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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+ ,dim=c(8
+ ,164)
+ ,dimnames=list(c('Gender'
+ ,'1'
+ ,'2'
+ ,'3'
+ ,'4'
+ ,'5'
+ ,'6'
+ ,'7')
+ ,1:164))
> y <- array(NA,dim=c(8,164),dimnames=list(c('Gender','1','2','3','4','5','6','7'),1:164))
> 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 = '3'
> #'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
2 Gender 1 3 4 5 6 7
1 7 1 7 6 1 5 7 7
2 6 1 5 4 1 4 5 5
3 6 1 6 6 2 5 5 6
4 5 2 4 4 2 4 5 6
5 6 1 5 2 2 4 5 6
6 7 1 6 5 1 6 7 5
7 7 2 7 1 1 5 7 7
8 7 1 6 6 1 3 5 6
9 7 1 6 3 1 4 3 7
10 6 1 6 4 1 4 6 6
11 4 2 5 3 1 2 7 7
12 6 2 5 2 1 5 6 7
13 6 1 4 4 1 3 5 6
14 7 1 6 3 1 5 3 6
15 6 1 6 5 1 6 7 7
16 6 2 5 3 2 4 5 6
17 4 1 3 3 1 3 7 7
18 7 2 7 6 1 6 7 7
19 7 2 3 1 1 7 7 7
20 6 1 5 1 2 2 6 7
21 3 2 3 1 1 6 5 5
22 7 1 5 5 1 4 7 7
23 5 1 2 2 1 1 4 5
24 7 2 6 3 1 4 7 6
25 6 1 3 3 1 3 7 7
26 5 1 6 5 1 6 7 6
27 5 1 6 5 1 6 7 6
28 6 1 5 1 1 3 3 6
29 5 2 5 2 1 6 7 6
30 6 1 7 3 1 4 5 6
31 6 2 6 5 1 7 7 6
32 5 1 5 3 1 4 5 5
33 4 1 5 5 4 4 5 3
34 5 2 4 3 3 3 4 3
35 4 2 4 2 1 4 5 5
36 6 2 6 4 2 6 6 6
37 6 1 5 3 1 3 7 7
38 7 2 5 3 1 2 5 7
39 7 2 7 6 1 7 7 7
40 7 1 5 6 1 7 7 6
41 7 1 5 3 1 5 6 7
42 5 1 6 3 1 5 6 7
43 6 1 5 4 2 6 7 6
44 6 2 6 4 1 7 7 6
45 3 2 7 2 1 6 6 5
46 6 1 5 2 4 3 6 6
47 5 2 5 3 1 4 4 6
48 4 2 5 2 3 4 7 7
49 6 1 6 5 2 4 5 6
50 6 2 2 5 3 2 6 7
51 6 1 4 6 2 4 5 6
52 5 1 4 3 1 3 3 5
53 6 1 6 3 2 5 7 7
54 5 1 3 2 1 3 6 4
55 7 2 6 5 1 5 6 7
56 6 1 6 2 1 5 5 5
57 6 1 5 4 1 4 5 6
58 7 1 6 4 1 5 7 7
59 4 2 1 1 1 5 7 7
60 3 2 5 6 2 6 7 7
61 4 1 7 2 1 4 6 7
62 4 1 4 3 3 4 6 6
63 5 1 5 4 1 7 7 6
64 4 2 6 3 1 6 7 6
65 6 1 4 4 4 5 5 4
66 7 2 6 3 1 6 7 6
67 6 2 6 6 1 6 6 6
68 6 2 5 4 1 5 5 7
69 6 1 5 5 1 4 6 7
70 6 1 3 3 1 2 5 7
71 7 1 5 4 1 7 5 7
72 6 2 6 3 1 5 6 7
73 6 2 5 3 1 5 6 6
74 6 1 6 6 1 6 6 5
75 7 1 6 6 1 6 7 7
76 5 1 4 2 2 4 6 5
77 4 1 4 2 2 4 5 5
78 7 2 6 6 1 6 7 7
79 7 2 7 5 1 6 7 7
80 6 2 4 1 1 5 6 2
81 7 2 5 2 1 7 7 6
82 6 1 6 5 1 3 6 6
83 5 1 6 3 1 6 6 6
84 7 1 5 3 1 5 7 6
85 6 2 3 4 2 6 6 5
86 5 2 7 6 1 7 7 6
87 6 1 6 4 1 4 7 7
88 5 1 4 2 4 2 5 5
89 7 1 4 4 3 3 3 7
90 6 1 5 4 2 5 6 6
91 2 1 3 2 1 5 6 5
92 5 2 7 4 1 6 5 6
93 7 1 6 3 1 3 6 7
94 7 1 6 6 1 3 6 7
95 7 1 4 4 2 5 6 6
96 7 1 5 4 1 5 7 7
97 6 2 6 3 1 3 6 6
98 5 1 5 3 2 4 6 5
99 6 2 6 3 1 1 6 5
100 6 2 6 4 3 7 7 6
101 5 1 4 2 1 4 6 6
102 7 2 5 2 1 7 5 6
103 5 1 6 3 2 4 5 6
104 6 1 5 3 1 5 6 6
105 5 2 5 4 1 5 6 5
106 5 1 4 4 2 6 6 5
107 5 2 4 2 2 4 5 5
108 5 1 6 5 1 4 6 7
109 7 1 5 5 1 4 4 7
110 6 1 6 4 1 6 6 6
111 7 1 5 4 1 4 7 7
112 6 1 6 3 1 3 7 7
113 5 1 5 4 1 3 5 4
114 5 1 4 4 2 5 5 5
115 7 1 6 5 1 5 7 7
116 6 2 4 4 1 3 3 7
117 5 1 5 5 2 4 7 7
118 7 1 5 3 2 5 5 6
119 4 2 6 2 1 5 7 5
120 3 1 3 1 2 3 5 7
121 7 1 5 5 2 3 3 1
122 5 4 5 2 5 6 6 1
123 3 5 6 2 3 5 6 1
124 4 5 4 4 4 4 3 1
125 4 7 7 1 7 7 7 2
126 5 5 7 2 6 6 6 1
127 5 7 5 1 5 7 7 1
128 3 5 7 1 2 2 6 2
129 4 4 3 1 4 5 5 2
130 6 6 6 1 6 6 6 1
131 4 4 5 3 6 6 6 2
132 5 4 5 2 5 6 7 2
133 4 4 6 1 4 2 6 2
134 4 4 5 1 4 6 7 2
135 5 6 6 1 5 7 6 1
136 4 6 6 2 3 4 6 1
137 3 5 7 5 5 7 7 2
138 3 3 5 1 5 7 7 2
139 6 6 7 1 5 6 7 1
140 5 5 6 2 6 6 7 1
141 2 4 6 1 4 2 6 1
142 4 5 7 2 3 7 7 1
143 3 2 7 1 3 7 7 2
144 3 5 5 1 4 5 6 1
145 5 7 7 1 5 5 7 1
146 3 4 5 1 5 6 6 2
147 3 4 6 2 3 5 7 2
148 4 7 7 1 6 6 6 2
149 5 6 6 1 5 6 5 2
150 5 5 5 2 4 6 4 1
151 5 5 6 1 5 5 7 1
152 5 5 7 1 4 6 7 1
153 3 7 6 1 7 7 5 2
154 4 6 7 1 5 7 7 2
155 4 6 7 1 3 6 6 2
156 3 5 6 2 5 6 5 1
157 4 2 6 2 2 6 6 1
158 4 4 4 4 4 7 7 1
159 3 6 7 1 3 6 7 1
160 2 5 6 1 4 5 6 2
161 4 5 4 1 5 5 5 1
162 4 5 5 1 4 5 6 1
163 3 4 6 1 4 4 5 1
