R version 2.12.0 (2010-10-15)
Copyright (C) 2010 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(11
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+ ,6)
+ ,dim=c(9
+ ,156)
+ ,dimnames=list(c('Maand'
+ ,'Schoolprestaties'
+ ,'Sport'
+ ,'GoingOut'
+ ,'Relation'
+ ,'Family'
+ ,'Friends'
+ ,'Coach'
+ ,'Job')
+ ,1:156))
> y <- array(NA,dim=c(9,156),dimnames=list(c('Maand','Schoolprestaties','Sport','GoingOut','Relation','Family','Friends','Coach','Job'),1:156))
> 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 = '2'
> 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
Schoolprestaties Maand Sport GoingOut Relation Family Friends Coach Job
1 14 11 3 2 3 3 3 7 6
2 8 11 5 6 0 7 7 2 7
3 12 11 6 6 0 6 8 3 8
4 7 11 6 6 6 6 9 8 8
5 10 11 7 8 5 5 5 7 9
6 9 11 3 1 0 7 7 7 8
7 16 11 8 9 8 8 8 9 8
8 7 11 4 4 0 2 3 2 7
9 14 11 7 7 0 4 8 4 7
10 6 11 4 4 9 9 4 4 4
11 16 11 6 6 6 6 6 6 6
12 11 11 6 5 6 6 4 4 7
13 17 11 7 7 5 5 8 9 5
14 12 11 4 5 4 4 8 8 8
15 7 11 6 6 0 2 2 7 5
16 13 11 5 5 0 4 9 4 4
17 9 11 0 2 2 2 2 2 9
18 15 11 9 9 6 6 8 8 8
19 7 11 4 4 0 4 8 4 4
20 9 11 4 4 4 4 4 4 6
21 7 11 2 5 5 5 5 2 6
22 14 11 7 7 7 7 7 9 7
23 15 11 5 5 5 5 3 3 3
24 7 11 9 9 4 4 4 4 4
25 13 11 6 6 6 6 6 6 6
26 17 11 6 6 6 6 6 6 6
27 15 11 7 3 0 7 9 7 7
28 14 11 3 3 1 2 2 2 5
29 14 11 6 5 0 6 6 6 8
30 8 11 6 5 4 4 4 4 6
31 8 11 4 4 4 4 8 2 4
32 12 11 7 7 7 7 3 9 9
33 14 11 7 6 7 7 7 7 7
34 8 11 7 7 0 4 4 4 4
35 11 11 4 4 4 4 4 4 6
36 16 11 5 5 5 5 8 7 8
37 11 11 6 6 0 6 6 6 6
38 8 11 5 5 5 5 5 5 5
39 14 11 6 0 1 6 6 6 6
40 16 11 6 6 2 2 9 2 6
41 14 11 6 5 0 6 4 2 4
42 5 11 3 3 9 9 7 7 7
43 8 11 3 3 3 3 3 3 9
44 10 11 3 3 0 4 4 4 8
45 8 11 6 7 6 6 6 6 6
46 13 11 7 7 1 5 8 5 6
47 15 11 5 1 5 5 5 7 5
48 6 11 5 5 0 4 4 4 7
49 12 11 5 5 0 2 2 2 5
50 14 11 6 6 0 6 9 6 8
51 5 11 6 2 6 6 6 9 6
52 15 11 6 6 7 7 8 8 8
53 11 11 5 5 0 5 5 5 5
54 8 11 4 2 4 4 4 4 4
55 13 11 7 7 5 5 5 2 5
56 14 11 5 5 1 5 9 9 6
57 12 12 3 3 4 4 4 4 4
58 16 12 6 6 9 9 8 6 6
59 10 12 2 2 2 2 2 2 9
60 15 12 8 8 8 8 8 8 7
61 8 12 3 5 3 3 3 3 3
62 16 12 0 2 1 6 3 3 6
63 19 12 6 6 0 6 6 7 6
64 14 12 8 2 6 6 6 2 6
65 7 12 4 1 0 5 5 9 5
66 13 12 5 5 0 5 5 5 5
67 15 12 6 6 6 6 4 4 5
68 7 12 5 2 2 2 9 2 9
69 13 12 6 6 1 6 6 6 8
70 4 12 2 2 5 5 5 5 5
71 14 12 6 6 5 5 5 5 6
72 13 12 5 5 5 5 3 9 7
73 11 12 5 0 5 5 8 2 5
74 14 12 6 2 6 6 9 6 6
75 12 12 4 4 6 6 6 6 6
76 15 12 6 1 0 9 6 6 6
77 14 12 5 5 0 5 5 5 6
78 13 12 5 5 1 5 3 3 9
79 7 12 4 2 7 7 4 2 7
80 5 12 2 2 2 2 9 2 9
81 7 12 7 7 4 4 4 4 4
82 13 12 5 5 0 6 8 8 8
83 13 12 6 2 5 5 5 5 5
84 11 12 5 5 5 5 5 9 8
85 6 12 3 3 3 3 8 2 9
86 12 12 6 6 0 6 6 6 6
87 8 12 4 1 4 4 9 4 4
88 11 12 5 5 9 9 5 5 7
89 12 12 7 7 0 8 8 8 8
90 9 12 4 2 4 4 3 3 9
91 12 12 6 6 2 2 2 2 9
92 13 12 8 8 7 7 7 7 7
93 16 12 7 7 7 7 7 7 8
94 16 12 6 6 6 6 4 9 4
95 11 12 7 7 0 5 5 5 6
96 8 12 4 4 5 5 9 5 7
97 4 12 0 5 6 6 6 2 6
98 7 12 3 2 0 3 3 3 7
99 14 12 5 5 5 5 5 5 5
100 11 12 6 2 9 9 2 2 9
101 17 12 5 5 0 7 7 7 7
102 15 12 7 7 7 7 7 7 7
103 14 12 6 5 1 6 6 6 6
104 5 12 8 8 3 3 8 3 6
105 4 12 7 2 7 7 9 3 9
106 19 12 8 8 8 8 8 2 9
107 11 12 3 3 0 3 3 3 8
108 15 12 8 2 5 5 5 5 8
109 10 12 3 3 3 3 3 3 3
110 9 12 4 5 0 4 4 4 6
111 12 12 2 2 5 5 5 5 5
112 15 12 7 2 7 7 9 7 7
113 7 12 6 6 0 6 6 6 6
114 13 12 2 2 0 7 7 7 7
115 14 12 7 7 0 9 7 2 7
116 14 12 6 6 6 6 6 6 6
117 14 12 6 2 0 6 3 9 8
118 8 12 6 2 6 6 9 4 9
119 15 12 6 5 6 6 6 6 6
120 15 12 6 6 2 2 2 2 9
121 9 12 4 4 5 5 5 2 5
122 16 12 5 5 0 5 5 5 6
