R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(1
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+ ,4)
+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('G'
+ ,'IM1'
+ ,'IM2'
+ ,'IM3'
+ ,'EM1'
+ ,'EM2'
+ ,'EM3'
+ ,'AM')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('G','IM1','IM2','IM3','EM1','EM2','EM3','AM'),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 = '8'
> #'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
AM G IM1 IM2 IM3 EM1 EM2 EM3
1 4 1 26 21 21 23 17 23
2 4 1 20 16 15 24 17 20
3 6 1 19 19 18 22 18 20
4 8 2 19 18 11 20 21 21
5 8 1 20 16 8 24 20 24
6 4 1 25 23 19 27 28 22
7 4 2 25 17 4 28 19 23
8 8 1 22 12 20 27 22 20
9 5 1 26 19 16 24 16 25
10 4 1 22 16 14 23 18 23
11 4 2 17 19 10 24 25 27
12 4 2 22 20 13 27 17 27
13 4 1 19 13 14 27 14 22
14 4 1 24 20 8 28 11 24
15 4 1 26 27 23 27 27 25
16 8 2 21 17 11 23 20 22
17 4 1 13 8 9 24 22 28
18 4 2 26 25 24 28 22 28
19 4 2 20 26 5 27 21 27
20 8 1 22 13 15 25 23 25
21 4 2 14 19 5 19 17 16
22 7 1 21 15 19 24 24 28
23 4 1 7 5 6 20 14 21
24 4 2 23 16 13 28 17 24
25 5 1 17 14 11 26 23 27
26 4 1 25 24 17 23 24 14
27 4 1 25 24 17 23 24 14
28 4 1 19 9 5 20 8 27
29 4 2 20 19 9 11 22 20
30 4 1 23 19 15 24 23 21
31 4 2 22 25 17 25 25 22
32 4 1 22 19 17 23 21 21
33 15 1 21 18 20 18 24 12
34 10 2 15 15 12 20 15 20
35 4 2 20 12 7 20 22 24
36 8 2 22 21 16 24 21 19
37 4 1 18 12 7 23 25 28
38 4 2 20 15 14 25 16 23
39 4 2 28 28 24 28 28 27
40 4 1 22 25 15 26 23 22
41 7 1 18 19 15 26 21 27
42 4 1 23 20 10 23 21 26
43 6 1 20 24 14 22 26 22
44 5 2 25 26 18 24 22 21
45 4 2 26 25 12 21 21 19
46 16 1 15 12 9 20 18 24
47 5 2 17 12 9 22 12 19
48 12 2 23 15 8 20 25 26
49 6 1 21 17 18 25 17 22
50 9 2 13 14 10 20 24 28
51 9 1 18 16 17 22 15 21
52 4 1 19 11 14 23 13 23
53 5 1 22 20 16 25 26 28
54 4 1 16 11 10 23 16 10
55 4 2 24 22 19 23 24 24
56 5 1 18 20 10 22 21 21
57 4 1 20 19 14 24 20 21
58 4 1 24 17 10 25 14 24
59 4 2 14 21 4 21 25 24
60 5 2 22 23 19 12 25 25
61 4 1 24 18 9 17 20 25
62 6 1 18 17 12 20 22 23
63 4 1 21 27 16 23 20 21
64 4 2 23 25 11 23 26 16
65 18 1 17 19 18 20 18 17
66 4 2 22 22 11 28 22 25
67 6 2 24 24 24 24 24 24
68 4 2 21 20 17 24 17 23
69 4 1 22 19 18 24 24 25
70 5 1 16 11 9 24 20 23
71 4 1 21 22 19 28 19 28
72 4 2 23 22 18 25 20 26
73 5 2 22 16 12 21 15 22
74 10 1 24 20 23 25 23 19
75 5 1 24 24 22 25 26 26
76 8 1 16 16 14 18 22 18
77 8 1 16 16 14 17 20 18
78 5 2 21 22 16 26 24 25
79 4 2 26 24 23 28 26 27
80 4 2 15 16 7 21 21 12
81 4 2 25 27 10 27 25 15
82 5 1 18 11 12 22 13 21
83 4 1 23 21 12 21 20 23
84 4 1 20 20 12 25 22 22
85 8 2 17 20 17 22 23 21
86 4 2 25 27 21 23 28 24
87 5 1 24 20 16 26 22 27
88 14 1 17 12 11 19 20 22
89 8 1 19 8 14 25 6 28
90 8 1 20 21 13 21 21 26
91 4 1 15 18 9 13 20 10
92 4 2 27 24 19 24 18 19
93 6 1 22 16 13 25 23 22
94 4 1 23 18 19 26 20 21
95 7 1 16 20 13 25 24 24
96 7 1 19 20 13 25 22 25
97 4 2 25 19 13 22 21 21
98 6 1 19 17 14 21 18 20
99 4 2 19 16 12 23 21 21
100 7 2 26 26 22 25 23 24
101 4 1 21 15 11 24 23 23
102 4 2 20 22 5 21 15 18
103 8 1 24 17 18 21 21 24
104 4 1 22 23 19 25 24 24
105 4 2 20 21 14 22 23 19
106 10 1 18 19 15 20 21 20
107 8 2 18 14 12 20 21 18
108 6 1 24 17 19 23 20 20
109 4 1 24 12 15 28 11 27
110 4 1 22 24 17 23 22 23
111 4 1 23 18 8 28 27 26
112 5 1 22 20 10 24 25 23
113 4 1 20 16 12 18 18 17
114 6 1 18 20 12 20 20 21
115 4 1 25 22 20 28 24 25
116 5 2 18 12 12 21 10 23
117 7 1 16 16 12 21 27 27
118 8 1 20 17 14 25 21 24
119 5 2 19 22 6 19 21 20
120 8 1 15 12 10 18 18 27
121 10 1 19 14 18 21 15 21
122 8 1 19 23 18 22 24 24
123 5 1 16 15 7 24 22 21
124 12 1 17 17 18 15 14 15
125 4 1 28 28 9 28 28 25
126 5 2 23 20 17 26 18 25
127 4 1 25 23 22 23 26 22
128 6 1 20 13 11 26 17 24
129 4 2 17 18 15 20 19 21
130 4 2 23 23 17 22 22 22
131 7 1 16 19 15 20 18 23
132 7 2 23 23 22 23 24 22
133 10 2 11 12 9 22 15 20
