.2901032
[20,] 0.65718928 0.68562143 0.3428107
[21,] 0.59840645 0.80318710 0.4015936
[22,] 0.56411287 0.87177425 0.4358871
[23,] 0.52679604 0.94640793 0.4732040
[24,] 0.46488298 0.92976595 0.5351170
[25,] 0.54130290 0.91739420 0.4586971
[26,] 0.48146399 0.96292797 0.5185360
[27,] 0.52940290 0.94119420 0.4705971
[28,] 0.61559799 0.76880401 0.3844020
[29,] 0.63504952 0.72990096 0.3649505
[30,] 0.59611868 0.80776264 0.4038813
[31,] 0.55418362 0.89163277 0.4458164
[32,] 0.51641474 0.96717052 0.4835853
[33,] 0.59922451 0.80155098 0.4007755
[34,] 0.54702573 0.90594853 0.4529743
[35,] 0.52853929 0.94292142 0.4714607
[36,] 0.55367683 0.89264633 0.4463232
[37,] 0.55652323 0.88695354 0.4434768
[38,] 0.50919982 0.98160037 0.4908002
[39,] 0.46241907 0.92483814 0.5375809
[40,] 0.48358104 0.96716209 0.5164190
[41,] 0.48891585 0.97783170 0.5110842
[42,] 0.44372787 0.88745574 0.5562721
[43,] 0.51943419 0.96113162 0.4805658
[44,] 0.48105910 0.96211819 0.5189409
[45,] 0.58275611 0.83448779 0.4172439
[46,] 0.82916999 0.34166003 0.1708300
[47,] 0.79806437 0.40387127 0.2019356
[48,] 0.76395748 0.47208505 0.2360425
[49,] 0.72500875 0.54998250 0.2749912
[50,] 0.73164703 0.53670595 0.2683530
[51,] 0.75550720 0.48898560 0.2444928
[52,] 0.72581900 0.54836201 0.2741810
[53,] 0.72367370 0.55265260 0.2763263
[54,] 0.69594458 0.60811084 0.3040554
[55,] 0.65651595 0.68696810 0.3434841
[56,] 0.61989758 0.76020483 0.3801024
[57,] 0.59393090 0.81213820 0.4060691
[58,] 0.57550944 0.84898111 0.4244906
[59,] 0.58700100 0.82599800 0.4129990
[60,] 0.54863102 0.90273797 0.4513690
[61,] 0.50447909 0.99104181 0.4955209
[62,] 0.45952666 0.91905332 0.5404733
[63,] 0.42202294 0.84404588 0.5779771
[64,] 0.40615880 0.81231760 0.5938412
[65,] 0.38652929 0.77305857 0.6134707
[66,] 0.45491255 0.90982510 0.5450875
[67,] 0.44071915 0.88143830 0.5592809
[68,] 0.39722541 0.79445082 0.6027746
[69,] 0.42216374 0.84432748 0.5778363
[70,] 0.39401600 0.78803200 0.6059840
[71,] 0.35449278 0.70898556 0.6455072
[72,] 0.34151255 0.68302509 0.6584875
[73,] 0.30145339 0.60290679 0.6985466
[74,] 0.28982142 0.57964283 0.7101786
[75,] 0.26184145 0.52368289 0.7381586
[76,] 0.25155944 0.50311889 0.7484406
[77,] 0.24065763 0.48131526 0.7593424
[78,] 0.20555307 0.41110614 0.7944469
[79,] 0.17397460 0.34794920 0.8260254
[80,] 0.15151208 0.30302417 0.8484879
[81,] 0.13191857 0.26383715 0.8680814
[82,] 0.16670001 0.33340003 0.8333000
[83,] 0.14204923 0.28409846 0.8579508
[84,] 0.12335572 0.24671145 0.8766443
[85,] 0.11493364 0.22986729 0.8850664
[86,] 0.11180698 0.22361396 0.8881930
[87,] 0.09767904 0.19535808 0.9023210
[88,] 0.13154215 0.26308431 0.8684578
[89,] 0.11237589 0.22475179 0.8876241
[90,] 0.09264150 0.18528301 0.9073585
[91,] 0.10281830 0.20563659 0.8971817
[92,] 0.08372616 0.16745232 0.9162738
[93,] 0.07809243 0.15618486 0.9219076