123 9 12 7 7 4 4 9 4 4
124 15 12 6 6 0 7 7 7 7
125 15 12 6 6 6 6 6 6 6
126 6 12 5 5 5 5 8 7 8
127 8 12 8 2 8 8 8 8 8
128 15 12 6 6 6 6 6 6 9
129 10 12 0 3 5 5 3 3 8
130 9 12 4 2 0 4 4 4 4
131 14 12 8 8 8 8 9 8 6
132 12 12 6 6 0 6 6 9 6
133 8 12 4 4 9 9 4 2 7
134 11 12 6 6 5 5 5 5 9
135 13 12 2 5 0 6 6 6 8
136 9 12 4 4 0 4 4 4 4
137 15 12 6 2 0 6 6 6 6
138 13 12 3 3 3 3 3 3 9
139 15 12 6 6 6 6 6 6 6
140 14 12 5 5 0 5 5 5 5
141 16 12 4 4 4 4 9 8 8
142 12 12 6 6 6 6 6 6 6
143 14 12 1 1 0 5 9 5 6
144 10 12 4 5 4 4 3 3 6
145 10 12 4 2 7 7 7 2 7
146 4 12 6 6 0 6 6 6 7
147 8 12 5 5 5 5 5 5 9
148 17 12 9 2 6 6 6 6 6
149 16 12 6 6 6 6 9 6 6
150 12 12 8 8 8 8 8 9 6
151 12 12 7 7 2 2 4 4 4
152 15 12 7 7 7 7 7 7 7
153 9 12 0 9 0 4 4 4 8
154 13 12 6 2 0 6 8 7 7
155 14 12 6 6 5 5 5 5 9
156 11 12 5 5 0 2 9 2 6
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Maand Sport GoingOut Relation Family
-1.0445311 0.6520827 0.4533694 0.1040109 -0.1392341 0.2677767
Friends Coach Job
-0.0757162 0.3315235 0.0008058
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.2719 -2.2644 0.6796 2.1445 6.7718
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.0445311 6.4896019 -0.161 0.8724
Maand 0.6520827 0.5514756 1.182 0.2389
Sport 0.4533694 0.1763692 2.571 0.0111 *
GoingOut 0.1040109 0.1441971 0.721 0.4719
Relation -0.1392341 0.1004351 -1.386 0.1678
Family 0.2677767 0.1905155 1.406 0.1620
Friends -0.0757162 0.1381356 -0.548 0.5844
Coach 0.3315235 0.1393361 2.379 0.0186 *
Job 0.0008058 0.1677641 0.005 0.9962
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.221 on 147 degrees of freedom
Multiple R-squared: 0.1939, Adjusted R-squared: 0.15
F-statistic: 4.42 on 8 and 147 DF, p-value: 8.167e-05
> 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,] 0.5928635 0.8142729 0.40713647
[2,] 0.6406681 0.7186638 0.35933191
[3,] 0.7105972 0.5788055 0.28940275
[4,] 0.9036762 0.1926476 0.09632378
[5,] 0.8517983 0.2964035 0.14820174
[6,] 0.8491009 0.3017982 0.15089908
[7,] 0.7868303 0.4263393 0.21316966
[8,] 0.8048555 0.3902891 0.19514453
[9,] 0.7431484 0.5137032 0.25685159
[10,] 0.6723742 0.6552517 0.32762584
[11,] 0.5975054 0.8049892 0.40249461
[12,] 0.6859313 0.6281375 0.31406874
[13,] 0.7849908 0.4300184 0.21500921
[14,] 0.7315006 0.5369988 0.26849939
[15,] 0.7877060 0.4245880 0.21229401
[16,] 0.7398961 0.5202078 0.26010392
[17,] 0.8006474 0.3987052 0.19935262
[18,] 0.7919321 0.4161357 0.20806787
[19,] 0.8021880 0.3956240 0.19781199
[20,] 0.7812268 0.4375463 0.21877316
[21,] 0.7358232 0.5283535 0.26417677
[22,] 0.6852422 0.6295156 0.31475781
[23,] 0.6667944 0.6664112 0.33320561
[24,] 0.6112308 0.7775384 0.38876918
[25,] 0.6143126 0.7713747 0.38568735
[26,] 0.5599520 0.8800959 0.44004797
[27,] 0.5518569 0.8962862 0.44814311
[28,] 0.4993075 0.9986149 0.50069253
[29,] 0.5481252 0.9037496 0.45187479
[30,] 0.6049461 0.7901078 0.39505391
[31,] 0.7028679 0.5942642 0.29713212
[32,] 0.6669876 0.6660248 0.33301239
[33,] 0.6165094 0.7669812 0.38349058
[34,] 0.6192636 0.7614728 0.38073638
[35,] 0.5683079 0.8633842 0.43169209
[36,] 0.5477799 0.9044403 0.45222013
[37,] 0.5925125 0.8149751 0.40748754
[38,] 0.5634866 0.8730267 0.43651336
[39,] 0.5309630 0.9380740 0.46903701
[40,] 0.7964436 0.4071129 0.20355644
[41,] 0.7778026 0.4443949 0.22219743
[42,] 0.7410296 0.5179408 0.25897042
[43,] 0.7247850 0.5504301 0.27521505
[44,] 0.6874464 0.6251072 0.31255362
[45,] 0.6466485 0.7067030 0.35335150
[46,] 0.6047020 0.7905960 0.39529801