134 4 2 18 16 13 24 18 23
135 5 2 24 23 20 23 26 25
136 8 1 23 13 14 22 11 23
137 11 1 21 22 14 26 26 22
138 7 2 16 18 12 23 21 25
139 4 2 24 23 20 27 23 26
140 8 1 23 20 20 23 23 22
141 6 1 18 10 8 21 15 24
142 7 1 20 17 17 26 22 24
143 5 1 9 18 9 23 26 25
144 4 2 24 15 18 21 16 20
145 8 1 25 23 22 27 20 26
146 4 1 20 17 10 19 18 21
147 8 2 21 17 13 23 22 26
148 6 2 25 22 15 25 16 21
149 4 2 22 20 18 23 19 22
150 9 2 21 20 18 22 20 16
151 5 1 21 19 12 22 19 26
152 6 1 22 18 12 25 23 28
153 4 1 27 22 20 25 24 18
154 4 2 24 20 12 28 25 25
155 4 2 24 22 16 28 21 23
156 5 2 21 18 16 20 21 21
157 6 1 18 16 18 25 23 20
158 16 1 16 16 16 19 27 25
159 6 1 22 16 13 25 23 22
160 6 1 20 16 17 22 18 21
161 4 2 18 17 13 18 16 16
162 4 1 20 18 17 20 16 18
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) G IM1 IM2 IM3 EM1
12.498874 -0.441931 -0.185414 -0.138080 0.195650 -0.182549
EM2 EM3
0.083052 0.000807
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.7712 -1.4438 -0.4795 0.9432 10.3392
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.498874 1.755983 7.118 3.90e-11 ***
G -0.441931 0.400716 -1.103 0.2718
IM1 -0.185414 0.073716 -2.515 0.0129 *
IM2 -0.138080 0.066287 -2.083 0.0389 *
IM3 0.195650 0.048662 4.021 9.06e-05 ***
EM1 -0.182549 0.071556 -2.551 0.0117 *
EM2 0.083052 0.055628 1.493 0.1375
EM3 0.000807 0.057360 0.014 0.9888
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.341 on 154 degrees of freedom
Multiple R-squared: 0.2398, Adjusted R-squared: 0.2053
F-statistic: 6.941 on 7 and 154 DF, p-value: 3.496e-07
> 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,] 0.498687555 0.99737511 0.501312445
[2,] 0.455390301 0.91078060 0.544609699
[3,] 0.327031869 0.65406374 0.672968131
[4,] 0.274588870 0.54917774 0.725411130
[5,] 0.194035242 0.38807048 0.805964758
[6,] 0.176128523 0.35225705 0.823871477
[7,] 0.143067556 0.28613511 0.856932444
[8,] 0.098562204 0.19712441 0.901437796
[9,] 0.065385370 0.13077074 0.934614630
[10,] 0.075199122 0.15039824 0.924800878
[11,] 0.075091528 0.15018306 0.924908472
[12,] 0.052829676 0.10565935 0.947170324
[13,] 0.043609149 0.08721830 0.956390851
[14,] 0.030510697 0.06102139 0.969489303
[15,] 0.019152278 0.03830456 0.980847722
[16,] 0.016852385 0.03370477 0.983147615
[17,] 0.011757624 0.02351525 0.988242376
[18,] 0.011540290 0.02308058 0.988459710
[19,] 0.016386367 0.03277273 0.983613633
[20,] 0.012469038 0.02493808 0.987530962
[21,] 0.008009009 0.01601802 0.991990991
[22,] 0.005644989 0.01128998 0.994355011
[23,] 0.336376943 0.67275389 0.663623057
[24,] 0.441751832 0.88350366 0.558248168
[25,] 0.450103847 0.90020769 0.549896153
[26,] 0.432130259 0.86426052 0.567869741
[27,] 0.392880884 0.78576177 0.607119116
[28,] 0.380837705 0.76167541 0.619162295
[29,] 0.330228314 0.66045663 0.669771686
[30,] 0.280064719 0.56012944 0.719935281
[31,] 0.298855073 0.59771015 0.701144927
[32,] 0.254041452 0.50808290 0.745958548
[33,] 0.220070447 0.44014089 0.779929553
[34,] 0.182903222 0.36580644 0.817096778
[35,] 0.153682570 0.30736514 0.846317430
[36,] 0.854369628 0.29126074 0.145630372
[37,] 0.828803628 0.34239274 0.171196372
[38,] 0.954958644 0.09008271 0.045041356
[39,] 0.942131344 0.11573731 0.057868656
[40,] 0.933281777 0.13343645 0.066718223
[41,] 0.931190053 0.13761989 0.068809947
[42,] 0.932667177 0.13466565 0.067332823
[43,] 0.916755810 0.16648838 0.083244190
[44,] 0.922495704 0.15500859 0.077504296
[45,] 0.915781272 0.16843746 0.084218728
[46,] 0.896221715 0.20755657 0.103778285
[47,] 0.884287857 0.23142429 0.115712143
[48,] 0.858734083 0.28253183 0.141265917
[49,] 0.836111175 0.32777765 0.163888825
[50,] 0.848355101 0.30328980 0.151644899
[51,] 0.827090165 0.34581967 0.172909835
[52,] 0.797638454 0.40472309 0.202361546
[53,] 0.768827298 0.46234540 0.231172702
[54,] 0.734848313 0.53030337 0.265151687
[55,] 0.997333755 0.00533249 0.002666245
