R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(15
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+ ,0)
+ ,dim=c(8
+ ,156)
+ ,dimnames=list(c('Popularity'
+ ,'FindingFriends'
+ ,'KnowingPeople'
+ ,'Liked'
+ ,'Celebrity'
+ ,'B'
+ ,'2B'
+ ,'3B')
+ ,1:156))
> y <- array(NA,dim=c(8,156),dimnames=list(c('Popularity','FindingFriends','KnowingPeople','Liked','Celebrity','B','2B','3B'),1:156))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> ylab = ''
> xlab = ''
> main = ''
> #'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
Attaching package: 'zoo'
The following object(s) are masked from package:base :
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
Popularity FindingFriends KnowingPeople Liked Celebrity B 2B 3B
1 15 10 12 16 6 1 1 3
2 12 9 7 12 6 1 0 0
3 9 12 11 11 4 1 0 3
4 10 12 11 12 6 1 3 0
5 13 9 14 14 6 1 1 3
6 16 11 16 16 7 1 1 0
7 14 12 13 13 6 1 2 0
8 16 11 13 14 7 2 0 1
9 10 12 5 13 6 1 1 1
10 8 12 8 13 4 0 0 0
11 12 11 14 13 5 2 1 0
12 15 11 15 15 8 1 0 2
13 14 12 8 14 4 1 0 0
14 14 6 13 12 6 1 0 0
15 12 13 12 12 6 1 0 1
16 12 11 11 12 5 0 2 1
17 10 12 8 11 4 0 3 1
18 4 10 4 10 2 0 2 0
19 14 11 15 15 8 0 2 1
20 15 12 12 16 7 0 0 0
21 16 12 14 14 6 1 0 0
22 12 12 9 13 4 2 0 0
23 12 11 16 13 4 0 0 0
24 12 12 10 13 4 0 1 0
25 12 12 8 13 5 0 2 0
26 12 12 14 14 4 1 0 0
27 11 6 6 9 4 1 1 0
28 11 5 16 14 6 3 0 0
29 11 12 11 12 6 0 1 3
30 11 14 7 13 6 0 1 2
31 11 12 13 11 4 1 0 0
32 11 9 7 13 2 2 0 1
33 15 11 14 15 7 1 0 0
34 15 11 17 16 6 1 0 1
35 9 11 15 15 7 0 2 2
36 16 12 8 14 4 0 2 1
37 13 10 8 8 4 0 0 1
38 9 12 11 11 4 2 2 0
39 16 11 16 15 6 1 2 0
40 12 12 10 15 6 1 0 0
41 15 9 5 11 3 2 1 0
42 5 15 8 12 3 0 3 0
43 11 11 8 12 6 1 2 0
44 17 11 15 14 5 2 0 0
45 9 15 6 8 4 0 2 1
46 13 12 16 16 6 2 0 0
47 16 9 16 16 6 0 1 1
48 16 12 16 14 6 0 1 0
49 14 9 19 12 6 1 1 0
50 16 11 14 15 6 0 1 1
51 11 12 15 12 6 1 0 0
52 11 11 11 14 5 0 1 2
53 11 6 14 17 6 1 2 1
54 12 10 12 13 6 1 0 0
55 12 12 15 13 6 1 1 1
56 12 13 14 12 5 1 1 0
57 14 11 13 16 6 1 1 1
58 10 10 11 12 5 1 0 2
59 9 11 8 10 4 0 1 0
60 12 7 11 15 5 0 1 0
61 10 11 9 12 4 1 0 0
62 14 11 10 16 6 2 2 0
63 8 7 4 13 6 1 0 0
64 16 12 15 15 7 0 2 1
65 14 14 17 18 6 0 1 3
66 14 11 12 12 4 0 0 1
67 12 12 12 13 4 0 1 0
68 14 11 15 14 6 0 1 0
69 7 12 13 12 3 0 1 0
70 19 12 15 15 6 2 1 0
71 15 12 14 16 4 0 0 2
72 8 12 8 14 5 0 0 0
73 10 15 15 15 6 1 0 0
74 13 11 12 13 7 1 1 0
75 13 13 14 13 3 1 0 3
76 10 10 10 11 5 1 0 1
77 12 12 7 12 3 1 0 0
78 15 13 16 18 8 0 0 1
79 7 14 12 12 4 0 0 1
80 14 11 15 16 6 0 0 1
81 10 11 7 9 4 1 1 1
82 6 7 9 11 4 2 0 0
83 11 11 15 10 5 1 1 0
84 12 12 7 11 4 2 2 0
85 14 12 15 13 6 3 1 0
86 12 10 14 13 7 1 2 0
87 14 12 14 15 7 0 1 2
88 11 8 8 13 4 2 1 1
89 10 7 8 9 5 1 0 0
90 13 11 14 13 6 0 1 2
91 8 11 10 12 4 0 0 1
92 9 11 12 13 5 2 0 4
93 6 9 15 11 6 1 1 0
94 12 12 12 14 5 1 0 0
95 14 13 13 13 5 0 0 0
96 11 9 12 12 4 2 2 0
97 8 11 10 15 2 0 0 1
98 7 12 8 12 3 1 0 0
99 9 9 6 12 5 0 2 0
100 14 12 13 13 5 3 1 0
101 13 12 7 12 5 0 0 0
102 15 12 13 13 6 0 1 2
103 5 14 4 5 2 1 1 2
104 15 11 14 13 5 0 2 2
105 13 12 13 13 5 0 1 0
106 12 8 13 13 5 0 0 1
107 6 12 6 11 2 1 0 0
108 7 12 7 12 4 1 0 0
109 13 12 5 12 3 3 0 0
110 16 11 14 15 8 2 0 0
111 10 11 13 15 6 0 1 0
112 16 12 16 16 7 1 0 0
113 15 10 16 13 6 1 0 0
114 8 13 7 10 3 1 0 1
115 11 8 14 15 5 1 0 0
116 13 12 11 13 6 1 1 2
117 16 11 17 16 7 0 2 1
118 11 10 5 13 3 0 1 3
119 14 13 10 16 8 0 1 1
120 9 10 11 13 3 0 0 2
121 8 10 10 14 3 0 1 0
122 8 7 9 15 4 2 0 0
123 11 10 12 14 5 1 3 0
124 12 8 15 13 7 0 2 0
125 11 12 7 13 6 2 1 0
126 14 12 13 15 6 1 0 0
127 11 12 8 16 6 1 0 0
128 14 11 16 12 5 1 1 0
129 13 13 15 14 6 0 0 1
130 12 12 6 14 5 1 1 0
131 4 8 6 4 4 1 0 0