[47,] 0.5756692 0.8486615 0.42433075
[48,] 0.5392603 0.9214794 0.46073968
[49,] 0.4939261 0.9878522 0.50607390
[50,] 0.4681685 0.9363370 0.53183149
[51,] 0.6321691 0.7356617 0.36783085
[52,] 0.6710791 0.6578419 0.32892094
[53,] 0.6422691 0.7154618 0.35773088
[54,] 0.7730456 0.4539087 0.22695436
[55,] 0.7363490 0.5273020 0.26365098
[56,] 0.7191030 0.5617940 0.28089702
[57,] 0.7708821 0.4582359 0.22911794
[58,] 0.7351121 0.5297757 0.26488787
[59,] 0.8328913 0.3342173 0.16710866
[60,] 0.8090612 0.3818776 0.19093878
[61,] 0.7771300 0.4457400 0.22286999
[62,] 0.7472535 0.5054930 0.25274652
[63,] 0.7223148 0.5553704 0.27768518
[64,] 0.6807774 0.6384451 0.31922256
[65,] 0.6448897 0.7102205 0.35511027
[66,] 0.6105346 0.7789308 0.38946538
[67,] 0.5700524 0.8598952 0.42994760
[68,] 0.5735766 0.8528467 0.42642336
[69,] 0.5856009 0.8287982 0.41439912
[70,] 0.6649748 0.6700505 0.33502523
[71,] 0.6215943 0.7568115 0.37840574
[72,] 0.5808769 0.8382462 0.41912308
[73,] 0.5594387 0.8811227 0.44056133
[74,] 0.5482146 0.9035708 0.45178541
[75,] 0.5073589 0.9852822 0.49264111
[76,] 0.4730848 0.9461696 0.52691520
[77,] 0.4313917 0.8627834 0.56860830
[78,] 0.4169398 0.8338796 0.58306020
[79,] 0.3775374 0.7550749 0.62246257
[80,] 0.3362985 0.6725971 0.66370146
[81,] 0.2968045 0.5936091 0.70319547
[82,] 0.2777229 0.5554459 0.72227707
[83,] 0.2530733 0.5061467 0.74692666
[84,] 0.2310921 0.4621843 0.76890786
[85,] 0.2162974 0.4325948 0.78370262
[86,] 0.2461934 0.4923868 0.75380660
[87,] 0.2409578 0.4819156 0.75904220
[88,] 0.2218996 0.4437992 0.77810040
[89,] 0.1881945 0.3763890 0.81180551
[90,] 0.2022969 0.4045937 0.79770314
[91,] 0.1761057 0.3522114 0.82389430
[92,] 0.1492935 0.2985870 0.85070651
[93,] 0.2624951 0.5249902 0.73750491
[94,] 0.4879830 0.9759660 0.51201698
[95,] 0.6548206 0.6903589 0.34517943
[96,] 0.6070779 0.7858441 0.39292207
[97,] 0.5765937 0.8468126 0.42340628
[98,] 0.5265442 0.9469116 0.47345578
[99,] 0.5008931 0.9982138 0.49910690
[100,] 0.4569645 0.9139291 0.54303545
[101,] 0.4255889 0.8511778 0.57441109
[102,] 0.5476144 0.9047711 0.45238555
[103,] 0.4995665 0.9991331 0.50043345
[104,] 0.4951111 0.9902221 0.50488894
[105,] 0.4500660 0.9001319 0.54993404
[106,] 0.3943514 0.7887028 0.60564860
[107,] 0.3946351 0.7892701 0.60536493
[108,] 0.3738000 0.7476000 0.62619999
[109,] 0.3910502 0.7821004 0.60894981
[110,] 0.3416484 0.6832969 0.65835157
[111,] 0.3831053 0.7662105 0.61689473
[112,] 0.3858033 0.7716066 0.61419668
[113,] 0.3904499 0.7808997 0.60955013
[114,] 0.3646369 0.7292739 0.63536306
[115,] 0.6590853 0.6818295 0.34091474
[116,] 0.8831772 0.2336456 0.11682282
[117,] 0.8708005 0.2583990 0.12919950
[118,] 0.8341268 0.3317463 0.16587316
[119,] 0.8399568 0.3200864 0.16004322
[120,] 0.7896849 0.4206301 0.21031507
[121,] 0.7368282 0.5263437 0.26317184
[122,] 0.6734786 0.6530429 0.32652143
[123,] 0.5949916 0.8100169 0.40500843
[124,] 0.5730572 0.8538856 0.42694281
[125,] 0.5733839 0.8532322 0.42661611
[126,] 0.5499940 0.9000120 0.45000602
[127,] 0.4763029 0.9526058 0.52369708
[128,] 0.4024943 0.8049886 0.59750569
[129,] 0.4515012 0.9030024 0.54849879
[130,] 0.4485251 0.8970502 0.55147488
[131,] 0.3388323 0.6776646 0.66116770
[132,] 0.2467660 0.4935320 0.75323399
[133,] 0.1520091 0.3040181 0.84799095
> postscript(file="/var/www/rcomp/tmp/1jsee1321982348.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/www/rcomp/tmp/2i2141321982348.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3mydm1321982348.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4h2fc1321982348.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5xnnk1321982348.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 156