164 3 4 5 5 2 4 6 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Gender `1` `3` `4` `5`
3.11942 -0.02483 0.14914 0.17480 0.01064 0.06767
`6` `7`
-0.15826 0.30595
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.32549 -0.71441 0.09762 0.67876 2.23020
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.11942 0.68506 4.554 1.06e-05 ***
Gender -0.02483 0.10288 -0.241 0.80963
`1` 0.14914 0.07963 1.873 0.06295 .
`3` 0.17480 0.06571 2.660 0.00862 **
`4` 0.01064 0.09364 0.114 0.90969
`5` 0.06767 0.06667 1.015 0.31168
`6` -0.15826 0.08681 -1.823 0.07023 .
`7` 0.30595 0.07044 4.344 2.51e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.034 on 156 degrees of freedom
Multiple R-squared: 0.426, Adjusted R-squared: 0.4002
F-statistic: 16.54 on 7 and 156 DF, p-value: 3.315e-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,] 0.26033000 0.52066000 0.7396700
[2,] 0.14108011 0.28216021 0.8589199
[3,] 0.11445915 0.22891830 0.8855409
[4,] 0.07575850 0.15151699 0.9242415
[5,] 0.06240387 0.12480774 0.9375961
[6,] 0.03942737 0.07885475 0.9605726
[7,] 0.02059620 0.04119239 0.9794038
[8,] 0.01028643 0.02057285 0.9897136
[9,] 0.02528418 0.05056835 0.9747158
[10,] 0.04029539 0.08059078 0.9597046
[11,] 0.31975255 0.63950511 0.6802474
[12,] 0.31572891 0.63145782 0.6842711
[13,] 0.33039776 0.66079552 0.6696022
[14,] 0.36138260 0.72276520 0.6386174
[15,] 0.33115881 0.66231762 0.6688412
[16,] 0.42150202 0.84300404 0.5784980
[17,] 0.45914732 0.91829464 0.5408527
[18,] 0.39985346 0.79970692 0.6001465
[19,] 0.35145393 0.70290786 0.6485461
[20,] 0.31601669 0.63203338 0.6839833
[21,] 0.25885546 0.51771091 0.7411445
[22,] 0.21842899 0.43685797 0.7815710
[23,] 0.18298075 0.36596150 0.8170193
[24,] 0.17756100 0.35512201 0.8224390
[25,] 0.17560102 0.35120203 0.8243990
[26,] 0.13819099 0.27638199 0.8618090
[27,] 0.10785207 0.21570413 0.8921479
[28,] 0.10312589 0.20625178 0.8968741
[29,] 0.07930826 0.15861651 0.9206917
[30,] 0.08466370 0.16932740 0.9153363
[31,] 0.07760537 0.15521074 0.9223946
[32,] 0.10674745 0.21349490 0.8932525
[33,] 0.08516348 0.17032696 0.9148365
[34,] 0.06523870 0.13047739 0.9347613
[35,] 0.18223966 0.36447932 0.8177603
[36,] 0.15518194 0.31036388 0.8448181
[37,] 0.14521147 0.29042294 0.8547885
[38,] 0.20089758 0.40179517 0.7991024
[39,] 0.16930130 0.33860261 0.8306987
[40,] 0.14062215 0.28124430 0.8593778
[41,] 0.11495221 0.22990443 0.8850478
[42,] 0.09615543 0.19231087 0.9038446
[43,] 0.07645896 0.15291792 0.9235410
[44,] 0.07220547 0.14441093 0.9277945
[45,] 0.06085401 0.12170801 0.9391460
[46,] 0.05210281 0.10420561 0.9478972
[47,] 0.03993248 0.07986496 0.9600675
[48,] 0.03500939 0.07001877 0.9649906
[49,] 0.03124662 0.06249324 0.9687534
[50,] 0.22255528 0.44511056 0.7774447
[51,] 0.37166257 0.74332514 0.6283374
[52,] 0.39692517 0.79385033 0.6030748
[53,] 0.37421482 0.74842964 0.6257852
[54,] 0.42183603 0.84367205 0.5781640
[55,] 0.43289198 0.86578396 0.5671080
[56,] 0.48600488 0.97200977 0.5139951
[57,] 0.44146242 0.88292484 0.5585376
[58,] 0.39639576 0.79279151 0.6036042
[59,] 0.35587606 0.71175213 0.6441239
[60,] 0.31532295 0.63064590 0.6846771
[61,] 0.28541077 0.57082153 0.7145892
[62,] 0.24669667 0.49339335 0.7533033
[63,] 0.21991903 0.43983806 0.7800810
[64,] 0.18666353 0.37332707 0.8133365
[65,] 0.16067091 0.32134182 0.8393291
[66,] 0.13390876 0.26781752 0.8660912
[67,] 0.13358201 0.26716401 0.8664180
[68,] 0.11665243 0.23330486 0.8833476
[69,] 0.10225267 0.20450535 0.8977473
[70,] 0.19818353 0.39636707 0.8018165
[71,] 0.25524683 0.51049365 0.7447532
[72,] 0.21967799 0.43935598 0.7803220
[73,] 0.20648488 0.41296976 0.7935151
[74,] 0.24575101 0.49150202 0.7542490