[94,] 0.06503975 0.13007950 0.9349602
[95,] 0.05392308 0.10784616 0.9460769
[96,] 0.05610182 0.11220365 0.9438982
[97,] 0.04368487 0.08736975 0.9563151
[98,] 0.03988763 0.07977525 0.9601124
[99,] 0.03121259 0.06242518 0.9687874
[100,] 0.03308808 0.06617617 0.9669119
[101,] 0.02557694 0.05115388 0.9744231
[102,] 0.02721561 0.05443122 0.9727844
[103,] 0.02558034 0.05116067 0.9744197
[104,] 0.02188733 0.04377466 0.9781127
[105,] 0.01654246 0.03308492 0.9834575
[106,] 0.01408864 0.02817728 0.9859114
[107,] 0.02356144 0.04712287 0.9764386
[108,] 0.03260378 0.06520755 0.9673962
[109,] 0.02596310 0.05192620 0.9740369
[110,] 0.02004189 0.04008379 0.9799581
[111,] 0.01576746 0.03153493 0.9842325
[112,] 0.02186892 0.04373784 0.9781311
[113,] 0.03060034 0.06120067 0.9693997
[114,] 0.04174208 0.08348417 0.9582579
[115,] 0.04038476 0.08076951 0.9596152
[116,] 0.08001926 0.16003853 0.9199807
[117,] 0.06342796 0.12685593 0.9365720
[118,] 0.04914400 0.09828801 0.9508560
[119,] 0.04162883 0.08325767 0.9583712
[120,] 0.03763333 0.07526666 0.9623667
[121,] 0.03601689 0.07203379 0.9639831
[122,] 0.03913730 0.07827459 0.9608627
[123,] 0.07890493 0.15780985 0.9210951
[124,] 0.07010893 0.14021785 0.9298911
[125,] 0.05708677 0.11417353 0.9429132
[126,] 0.07021631 0.14043263 0.9297837
[127,] 0.05439653 0.10879306 0.9456035
[128,] 0.04047882 0.08095763 0.9595212
[129,] 0.08686718 0.17373436 0.9131328
[130,] 0.06576291 0.13152581 0.9342371
[131,] 0.12116027 0.24232054 0.8788397
[132,] 0.10874156 0.21748313 0.8912584
[133,] 0.09961663 0.19923325 0.9003834
[134,] 0.40141432 0.80282864 0.5985857
[135,] 0.47099720 0.94199440 0.5290028
[136,] 0.55375628 0.89248744 0.4462437
[137,] 0.60294895 0.79410210 0.3970510
[138,] 0.77657192 0.44685616 0.2234281
[139,] 0.69278742 0.61442515 0.3072126
[140,] 0.63686507 0.72626987 0.3631349
[141,] 0.54623144 0.90753713 0.4537686
> postscript(file="/var/wessaorg/rcomp/tmp/1ofrq1322149105.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/22jhu1322149105.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3tzz81322149105.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/444uj1322149105.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5o82c1322149105.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
2.07973411 -0.52994963 1.17211199 -5.16842895 2.94785820 0.14259694
7 8 9 10 11 12
-0.93053056 2.62630870 1.10321199 -5.49663764 -0.96938185 0.27671221
13 14 15 16 17 18
0.50477250 -0.51060076 2.92348152 1.23686852 -0.43166747 -1.66498806
19 20 21 22 23 24
-0.98088324 0.48503647 -0.43720864 -0.15333138 -0.10582894 -0.70216626
25 26 27 28 29 30
-2.60073984 0.19543123 -0.49418509 2.71412514 0.14179659 -0.56962427
31 32 33 34 35 36
0.57445052 -1.56352975 -0.48211135 -0.01204077 1.68892128 0.13626292
37 38 39 40 41 42
-2.51448534 1.73019329 -1.97675797 -1.54771033 -1.35892514 1.61135382