Frequency = 1
1 2 3 4 5 6
3.81951318 -3.03240183 0.52539234 -5.22110445 -2.72609992 -2.26403192
7 8 9 10 11 12
1.89579926 -2.33499184 2.17284772 -4.54123530 4.21640566 -0.16877453
13 14 15 16 17 18
3.94523555 0.97101424 -5.18147452 2.36574177 1.88764795 1.03103857
19 20 21 22 23 24
-3.15259411 -0.90013390 -1.48718545 0.61082265 4.67216793 -5.68542372
25 26 27 28 29 30
1.21640566 5.21640566 1.86670685 5.28751794 1.48340040 -2.91088356
31 32 33 34 35 36
-0.93261070 -1.69365354 1.37788055 -4.12759956 1.09986610 4.72062577
37 38 39 40 41 42
-1.61899893 -2.84105834 2.14430052 6.28381852 2.66128524 -5.75369442
43 44 45 46 47 48
-0.96082103 0.09869841 -3.88760523 0.71358740 3.91193821 -5.01525635
49 50 51 52 53 54
2.03352329 1.60653798 -7.36212129 2.57463680 -0.53722883 -1.69050056
55 56 57 58 59 60
2.03875163 1.57797008 2.00677525 3.33012752 1.32882650 0.68005677
61 62 63 64 65 66
-1.81609080 6.77183441 5.39739488 2.39972181 -5.64599259 0.81068849
67 68 69 70 71 72
3.07674346 -2.50126855 -0.13345907 -5.82100019 1.94867292 0.02772108
73 74 75 76 77 78
1.24863243 2.20751502 0.67908353 1.44564278 1.80988271 1.45831416
79 80 81 82 83 84
-3.06758134 -3.14116040 -5.22274585 -0.22692760 1.36552227 -1.82165239
85 86 87 88 89 90
-2.90279943 -1.27108161 -1.85999156 -1.00892295 -2.87724153 -1.09080480
91 92 93 94 95 96
1.09930540 -0.93559329 2.62098120 2.41993171 -2.30487784 -2.63450768
97 98 99 100 101 102
-4.28535580 -2.92498355 2.50685898 -0.38444917 3.76190884 1.62178698
103 104 105 106 107 108
0.97216338 -7.01880696 -7.38224378 6.66758624 0.97019977 2.45636616
109 110 111 112 113 114
0.39193098 -2.21316387 2.17899981 2.29327375 -6.27108161 1.43404967
115 116 117 118 119 120
0.76921245 1.56432298 -0.07836859 -3.13185532 2.66833387 4.09930540
121 122 123 124 125 126
-0.94119022 3.80988271 -2.84416507 1.20452857 2.56432298 -5.93145691
127 128 129 130 131 132
-5.69668367 2.56190564 1.49092504 -1.89951963 -0.24342130 -2.26565213
133 134 135 136 137 138
-2.53268831 -1.05374442 1.64479525 -2.10754141 2.14496196 3.38709629
139 140 141 142 143 144
2.56432298 1.81068849 4.49865861 -0.43567702 4.34226843 -0.40042013
145 146 147 148 149 150
0.15956712 -9.27188739 -3.49636415 3.62025840 3.79147145 -2.65066096
151 152 153 154 155 156
0.03433934 1.62178698 -0.81734147 -0.03593502 1.94625558 0.91064792
> postscript(file="/var/www/rcomp/tmp/6jgpa1321982348.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 3.81951318 NA
1 -3.03240183 3.81951318
2 0.52539234 -3.03240183
3 -5.22110445 0.52539234
4 -2.72609992 -5.22110445
5 -2.26403192 -2.72609992
6 1.89579926 -2.26403192
7 -2.33499184 1.89579926
8 2.17284772 -2.33499184
9 -4.54123530 2.17284772
10 4.21640566 -4.54123530
11 -0.16877453 4.21640566
12 3.94523555 -0.16877453
13 0.97101424 3.94523555
14 -5.18147452 0.97101424
15 2.36574177 -5.18147452
16 1.88764795 2.36574177
17 1.03103857 1.88764795
18 -3.15259411 1.03103857
19 -0.90013390 -3.15259411
20 -1.48718545 -0.90013390
21 0.61082265 -1.48718545
22 4.67216793 0.61082265
23 -5.68542372 4.67216793
24 1.21640566 -5.68542372
25 5.21640566 1.21640566
26 1.86670685 5.21640566
27 5.28751794 1.86670685
28 1.48340040 5.28751794
29 -2.91088356 1.48340040
30 -0.93261070 -2.91088356
31 -1.69365354 -0.93261070
32 1.37788055 -1.69365354