[75,] 0.23601489 0.47202978 0.7639851
[76,] 0.26170298 0.52340595 0.7382970
[77,] 0.22548977 0.45097954 0.7745102
[78,] 0.19322738 0.38645476 0.8067726
[79,] 0.18085069 0.36170138 0.8191493
[80,] 0.15283871 0.30567742 0.8471613
[81,] 0.37370172 0.74740344 0.6262983
[82,] 0.39144267 0.78288534 0.6085573
[83,] 0.39242407 0.78484814 0.6075759
[84,] 0.35768114 0.71536228 0.6423189
[85,] 0.38908187 0.77816374 0.6109181
[86,] 0.39344310 0.78688619 0.6065569
[87,] 0.36161841 0.72323683 0.6383816
[88,] 0.31956527 0.63913053 0.6804347
[89,] 0.32747544 0.65495088 0.6725246
[90,] 0.28863093 0.57726186 0.7113691
[91,] 0.25153089 0.50306177 0.7484691
[92,] 0.28085777 0.56171555 0.7191422
[93,] 0.25897469 0.51794938 0.7410253
[94,] 0.23322317 0.46644635 0.7667768
[95,] 0.20060520 0.40121039 0.7993948
[96,] 0.17059677 0.34119354 0.8294032
[97,] 0.14267549 0.28535099 0.8573245
[98,] 0.15274560 0.30549120 0.8472544
[99,] 0.13341346 0.26682691 0.8665865
[100,] 0.10910339 0.21820678 0.8908966
[101,] 0.13094818 0.26189636 0.8690518
[102,] 0.12421611 0.24843222 0.8757839
[103,] 0.10196775 0.20393551 0.8980322
[104,] 0.08242496 0.16484992 0.9175750
[105,] 0.10205667 0.20411335 0.8979433
[106,] 0.09140267 0.18280534 0.9085973
[107,] 0.08292824 0.16585647 0.9170718
[108,] 0.23367398 0.46734795 0.7663260
[109,] 0.25394774 0.50789548 0.7460523
[110,] 0.38640530 0.77281060 0.6135947
[111,] 0.83933507 0.32132987 0.1606649
[112,] 0.82915193 0.34169614 0.1708481
[113,] 0.82596452 0.34807095 0.1740355
[114,] 0.79663492 0.40673016 0.2033651
[115,] 0.77217696 0.45564607 0.2278230
[116,] 0.74778352 0.50443296 0.2522165
[117,] 0.71943432 0.56113137 0.2805657
[118,] 0.69228713 0.61542575 0.3077129
[119,] 0.66203013 0.67593975 0.3379699
[120,] 0.71866032 0.56267935 0.2813397
[121,] 0.68143031 0.63713939 0.3185697
[122,] 0.74174033 0.51651934 0.2582597
[123,] 0.84442070 0.31115859 0.1555793
[124,] 0.83240558 0.33518884 0.1675944
[125,] 0.78862035 0.42275930 0.2113796
[126,] 0.74041922 0.51916155 0.2595808
[127,] 0.72931989 0.54136022 0.2706801
[128,] 0.68243622 0.63512757 0.3175638
[129,] 0.75097625 0.49804751 0.2490238
[130,] 0.72714005 0.54571991 0.2728600
[131,] 0.71885142 0.56229715 0.2811486
[132,] 0.65801236 0.68397528 0.3419876
[133,] 0.59566473 0.80867055 0.4043353
[134,] 0.58533722 0.82932556 0.4146628
[135,] 0.54498828 0.91002345 0.4550117
[136,] 0.47987829 0.95975657 0.5201217
[137,] 0.39320200 0.78640400 0.6067980
[138,] 0.32152057 0.64304114 0.6784794
[139,] 0.45000635 0.90001269 0.5499937
[140,] 0.59599258 0.80801485 0.4040074
[141,] 0.54466206 0.91067588 0.4553379
[142,] 0.64778005 0.70443990 0.3522199
[143,] 0.50459066 0.99081867 0.4954093
> postscript(file="/var/www/html/freestat/rcomp/tmp/1hb4i1290499179.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/29kll1290499179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/39kll1290499179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4kbko1290499179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5kbko1290499179.ps",horizontal=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 = 164
Frequency = 1
1 2 3 4 5 6
0.429711646 0.440669760 -0.442345119 -0.701953920 0.473684111 1.297892017
7 8 9 10 11 12
1.328553808 0.703639999 0.537907471 0.143830679 -1.519747076 0.293778460
13 14 15 16 17 18
0.351532008 0.776187954 -0.314015751 0.323706625 -1.313960806 0.386864032
19 20 21 22 23 24
1.789777924 0.636137838 -1.847155971 0.970473782 0.282467172 1.501717274
25 26 27 28 29 30
0.686039194 -1.008061867 -1.008061867 0.410284037 -0.309683525 0.011233691
31 32 33 34 35 36
-0.050909481 -0.384526965 -1.154140689 0.289488562 -1.035755172 0.022671349