43 44 45 46 47 48
1.63214670 -0.26626284 -0.51184704 2.87142268 2.05830215 -0.46849108
49 50 51 52 53 54
-0.84804716 1.65602617 1.75909855 -0.34794398 2.57580223 1.20868269
55 56 57 58 59 60
-3.03515169 -4.72652917 0.53497638 -0.37065720 0.08192726 -1.80170644
61 62 63 64 65 66
-2.51986042 0.94362423 1.96698623 0.49428909 0.51280539 0.69074833
67 68 69 70 71 72
1.18353928 -1.47365403 2.25668174 0.64678254 -0.21261505 0.10595047
73 74 75 76 77 78
0.81707384 1.65720430 0.73951247 -2.94776306 1.46631991 -0.47387894
79 80 81 82 83 84
2.03377590 0.94160519 0.65046304 -1.45465165 -0.04790508 1.14881920
85 86 87 88 89 90
0.02585667 -1.42457049 -1.28320819 -0.01678118 0.08572953 0.25933180
91 92 93 94 95 96
-0.82231987 2.65780702 0.01511164 0.69669371 1.50187723 -1.57830708
97 98 99 100 101 102
1.12221715 -2.75369079 0.80405937 -0.21987278 2.24980763 -0.19919120
103 104 105 106 107 108
1.25285238 0.03327930 -1.07336396 2.27447070 -0.27803869 1.22726815
109 110 111 112 113 114
-0.40339692 -1.90901704 0.17665179 2.00687282 -1.81710522 1.31056642
115 116 117 118 119 120
0.46729745 1.20019235 2.30023963 -3.02921432 0.74021870 0.39847698
121 122 123 124 125 126
0.62014893 1.78523315 1.97923563 2.13341021 1.59284024 2.82866335
127 128 129 130 131 132
-0.50693995 -0.94619022 -1.91663873 1.25366174 -1.62110720 2.42853928
133 134 135 136 137 138
-3.66415528 1.44394015 1.66450602 1.10131573 -0.68320287 0.43393798
139 140 141 142 143 144
0.72429529 -1.67428415 1.48767138 -3.56528922 -1.89005613 2.33443025
145 146 147 148 149 150
-0.28552695 -0.32705676 0.01887605 1.96085759 0.35404228 -0.57839974
151 152 153 154 155 156
-1.52747222 2.21446125 -1.70097554 -6.01251919 -2.48684130 -0.85639416
157 158 159 160 161 162
2.65780702 -4.32110448 -1.91663873 -0.78390636 -1.74815129 -2.22093965
> postscript(file="/var/wessaorg/rcomp/tmp/6eq2b1322149105.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 2.07973411 NA
1 -0.52994963 2.07973411
2 1.17211199 -0.52994963
3 -5.16842895 1.17211199
4 2.94785820 -5.16842895
5 0.14259694 2.94785820
6 -0.93053056 0.14259694
7 2.62630870 -0.93053056
8 1.10321199 2.62630870
9 -5.49663764 1.10321199
10 -0.96938185 -5.49663764
11 0.27671221 -0.96938185
12 0.50477250 0.27671221
13 -0.51060076 0.50477250
14 2.92348152 -0.51060076
15 1.23686852 2.92348152
16 -0.43166747 1.23686852
17 -1.66498806 -0.43166747
18 -0.98088324 -1.66498806
19 0.48503647 -0.98088324
20 -0.43720864 0.48503647
21 -0.15333138 -0.43720864
22 -0.10582894 -0.15333138
23 -0.70216626 -0.10582894
24 -2.60073984 -0.70216626
25 0.19543123 -2.60073984
26 -0.49418509 0.19543123
27 2.71412514 -0.49418509
28 0.14179659 2.71412514
29 -0.56962427 0.14179659
30 0.57445052 -0.56962427
31 -1.56352975 0.57445052
32 -0.48211135 -1.56352975
33 -0.01204077 -0.48211135
34 1.68892128 -0.01204077