33 -4.12759956 1.37788055
34 1.09986610 -4.12759956
35 4.72062577 1.09986610
36 -1.61899893 4.72062577
37 -2.84105834 -1.61899893
38 2.14430052 -2.84105834
39 6.28381852 2.14430052
40 2.66128524 6.28381852
41 -5.75369442 2.66128524
42 -0.96082103 -5.75369442
43 0.09869841 -0.96082103
44 -3.88760523 0.09869841
45 0.71358740 -3.88760523
46 3.91193821 0.71358740
47 -5.01525635 3.91193821
48 2.03352329 -5.01525635
49 1.60653798 2.03352329
50 -7.36212129 1.60653798
51 2.57463680 -7.36212129
52 -0.53722883 2.57463680
53 -1.69050056 -0.53722883
54 2.03875163 -1.69050056
55 1.57797008 2.03875163
56 2.00677525 1.57797008
57 3.33012752 2.00677525
58 1.32882650 3.33012752
59 0.68005677 1.32882650
60 -1.81609080 0.68005677
61 6.77183441 -1.81609080
62 5.39739488 6.77183441
63 2.39972181 5.39739488
64 -5.64599259 2.39972181
65 0.81068849 -5.64599259
66 3.07674346 0.81068849
67 -2.50126855 3.07674346
68 -0.13345907 -2.50126855
69 -5.82100019 -0.13345907
70 1.94867292 -5.82100019
71 0.02772108 1.94867292
72 1.24863243 0.02772108
73 2.20751502 1.24863243
74 0.67908353 2.20751502
75 1.44564278 0.67908353
76 1.80988271 1.44564278
77 1.45831416 1.80988271
78 -3.06758134 1.45831416
79 -3.14116040 -3.06758134
80 -5.22274585 -3.14116040
81 -0.22692760 -5.22274585
82 1.36552227 -0.22692760
83 -1.82165239 1.36552227
84 -2.90279943 -1.82165239
85 -1.27108161 -2.90279943
86 -1.85999156 -1.27108161
87 -1.00892295 -1.85999156
88 -2.87724153 -1.00892295
89 -1.09080480 -2.87724153
90 1.09930540 -1.09080480
91 -0.93559329 1.09930540
92 2.62098120 -0.93559329
93 2.41993171 2.62098120
94 -2.30487784 2.41993171
95 -2.63450768 -2.30487784
96 -4.28535580 -2.63450768
97 -2.92498355 -4.28535580
98 2.50685898 -2.92498355
99 -0.38444917 2.50685898
100 3.76190884 -0.38444917
101 1.62178698 3.76190884
102 0.97216338 1.62178698
103 -7.01880696 0.97216338
104 -7.38224378 -7.01880696
105 6.66758624 -7.38224378
106 0.97019977 6.66758624
107 2.45636616 0.97019977
108 0.39193098 2.45636616
109 -2.21316387 0.39193098
110 2.17899981 -2.21316387
111 2.29327375 2.17899981
112 -6.27108161 2.29327375
113 1.43404967 -6.27108161
114 0.76921245 1.43404967
115 1.56432298 0.76921245
116 -0.07836859 1.56432298
117 -3.13185532 -0.07836859
118 2.66833387 -3.13185532
119 4.09930540 2.66833387
120 -0.94119022 4.09930540
121 3.80988271 -0.94119022
122 -2.84416507 3.80988271
123 1.20452857 -2.84416507
124 2.56432298 1.20452857
125 -5.93145691 2.56432298
126 -5.69668367 -5.93145691
127 2.56190564 -5.69668367
128 1.49092504 2.56190564
129 -1.89951963 1.49092504
130 -0.24342130 -1.89951963
131 -2.26565213 -0.24342130
132 -2.53268831 -2.26565213
133 -1.05374442 -2.53268831
134 1.64479525 -1.05374442
135 -2.10754141 1.64479525
136 2.14496196 -2.10754141
137 3.38709629 2.14496196
138 2.56432298 3.38709629
139 1.81068849 2.56432298
140 4.49865861 1.81068849
141 -0.43567702 4.49865861
142 4.34226843 -0.43567702
143 -0.40042013 4.34226843
144 0.15956712 -0.40042013
145 -9.27188739 0.15956712
146 -3.49636415 -9.27188739
147 3.62025840 -3.49636415
148 3.79147145 3.62025840
149 -2.65066096 3.79147145
150 0.03433934 -2.65066096
151 1.62178698 0.03433934
152 -0.81734147 1.62178698
153 -0.03593502 -0.81734147
154 1.94625558 -0.03593502
155 0.91064792 1.94625558
156 NA 0.91064792
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.03240183 3.81951318
[2,] 0.52539234 -3.03240183
[3,] -5.22110445 0.52539234
[4,] -2.72609992 -5.22110445
[5,] -2.26403192 -2.72609992