37 38 39 40 41 42
0.387753734 1.163737859 0.319190631 0.898604186 1.094149397 -1.054993333
43 44 45 46 47 48
0.305245824 0.123893794 -2.460272633 0.678338417 -0.823912594 -1.501567233
49 50 51 52 53 54
-0.199868443 0.398540403 -0.076386258 -0.484225898 0.092625886 0.620446588
55 56 57 58 59 60
0.620225906 0.573460178 0.134715876 0.928460925 -0.776589812 -3.325488822
61 62 63 64 65 66
-1.961659386 -1.404357215 -0.751789264 -1.633629529 0.796178030 1.366370471
67 68 69 70 71 72
-0.316296886 -0.214085622 -0.187783750 0.437197530 0.625741786 -0.030167544
73 74 75 76 77 78
0.424929070 -0.035168790 0.511180974 0.087038258 -1.071219274 0.536006762
79 80 81 82 83 84
0.561667307 2.147493886 1.622643074 0.036700806 -0.816712850 1.558360814
85 86 87 88 89 90
0.776053423 -1.374855485 -0.003865673 0.042850901 0.707786430 0.214661693
91 92 93 94 95 96
-2.820854100 -1.274090599 1.080353471 0.555943647 1.363804423 1.077603655
97 98 99 100 101 102
0.411133144 -0.236907747 0.852433831 0.102617166 -0.208277312 1.306128008
103 104 105 106 107 108
-0.850261893 0.400103282 -0.443920321 -0.397915095 -0.046393486 -1.336926480
109 110 111 112 113 114
0.495701185 0.008483875 1.145277057 0.238611004 -0.185702954 -0.488499226
115 116 117 118 119 120
0.753657651 -0.246111153 -1.040164532 1.231207435 -1.085198969 -2.291507636
121 122 123 124 125 126
2.230202045 1.068926684 -0.966440228 -0.435498828 -0.216724519 0.784828698
127 128 129 130 131 132
1.408791454 -1.033075048 0.156115717 2.133600491 -0.422468790 0.921230332
133 134 135 136 137 138
0.069965266 0.106671921 1.076565403 0.126058962 -1.944312566 -0.996465583
139 140 141 142 143 144
2.153353607 1.092228960 -1.624080850 -0.092672229 -1.298300203 -0.653132537
145 146 147 148 149 150
1.245852798 -1.062223926 -1.138962368 -0.296670335 0.680027388 0.787875721
151 152 153 154 155 156
1.345343951 1.139166133 -1.384096854 -0.220273679 -0.289581181 -1.213647791
157 158 159 160 161 162
-0.097952680 -0.030314691 -0.825369764 -2.108229152 0.327114346 0.346867463
163 164
-0.917685186 -1.288221394
> postscript(file="/var/www/html/freestat/rcomp/tmp/6kbko1290499179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 164
Frequency = 1
lag(myerror, k = 1) myerror
0 0.429711646 NA
1 0.440669760 0.429711646
2 -0.442345119 0.440669760
3 -0.701953920 -0.442345119
4 0.473684111 -0.701953920
5 1.297892017 0.473684111
6 1.328553808 1.297892017
7 0.703639999 1.328553808
8 0.537907471 0.703639999
9 0.143830679 0.537907471
10 -1.519747076 0.143830679
11 0.293778460 -1.519747076
12 0.351532008 0.293778460
13 0.776187954 0.351532008
14 -0.314015751 0.776187954
15 0.323706625 -0.314015751
16 -1.313960806 0.323706625
17 0.386864032 -1.313960806
18 1.789777924 0.386864032
19 0.636137838 1.789777924
20 -1.847155971 0.636137838
21 0.970473782 -1.847155971
22 0.282467172 0.970473782
23 1.501717274 0.282467172
24 0.686039194 1.501717274
25 -1.008061867 0.686039194
26 -1.008061867 -1.008061867
27 0.410284037 -1.008061867
28 -0.309683525 0.410284037
29 0.011233691 -0.309683525
30 -0.050909481 0.011233691
31 -0.384526965 -0.050909481
32 -1.154140689 -0.384526965
33 0.289488562 -1.154140689
34 -1.035755172 0.289488562
35 0.022671349 -1.035755172
36 0.387753734 0.022671349
37 1.163737859 0.387753734
38 0.319190631 1.163737859
39 0.898604186 0.319190631
40 1.094149397 0.898604186
41 -1.054993333 1.094149397
42 0.305245824 -1.054993333
43 0.123893794 0.305245824
44 -2.460272633 0.123893794
45 0.678338417 -2.460272633
46 -0.823912594 0.678338417
47 -1.501567233 -0.823912594
48 -0.199868443 -1.501567233
49 0.398540403 -0.199868443
50 -0.076386258 0.398540403