132 15 11 12 13 6 0 0 0
133 10 12 8 16 4 0 0 1
134 13 13 11 15 6 0 0 0
135 15 12 13 14 6 0 0 2
136 12 10 14 14 5 0 0 0
137 13 12 14 14 6 1 0 0
138 8 10 10 6 4 0 0 0
139 10 13 4 13 6 0 0 1
140 15 11 16 14 6 1 0 0
141 16 12 12 15 8 0 1 0
142 16 12 15 16 7 1 0 0
143 14 10 12 15 6 0 0 0
144 14 11 14 12 6 1 0 0
145 12 11 11 14 2 0 0 0
146 15 11 16 11 5 0 0 0
147 13 8 14 14 5 0 0 1
148 16 11 14 14 6 1 0 0
149 14 12 15 14 6 0 0 1
150 8 11 9 12 4 0 0 0
151 16 12 15 14 6 0 0 0
152 16 12 14 16 8 1 0 1
153 12 12 15 13 6 0 1 0
154 11 8 10 14 5 0 0 0
155 16 12 14 16 8 0 0 0
156 9 11 9 12 4 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) FindingFriends KnowingPeople Liked Celebrity
-0.23026 0.12442 0.24245 0.35248 0.63332
B `2B` `3B`
0.32079 -0.11246 -0.02646
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.41179 -1.12493 -0.05509 1.27452 6.59187
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.23026 1.49695 -0.154 0.877960
FindingFriends 0.12442 0.09824 1.267 0.207313
KnowingPeople 0.24245 0.06161 3.935 0.000128 ***
Liked 0.35248 0.09736 3.620 0.000403 ***
Celebrity 0.63332 0.15756 4.020 9.25e-05 ***
B 0.32079 0.22771 1.409 0.161003
`2B` -0.11246 0.21191 -0.531 0.596422
`3B` -0.02646 0.19725 -0.134 0.893463
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.109 on 148 degrees of freedom
Multiple R-squared: 0.5076, Adjusted R-squared: 0.4843
F-statistic: 21.79 on 7 and 148 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.27842126 0.5568425213 0.7215787393
[2,] 0.24316503 0.4863300520 0.7568349740
[3,] 0.34040270 0.6808053986 0.6595973007
[4,] 0.24231903 0.4846380604 0.7576809698
[5,] 0.17202341 0.3440468252 0.8279765874
[6,] 0.22539947 0.4507989306 0.7746005347
[7,] 0.20020203 0.4004040554 0.7997979723
[8,] 0.23835304 0.4767060854 0.7616469573
[9,] 0.18874656 0.3774931203 0.8112534399
[10,] 0.13242326 0.2648465231 0.8675767385
[11,] 0.12819686 0.2563937228 0.8718031386
[12,] 0.08759866 0.1751973108 0.9124013446
[13,] 0.05885796 0.1177159112 0.9411420444
[14,] 0.04898931 0.0979786271 0.9510106864
[15,] 0.04065573 0.0813114670 0.9593442665
[16,] 0.03128717 0.0625743419 0.9687128291
[17,] 0.03772714 0.0754542716 0.9622728642
[18,] 0.15041427 0.3008285475 0.8495857263
[19,] 0.11589755 0.2317950982 0.8841024509
[20,] 0.08799280 0.1759856036 0.9120071982
[21,] 0.06328089 0.1265617775 0.9367191113
[22,] 0.05667238 0.1133447580 0.9433276210
[23,] 0.04003389 0.0800677885 0.9599661058
[24,] 0.02768266 0.0553653211 0.9723173394
[25,] 0.12237112 0.2447422430 0.8776288785
[26,] 0.37375689 0.7475137897 0.6262431052
[27,] 0.56048717 0.8790256562 0.4395128281
[28,] 0.52042869 0.9591426220 0.4795713110
[29,] 0.53490441 0.9301911821 0.4650955910
[30,] 0.51703002 0.9659399655 0.4829699828
[31,] 0.81364430 0.3727114037 0.1863557019
[32,] 0.89822317 0.2035536529 0.1017768265
[33,] 0.87325532 0.2534893700 0.1267446850
[34,] 0.91291854 0.1741629228 0.0870814614
[35,] 0.89627916 0.2074416863 0.1037208432
[36,] 0.89428683 0.2114263309 0.1057131655
[37,] 0.88770734 0.2245853218 0.1122926609
[38,] 0.89262677 0.2147464597 0.1073732299
[39,] 0.86720046 0.2655990806 0.1327995403
[40,] 0.87212026 0.2557594726 0.1278797363
[41,] 0.87467023 0.2506595341 0.1253297670
[42,] 0.85489650 0.2902069935 0.1451034967
[43,] 0.88461301 0.2307739751 0.1153869876
[44,] 0.86774510 0.2645098001 0.1322549000
[45,] 0.84975667 0.3004866700 0.1502433350
[46,] 0.81983779 0.3603244149 0.1801622074
[47,] 0.78549466 0.4290106701 0.2145053350
[48,] 0.77107270 0.4578545955 0.2289272978
[49,] 0.74006009 0.5198798227 0.2599399113
[50,] 0.70963491 0.5807301888 0.2903650944
[51,] 0.68057651 0.6388469706 0.3194234853
[52,] 0.64061385 0.7187723089 0.3593861545