[6,] 1.89579926 -2.26403192
[7,] -2.33499184 1.89579926
[8,] 2.17284772 -2.33499184
[9,] -4.54123530 2.17284772
[10,] 4.21640566 -4.54123530
[11,] -0.16877453 4.21640566
[12,] 3.94523555 -0.16877453
[13,] 0.97101424 3.94523555
[14,] -5.18147452 0.97101424
[15,] 2.36574177 -5.18147452
[16,] 1.88764795 2.36574177
[17,] 1.03103857 1.88764795
[18,] -3.15259411 1.03103857
[19,] -0.90013390 -3.15259411
[20,] -1.48718545 -0.90013390
[21,] 0.61082265 -1.48718545
[22,] 4.67216793 0.61082265
[23,] -5.68542372 4.67216793
[24,] 1.21640566 -5.68542372
[25,] 5.21640566 1.21640566
[26,] 1.86670685 5.21640566
[27,] 5.28751794 1.86670685
[28,] 1.48340040 5.28751794
[29,] -2.91088356 1.48340040
[30,] -0.93261070 -2.91088356
[31,] -1.69365354 -0.93261070
[32,] 1.37788055 -1.69365354
[33,] -4.12759956 1.37788055
[34,] 1.09986610 -4.12759956
[35,] 4.72062577 1.09986610
[36,] -1.61899893 4.72062577
[37,] -2.84105834 -1.61899893
[38,] 2.14430052 -2.84105834
[39,] 6.28381852 2.14430052
[40,] 2.66128524 6.28381852
[41,] -5.75369442 2.66128524
[42,] -0.96082103 -5.75369442
[43,] 0.09869841 -0.96082103
[44,] -3.88760523 0.09869841
[45,] 0.71358740 -3.88760523
[46,] 3.91193821 0.71358740
[47,] -5.01525635 3.91193821
[48,] 2.03352329 -5.01525635
[49,] 1.60653798 2.03352329
[50,] -7.36212129 1.60653798
[51,] 2.57463680 -7.36212129
[52,] -0.53722883 2.57463680
[53,] -1.69050056 -0.53722883
[54,] 2.03875163 -1.69050056
[55,] 1.57797008 2.03875163
[56,] 2.00677525 1.57797008
[57,] 3.33012752 2.00677525
[58,] 1.32882650 3.33012752
[59,] 0.68005677 1.32882650
[60,] -1.81609080 0.68005677
[61,] 6.77183441 -1.81609080
[62,] 5.39739488 6.77183441
[63,] 2.39972181 5.39739488
[64,] -5.64599259 2.39972181
[65,] 0.81068849 -5.64599259
[66,] 3.07674346 0.81068849
[67,] -2.50126855 3.07674346
[68,] -0.13345907 -2.50126855
[69,] -5.82100019 -0.13345907
[70,] 1.94867292 -5.82100019
[71,] 0.02772108 1.94867292
[72,] 1.24863243 0.02772108
[73,] 2.20751502 1.24863243
[74,] 0.67908353 2.20751502
[75,] 1.44564278 0.67908353
[76,] 1.80988271 1.44564278
[77,] 1.45831416 1.80988271
[78,] -3.06758134 1.45831416
[79,] -3.14116040 -3.06758134
[80,] -5.22274585 -3.14116040
[81,] -0.22692760 -5.22274585
[82,] 1.36552227 -0.22692760
[83,] -1.82165239 1.36552227
[84,] -2.90279943 -1.82165239
[85,] -1.27108161 -2.90279943
[86,] -1.85999156 -1.27108161
[87,] -1.00892295 -1.85999156
[88,] -2.87724153 -1.00892295
[89,] -1.09080480 -2.87724153
[90,] 1.09930540 -1.09080480
[91,] -0.93559329 1.09930540
[92,] 2.62098120 -0.93559329
[93,] 2.41993171 2.62098120
[94,] -2.30487784 2.41993171
[95,] -2.63450768 -2.30487784
[96,] -4.28535580 -2.63450768
[97,] -2.92498355 -4.28535580
[98,] 2.50685898 -2.92498355
[99,] -0.38444917 2.50685898
[100,] 3.76190884 -0.38444917
[101,] 1.62178698 3.76190884
[102,] 0.97216338 1.62178698
[103,] -7.01880696 0.97216338
[104,] -7.38224378 -7.01880696
[105,] 6.66758624 -7.38224378
[106,] 0.97019977 6.66758624
[107,] 2.45636616 0.97019977
[108,] 0.39193098 2.45636616
[109,] -2.21316387 0.39193098
[110,] 2.17899981 -2.21316387
[111,] 2.29327375 2.17899981
[112,] -6.27108161 2.29327375
[113,] 1.43404967 -6.27108161
[114,] 0.76921245 1.43404967
[115,] 1.56432298 0.76921245
[116,] -0.07836859 1.56432298
[117,] -3.13185532 -0.07836859
[118,] 2.66833387 -3.13185532
[119,] 4.09930540 2.66833387
[120,] -0.94119022 4.09930540
[121,] 3.80988271 -0.94119022
[122,] -2.84416507 3.80988271
[123,] 1.20452857 -2.84416507
[124,] 2.56432298 1.20452857