35 0.13626292 1.68892128
36 -2.51448534 0.13626292
37 1.73019329 -2.51448534
38 -1.97675797 1.73019329
39 -1.54771033 -1.97675797
40 -1.35892514 -1.54771033
41 1.61135382 -1.35892514
42 1.63214670 1.61135382
43 -0.26626284 1.63214670
44 -0.51184704 -0.26626284
45 2.87142268 -0.51184704
46 2.05830215 2.87142268
47 -0.46849108 2.05830215
48 -0.84804716 -0.46849108
49 1.65602617 -0.84804716
50 1.75909855 1.65602617
51 -0.34794398 1.75909855
52 2.57580223 -0.34794398
53 1.20868269 2.57580223
54 -3.03515169 1.20868269
55 -4.72652917 -3.03515169
56 0.53497638 -4.72652917
57 -0.37065720 0.53497638
58 0.08192726 -0.37065720
59 -1.80170644 0.08192726
60 -2.51986042 -1.80170644
61 0.94362423 -2.51986042
62 1.96698623 0.94362423
63 0.49428909 1.96698623
64 0.51280539 0.49428909
65 0.69074833 0.51280539
66 1.18353928 0.69074833
67 -1.47365403 1.18353928
68 2.25668174 -1.47365403
69 0.64678254 2.25668174
70 -0.21261505 0.64678254
71 0.10595047 -0.21261505
72 0.81707384 0.10595047
73 1.65720430 0.81707384
74 0.73951247 1.65720430
75 -2.94776306 0.73951247
76 1.46631991 -2.94776306
77 -0.47387894 1.46631991
78 2.03377590 -0.47387894
79 0.94160519 2.03377590
80 0.65046304 0.94160519
81 -1.45465165 0.65046304
82 -0.04790508 -1.45465165
83 1.14881920 -0.04790508
84 0.02585667 1.14881920
85 -1.42457049 0.02585667
86 -1.28320819 -1.42457049
87 -0.01678118 -1.28320819
88 0.08572953 -0.01678118
89 0.25933180 0.08572953
90 -0.82231987 0.25933180
91 2.65780702 -0.82231987
92 0.01511164 2.65780702
93 0.69669371 0.01511164
94 1.50187723 0.69669371
95 -1.57830708 1.50187723
96 1.12221715 -1.57830708
97 -2.75369079 1.12221715
98 0.80405937 -2.75369079
99 -0.21987278 0.80405937
100 2.24980763 -0.21987278
101 -0.19919120 2.24980763
102 1.25285238 -0.19919120
103 0.03327930 1.25285238
104 -1.07336396 0.03327930
105 2.27447070 -1.07336396
106 -0.27803869 2.27447070
107 1.22726815 -0.27803869
108 -0.40339692 1.22726815
109 -1.90901704 -0.40339692
110 0.17665179 -1.90901704
111 2.00687282 0.17665179
112 -1.81710522 2.00687282
113 1.31056642 -1.81710522
114 0.46729745 1.31056642
115 1.20019235 0.46729745
116 2.30023963 1.20019235
117 -3.02921432 2.30023963
118 0.74021870 -3.02921432
119 0.39847698 0.74021870
120 0.62014893 0.39847698
121 1.78523315 0.62014893
122 1.97923563 1.78523315
123 2.13341021 1.97923563
124 1.59284024 2.13341021
125 2.82866335 1.59284024
126 -0.50693995 2.82866335
127 -0.94619022 -0.50693995
128 -1.91663873 -0.94619022
129 1.25366174 -1.91663873
130 -1.62110720 1.25366174
131 2.42853928 -1.62110720
132 -3.66415528 2.42853928
133 1.44394015 -3.66415528
134 1.66450602 1.44394015
135 1.10131573 1.66450602
136 -0.68320287 1.10131573
137 0.43393798 -0.68320287
138 0.72429529 0.43393798
139 -1.67428415 0.72429529
140 1.48767138 -1.67428415
141 -3.56528922 1.48767138
142 -1.89005613 -3.56528922