51 -0.484225898 -0.076386258
52 0.092625886 -0.484225898
53 0.620446588 0.092625886
54 0.620225906 0.620446588
55 0.573460178 0.620225906
56 0.134715876 0.573460178
57 0.928460925 0.134715876
58 -0.776589812 0.928460925
59 -3.325488822 -0.776589812
60 -1.961659386 -3.325488822
61 -1.404357215 -1.961659386
62 -0.751789264 -1.404357215
63 -1.633629529 -0.751789264
64 0.796178030 -1.633629529
65 1.366370471 0.796178030
66 -0.316296886 1.366370471
67 -0.214085622 -0.316296886
68 -0.187783750 -0.214085622
69 0.437197530 -0.187783750
70 0.625741786 0.437197530
71 -0.030167544 0.625741786
72 0.424929070 -0.030167544
73 -0.035168790 0.424929070
74 0.511180974 -0.035168790
75 0.087038258 0.511180974
76 -1.071219274 0.087038258
77 0.536006762 -1.071219274
78 0.561667307 0.536006762
79 2.147493886 0.561667307
80 1.622643074 2.147493886
81 0.036700806 1.622643074
82 -0.816712850 0.036700806
83 1.558360814 -0.816712850
84 0.776053423 1.558360814
85 -1.374855485 0.776053423
86 -0.003865673 -1.374855485
87 0.042850901 -0.003865673
88 0.707786430 0.042850901
89 0.214661693 0.707786430
90 -2.820854100 0.214661693
91 -1.274090599 -2.820854100
92 1.080353471 -1.274090599
93 0.555943647 1.080353471
94 1.363804423 0.555943647
95 1.077603655 1.363804423
96 0.411133144 1.077603655
97 -0.236907747 0.411133144
98 0.852433831 -0.236907747
99 0.102617166 0.852433831
100 -0.208277312 0.102617166
101 1.306128008 -0.208277312
102 -0.850261893 1.306128008
103 0.400103282 -0.850261893
104 -0.443920321 0.400103282
105 -0.397915095 -0.443920321
106 -0.046393486 -0.397915095
107 -1.336926480 -0.046393486
108 0.495701185 -1.336926480
109 0.008483875 0.495701185
110 1.145277057 0.008483875
111 0.238611004 1.145277057
112 -0.185702954 0.238611004
113 -0.488499226 -0.185702954
114 0.753657651 -0.488499226
115 -0.246111153 0.753657651
116 -1.040164532 -0.246111153
117 1.231207435 -1.040164532
118 -1.085198969 1.231207435
119 -2.291507636 -1.085198969
120 2.230202045 -2.291507636
121 1.068926684 2.230202045
122 -0.966440228 1.068926684
123 -0.435498828 -0.966440228
124 -0.216724519 -0.435498828
125 0.784828698 -0.216724519
126 1.408791454 0.784828698
127 -1.033075048 1.408791454
128 0.156115717 -1.033075048
129 2.133600491 0.156115717
130 -0.422468790 2.133600491
131 0.921230332 -0.422468790
132 0.069965266 0.921230332
133 0.106671921 0.069965266
134 1.076565403 0.106671921
135 0.126058962 1.076565403
136 -1.944312566 0.126058962
137 -0.996465583 -1.944312566
138 2.153353607 -0.996465583
139 1.092228960 2.153353607
140 -1.624080850 1.092228960
141 -0.092672229 -1.624080850
142 -1.298300203 -0.092672229
143 -0.653132537 -1.298300203
144 1.245852798 -0.653132537
145 -1.062223926 1.245852798
146 -1.138962368 -1.062223926
147 -0.296670335 -1.138962368
148 0.680027388 -0.296670335
149 0.787875721 0.680027388
150 1.345343951 0.787875721
151 1.139166133 1.345343951
152 -1.384096854 1.139166133
153 -0.220273679 -1.384096854
154 -0.289581181 -0.220273679
155 -1.213647791 -0.289581181
156 -0.097952680 -1.213647791
157 -0.030314691 -0.097952680
158 -0.825369764 -0.030314691
159 -2.108229152 -0.825369764
160 0.327114346 -2.108229152
161 0.346867463 0.327114346
162 -0.917685186 0.346867463
163 -1.288221394 -0.917685186
164 NA -1.288221394
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.440669760 0.429711646
[2,] -0.442345119 0.440669760
[3,] -0.701953920 -0.442345119
[4,] 0.473684111 -0.701953920
[5,] 1.297892017 0.473684111
[6,] 1.328553808 1.297892017
[7,] 0.703639999 1.328553808
[8,] 0.537907471 0.703639999
[9,] 0.143830679 0.537907471
[10,] -1.519747076 0.143830679
[11,] 0.293778460 -1.519747076