[56,] 0.996261055 0.00747789 0.003738945
[57,] 0.994729809 0.01054038 0.005270191
[58,] 0.993433433 0.01313313 0.006566567
[59,] 0.993677739 0.01264452 0.006322261
[60,] 0.992617150 0.01476570 0.007382850
[61,] 0.991046537 0.01790693 0.008953463
[62,] 0.988470821 0.02305836 0.011529179
[63,] 0.984497374 0.03100525 0.015502626
[64,] 0.988888116 0.02222377 0.011111884
[65,] 0.986302460 0.02739508 0.013697540
[66,] 0.981623468 0.03675306 0.018376532
[67,] 0.975648829 0.04870234 0.024351171
[68,] 0.968196154 0.06360769 0.031803846
[69,] 0.961674385 0.07665123 0.038325615
[70,] 0.958560364 0.08287927 0.041439636
[71,] 0.958455640 0.08308872 0.041544360
[72,] 0.953133707 0.09373259 0.046866293
[73,] 0.943520224 0.11295955 0.056479776
[74,] 0.932489260 0.13502148 0.067510740
[75,] 0.919436934 0.16112613 0.080563066
[76,] 0.909507718 0.18098456 0.090492282
[77,] 0.891070777 0.21785845 0.108929223
[78,] 0.977832634 0.04433473 0.022167366
[79,] 0.973733243 0.05253351 0.026266757
[80,] 0.970756332 0.05848734 0.029243668
[81,] 0.978397874 0.04320425 0.021602126
[82,] 0.971566220 0.05686756 0.028433780
[83,] 0.963051862 0.07389628 0.036948138
[84,] 0.960141192 0.07971762 0.039858808
[85,] 0.949733270 0.10053346 0.050266730
[86,] 0.939966184 0.12006763 0.060033816
[87,] 0.924893161 0.15021368 0.075106839
[88,] 0.907466756 0.18506649 0.092533244
[89,] 0.897392301 0.20521540 0.102607699
[90,] 0.892374053 0.21525189 0.107625947
[91,] 0.884920341 0.23015932 0.115079659
[92,] 0.865178219 0.26964356 0.134821781
[93,] 0.843505879 0.31298824 0.156494121
[94,] 0.840714451 0.31857110 0.159285549
[95,] 0.823128909 0.35374218 0.176871091
[96,] 0.835616867 0.32876627 0.164383133
[97,] 0.814932300 0.37013540 0.185067700
[98,] 0.780210484 0.43957903 0.219789516
[99,] 0.750693484 0.49861303 0.249306516
[100,] 0.737340203 0.52531959 0.262659797
[101,] 0.695586190 0.60882762 0.304413810
[102,] 0.648978845 0.70204231 0.351021155
[103,] 0.657597545 0.68480491 0.342402455
[104,] 0.611684135 0.77663173 0.388315865
[105,] 0.592188001 0.81562400 0.407811999
[106,] 0.547187449 0.90562510 0.452812551
[107,] 0.508465683 0.98306863 0.491534317
[108,] 0.478597973 0.95719595 0.521402027
[109,] 0.432770166 0.86554033 0.567229834
[110,] 0.381967168 0.76393434 0.618032832
[111,] 0.370052913 0.74010583 0.629947087
[112,] 0.322979423 0.64595885 0.677020577
[113,] 0.287464820 0.57492964 0.712535180
[114,] 0.410067793 0.82013559 0.589932207
[115,] 0.375093120 0.75018624 0.624906880
[116,] 0.320705554 0.64141111 0.679294446
[117,] 0.339468655 0.67893731 0.660531345
[118,] 0.287868998 0.57573800 0.712131002
[119,] 0.288005096 0.57601019 0.711994904
[120,] 0.247951732 0.49590346 0.752048268
[121,] 0.201297872 0.40259574 0.798702128
[122,] 0.161074223 0.32214845 0.838925777
[123,] 0.246527233 0.49305447 0.753472767
[124,] 0.206728097 0.41345619 0.793271903
[125,] 0.213047585 0.42609517 0.786952415
[126,] 0.286628131 0.57325626 0.713371869
[127,] 0.530895047 0.93820991 0.469104953
[128,] 0.467856700 0.93571340 0.532143300
[129,] 0.506323152 0.98735370 0.493676848
[130,] 0.430266152 0.86053230 0.569733848
[131,] 0.434367577 0.86873515 0.565632423
[132,] 0.361154556 0.72230911 0.638845444
[133,] 0.490782336 0.98156467 0.509217664
[134,] 0.425384438 0.85076888 0.574615562
[135,] 0.357084494 0.71416899 0.642915506
[136,] 0.282901709 0.56580342 0.717098291
[137,] 0.239112063 0.47822413 0.760887937
[138,] 0.570410752 0.85917850 0.429589248
[139,] 0.453088801 0.90617760 0.546911199
[140,] 0.687968763 0.62406247 0.312031237
[141,] 0.570847465 0.85830507 0.429152535
> postscript(file="/var/www/html/freestat/rcomp/tmp/1k8gx1291999235.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2k8gx1291999235.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3uzxi1291999235.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4uzxi1291999235.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5uzxi1291999235.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