[125,] -5.93145691 2.56432298
[126,] -5.69668367 -5.93145691
[127,] 2.56190564 -5.69668367
[128,] 1.49092504 2.56190564
[129,] -1.89951963 1.49092504
[130,] -0.24342130 -1.89951963
[131,] -2.26565213 -0.24342130
[132,] -2.53268831 -2.26565213
[133,] -1.05374442 -2.53268831
[134,] 1.64479525 -1.05374442
[135,] -2.10754141 1.64479525
[136,] 2.14496196 -2.10754141
[137,] 3.38709629 2.14496196
[138,] 2.56432298 3.38709629
[139,] 1.81068849 2.56432298
[140,] 4.49865861 1.81068849
[141,] -0.43567702 4.49865861
[142,] 4.34226843 -0.43567702
[143,] -0.40042013 4.34226843
[144,] 0.15956712 -0.40042013
[145,] -9.27188739 0.15956712
[146,] -3.49636415 -9.27188739
[147,] 3.62025840 -3.49636415
[148,] 3.79147145 3.62025840
[149,] -2.65066096 3.79147145
[150,] 0.03433934 -2.65066096
[151,] 1.62178698 0.03433934
[152,] -0.81734147 1.62178698
[153,] -0.03593502 -0.81734147
[154,] 1.94625558 -0.03593502
[155,] 0.91064792 1.94625558
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.03240183 3.81951318
2 0.52539234 -3.03240183
3 -5.22110445 0.52539234
4 -2.72609992 -5.22110445
5 -2.26403192 -2.72609992
6 1.89579926 -2.26403192
7 -2.33499184 1.89579926
8 2.17284772 -2.33499184
9 -4.54123530 2.17284772
10 4.21640566 -4.54123530
11 -0.16877453 4.21640566
12 3.94523555 -0.16877453
13 0.97101424 3.94523555
14 -5.18147452 0.97101424
15 2.36574177 -5.18147452
16 1.88764795 2.36574177
17 1.03103857 1.88764795
18 -3.15259411 1.03103857
19 -0.90013390 -3.15259411
20 -1.48718545 -0.90013390
21 0.61082265 -1.48718545
22 4.67216793 0.61082265
23 -5.68542372 4.67216793
24 1.21640566 -5.68542372
25 5.21640566 1.21640566
26 1.86670685 5.21640566
27 5.28751794 1.86670685
28 1.48340040 5.28751794
29 -2.91088356 1.48340040
30 -0.93261070 -2.91088356
31 -1.69365354 -0.93261070
32 1.37788055 -1.69365354
33 -4.12759956 1.37788055
34 1.09986610 -4.12759956
35 4.72062577 1.09986610
36 -1.61899893 4.72062577
37 -2.84105834 -1.61899893
38 2.14430052 -2.84105834
39 6.28381852 2.14430052
40 2.66128524 6.28381852
41 -5.75369442 2.66128524
42 -0.96082103 -5.75369442
43 0.09869841 -0.96082103
44 -3.88760523 0.09869841
45 0.71358740 -3.88760523
46 3.91193821 0.71358740
47 -5.01525635 3.91193821
48 2.03352329 -5.01525635
49 1.60653798 2.03352329
50 -7.36212129 1.60653798
51 2.57463680 -7.36212129
52 -0.53722883 2.57463680
53 -1.69050056 -0.53722883
54 2.03875163 -1.69050056
55 1.57797008 2.03875163
56 2.00677525 1.57797008
57 3.33012752 2.00677525
58 1.32882650 3.33012752
59 0.68005677 1.32882650
60 -1.81609080 0.68005677
61 6.77183441 -1.81609080
62 5.39739488 6.77183441
63 2.39972181 5.39739488
64 -5.64599259 2.39972181
65 0.81068849 -5.64599259
66 3.07674346 0.81068849
67 -2.50126855 3.07674346
68 -0.13345907 -2.50126855
69 -5.82100019 -0.13345907
70 1.94867292 -5.82100019
71 0.02772108 1.94867292
72 1.24863243 0.02772108
73 2.20751502 1.24863243
74 0.67908353 2.20751502
75 1.44564278 0.67908353
76 1.80988271 1.44564278
77 1.45831416 1.80988271
78 -3.06758134 1.45831416
79 -3.14116040 -3.06758134
80 -5.22274585 -3.14116040
81 -0.22692760 -5.22274585
82 1.36552227 -0.22692760
83 -1.82165239 1.36552227
84 -2.90279943 -1.82165239
85 -1.27108161 -2.90279943
86 -1.85999156 -1.27108161
87 -1.00892295 -1.85999156
88 -2.87724153 -1.00892295
89 -1.09080480 -2.87724153
90 1.09930540 -1.09080480
91 -0.93559329 1.09930540
92 2.62098120 -0.93559329
93 2.41993171 2.62098120
94 -2.30487784 2.41993171
95 -2.63450768 -2.30487784