[12,] 0.351532008 0.293778460
[13,] 0.776187954 0.351532008
[14,] -0.314015751 0.776187954
[15,] 0.323706625 -0.314015751
[16,] -1.313960806 0.323706625
[17,] 0.386864032 -1.313960806
[18,] 1.789777924 0.386864032
[19,] 0.636137838 1.789777924
[20,] -1.847155971 0.636137838
[21,] 0.970473782 -1.847155971
[22,] 0.282467172 0.970473782
[23,] 1.501717274 0.282467172
[24,] 0.686039194 1.501717274
[25,] -1.008061867 0.686039194
[26,] -1.008061867 -1.008061867
[27,] 0.410284037 -1.008061867
[28,] -0.309683525 0.410284037
[29,] 0.011233691 -0.309683525
[30,] -0.050909481 0.011233691
[31,] -0.384526965 -0.050909481
[32,] -1.154140689 -0.384526965
[33,] 0.289488562 -1.154140689
[34,] -1.035755172 0.289488562
[35,] 0.022671349 -1.035755172
[36,] 0.387753734 0.022671349
[37,] 1.163737859 0.387753734
[38,] 0.319190631 1.163737859
[39,] 0.898604186 0.319190631
[40,] 1.094149397 0.898604186
[41,] -1.054993333 1.094149397
[42,] 0.305245824 -1.054993333
[43,] 0.123893794 0.305245824
[44,] -2.460272633 0.123893794
[45,] 0.678338417 -2.460272633
[46,] -0.823912594 0.678338417
[47,] -1.501567233 -0.823912594
[48,] -0.199868443 -1.501567233
[49,] 0.398540403 -0.199868443
[50,] -0.076386258 0.398540403
[51,] -0.484225898 -0.076386258
[52,] 0.092625886 -0.484225898
[53,] 0.620446588 0.092625886
[54,] 0.620225906 0.620446588
[55,] 0.573460178 0.620225906
[56,] 0.134715876 0.573460178
[57,] 0.928460925 0.134715876
[58,] -0.776589812 0.928460925
[59,] -3.325488822 -0.776589812
[60,] -1.961659386 -3.325488822
[61,] -1.404357215 -1.961659386
[62,] -0.751789264 -1.404357215
[63,] -1.633629529 -0.751789264
[64,] 0.796178030 -1.633629529
[65,] 1.366370471 0.796178030
[66,] -0.316296886 1.366370471
[67,] -0.214085622 -0.316296886
[68,] -0.187783750 -0.214085622
[69,] 0.437197530 -0.187783750
[70,] 0.625741786 0.437197530
[71,] -0.030167544 0.625741786
[72,] 0.424929070 -0.030167544
[73,] -0.035168790 0.424929070
[74,] 0.511180974 -0.035168790
[75,] 0.087038258 0.511180974
[76,] -1.071219274 0.087038258
[77,] 0.536006762 -1.071219274
[78,] 0.561667307 0.536006762
[79,] 2.147493886 0.561667307
[80,] 1.622643074 2.147493886
[81,] 0.036700806 1.622643074
[82,] -0.816712850 0.036700806
[83,] 1.558360814 -0.816712850
[84,] 0.776053423 1.558360814
[85,] -1.374855485 0.776053423
[86,] -0.003865673 -1.374855485
[87,] 0.042850901 -0.003865673
[88,] 0.707786430 0.042850901
[89,] 0.214661693 0.707786430
[90,] -2.820854100 0.214661693
[91,] -1.274090599 -2.820854100
[92,] 1.080353471 -1.274090599
[93,] 0.555943647 1.080353471
[94,] 1.363804423 0.555943647
[95,] 1.077603655 1.363804423
[96,] 0.411133144 1.077603655
[97,] -0.236907747 0.411133144
[98,] 0.852433831 -0.236907747
[99,] 0.102617166 0.852433831
[100,] -0.208277312 0.102617166
[101,] 1.306128008 -0.208277312
[102,] -0.850261893 1.306128008
[103,] 0.400103282 -0.850261893
[104,] -0.443920321 0.400103282
[105,] -0.397915095 -0.443920321
[106,] -0.046393486 -0.397915095
[107,] -1.336926480 -0.046393486
[108,] 0.495701185 -1.336926480
[109,] 0.008483875 0.495701185
[110,] 1.145277057 0.008483875
[111,] 0.238611004 1.145277057
[112,] -0.185702954 0.238611004
[113,] -0.488499226 -0.185702954
[114,] 0.753657651 -0.488499226
[115,] -0.246111153 0.753657651
[116,] -1.040164532 -0.246111153
[117,] 1.231207435 -1.040164532
[118,] -1.085198969 1.231207435
[119,] -2.291507636 -1.085198969
[120,] 2.230202045 -2.291507636
[121,] 1.068926684 2.230202045
[122,] -0.966440228 1.068926684
[123,] -0.435498828 -0.966440228
[124,] -0.216724519 -0.435498828
[125,] 0.784828698 -0.216724519
[126,] 1.408791454 0.784828698
[127,] -1.033075048 1.408791454