[53,] 0.69496552 0.6100689531 0.3050344766
[54,] 0.68558346 0.6288330706 0.3144165353
[55,] 0.67136753 0.6572649490 0.3286324745
[56,] 0.70862712 0.5827457688 0.2913728844
[57,] 0.67019731 0.6596053786 0.3298026893
[58,] 0.62757265 0.7448547048 0.3724273524
[59,] 0.72111057 0.5577788512 0.2788894256
[60,] 0.84432816 0.3113436780 0.1556718390
[61,] 0.84166579 0.3166684243 0.1583342121
[62,] 0.88614356 0.2277128864 0.1138564432
[63,] 0.95604011 0.0879197704 0.0439598852
[64,] 0.94382460 0.1123507900 0.0561753950
[65,] 0.93445179 0.1310964261 0.0655482131
[66,] 0.92094313 0.1581137432 0.0790568716
[67,] 0.92974565 0.1405087069 0.0702543534
[68,] 0.92626601 0.1474679840 0.0737339920
[69,] 0.97070015 0.0585996935 0.0292998467
[70,] 0.96185337 0.0762932504 0.0381466252
[71,] 0.95595009 0.0880998278 0.0440499139
[72,] 0.97452069 0.0509586115 0.0254793058
[73,] 0.96765156 0.0646968803 0.0323484402
[74,] 0.97022060 0.0595587949 0.0297793974
[75,] 0.96105075 0.0778984956 0.0389492478
[76,] 0.95549300 0.0890139929 0.0445069965
[77,] 0.94472454 0.1105509137 0.0552754569
[78,] 0.94011387 0.1197722677 0.0598861338
[79,] 0.93868220 0.1226355955 0.0613177977
[80,] 0.92277702 0.1544459506 0.0772229753
[81,] 0.92685832 0.1462833681 0.0731416840
[82,] 0.96327863 0.0734427396 0.0367213698
[83,] 0.99864611 0.0027077862 0.0013538931
[84,] 0.99805935 0.0038812915 0.0019406458
[85,] 0.99770554 0.0045889139 0.0022944570
[86,] 0.99664325 0.0067134941 0.0033567471
[87,] 0.99676234 0.0064753265 0.0032376632
[88,] 0.99737742 0.0052451684 0.0026225842
[89,] 0.99658727 0.0068254659 0.0034127330
[90,] 0.99512357 0.0097528572 0.0048764286
[91,] 0.99780620 0.0043876053 0.0021938026
[92,] 0.99744826 0.0051034761 0.0025517381
[93,] 0.99687548 0.0062490434 0.0031245217
[94,] 0.99717935 0.0056412982 0.0028206491
[95,] 0.99614044 0.0077191215 0.0038595608
[96,] 0.99436496 0.0112700717 0.0056350358
[97,] 0.99413925 0.0117214973 0.0058607487
[98,] 0.99647439 0.0070512114 0.0035256057
[99,] 0.99927779 0.0014444176 0.0007222088
[100,] 0.99885542 0.0022891535 0.0011445767
[101,] 0.99965567 0.0006886573 0.0003443286
[102,] 0.99942389 0.0011522149 0.0005761075
[103,] 0.99921438 0.0015712352 0.0007856176
[104,] 0.99880952 0.0023809685 0.0011904843
[105,] 0.99855804 0.0028839288 0.0014419644
[106,] 0.99764613 0.0047077401 0.0023538701
[107,] 0.99630134 0.0073973233 0.0036986616
[108,] 0.99928918 0.0014216436 0.0007108218
[109,] 0.99878429 0.0024314266 0.0012157133
[110,] 0.99802136 0.0039572701 0.0019786351
[111,] 0.99781866 0.0043626843 0.0021813422
[112,] 0.99814330 0.0037134042 0.0018567021
[113,] 0.99689207 0.0062158662 0.0031079331
[114,] 0.99709515 0.0058096926 0.0029048463
[115,] 0.99509923 0.0098015307 0.0049007654
[116,] 0.99186931 0.0162613887 0.0081306943
[117,] 0.99110395 0.0177921075 0.0088960537
[118,] 0.98578932 0.0284213615 0.0142106808
[119,] 0.98547052 0.0290589549 0.0145294775
[120,] 0.99132720 0.0173456045 0.0086728023
[121,] 0.98693603 0.0261279321 0.0130639661
[122,] 0.98951106 0.0209778774 0.0104889387
[123,] 0.98447549 0.0310490278 0.0155245139
[124,] 0.97448738 0.0510252378 0.0255126189
[125,] 0.96516448 0.0696710302 0.0348355151
[126,] 0.96100292 0.0779941565 0.0389970782
[127,] 0.95768971 0.0846205726 0.0423102863
[128,] 0.92864508 0.1427098499 0.0713549249
[129,] 0.92593959 0.1481208274 0.0740604137
[130,] 0.90269559 0.1946088236 0.0973044118
[131,] 0.99476428 0.0104714421 0.0052357210
[132,] 0.99962754 0.0007449143 0.0003724571
[133,] 0.99860231 0.0027953834 0.0013976917
[134,] 0.99707568 0.0058486312 0.0029243156
[135,] 0.98551420 0.0289716031 0.0144858016
