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 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(2
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+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('Gender'
+ ,'Age'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('Gender','Age','Connected','Separate','Learning','Software','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 = '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
Software Gender Age Connected Separate Learning Happiness Depression
1 12 2 7 41 38 13 14 12
2 11 2 5 39 32 16 18 11
3 15 2 5 30 35 19 11 14
4 6 1 5 31 33 15 12 12
5 13 2 8 34 37 14 16 21
6 10 2 6 35 29 13 18 12
7 12 2 5 39 31 19 14 22
8 14 2 6 34 36 15 14 11
9 12 2 5 36 35 14 15 10
10 6 2 4 37 38 15 15 13
11 10 1 6 38 31 16 17 10
12 12 2 5 36 34 16 19 8
13 12 1 5 38 35 16 10 15
14 11 2 6 39 38 16 16 14
15 15 2 7 33 37 17 18 10
16 12 1 6 32 33 15 14 14
17 10 1 7 36 32 15 14 14
18 12 2 6 38 38 20 17 11
19 11 1 8 39 38 18 14 10
20 12 2 7 32 32 16 16 13
21 11 1 5 32 33 16 18 7
22 12 2 5 31 31 16 11 14
23 13 2 7 39 38 19 14 12
24 11 2 7 37 39 16 12 14
25 9 1 5 39 32 17 17 11
26 13 2 4 41 32 17 9 9
27 10 1 10 36 35 16 16 11
28 14 2 6 33 37 15 14 15
29 12 2 5 33 33 16 15 14
30 10 1 5 34 33 14 11 13
31 12 2 5 31 28 15 16 9
32 8 1 5 27 32 12 13 15
33 10 2 6 37 31 14 17 10
34 12 2 5 34 37 16 15 11
35 12 1 5 34 30 14 14 13
36 7 1 5 32 33 7 16 8
37 6 1 5 29 31 10 9 20
38 12 1 5 36 33 14 15 12
39 10 2 5 29 31 16 17 10
40 10 1 5 35 33 16 13 10
41 10 1 5 37 32 16 15 9
42 12 2 7 34 33 14 16 14
43 15 1 5 38 32 20 16 8
44 10 1 6 35 33 14 12 14
45 10 2 7 38 28 14 12 11
46 12 2 7 37 35 11 11 13
47 13 2 5 38 39 14 15 9
48 11 2 5 33 34 15 15 11
49 11 2 4 36 38 16 17 15
50 12 1 5 38 32 14 13 11
51 14 2 4 32 38 16 16 10
52 10 1 5 32 30 14 14 14
53 12 1 5 32 33 12 11 18
54 13 2 7 34 38 16 12 14
55 5 1 5 32 32 9 12 11
56 6 2 5 37 32 14 15 12
57 12 2 6 39 34 16 16 13
58 12 2 4 29 34 16 15 9
59 11 1 6 37 36 15 12 10
60 10 2 6 35 34 16 12 15
61 7 1 5 30 28 12 8 20
62 12 1 7 38 34 16 13 12
63 14 2 6 34 35 16 11 12
64 11 2 8 31 35 14 14 14
65 12 2 7 34 31 16 15 13
66 13 1 5 35 37 17 10 11
67 14 2 6 36 35 18 11 17
68 11 1 6 30 27 18 12 12
69 12 2 5 39 40 12 15 13
70 12 1 5 35 37 16 15 14
71 8 1 5 38 36 10 14 13
72 11 2 5 31 38 14 16 15
73 14 2 4 34 39 18 15 13
74 14 1 6 38 41 18 15 10
75 12 1 6 34 27 16 13 11
76 9 2 6 39 30 17 12 19
77 13 2 6 37 37 16 17 13
78 11 2 7 34 31 16 13 17
79 12 1 5 28 31 13 15 13
80 12 1 7 37 27 16 13 9
81 12 1 6 33 36 16 15 11
82 12 1 5 37 38 20 16 10
83 12 2 5 35 37 16 15 9
84 12 1 4 37 33 15 16 12
85 11 2 8 32 34 15 15 12
86 10 2 8 33 31 16 14 13
87 9 1 5 38 39 14 15 13
88 12 2 5 33 34 16 14 12
89 12 2 6 29 32 16 13 15
90 12 2 4 33 33 15 7 22
91 9 2 5 31 36 12 17 13
92 15 2 5 36 32 17 13 15
93 12 2 5 35 41 16 15 13
94 12 2 5 32 28 15 14 15
95 12 2 6 29 30 13 13 10
96 10 2 6 39 36 16 16 11
97 13 2 5 37 35 16 12 16
98 9 2 6 35 31 16 14 11
99 12 1 5 37 34 16 17 11
100 10 1 7 32 36 14 15 10
101 14 2 5 38 36 16 17 10
102 11 1 6 37 35 16 12 16
103 15 2 6 36 37 20 16 12
104 11 1 6 32 28 15 11 11
105 11 2 4 33 39 16 15 16
106 12 1 5 40 32 13 9 19
107 12 2 5 38 35 17 16 11
108 12 1 7 41 39 16 15 16
109 11 1 6 36 35 16 10 15
110 7 2 9 43 42 12 10 24
111 12 2 6 30 34 16 15 14
112 14 2 6 31 33 16 11 15
113 11 2 5 32 41 17 13 11
114 11 1 6 32 33 13 14 15
115 10 2 5 37 34 12 18 12