143 2.33443025 -1.89005613
144 -0.28552695 2.33443025
145 -0.32705676 -0.28552695
146 0.01887605 -0.32705676
147 1.96085759 0.01887605
148 0.35404228 1.96085759
149 -0.57839974 0.35404228
150 -1.52747222 -0.57839974
151 2.21446125 -1.52747222
152 -1.70097554 2.21446125
153 -6.01251919 -1.70097554
154 -2.48684130 -6.01251919
155 -0.85639416 -2.48684130
156 2.65780702 -0.85639416
157 -4.32110448 2.65780702
158 -1.91663873 -4.32110448
159 -0.78390636 -1.91663873
160 -1.74815129 -0.78390636
161 -2.22093965 -1.74815129
162 NA -2.22093965
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.52994963 2.07973411
[2,] 1.17211199 -0.52994963
[3,] -5.16842895 1.17211199
[4,] 2.94785820 -5.16842895
[5,] 0.14259694 2.94785820
[6,] -0.93053056 0.14259694
[7,] 2.62630870 -0.93053056
[8,] 1.10321199 2.62630870
[9,] -5.49663764 1.10321199
[10,] -0.96938185 -5.49663764
[11,] 0.27671221 -0.96938185
[12,] 0.50477250 0.27671221
[13,] -0.51060076 0.50477250
[14,] 2.92348152 -0.51060076
[15,] 1.23686852 2.92348152
[16,] -0.43166747 1.23686852
[17,] -1.66498806 -0.43166747
[18,] -0.98088324 -1.66498806
[19,] 0.48503647 -0.98088324
[20,] -0.43720864 0.48503647
[21,] -0.15333138 -0.43720864
[22,] -0.10582894 -0.15333138
[23,] -0.70216626 -0.10582894
[24,] -2.60073984 -0.70216626
[25,] 0.19543123 -2.60073984
[26,] -0.49418509 0.19543123
[27,] 2.71412514 -0.49418509
[28,] 0.14179659 2.71412514
[29,] -0.56962427 0.14179659
[30,] 0.57445052 -0.56962427
[31,] -1.56352975 0.57445052
[32,] -0.48211135 -1.56352975
[33,] -0.01204077 -0.48211135
[34,] 1.68892128 -0.01204077
[35,] 0.13626292 1.68892128
[36,] -2.51448534 0.13626292
[37,] 1.73019329 -2.51448534
[38,] -1.97675797 1.73019329
[39,] -1.54771033 -1.97675797
[40,] -1.35892514 -1.54771033
[41,] 1.61135382 -1.35892514
[42,] 1.63214670 1.61135382
[43,] -0.26626284 1.63214670
[44,] -0.51184704 -0.26626284
[45,] 2.87142268 -0.51184704
[46,] 2.05830215 2.87142268
[47,] -0.46849108 2.05830215
[48,] -0.84804716 -0.46849108
[49,] 1.65602617 -0.84804716
[50,] 1.75909855 1.65602617
[51,] -0.34794398 1.75909855
[52,] 2.57580223 -0.34794398
[53,] 1.20868269 2.57580223
[54,] -3.03515169 1.20868269
[55,] -4.72652917 -3.03515169
[56,] 0.53497638 -4.72652917
[57,] -0.37065720 0.53497638
[58,] 0.08192726 -0.37065720
[59,] -1.80170644 0.08192726
[60,] -2.51986042 -1.80170644
[61,] 0.94362423 -2.51986042
[62,] 1.96698623 0.94362423
[63,] 0.49428909 1.96698623
[64,] 0.51280539 0.49428909
[65,] 0.69074833 0.51280539
[66,] 1.18353928 0.69074833
[67,] -1.47365403 1.18353928
[68,] 2.25668174 -1.47365403
[69,] 0.64678254 2.25668174
[70,] -0.21261505 0.64678254
[71,] 0.10595047 -0.21261505
[72,] 0.81707384 0.10595047
[73,] 1.65720430 0.81707384
[74,] 0.73951247 1.65720430
[75,] -2.94776306 0.73951247
[76,] 1.46631991 -2.94776306
[77,] -0.47387894 1.46631991
[78,] 2.03377590 -0.47387894