> postscript(file="/var/www/html/rcomp/tmp/1tc3p1293205930.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/24m2a1293205930.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/34m2a1293205930.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/44m2a1293205930.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5fdkv1293205930.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
1.508116028 1.062851244 -1.581693625 -1.942823715 -0.147395860 0.701196370
7 8 9 10 11 12
1.107340636 1.726716092 -1.039077541 -2.317912868 -0.810609567 -0.396732928
13 14 15 16 17 18
3.008820441 1.981431708 -0.620611004 1.049707778 0.750890753 -2.550274171
19 20 21 22 23 24
-0.877488887 0.754890468 2.287492392 0.798067571 -0.133070861 1.309652259
25 26 27 28 29 30
1.273685645 -0.445864374 3.115109227 -2.968022323 -0.767571394 -0.425567817
31 32 33 34 35 36
-0.145974969 1.949333157 0.426111888 0.006072588 -5.217705135 5.580988752
37 38 39 40 41 42
4.719796013 -1.756945946 1.799458698 -1.095198377 6.591865699 -4.367995688
43 44 45 46 47 48
-0.203519613 3.482002253 0.807502285 -2.223149883 1.930609687 2.235843564
49 50 51 52 53 54
0.265941680 2.519141752 -2.249993787 -0.741251443 -2.772036723 -0.626288506
55 56 57 58 59 60
-1.463552232 -0.386186395 0.088322529 -1.345114504 -0.023589107 0.351030679
61 62 63 64 65 66
-0.404243978 0.580876821 -2.313443475 1.631410971 -1.585983600 3.215661795
67 68 69 70 71 72
0.824757321 0.602712793 -3.431887886 4.484238296 2.222884651 -3.303715130
73 74 75 76 77 78
-4.680701001 -0.271571817 1.494902534 -0.776648525 2.589550818 -1.651141941
79 80 81 82 83 84
-4.157603482 -0.188246428 1.277015097 -3.874862341 -0.674829054 2.212843931
85 86 87 88 89 90
-0.131586459 -1.519584931 -0.212139490 0.677124278 0.760010847 0.250565177
91 92 93 94 95 96
-2.299443266 -3.332326154 -6.411787798 -0.594291053 1.712106410 0.021391217
97 98 99 100 101 102
-2.090241968 -2.652896651 -0.515673493 0.986630097 2.643693630 2.368590887
103 104 105 106 107 108
-0.665879823 2.996346860 0.948988235 0.360677342 -2.182199450 -3.043770799
109 110 111 112 113 114
3.432873664 0.472004225 -3.264872914 0.464314545 1.403921617 -0.803447515
115 116 117 118 119 120
-1.933979599 0.532699779 0.918457147 2.483441146 -0.379035770 -1.110165869
121 122 123 124 125 126
-2.160663250 -3.284784923 -1.008067339 -1.192402836 -0.871220660 0.177459215
127 128 129 130 131 132
-1.962784084 1.377762186 -0.732128656 0.972853827 -2.483791129 2.570075781
133 134 135 136 137 138
-1.348892669 -0.141281560 1.903650177 -0.509556426 -0.712507608 -0.086599769
139 140 141 142 143 144
-0.712725850 0.927019213 1.586509561 0.706762014 0.989536249 1.116875442
145 146 147 148 149 150
1.993329073 2.938568812 0.765749227 2.411914151 0.392293103 -2.083457932
151 152 153 154 155 156
2.365830969 0.342350001 -1.169228321 -0.290923031 0.636673912 -1.083457932
> postscript(file="/var/www/html/rcomp/tmp/6fdkv1293205930.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 1.508116028 NA
1 1.062851244 1.508116028
2 -1.581693625 1.062851244
3 -1.942823715 -1.581693625
4 -0.147395860 -1.942823715
5 0.701196370 -0.147395860
6 1.107340636 0.701196370
7 1.726716092 1.107340636
8 -1.039077541 1.726716092
9 -2.317912868 -1.039077541
10 -0.810609567 -2.317912868
11 -0.396732928 -0.810609567
12 3.008820441 -0.396732928
13 1.981431708 3.008820441
14 -0.620611004 1.981431708
15 1.049707778 -0.620611004
16 0.750890753 1.049707778
17 -2.550274171 0.750890753
18 -0.877488887 -2.550274171
19 0.754890468 -0.877488887
20 2.287492392 0.754890468
21 0.798067571 2.287492392
22 -0.133070861 0.798067571
23 1.309652259 -0.133070861
24 1.273685645 1.309652259
25 -0.445864374 1.273685645
26 3.115109227 -0.445864374
27 -2.968022323 3.115109227
28 -0.767571394 -2.968022323
29 -0.425567817 -0.767571394
30 -0.145974969 -0.425567817