-1.676958073 -2.120977935 -0.927249906 2.131088326 2.996185286 -1.377484465
7 8 9 10 11 12
2.099920395 0.851667299 0.289116353 -1.822521103 -1.512867059 0.177398303
13 14 15 16 17 18
-1.729795517 1.767826208 -1.341717008 2.993727590 -3.771338808 -0.776208117
19 20 21 22 23 24
1.868039321 1.515811453 -1.330310900 -0.444062886 -4.771245272 -0.004538312
25 26 27 28 29 30
-1.309647462 -1.239634621 -1.239634621 -1.304831730 -2.879299472 -1.649613582
31 32 33 34 35 36
-0.940277859 -2.242771688 6.692149288 3.278659906 -1.814852766 2.855131959
37 38 39 40 41 42
-2.332351471 -1.358297838 -0.488644132 -0.642257053 0.949673295 -0.553764733
43 44 45 46 47 48
0.065133349 0.625810614 0.384063313 8.757052959 -0.562742554 6.709211097
49 50 51 52 53 54
-0.203497827 1.407125435 1.917093995 -2.653904860 -0.964854345 -2.666214378
55 56 57 58 59 60
-1.658647801 -0.659350223 -1.761051024 0.165484720 -1.164276608 -2.983289238
61 62 63 64 65 66
-1.460292985 -0.914653130 -1.044843140 -0.024272499 10.339245302 0.613760176
67 68 69 70 71 72
-0.178187191 -1.335032133 -2.508257105 -1.630715811 -1.332047418 -0.952721363
73 74 75 76 77 78
-0.104424520 3.292846413 -1.213988680 -0.175923316 -0.192367710 -0.081103618
79 80 81 82 83 84
-1.050039845 -1.914319344 1.632419452 -1.628954454 -1.086607751 -1.216034210
85 86 87 88 89 90
1.061514842 -1.506340117 -0.078461719 6.389543933 1.874280981 2.076026197
91 92 93 94 95 96
-3.847111438 -0.141348208 0.323772297 -1.956039107 0.678940283 1.400480643
97 98 99 100 101 102
-0.644547242 -0.603360160 -1.793076090 2.125701833 -1.791778537 0.726003203
103 104 105 106 107 108
1.288729085 -1.968230575 -1.655598747 2.860031395 1.200124708 -0.455543361
109 110 111 112 113 114
-0.708783649 -1.637037011 -0.024195547 0.113582200 -2.709951071 -0.332694481
115 116 117 118 119 120
-1.198878594 -0.983949567 -0.659502027 2.059864033 0.479915692 0.193885204
121 122 123 124 125 126
2.448149942 1.123530258 -0.851585956 3.484164706 2.005783468 0.316227627
127 128 129 130 131 132
-2.528523337 0.609249088 -2.856234975 -1.329513117 -0.264062350 0.708683056
133 134 135 136 137 138
3.074807706 -1.744049515 -0.883128435 2.347468751 5.704581514 0.923612852
139 140 141 142 143 144
-0.904585222 1.326862813 -0.335509752 0.572411555 -2.644530589 -2.127011705
145 146 147 148 149 150
2.696754975 -2.001251218 2.433096073 2.341299790 -1.693112963 2.860714202
151 152 153 154 155 156
-0.470417418 0.790740096 -1.370046105 0.263622879 0.091006913 -1.476331051
157 158 159 160 161 162
-1.394519559 7.194416421 0.323772297 -0.961233341 -2.529507137 -2.881644853
> postscript(file="/var/www/html/freestat/rcomp/tmp/658ek1291999235.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 -1.676958073 NA
1 -2.120977935 -1.676958073
2 -0.927249906 -2.120977935
3 2.131088326 -0.927249906
4 2.996185286 2.131088326
5 -1.377484465 2.996185286
6 2.099920395 -1.377484465
7 0.851667299 2.099920395
8 0.289116353 0.851667299
9 -1.822521103 0.289116353
10 -1.512867059 -1.822521103
11 0.177398303 -1.512867059
12 -1.729795517 0.177398303
13 1.767826208 -1.729795517
14 -1.341717008 1.767826208
15 2.993727590 -1.341717008
16 -3.771338808 2.993727590
17 -0.776208117 -3.771338808
18 1.868039321 -0.776208117
19 1.515811453 1.868039321
20 -1.330310900 1.515811453
21 -0.444062886 -1.330310900
22 -4.771245272 -0.444062886
23 -0.004538312 -4.771245272
24 -1.309647462 -0.004538312
25 -1.239634621 -1.309647462
26 -1.239634621 -1.239634621
27 -1.304831730 -1.239634621
28 -2.879299472 -1.304831730
29 -1.649613582 -2.879299472
30 -0.940277859 -1.649613582
31 -2.242771688 -0.940277859
32 6.692149288 -2.242771688
33 3.278659906 6.692149288
34 -1.814852766 3.278659906