[79,] 0.94160519 2.03377590
[80,] 0.65046304 0.94160519
[81,] -1.45465165 0.65046304
[82,] -0.04790508 -1.45465165
[83,] 1.14881920 -0.04790508
[84,] 0.02585667 1.14881920
[85,] -1.42457049 0.02585667
[86,] -1.28320819 -1.42457049
[87,] -0.01678118 -1.28320819
[88,] 0.08572953 -0.01678118
[89,] 0.25933180 0.08572953
[90,] -0.82231987 0.25933180
[91,] 2.65780702 -0.82231987
[92,] 0.01511164 2.65780702
[93,] 0.69669371 0.01511164
[94,] 1.50187723 0.69669371
[95,] -1.57830708 1.50187723
[96,] 1.12221715 -1.57830708
[97,] -2.75369079 1.12221715
[98,] 0.80405937 -2.75369079
[99,] -0.21987278 0.80405937
[100,] 2.24980763 -0.21987278
[101,] -0.19919120 2.24980763
[102,] 1.25285238 -0.19919120
[103,] 0.03327930 1.25285238
[104,] -1.07336396 0.03327930
[105,] 2.27447070 -1.07336396
[106,] -0.27803869 2.27447070
[107,] 1.22726815 -0.27803869
[108,] -0.40339692 1.22726815
[109,] -1.90901704 -0.40339692
[110,] 0.17665179 -1.90901704
[111,] 2.00687282 0.17665179
[112,] -1.81710522 2.00687282
[113,] 1.31056642 -1.81710522
[114,] 0.46729745 1.31056642
[115,] 1.20019235 0.46729745
[116,] 2.30023963 1.20019235
[117,] -3.02921432 2.30023963
[118,] 0.74021870 -3.02921432
[119,] 0.39847698 0.74021870
[120,] 0.62014893 0.39847698
[121,] 1.78523315 0.62014893
[122,] 1.97923563 1.78523315
[123,] 2.13341021 1.97923563
[124,] 1.59284024 2.13341021
[125,] 2.82866335 1.59284024
[126,] -0.50693995 2.82866335
[127,] -0.94619022 -0.50693995
[128,] -1.91663873 -0.94619022
[129,] 1.25366174 -1.91663873
[130,] -1.62110720 1.25366174
[131,] 2.42853928 -1.62110720
[132,] -3.66415528 2.42853928
[133,] 1.44394015 -3.66415528
[134,] 1.66450602 1.44394015
[135,] 1.10131573 1.66450602
[136,] -0.68320287 1.10131573
[137,] 0.43393798 -0.68320287
[138,] 0.72429529 0.43393798
[139,] -1.67428415 0.72429529
[140,] 1.48767138 -1.67428415
[141,] -3.56528922 1.48767138
[142,] -1.89005613 -3.56528922
[143,] 2.33443025 -1.89005613
[144,] -0.28552695 2.33443025
[145,] -0.32705676 -0.28552695
[146,] 0.01887605 -0.32705676
[147,] 1.96085759 0.01887605
[148,] 0.35404228 1.96085759
[149,] -0.57839974 0.35404228
[150,] -1.52747222 -0.57839974
[151,] 2.21446125 -1.52747222
[152,] -1.70097554 2.21446125
[153,] -6.01251919 -1.70097554
[154,] -2.48684130 -6.01251919
[155,] -0.85639416 -2.48684130
[156,] 2.65780702 -0.85639416
[157,] -4.32110448 2.65780702
[158,] -1.91663873 -4.32110448
[159,] -0.78390636 -1.91663873
[160,] -1.74815129 -0.78390636
[161,] -2.22093965 -1.74815129
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.52994963 2.07973411
2 1.17211199 -0.52994963
3 -5.16842895 1.17211199
4 2.94785820 -5.16842895
5 0.14259694 2.94785820
6 -0.93053056 0.14259694
7 2.62630870 -0.93053056
8 1.10321199 2.62630870
9 -5.49663764 1.10321199
10 -0.96938185 -5.49663764
11 0.27671221 -0.96938185
12 0.50477250 0.27671221