31 1.949333157 -0.145974969
32 0.426111888 1.949333157
33 0.006072588 0.426111888
34 -5.217705135 0.006072588
35 5.580988752 -5.217705135
36 4.719796013 5.580988752
37 -1.756945946 4.719796013
38 1.799458698 -1.756945946
39 -1.095198377 1.799458698
40 6.591865699 -1.095198377
41 -4.367995688 6.591865699
42 -0.203519613 -4.367995688
43 3.482002253 -0.203519613
44 0.807502285 3.482002253
45 -2.223149883 0.807502285
46 1.930609687 -2.223149883
47 2.235843564 1.930609687
48 0.265941680 2.235843564
49 2.519141752 0.265941680
50 -2.249993787 2.519141752
51 -0.741251443 -2.249993787
52 -2.772036723 -0.741251443
53 -0.626288506 -2.772036723
54 -1.463552232 -0.626288506
55 -0.386186395 -1.463552232
56 0.088322529 -0.386186395
57 -1.345114504 0.088322529
58 -0.023589107 -1.345114504
59 0.351030679 -0.023589107
60 -0.404243978 0.351030679
61 0.580876821 -0.404243978
62 -2.313443475 0.580876821
63 1.631410971 -2.313443475
64 -1.585983600 1.631410971
65 3.215661795 -1.585983600
66 0.824757321 3.215661795
67 0.602712793 0.824757321
68 -3.431887886 0.602712793
69 4.484238296 -3.431887886
70 2.222884651 4.484238296
71 -3.303715130 2.222884651
72 -4.680701001 -3.303715130
73 -0.271571817 -4.680701001
74 1.494902534 -0.271571817
75 -0.776648525 1.494902534
76 2.589550818 -0.776648525
77 -1.651141941 2.589550818
78 -4.157603482 -1.651141941
79 -0.188246428 -4.157603482
80 1.277015097 -0.188246428
81 -3.874862341 1.277015097
82 -0.674829054 -3.874862341
83 2.212843931 -0.674829054
84 -0.131586459 2.212843931
85 -1.519584931 -0.131586459
86 -0.212139490 -1.519584931
87 0.677124278 -0.212139490
88 0.760010847 0.677124278
89 0.250565177 0.760010847
90 -2.299443266 0.250565177
91 -3.332326154 -2.299443266
92 -6.411787798 -3.332326154
93 -0.594291053 -6.411787798
94 1.712106410 -0.594291053
95 0.021391217 1.712106410
96 -2.090241968 0.021391217
97 -2.652896651 -2.090241968
98 -0.515673493 -2.652896651
99 0.986630097 -0.515673493
100 2.643693630 0.986630097
101 2.368590887 2.643693630
102 -0.665879823 2.368590887
103 2.996346860 -0.665879823
104 0.948988235 2.996346860
105 0.360677342 0.948988235
106 -2.182199450 0.360677342
107 -3.043770799 -2.182199450
108 3.432873664 -3.043770799
109 0.472004225 3.432873664
110 -3.264872914 0.472004225
111 0.464314545 -3.264872914
112 1.403921617 0.464314545
113 -0.803447515 1.403921617
114 -1.933979599 -0.803447515
115 0.532699779 -1.933979599
116 0.918457147 0.532699779
117 2.483441146 0.918457147
118 -0.379035770 2.483441146
119 -1.110165869 -0.379035770
120 -2.160663250 -1.110165869
121 -3.284784923 -2.160663250
122 -1.008067339 -3.284784923
123 -1.192402836 -1.008067339
124 -0.871220660 -1.192402836
125 0.177459215 -0.871220660
126 -1.962784084 0.177459215
127 1.377762186 -1.962784084
128 -0.732128656 1.377762186
129 0.972853827 -0.732128656
130 -2.483791129 0.972853827
131 2.570075781 -2.483791129
132 -1.348892669 2.570075781
133 -0.141281560 -1.348892669
134 1.903650177 -0.141281560
135 -0.509556426 1.903650177
136 -0.712507608 -0.509556426
137 -0.086599769 -0.712507608
138 -0.712725850 -0.086599769
139 0.927019213 -0.712725850
140 1.586509561 0.927019213
141 0.706762014 1.586509561
142 0.989536249 0.706762014
143 1.116875442 0.989536249
144 1.993329073 1.116875442
145 2.938568812 1.993329073
146 0.765749227 2.938568812
147 2.411914151 0.765749227
148 0.392293103 2.411914151
149 -2.083457932 0.392293103
150 2.365830969 -2.083457932
151 0.342350001 2.365830969
152 -1.169228321 0.342350001
153 -0.290923031 -1.169228321
154 0.636673912 -0.290923031
155 -1.083457932 0.636673912
156 NA -1.083457932
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.062851244 1.508116028
[2,] -1.581693625 1.062851244