35 2.855131959 -1.814852766
36 -2.332351471 2.855131959
37 -1.358297838 -2.332351471
38 -0.488644132 -1.358297838
39 -0.642257053 -0.488644132
40 0.949673295 -0.642257053
41 -0.553764733 0.949673295
42 0.065133349 -0.553764733
43 0.625810614 0.065133349
44 0.384063313 0.625810614
45 8.757052959 0.384063313
46 -0.562742554 8.757052959
47 6.709211097 -0.562742554
48 -0.203497827 6.709211097
49 1.407125435 -0.203497827
50 1.917093995 1.407125435
51 -2.653904860 1.917093995
52 -0.964854345 -2.653904860
53 -2.666214378 -0.964854345
54 -1.658647801 -2.666214378
55 -0.659350223 -1.658647801
56 -1.761051024 -0.659350223
57 0.165484720 -1.761051024
58 -1.164276608 0.165484720
59 -2.983289238 -1.164276608
60 -1.460292985 -2.983289238
61 -0.914653130 -1.460292985
62 -1.044843140 -0.914653130
63 -0.024272499 -1.044843140
64 10.339245302 -0.024272499
65 0.613760176 10.339245302
66 -0.178187191 0.613760176
67 -1.335032133 -0.178187191
68 -2.508257105 -1.335032133
69 -1.630715811 -2.508257105
70 -1.332047418 -1.630715811
71 -0.952721363 -1.332047418
72 -0.104424520 -0.952721363
73 3.292846413 -0.104424520
74 -1.213988680 3.292846413
75 -0.175923316 -1.213988680
76 -0.192367710 -0.175923316
77 -0.081103618 -0.192367710
78 -1.050039845 -0.081103618
79 -1.914319344 -1.050039845
80 1.632419452 -1.914319344
81 -1.628954454 1.632419452
82 -1.086607751 -1.628954454
83 -1.216034210 -1.086607751
84 1.061514842 -1.216034210
85 -1.506340117 1.061514842
86 -0.078461719 -1.506340117
87 6.389543933 -0.078461719
88 1.874280981 6.389543933
89 2.076026197 1.874280981
90 -3.847111438 2.076026197
91 -0.141348208 -3.847111438
92 0.323772297 -0.141348208
93 -1.956039107 0.323772297
94 0.678940283 -1.956039107
95 1.400480643 0.678940283
96 -0.644547242 1.400480643
97 -0.603360160 -0.644547242
98 -1.793076090 -0.603360160
99 2.125701833 -1.793076090
100 -1.791778537 2.125701833
101 0.726003203 -1.791778537
102 1.288729085 0.726003203
103 -1.968230575 1.288729085
104 -1.655598747 -1.968230575
105 2.860031395 -1.655598747
106 1.200124708 2.860031395
107 -0.455543361 1.200124708
108 -0.708783649 -0.455543361
109 -1.637037011 -0.708783649
110 -0.024195547 -1.637037011
111 0.113582200 -0.024195547
112 -2.709951071 0.113582200
113 -0.332694481 -2.709951071
114 -1.198878594 -0.332694481
115 -0.983949567 -1.198878594
116 -0.659502027 -0.983949567
117 2.059864033 -0.659502027
118 0.479915692 2.059864033
119 0.193885204 0.479915692
120 2.448149942 0.193885204
121 1.123530258 2.448149942
122 -0.851585956 1.123530258
123 3.484164706 -0.851585956
124 2.005783468 3.484164706
125 0.316227627 2.005783468
126 -2.528523337 0.316227627
127 0.609249088 -2.528523337
128 -2.856234975 0.609249088
129 -1.329513117 -2.856234975
130 -0.264062350 -1.329513117
131 0.708683056 -0.264062350
132 3.074807706 0.708683056
133 -1.744049515 3.074807706
134 -0.883128435 -1.744049515
135 2.347468751 -0.883128435
136 5.704581514 2.347468751
137 0.923612852 5.704581514
138 -0.904585222 0.923612852
139 1.326862813 -0.904585222
140 -0.335509752 1.326862813
141 0.572411555 -0.335509752
142 -2.644530589 0.572411555
143 -2.127011705 -2.644530589
144 2.696754975 -2.127011705
145 -2.001251218 2.696754975
146 2.433096073 -2.001251218
147 2.341299790 2.433096073
148 -1.693112963 2.341299790
149 2.860714202 -1.693112963
150 -0.470417418 2.860714202
151 0.790740096 -0.470417418
152 -1.370046105 0.790740096
153 0.263622879 -1.370046105
154 0.091006913 0.263622879
155 -1.476331051 0.091006913
156 -1.394519559 -1.476331051
157 7.194416421 -1.394519559
158 0.323772297 7.194416421
159 -0.961233341 0.323772297
160 -2.529507137 -0.961233341
161 -2.881644853 -2.529507137
162 NA -2.881644853
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.120977935 -1.676958073
[2,] -0.927249906 -2.120977935
[3,] 2.131088326 -0.927249906