13 -0.51060076 0.50477250
14 2.92348152 -0.51060076
15 1.23686852 2.92348152
16 -0.43166747 1.23686852
17 -1.66498806 -0.43166747
18 -0.98088324 -1.66498806
19 0.48503647 -0.98088324
20 -0.43720864 0.48503647
21 -0.15333138 -0.43720864
22 -0.10582894 -0.15333138
23 -0.70216626 -0.10582894
24 -2.60073984 -0.70216626
25 0.19543123 -2.60073984
26 -0.49418509 0.19543123
27 2.71412514 -0.49418509
28 0.14179659 2.71412514
29 -0.56962427 0.14179659
30 0.57445052 -0.56962427
31 -1.56352975 0.57445052
32 -0.48211135 -1.56352975
33 -0.01204077 -0.48211135
34 1.68892128 -0.01204077
35 0.13626292 1.68892128
36 -2.51448534 0.13626292
37 1.73019329 -2.51448534
38 -1.97675797 1.73019329
39 -1.54771033 -1.97675797
40 -1.35892514 -1.54771033
41 1.61135382 -1.35892514
42 1.63214670 1.61135382
43 -0.26626284 1.63214670
44 -0.51184704 -0.26626284
45 2.87142268 -0.51184704
46 2.05830215 2.87142268
47 -0.46849108 2.05830215
48 -0.84804716 -0.46849108
49 1.65602617 -0.84804716
50 1.75909855 1.65602617
51 -0.34794398 1.75909855
52 2.57580223 -0.34794398
53 1.20868269 2.57580223
54 -3.03515169 1.20868269
55 -4.72652917 -3.03515169
56 0.53497638 -4.72652917
57 -0.37065720 0.53497638
58 0.08192726 -0.37065720
59 -1.80170644 0.08192726
60 -2.51986042 -1.80170644
61 0.94362423 -2.51986042
62 1.96698623 0.94362423
63 0.49428909 1.96698623
64 0.51280539 0.49428909
65 0.69074833 0.51280539
66 1.18353928 0.69074833
67 -1.47365403 1.18353928
68 2.25668174 -1.47365403
69 0.64678254 2.25668174
70 -0.21261505 0.64678254
71 0.10595047 -0.21261505
72 0.81707384 0.10595047
73 1.65720430 0.81707384
74 0.73951247 1.65720430
75 -2.94776306 0.73951247
76 1.46631991 -2.94776306
77 -0.47387894 1.46631991
78 2.03377590 -0.47387894
79 0.94160519 2.03377590
80 0.65046304 0.94160519
81 -1.45465165 0.65046304
82 -0.04790508 -1.45465165
83 1.14881920 -0.04790508
84 0.02585667 1.14881920
85 -1.42457049 0.02585667
86 -1.28320819 -1.42457049
87 -0.01678118 -1.28320819
88 0.08572953 -0.01678118
89 0.25933180 0.08572953
90 -0.82231987 0.25933180
91 2.65780702 -0.82231987
92 0.01511164 2.65780702
93 0.69669371 0.01511164
94 1.50187723 0.69669371
95 -1.57830708 1.50187723
96 1.12221715 -1.57830708
97 -2.75369079 1.12221715
98 0.80405937 -2.75369079
99 -0.21987278 0.80405937
100 2.24980763 -0.21987278
101 -0.19919120 2.24980763
102 1.25285238 -0.19919120
103 0.03327930 1.25285238
104 -1.07336396 0.03327930
105 2.27447070 -1.07336396
106 -0.27803869 2.27447070
107 1.22726815 -0.27803869
108 -0.40339692 1.22726815
109 -1.90901704 -0.40339692
110 0.17665179 -1.90901704
111 2.00687282 0.17665179
112 -1.81710522 2.00687282
113 1.31056642 -1.81710522
114 0.46729745 1.31056642
115 1.20019235 0.46729745
116 2.30023963 1.20019235
117 -3.02921432 2.30023963
118 0.74021870 -3.02921432
119 0.39847698 0.74021870
120 0.62014893 0.39847698