[3,] -1.942823715 -1.581693625
[4,] -0.147395860 -1.942823715
[5,] 0.701196370 -0.147395860
[6,] 1.107340636 0.701196370
[7,] 1.726716092 1.107340636
[8,] -1.039077541 1.726716092
[9,] -2.317912868 -1.039077541
[10,] -0.810609567 -2.317912868
[11,] -0.396732928 -0.810609567
[12,] 3.008820441 -0.396732928
[13,] 1.981431708 3.008820441
[14,] -0.620611004 1.981431708
[15,] 1.049707778 -0.620611004
[16,] 0.750890753 1.049707778
[17,] -2.550274171 0.750890753
[18,] -0.877488887 -2.550274171
[19,] 0.754890468 -0.877488887
[20,] 2.287492392 0.754890468
[21,] 0.798067571 2.287492392
[22,] -0.133070861 0.798067571
[23,] 1.309652259 -0.133070861
[24,] 1.273685645 1.309652259
[25,] -0.445864374 1.273685645
[26,] 3.115109227 -0.445864374
[27,] -2.968022323 3.115109227
[28,] -0.767571394 -2.968022323
[29,] -0.425567817 -0.767571394
[30,] -0.145974969 -0.425567817
[31,] 1.949333157 -0.145974969
[32,] 0.426111888 1.949333157
[33,] 0.006072588 0.426111888
[34,] -5.217705135 0.006072588
[35,] 5.580988752 -5.217705135
[36,] 4.719796013 5.580988752
[37,] -1.756945946 4.719796013
[38,] 1.799458698 -1.756945946
[39,] -1.095198377 1.799458698
[40,] 6.591865699 -1.095198377
[41,] -4.367995688 6.591865699
[42,] -0.203519613 -4.367995688
[43,] 3.482002253 -0.203519613
[44,] 0.807502285 3.482002253
[45,] -2.223149883 0.807502285
[46,] 1.930609687 -2.223149883
[47,] 2.235843564 1.930609687
[48,] 0.265941680 2.235843564
[49,] 2.519141752 0.265941680
[50,] -2.249993787 2.519141752
[51,] -0.741251443 -2.249993787
[52,] -2.772036723 -0.741251443
[53,] -0.626288506 -2.772036723
[54,] -1.463552232 -0.626288506
[55,] -0.386186395 -1.463552232
[56,] 0.088322529 -0.386186395
[57,] -1.345114504 0.088322529
[58,] -0.023589107 -1.345114504
[59,] 0.351030679 -0.023589107
[60,] -0.404243978 0.351030679
[61,] 0.580876821 -0.404243978
[62,] -2.313443475 0.580876821
[63,] 1.631410971 -2.313443475
[64,] -1.585983600 1.631410971
[65,] 3.215661795 -1.585983600
[66,] 0.824757321 3.215661795
[67,] 0.602712793 0.824757321
[68,] -3.431887886 0.602712793
[69,] 4.484238296 -3.431887886
[70,] 2.222884651 4.484238296
[71,] -3.303715130 2.222884651
[72,] -4.680701001 -3.303715130
[73,] -0.271571817 -4.680701001
[74,] 1.494902534 -0.271571817
[75,] -0.776648525 1.494902534
[76,] 2.589550818 -0.776648525
[77,] -1.651141941 2.589550818
[78,] -4.157603482 -1.651141941
[79,] -0.188246428 -4.157603482
[80,] 1.277015097 -0.188246428
[81,] -3.874862341 1.277015097
[82,] -0.674829054 -3.874862341
[83,] 2.212843931 -0.674829054
[84,] -0.131586459 2.212843931
[85,] -1.519584931 -0.131586459
[86,] -0.212139490 -1.519584931
[87,] 0.677124278 -0.212139490
[88,] 0.760010847 0.677124278
[89,] 0.250565177 0.760010847
[90,] -2.299443266 0.250565177
[91,] -3.332326154 -2.299443266
[92,] -6.411787798 -3.332326154
[93,] -0.594291053 -6.411787798
[94,] 1.712106410 -0.594291053
[95,] 0.021391217 1.712106410
[96,] -2.090241968 0.021391217
[97,] -2.652896651 -2.090241968
[98,] -0.515673493 -2.652896651
[99,] 0.986630097 -0.515673493
[100,] 2.643693630 0.986630097
[101,] 2.368590887 2.643693630
[102,] -0.665879823 2.368590887
[103,] 2.996346860 -0.665879823
[104,] 0.948988235 2.996346860
[105,] 0.360677342 0.948988235
[106,] -2.182199450 0.360677342
[107,] -3.043770799 -2.182199450
[108,] 3.432873664 -3.043770799
[109,] 0.472004225 3.432873664
[110,] -3.264872914 0.472004225
[111,] 0.464314545 -3.264872914
[112,] 1.403921617 0.464314545
[113,] -0.803447515 1.403921617
[114,] -1.933979599 -0.803447515
[115,] 0.532699779 -1.933979599
[116,] 0.918457147 0.532699779
[117,] 2.483441146 0.918457147
[118,] -0.379035770 2.483441146
[119,] -1.110165869 -0.379035770
[120,] -2.160663250 -1.110165869