[4,] 2.996185286 2.131088326
[5,] -1.377484465 2.996185286
[6,] 2.099920395 -1.377484465
[7,] 0.851667299 2.099920395
[8,] 0.289116353 0.851667299
[9,] -1.822521103 0.289116353
[10,] -1.512867059 -1.822521103
[11,] 0.177398303 -1.512867059
[12,] -1.729795517 0.177398303
[13,] 1.767826208 -1.729795517
[14,] -1.341717008 1.767826208
[15,] 2.993727590 -1.341717008
[16,] -3.771338808 2.993727590
[17,] -0.776208117 -3.771338808
[18,] 1.868039321 -0.776208117
[19,] 1.515811453 1.868039321
[20,] -1.330310900 1.515811453
[21,] -0.444062886 -1.330310900
[22,] -4.771245272 -0.444062886
[23,] -0.004538312 -4.771245272
[24,] -1.309647462 -0.004538312
[25,] -1.239634621 -1.309647462
[26,] -1.239634621 -1.239634621
[27,] -1.304831730 -1.239634621
[28,] -2.879299472 -1.304831730
[29,] -1.649613582 -2.879299472
[30,] -0.940277859 -1.649613582
[31,] -2.242771688 -0.940277859
[32,] 6.692149288 -2.242771688
[33,] 3.278659906 6.692149288
[34,] -1.814852766 3.278659906
[35,] 2.855131959 -1.814852766
[36,] -2.332351471 2.855131959
[37,] -1.358297838 -2.332351471
[38,] -0.488644132 -1.358297838
[39,] -0.642257053 -0.488644132
[40,] 0.949673295 -0.642257053
[41,] -0.553764733 0.949673295
[42,] 0.065133349 -0.553764733
[43,] 0.625810614 0.065133349
[44,] 0.384063313 0.625810614
[45,] 8.757052959 0.384063313
[46,] -0.562742554 8.757052959
[47,] 6.709211097 -0.562742554
[48,] -0.203497827 6.709211097
[49,] 1.407125435 -0.203497827
[50,] 1.917093995 1.407125435
[51,] -2.653904860 1.917093995
[52,] -0.964854345 -2.653904860
[53,] -2.666214378 -0.964854345
[54,] -1.658647801 -2.666214378
[55,] -0.659350223 -1.658647801
[56,] -1.761051024 -0.659350223
[57,] 0.165484720 -1.761051024
[58,] -1.164276608 0.165484720
[59,] -2.983289238 -1.164276608
[60,] -1.460292985 -2.983289238
[61,] -0.914653130 -1.460292985
[62,] -1.044843140 -0.914653130
[63,] -0.024272499 -1.044843140
[64,] 10.339245302 -0.024272499
[65,] 0.613760176 10.339245302
[66,] -0.178187191 0.613760176
[67,] -1.335032133 -0.178187191
[68,] -2.508257105 -1.335032133
[69,] -1.630715811 -2.508257105
[70,] -1.332047418 -1.630715811
[71,] -0.952721363 -1.332047418
[72,] -0.104424520 -0.952721363
[73,] 3.292846413 -0.104424520
[74,] -1.213988680 3.292846413
[75,] -0.175923316 -1.213988680
[76,] -0.192367710 -0.175923316
[77,] -0.081103618 -0.192367710
[78,] -1.050039845 -0.081103618
[79,] -1.914319344 -1.050039845
[80,] 1.632419452 -1.914319344
[81,] -1.628954454 1.632419452
[82,] -1.086607751 -1.628954454
[83,] -1.216034210 -1.086607751
[84,] 1.061514842 -1.216034210
[85,] -1.506340117 1.061514842
[86,] -0.078461719 -1.506340117
[87,] 6.389543933 -0.078461719
[88,] 1.874280981 6.389543933
[89,] 2.076026197 1.874280981
[90,] -3.847111438 2.076026197
[91,] -0.141348208 -3.847111438
[92,] 0.323772297 -0.141348208
[93,] -1.956039107 0.323772297
[94,] 0.678940283 -1.956039107
[95,] 1.400480643 0.678940283
[96,] -0.644547242 1.400480643
[97,] -0.603360160 -0.644547242
[98,] -1.793076090 -0.603360160
[99,] 2.125701833 -1.793076090
[100,] -1.791778537 2.125701833
[101,] 0.726003203 -1.791778537
[102,] 1.288729085 0.726003203
[103,] -1.968230575 1.288729085
[104,] -1.655598747 -1.968230575
[105,] 2.860031395 -1.655598747
[106,] 1.200124708 2.860031395
[107,] -0.455543361 1.200124708
[108,] -0.708783649 -0.455543361
[109,] -1.637037011 -0.708783649
[110,] -0.024195547 -1.637037011
[111,] 0.113582200 -0.024195547
[112,] -2.709951071 0.113582200
[113,] -0.332694481 -2.709951071
[114,] -1.198878594 -0.332694481
[115,] -0.983949567 -1.198878594
[116,] -0.659502027 -0.983949567
[117,] 2.059864033 -0.659502027
[118,] 0.479915692 2.059864033
[119,] 0.193885204 0.479915692
[120,] 2.448149942 0.193885204
[121,] 1.123530258 2.448149942
[122,] -0.851585956 1.123530258
[123,] 3.484164706 -0.851585956
[124,] 2.005783468 3.484164706
[125,] 0.316227627 2.005783468