[128,] 0.156115717 -1.033075048
[129,] 2.133600491 0.156115717
[130,] -0.422468790 2.133600491
[131,] 0.921230332 -0.422468790
[132,] 0.069965266 0.921230332
[133,] 0.106671921 0.069965266
[134,] 1.076565403 0.106671921
[135,] 0.126058962 1.076565403
[136,] -1.944312566 0.126058962
[137,] -0.996465583 -1.944312566
[138,] 2.153353607 -0.996465583
[139,] 1.092228960 2.153353607
[140,] -1.624080850 1.092228960
[141,] -0.092672229 -1.624080850
[142,] -1.298300203 -0.092672229
[143,] -0.653132537 -1.298300203
[144,] 1.245852798 -0.653132537
[145,] -1.062223926 1.245852798
[146,] -1.138962368 -1.062223926
[147,] -0.296670335 -1.138962368
[148,] 0.680027388 -0.296670335
[149,] 0.787875721 0.680027388
[150,] 1.345343951 0.787875721
[151,] 1.139166133 1.345343951
[152,] -1.384096854 1.139166133
[153,] -0.220273679 -1.384096854
[154,] -0.289581181 -0.220273679
[155,] -1.213647791 -0.289581181
[156,] -0.097952680 -1.213647791
[157,] -0.030314691 -0.097952680
[158,] -0.825369764 -0.030314691
[159,] -2.108229152 -0.825369764
[160,] 0.327114346 -2.108229152
[161,] 0.346867463 0.327114346
[162,] -0.917685186 0.346867463
[163,] -1.288221394 -0.917685186
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.440669760 0.429711646
2 -0.442345119 0.440669760
3 -0.701953920 -0.442345119
4 0.473684111 -0.701953920
5 1.297892017 0.473684111
6 1.328553808 1.297892017
7 0.703639999 1.328553808
8 0.537907471 0.703639999
9 0.143830679 0.537907471
10 -1.519747076 0.143830679
11 0.293778460 -1.519747076
12 0.351532008 0.293778460
13 0.776187954 0.351532008
14 -0.314015751 0.776187954
15 0.323706625 -0.314015751
16 -1.313960806 0.323706625
17 0.386864032 -1.313960806
18 1.789777924 0.386864032
19 0.636137838 1.789777924
20 -1.847155971 0.636137838
21 0.970473782 -1.847155971
22 0.282467172 0.970473782
23 1.501717274 0.282467172
24 0.686039194 1.501717274
25 -1.008061867 0.686039194
26 -1.008061867 -1.008061867
27 0.410284037 -1.008061867
28 -0.309683525 0.410284037
29 0.011233691 -0.309683525
30 -0.050909481 0.011233691
31 -0.384526965 -0.050909481
32 -1.154140689 -0.384526965
33 0.289488562 -1.154140689
34 -1.035755172 0.289488562
35 0.022671349 -1.035755172
36 0.387753734 0.022671349
37 1.163737859 0.387753734
38 0.319190631 1.163737859
39 0.898604186 0.319190631
40 1.094149397 0.898604186
41 -1.054993333 1.094149397
42 0.305245824 -1.054993333
43 0.123893794 0.305245824
44 -2.460272633 0.123893794
45 0.678338417 -2.460272633
46 -0.823912594 0.678338417
47 -1.501567233 -0.823912594
48 -0.199868443 -1.501567233
49 0.398540403 -0.199868443
50 -0.076386258 0.398540403
51 -0.484225898 -0.076386258
52 0.092625886 -0.484225898
53 0.620446588 0.092625886
54 0.620225906 0.620446588
55 0.573460178 0.620225906
56 0.134715876 0.573460178
57 0.928460925 0.134715876
58 -0.776589812 0.928460925
59 -3.325488822 -0.776589812
60 -1.961659386 -3.325488822
61 -1.404357215 -1.961659386
62 -0.751789264 -1.404357215
63 -1.633629529 -0.751789264
64 0.796178030 -1.633629529
65 1.366370471 0.796178030
66 -0.316296886 1.366370471
67 -0.214085622 -0.316296886
68 -0.187783750 -0.214085622
69 0.437197530 -0.187783750
70 0.625741786 0.437197530
71 -0.030167544 0.625741786
72 0.424929070 -0.030167544
73 -0.035168790 0.424929070
74 0.511180974 -0.035168790
75 0.087038258 0.511180974
76 -1.071219274 0.087038258
77 0.536006762 -1.071219274
78 0.561667307 0.536006762
79 2.147493886 0.561667307
80 1.622643074 2.147493886
81 0.036700806 1.622643074
82 -0.816712850 0.036700806
83 1.558360814 -0.816712850
84 0.776053423 1.558360814
85 -1.374855485 0.776053423
86 -0.003865673 -1.374855485
87 0.042850901 -0.003865673