[121,] -3.284784923 -2.160663250
[122,] -1.008067339 -3.284784923
[123,] -1.192402836 -1.008067339
[124,] -0.871220660 -1.192402836
[125,] 0.177459215 -0.871220660
[126,] -1.962784084 0.177459215
[127,] 1.377762186 -1.962784084
[128,] -0.732128656 1.377762186
[129,] 0.972853827 -0.732128656
[130,] -2.483791129 0.972853827
[131,] 2.570075781 -2.483791129
[132,] -1.348892669 2.570075781
[133,] -0.141281560 -1.348892669
[134,] 1.903650177 -0.141281560
[135,] -0.509556426 1.903650177
[136,] -0.712507608 -0.509556426
[137,] -0.086599769 -0.712507608
[138,] -0.712725850 -0.086599769
[139,] 0.927019213 -0.712725850
[140,] 1.586509561 0.927019213
[141,] 0.706762014 1.586509561
[142,] 0.989536249 0.706762014
[143,] 1.116875442 0.989536249
[144,] 1.993329073 1.116875442
[145,] 2.938568812 1.993329073
[146,] 0.765749227 2.938568812
[147,] 2.411914151 0.765749227
[148,] 0.392293103 2.411914151
[149,] -2.083457932 0.392293103
[150,] 2.365830969 -2.083457932
[151,] 0.342350001 2.365830969
[152,] -1.169228321 0.342350001
[153,] -0.290923031 -1.169228321
[154,] 0.636673912 -0.290923031
[155,] -1.083457932 0.636673912
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.062851244 1.508116028
2 -1.581693625 1.062851244
3 -1.942823715 -1.581693625
4 -0.147395860 -1.942823715
5 0.701196370 -0.147395860
6 1.107340636 0.701196370
7 1.726716092 1.107340636
8 -1.039077541 1.726716092
9 -2.317912868 -1.039077541
10 -0.810609567 -2.317912868
11 -0.396732928 -0.810609567
12 3.008820441 -0.396732928
13 1.981431708 3.008820441
14 -0.620611004 1.981431708
15 1.049707778 -0.620611004
16 0.750890753 1.049707778
17 -2.550274171 0.750890753
18 -0.877488887 -2.550274171
19 0.754890468 -0.877488887
20 2.287492392 0.754890468
21 0.798067571 2.287492392
22 -0.133070861 0.798067571
23 1.309652259 -0.133070861
24 1.273685645 1.309652259
25 -0.445864374 1.273685645
26 3.115109227 -0.445864374
27 -2.968022323 3.115109227
28 -0.767571394 -2.968022323
29 -0.425567817 -0.767571394
30 -0.145974969 -0.425567817
31 1.949333157 -0.145974969
32 0.426111888 1.949333157
33 0.006072588 0.426111888
34 -5.217705135 0.006072588
35 5.580988752 -5.217705135
36 4.719796013 5.580988752
37 -1.756945946 4.719796013
38 1.799458698 -1.756945946
39 -1.095198377 1.799458698
40 6.591865699 -1.095198377
41 -4.367995688 6.591865699
42 -0.203519613 -4.367995688
43 3.482002253 -0.203519613
44 0.807502285 3.482002253
45 -2.223149883 0.807502285
46 1.930609687 -2.223149883
47 2.235843564 1.930609687
48 0.265941680 2.235843564
49 2.519141752 0.265941680
50 -2.249993787 2.519141752
51 -0.741251443 -2.249993787
52 -2.772036723 -0.741251443
53 -0.626288506 -2.772036723
54 -1.463552232 -0.626288506
55 -0.386186395 -1.463552232
56 0.088322529 -0.386186395
57 -1.345114504 0.088322529
58 -0.023589107 -1.345114504
59 0.351030679 -0.023589107
60 -0.404243978 0.351030679
61 0.580876821 -0.404243978
62 -2.313443475 0.580876821
63 1.631410971 -2.313443475
64 -1.585983600 1.631410971
65 3.215661795 -1.585983600
66 0.824757321 3.215661795
67 0.602712793 0.824757321
68 -3.431887886 0.602712793
69 4.484238296 -3.431887886
70 2.222884651 4.484238296
71 -3.303715130 2.222884651
72 -4.680701001 -3.303715130
73 -0.271571817 -4.680701001
74 1.494902534 -0.271571817
75 -0.776648525 1.494902534
76 2.589550818 -0.776648525
77 -1.651141941 2.589550818
78 -4.157603482 -1.651141941
79 -0.188246428 -4.157603482
80 1.277015097 -0.188246428
81 -3.874862341 1.277015097
82 -0.674829054 -3.874862341
83 2.212843931 -0.674829054
84 -0.131586459 2.212843931
85 -1.519584931 -0.131586459
86 -0.212139490 -1.519584931
87 0.677124278 -0.212139490
88 0.760010847 0.677124278
89 0.250565177 0.760010847
90 -2.299443266 0.250565177
91 -3.332326154 -2.299443266