[126,] -2.528523337 0.316227627
[127,] 0.609249088 -2.528523337
[128,] -2.856234975 0.609249088
[129,] -1.329513117 -2.856234975
[130,] -0.264062350 -1.329513117
[131,] 0.708683056 -0.264062350
[132,] 3.074807706 0.708683056
[133,] -1.744049515 3.074807706
[134,] -0.883128435 -1.744049515
[135,] 2.347468751 -0.883128435
[136,] 5.704581514 2.347468751
[137,] 0.923612852 5.704581514
[138,] -0.904585222 0.923612852
[139,] 1.326862813 -0.904585222
[140,] -0.335509752 1.326862813
[141,] 0.572411555 -0.335509752
[142,] -2.644530589 0.572411555
[143,] -2.127011705 -2.644530589
[144,] 2.696754975 -2.127011705
[145,] -2.001251218 2.696754975
[146,] 2.433096073 -2.001251218
[147,] 2.341299790 2.433096073
[148,] -1.693112963 2.341299790
[149,] 2.860714202 -1.693112963
[150,] -0.470417418 2.860714202
[151,] 0.790740096 -0.470417418
[152,] -1.370046105 0.790740096
[153,] 0.263622879 -1.370046105
[154,] 0.091006913 0.263622879
[155,] -1.476331051 0.091006913
[156,] -1.394519559 -1.476331051
[157,] 7.194416421 -1.394519559
[158,] 0.323772297 7.194416421
[159,] -0.961233341 0.323772297
[160,] -2.529507137 -0.961233341
[161,] -2.881644853 -2.529507137
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.120977935 -1.676958073
2 -0.927249906 -2.120977935
3 2.131088326 -0.927249906
4 2.996185286 2.131088326
5 -1.377484465 2.996185286
6 2.099920395 -1.377484465
7 0.851667299 2.099920395
8 0.289116353 0.851667299
9 -1.822521103 0.289116353
10 -1.512867059 -1.822521103
11 0.177398303 -1.512867059
12 -1.729795517 0.177398303
13 1.767826208 -1.729795517
14 -1.341717008 1.767826208
15 2.993727590 -1.341717008
16 -3.771338808 2.993727590
17 -0.776208117 -3.771338808
18 1.868039321 -0.776208117
19 1.515811453 1.868039321
20 -1.330310900 1.515811453
21 -0.444062886 -1.330310900
22 -4.771245272 -0.444062886
23 -0.004538312 -4.771245272
24 -1.309647462 -0.004538312
25 -1.239634621 -1.309647462
26 -1.239634621 -1.239634621
27 -1.304831730 -1.239634621
28 -2.879299472 -1.304831730
29 -1.649613582 -2.879299472
30 -0.940277859 -1.649613582
31 -2.242771688 -0.940277859
32 6.692149288 -2.242771688
33 3.278659906 6.692149288
34 -1.814852766 3.278659906
35 2.855131959 -1.814852766
36 -2.332351471 2.855131959
37 -1.358297838 -2.332351471
38 -0.488644132 -1.358297838
39 -0.642257053 -0.488644132
40 0.949673295 -0.642257053
41 -0.553764733 0.949673295
42 0.065133349 -0.553764733
43 0.625810614 0.065133349
44 0.384063313 0.625810614
45 8.757052959 0.384063313
46 -0.562742554 8.757052959
47 6.709211097 -0.562742554
48 -0.203497827 6.709211097
49 1.407125435 -0.203497827
50 1.917093995 1.407125435
51 -2.653904860 1.917093995
52 -0.964854345 -2.653904860
53 -2.666214378 -0.964854345
54 -1.658647801 -2.666214378
55 -0.659350223 -1.658647801
56 -1.761051024 -0.659350223
57 0.165484720 -1.761051024
58 -1.164276608 0.165484720
59 -2.983289238 -1.164276608
60 -1.460292985 -2.983289238
61 -0.914653130 -1.460292985
62 -1.044843140 -0.914653130
63 -0.024272499 -1.044843140
64 10.339245302 -0.024272499
65 0.613760176 10.339245302
66 -0.178187191 0.613760176
67 -1.335032133 -0.178187191
68 -2.508257105 -1.335032133
69 -1.630715811 -2.508257105
70 -1.332047418 -1.630715811
71 -0.952721363 -1.332047418
72 -0.104424520 -0.952721363
73 3.292846413 -0.104424520
74 -1.213988680 3.292846413
75 -0.175923316 -1.213988680
76 -0.192367710 -0.175923316
77 -0.081103618 -0.192367710
78 -1.050039845 -0.081103618
79 -1.914319344 -1.050039845
80 1.632419452 -1.914319344
81 -1.628954454 1.632419452
82 -1.086607751 -1.628954454
83 -1.216034210 -1.086607751
84 1.061514842 -1.216034210
85 -1.506340117 1.061514842
86 -0.078461719 -1.506340117
87 6.389543933 -0.078461719
88 1.874280981 6.389543933
89 2.076026197 1.874280981
90 -3.847111438 2.076026197
91 -0.141348208 -3.847111438
92 0.323772297 -0.141348208
93 -1.956039107 0.323772297