121 1.78523315 0.62014893
122 1.97923563 1.78523315
123 2.13341021 1.97923563
124 1.59284024 2.13341021
125 2.82866335 1.59284024
126 -0.50693995 2.82866335
127 -0.94619022 -0.50693995
128 -1.91663873 -0.94619022
129 1.25366174 -1.91663873
130 -1.62110720 1.25366174
131 2.42853928 -1.62110720
132 -3.66415528 2.42853928
133 1.44394015 -3.66415528
134 1.66450602 1.44394015
135 1.10131573 1.66450602
136 -0.68320287 1.10131573
137 0.43393798 -0.68320287
138 0.72429529 0.43393798
139 -1.67428415 0.72429529
140 1.48767138 -1.67428415
141 -3.56528922 1.48767138
142 -1.89005613 -3.56528922
143 2.33443025 -1.89005613
144 -0.28552695 2.33443025
145 -0.32705676 -0.28552695
146 0.01887605 -0.32705676
147 1.96085759 0.01887605
148 0.35404228 1.96085759
149 -0.57839974 0.35404228
150 -1.52747222 -0.57839974
151 2.21446125 -1.52747222
152 -1.70097554 2.21446125
153 -6.01251919 -1.70097554
154 -2.48684130 -6.01251919
155 -0.85639416 -2.48684130
156 2.65780702 -0.85639416
157 -4.32110448 2.65780702
158 -1.91663873 -4.32110448
159 -0.78390636 -1.91663873
160 -1.74815129 -0.78390636
161 -2.22093965 -1.74815129
> 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/753qd1322149105.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8b5cz1322149105.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9l3qn1322149105.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10n8ih1322149105.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/117ipc1322149105.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/12nkvb1322149105.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/13syo71322149105.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/14pqiu1322149105.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/15u8861322149105.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/16ek601322149105.tab")
+ }
>
> try(system("convert tmp/1ofrq1322149105.ps tmp/1ofrq1322149105.png",intern=TRUE))
character(0)
> try(system("convert tmp/22jhu1322149105.ps tmp/22jhu1322149105.png",intern=TRUE))
character(0)
> try(system("convert tmp/3tzz81322149105.ps tmp/3tzz81322149105.png",intern=TRUE))
character(0)
> try(system("convert tmp/444uj1322149105.ps tmp/444uj1322149105.png",intern=TRUE))
character(0)
> try(system("convert tmp/5o82c1322149105.ps tmp/5o82c1322149105.png",intern=TRUE))
character(0)
> try(system("convert tmp/6eq2b1322149105.ps tmp/6eq2b1322149105.png",intern=TRUE))
character(0)
> try(system("convert tmp/753qd1322149105.ps tmp/753qd1322149105.png",intern=TRUE))
character(0)
> try(system("convert tmp/8b5cz1322149105.ps tmp/8b5cz1322149105.png",intern=TRUE))
character(0)
> try(system("convert tmp/9l3qn1322149105.ps tmp/9l3qn1322149105.png",intern=TRUE))
character(0)
> try(system("convert tmp/10n8ih1322149105.ps tmp/10n8ih1322149105.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.006 0.781 5.845