116 13 1 8 37 32 18 16 10
117 13 2 7 33 40 14 14 14
118 8 2 5 34 40 14 14 13
119 11 2 7 33 35 13 14 9
120 12 2 6 38 36 16 14 15
121 11 2 6 33 37 13 12 15
122 13 2 9 31 27 16 14 14
123 12 2 7 38 39 13 15 11
124 14 2 6 37 38 16 15 8
125 13 2 5 33 31 15 15 11
126 15 2 5 31 33 16 13 11
127 10 1 6 39 32 15 17 8
128 11 2 6 44 39 17 17 10
129 9 2 7 33 36 15 19 11
130 11 2 5 35 33 12 15 13
131 10 1 5 32 33 16 13 11
132 11 1 5 28 32 10 9 20
133 8 2 6 40 37 16 15 10
134 11 1 4 27 30 12 15 15
135 12 1 5 37 38 14 15 12
136 12 2 7 32 29 15 16 14
137 9 1 5 28 22 13 11 23
138 11 1 7 34 35 15 14 14
139 10 2 7 30 35 11 11 16
140 8 2 6 35 34 12 15 11
141 9 1 5 31 35 8 13 12
142 8 2 8 32 34 16 15 10
143 9 1 5 30 34 15 16 14
144 15 2 5 30 35 17 14 12
145 11 1 5 31 23 16 15 12
146 8 2 6 40 31 10 16 11
147 13 2 4 32 27 18 16 12
148 12 1 5 36 36 13 11 13
149 12 1 5 32 31 16 12 11
150 9 1 7 35 32 13 9 19
151 7 2 6 38 39 10 16 12
152 13 2 7 42 37 15 13 17
153 9 1 10 34 38 16 16 9
154 6 2 6 35 39 16 12 12
155 8 2 8 35 34 14 9 19
156 8 2 4 33 31 10 13 18
157 15 2 5 36 32 17 13 15
158 6 2 6 32 37 13 14 14
159 9 2 7 33 36 15 19 11
160 11 2 7 34 32 16 13 9
161 8 2 6 32 35 12 12 18
162 8 2 6 34 36 13 13 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/10eqk1354801787.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/2uglf1354801787.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3r78y1354801787.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/410cp1354801787.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5p5xj1354801787.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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/6pafo1354801787.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/7ui2g1354801787.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/8dn7k1354801787.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/94kob1354801787.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/10am331354801787.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11axv61354801787.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12f9ln1354801787.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13f1n11354801787.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/142ftm1354801787.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/15pn1w1354801787.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/16ea7c1354801788.tab")
+ }
>
> try(system("convert tmp/10eqk1354801787.ps tmp/10eqk1354801787.png",intern=TRUE))
character(0)
> try(system("convert tmp/2uglf1354801787.ps tmp/2uglf1354801787.png",intern=TRUE))
character(0)
> try(system("convert tmp/3r78y1354801787.ps tmp/3r78y1354801787.png",intern=TRUE))
character(0)
> try(system("convert tmp/410cp1354801787.ps tmp/410cp1354801787.png",intern=TRUE))
character(0)
> try(system("convert tmp/5p5xj1354801787.ps tmp/5p5xj1354801787.png",intern=TRUE))
character(0)
> try(system("convert tmp/6pafo1354801787.ps tmp/6pafo1354801787.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ui2g1354801787.ps tmp/7ui2g1354801787.png",intern=TRUE))
character(0)
> try(system("convert tmp/8dn7k1354801787.ps tmp/8dn7k1354801787.png",intern=TRUE))
character(0)
> try(system("convert tmp/94kob1354801787.ps tmp/94kob1354801787.png",intern=TRUE))
character(0)
> try(system("convert tmp/10am331354801787.ps tmp/10am331354801787.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
14.478 1.900 16.436