94 0.678940283 -1.956039107
95 1.400480643 0.678940283
96 -0.644547242 1.400480643
97 -0.603360160 -0.644547242
98 -1.793076090 -0.603360160
99 2.125701833 -1.793076090
100 -1.791778537 2.125701833
101 0.726003203 -1.791778537
102 1.288729085 0.726003203
103 -1.968230575 1.288729085
104 -1.655598747 -1.968230575
105 2.860031395 -1.655598747
106 1.200124708 2.860031395
107 -0.455543361 1.200124708
108 -0.708783649 -0.455543361
109 -1.637037011 -0.708783649
110 -0.024195547 -1.637037011
111 0.113582200 -0.024195547
112 -2.709951071 0.113582200
113 -0.332694481 -2.709951071
114 -1.198878594 -0.332694481
115 -0.983949567 -1.198878594
116 -0.659502027 -0.983949567
117 2.059864033 -0.659502027
118 0.479915692 2.059864033
119 0.193885204 0.479915692
120 2.448149942 0.193885204
121 1.123530258 2.448149942
122 -0.851585956 1.123530258
123 3.484164706 -0.851585956
124 2.005783468 3.484164706
125 0.316227627 2.005783468
126 -2.528523337 0.316227627
127 0.609249088 -2.528523337
128 -2.856234975 0.609249088
129 -1.329513117 -2.856234975
130 -0.264062350 -1.329513117
131 0.708683056 -0.264062350
132 3.074807706 0.708683056
133 -1.744049515 3.074807706
134 -0.883128435 -1.744049515
135 2.347468751 -0.883128435
136 5.704581514 2.347468751
137 0.923612852 5.704581514
138 -0.904585222 0.923612852
139 1.326862813 -0.904585222
140 -0.335509752 1.326862813
141 0.572411555 -0.335509752
142 -2.644530589 0.572411555
143 -2.127011705 -2.644530589
144 2.696754975 -2.127011705
145 -2.001251218 2.696754975
146 2.433096073 -2.001251218
147 2.341299790 2.433096073
148 -1.693112963 2.341299790
149 2.860714202 -1.693112963
150 -0.470417418 2.860714202
151 0.790740096 -0.470417418
152 -1.370046105 0.790740096
153 0.263622879 -1.370046105
154 0.091006913 0.263622879
155 -1.476331051 0.091006913
156 -1.394519559 -1.476331051
157 7.194416421 -1.394519559
158 0.323772297 7.194416421
159 -0.961233341 0.323772297
160 -2.529507137 -0.961233341
161 -2.881644853 -2.529507137
> 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/freestat/rcomp/tmp/758ek1291999235.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8gid51291999235.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9gid51291999235.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/109rd91291999235.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11c9te1291999235.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/freestat/rcomp/tmp/12xsa21291999235.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/freestat/rcomp/tmp/13tk7b1291999235.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/freestat/rcomp/tmp/14w26z1291999235.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/freestat/rcomp/tmp/1503m51291999235.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/freestat/rcomp/tmp/16wv2d1291999235.tab")
+ }
> try(system("convert tmp/1k8gx1291999235.ps tmp/1k8gx1291999235.png",intern=TRUE))
character(0)
> try(system("convert tmp/2k8gx1291999235.ps tmp/2k8gx1291999235.png",intern=TRUE))
character(0)
> try(system("convert tmp/3uzxi1291999235.ps tmp/3uzxi1291999235.png",intern=TRUE))
character(0)
> try(system("convert tmp/4uzxi1291999235.ps tmp/4uzxi1291999235.png",intern=TRUE))
character(0)
> try(system("convert tmp/5uzxi1291999235.ps tmp/5uzxi1291999235.png",intern=TRUE))
character(0)
> try(system("convert tmp/658ek1291999235.ps tmp/658ek1291999235.png",intern=TRUE))
character(0)
> try(system("convert tmp/758ek1291999235.ps tmp/758ek1291999235.png",intern=TRUE))
character(0)
> try(system("convert tmp/8gid51291999235.ps tmp/8gid51291999235.png",intern=TRUE))
character(0)
> try(system("convert tmp/9gid51291999235.ps tmp/9gid51291999235.png",intern=TRUE))
character(0)
> try(system("convert tmp/109rd91291999235.ps tmp/109rd91291999235.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.577 2.801 14.232