96 -4.28535580 -2.63450768
97 -2.92498355 -4.28535580
98 2.50685898 -2.92498355
99 -0.38444917 2.50685898
100 3.76190884 -0.38444917
101 1.62178698 3.76190884
102 0.97216338 1.62178698
103 -7.01880696 0.97216338
104 -7.38224378 -7.01880696
105 6.66758624 -7.38224378
106 0.97019977 6.66758624
107 2.45636616 0.97019977
108 0.39193098 2.45636616
109 -2.21316387 0.39193098
110 2.17899981 -2.21316387
111 2.29327375 2.17899981
112 -6.27108161 2.29327375
113 1.43404967 -6.27108161
114 0.76921245 1.43404967
115 1.56432298 0.76921245
116 -0.07836859 1.56432298
117 -3.13185532 -0.07836859
118 2.66833387 -3.13185532
119 4.09930540 2.66833387
120 -0.94119022 4.09930540
121 3.80988271 -0.94119022
122 -2.84416507 3.80988271
123 1.20452857 -2.84416507
124 2.56432298 1.20452857
125 -5.93145691 2.56432298
126 -5.69668367 -5.93145691
127 2.56190564 -5.69668367
128 1.49092504 2.56190564
129 -1.89951963 1.49092504
130 -0.24342130 -1.89951963
131 -2.26565213 -0.24342130
132 -2.53268831 -2.26565213
133 -1.05374442 -2.53268831
134 1.64479525 -1.05374442
135 -2.10754141 1.64479525
136 2.14496196 -2.10754141
137 3.38709629 2.14496196
138 2.56432298 3.38709629
139 1.81068849 2.56432298
140 4.49865861 1.81068849
141 -0.43567702 4.49865861
142 4.34226843 -0.43567702
143 -0.40042013 4.34226843
144 0.15956712 -0.40042013
145 -9.27188739 0.15956712
146 -3.49636415 -9.27188739
147 3.62025840 -3.49636415
148 3.79147145 3.62025840
149 -2.65066096 3.79147145
150 0.03433934 -2.65066096
151 1.62178698 0.03433934
152 -0.81734147 1.62178698
153 -0.03593502 -0.81734147
154 1.94625558 -0.03593502
155 0.91064792 1.94625558
> 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/www/rcomp/tmp/739cj1321982348.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/www/rcomp/tmp/8hpz11321982348.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/www/rcomp/tmp/9ief01321982348.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/www/rcomp/tmp/10hh531321982348.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/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11fipw1321982348.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12l49j1321982348.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13sw4c1321982348.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/14dtmh1321982348.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/www/rcomp/tmp/153y0k1321982348.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/www/rcomp/tmp/16o0m61321982348.tab")
+ }
>
> try(system("convert tmp/1jsee1321982348.ps tmp/1jsee1321982348.png",intern=TRUE))
character(0)
> try(system("convert tmp/2i2141321982348.ps tmp/2i2141321982348.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mydm1321982348.ps tmp/3mydm1321982348.png",intern=TRUE))
character(0)
> try(system("convert tmp/4h2fc1321982348.ps tmp/4h2fc1321982348.png",intern=TRUE))
character(0)
> try(system("convert tmp/5xnnk1321982348.ps tmp/5xnnk1321982348.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jgpa1321982348.ps tmp/6jgpa1321982348.png",intern=TRUE))
character(0)
> try(system("convert tmp/739cj1321982348.ps tmp/739cj1321982348.png",intern=TRUE))
character(0)
> try(system("convert tmp/8hpz11321982348.ps tmp/8hpz11321982348.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ief01321982348.ps tmp/9ief01321982348.png",intern=TRUE))
character(0)
> try(system("convert tmp/10hh531321982348.ps tmp/10hh531321982348.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.500 0.400 6.975