92 -6.411787798 -3.332326154
93 -0.594291053 -6.411787798
94 1.712106410 -0.594291053
95 0.021391217 1.712106410
96 -2.090241968 0.021391217
97 -2.652896651 -2.090241968
98 -0.515673493 -2.652896651
99 0.986630097 -0.515673493
100 2.643693630 0.986630097
101 2.368590887 2.643693630
102 -0.665879823 2.368590887
103 2.996346860 -0.665879823
104 0.948988235 2.996346860
105 0.360677342 0.948988235
106 -2.182199450 0.360677342
107 -3.043770799 -2.182199450
108 3.432873664 -3.043770799
109 0.472004225 3.432873664
110 -3.264872914 0.472004225
111 0.464314545 -3.264872914
112 1.403921617 0.464314545
113 -0.803447515 1.403921617
114 -1.933979599 -0.803447515
115 0.532699779 -1.933979599
116 0.918457147 0.532699779
117 2.483441146 0.918457147
118 -0.379035770 2.483441146
119 -1.110165869 -0.379035770
120 -2.160663250 -1.110165869
121 -3.284784923 -2.160663250
122 -1.008067339 -3.284784923
123 -1.192402836 -1.008067339
124 -0.871220660 -1.192402836
125 0.177459215 -0.871220660
126 -1.962784084 0.177459215
127 1.377762186 -1.962784084
128 -0.732128656 1.377762186
129 0.972853827 -0.732128656
130 -2.483791129 0.972853827
131 2.570075781 -2.483791129
132 -1.348892669 2.570075781
133 -0.141281560 -1.348892669
134 1.903650177 -0.141281560
135 -0.509556426 1.903650177
136 -0.712507608 -0.509556426
137 -0.086599769 -0.712507608
138 -0.712725850 -0.086599769
139 0.927019213 -0.712725850
140 1.586509561 0.927019213
141 0.706762014 1.586509561
142 0.989536249 0.706762014
143 1.116875442 0.989536249
144 1.993329073 1.116875442
145 2.938568812 1.993329073
146 0.765749227 2.938568812
147 2.411914151 0.765749227
148 0.392293103 2.411914151
149 -2.083457932 0.392293103
150 2.365830969 -2.083457932
151 0.342350001 2.365830969
152 -1.169228321 0.342350001
153 -0.290923031 -1.169228321
154 0.636673912 -0.290923031
155 -1.083457932 0.636673912
> 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/html/rcomp/tmp/7pm1y1293205930.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8pm1y1293205930.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/90dij1293205930.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/100dij1293205930.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/html/rcomp/tmp/113ezp1293205930.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/html/rcomp/tmp/127wfc1293205930.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/html/rcomp/tmp/13wfu61293205930.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/html/rcomp/tmp/14o7t91293205930.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/html/rcomp/tmp/15a7ax1293205930.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/html/rcomp/tmp/16dqql1293205930.tab")
+ }
>
> try(system("convert tmp/1tc3p1293205930.ps tmp/1tc3p1293205930.png",intern=TRUE))
character(0)
> try(system("convert tmp/24m2a1293205930.ps tmp/24m2a1293205930.png",intern=TRUE))
character(0)
> try(system("convert tmp/34m2a1293205930.ps tmp/34m2a1293205930.png",intern=TRUE))
character(0)
> try(system("convert tmp/44m2a1293205930.ps tmp/44m2a1293205930.png",intern=TRUE))
character(0)
> try(system("convert tmp/5fdkv1293205930.ps tmp/5fdkv1293205930.png",intern=TRUE))
character(0)
> try(system("convert tmp/6fdkv1293205930.ps tmp/6fdkv1293205930.png",intern=TRUE))
character(0)
> try(system("convert tmp/7pm1y1293205930.ps tmp/7pm1y1293205930.png",intern=TRUE))
character(0)
> try(system("convert tmp/8pm1y1293205930.ps tmp/8pm1y1293205930.png",intern=TRUE))
character(0)
> try(system("convert tmp/90dij1293205930.ps tmp/90dij1293205930.png",intern=TRUE))
character(0)
> try(system("convert tmp/100dij1293205930.ps tmp/100dij1293205930.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.208 1.807 9.509