88 0.707786430 0.042850901
89 0.214661693 0.707786430
90 -2.820854100 0.214661693
91 -1.274090599 -2.820854100
92 1.080353471 -1.274090599
93 0.555943647 1.080353471
94 1.363804423 0.555943647
95 1.077603655 1.363804423
96 0.411133144 1.077603655
97 -0.236907747 0.411133144
98 0.852433831 -0.236907747
99 0.102617166 0.852433831
100 -0.208277312 0.102617166
101 1.306128008 -0.208277312
102 -0.850261893 1.306128008
103 0.400103282 -0.850261893
104 -0.443920321 0.400103282
105 -0.397915095 -0.443920321
106 -0.046393486 -0.397915095
107 -1.336926480 -0.046393486
108 0.495701185 -1.336926480
109 0.008483875 0.495701185
110 1.145277057 0.008483875
111 0.238611004 1.145277057
112 -0.185702954 0.238611004
113 -0.488499226 -0.185702954
114 0.753657651 -0.488499226
115 -0.246111153 0.753657651
116 -1.040164532 -0.246111153
117 1.231207435 -1.040164532
118 -1.085198969 1.231207435
119 -2.291507636 -1.085198969
120 2.230202045 -2.291507636
121 1.068926684 2.230202045
122 -0.966440228 1.068926684
123 -0.435498828 -0.966440228
124 -0.216724519 -0.435498828
125 0.784828698 -0.216724519
126 1.408791454 0.784828698
127 -1.033075048 1.408791454
128 0.156115717 -1.033075048
129 2.133600491 0.156115717
130 -0.422468790 2.133600491
131 0.921230332 -0.422468790
132 0.069965266 0.921230332
133 0.106671921 0.069965266
134 1.076565403 0.106671921
135 0.126058962 1.076565403
136 -1.944312566 0.126058962
137 -0.996465583 -1.944312566
138 2.153353607 -0.996465583
139 1.092228960 2.153353607
140 -1.624080850 1.092228960
141 -0.092672229 -1.624080850
142 -1.298300203 -0.092672229
143 -0.653132537 -1.298300203
144 1.245852798 -0.653132537
145 -1.062223926 1.245852798
146 -1.138962368 -1.062223926
147 -0.296670335 -1.138962368
148 0.680027388 -0.296670335
149 0.787875721 0.680027388
150 1.345343951 0.787875721
151 1.139166133 1.345343951
152 -1.384096854 1.139166133
153 -0.220273679 -1.384096854
154 -0.289581181 -0.220273679
155 -1.213647791 -0.289581181
156 -0.097952680 -1.213647791
157 -0.030314691 -0.097952680
158 -0.825369764 -0.030314691
159 -2.108229152 -0.825369764
160 0.327114346 -2.108229152
161 0.346867463 0.327114346
162 -0.917685186 0.346867463
163 -1.288221394 -0.917685186
> 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/7dl2r1290499179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8nujb1290499179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9nujb1290499179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10nujb1290499179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11k4yk1290499179.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/12nmfq1290499179.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/131wvz1290499179.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/14u5uk1290499179.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/15fob81290499179.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/16tg8h1290499179.tab")
+ }
>
> try(system("convert tmp/1hb4i1290499179.ps tmp/1hb4i1290499179.png",intern=TRUE))
character(0)
> try(system("convert tmp/29kll1290499179.ps tmp/29kll1290499179.png",intern=TRUE))
character(0)
> try(system("convert tmp/39kll1290499179.ps tmp/39kll1290499179.png",intern=TRUE))
character(0)
> try(system("convert tmp/4kbko1290499179.ps tmp/4kbko1290499179.png",intern=TRUE))
character(0)
> try(system("convert tmp/5kbko1290499179.ps tmp/5kbko1290499179.png",intern=TRUE))
character(0)
> try(system("convert tmp/6kbko1290499179.ps tmp/6kbko1290499179.png",intern=TRUE))
character(0)
> try(system("convert tmp/7dl2r1290499179.ps tmp/7dl2r1290499179.png",intern=TRUE))
character(0)
> try(system("convert tmp/8nujb1290499179.ps tmp/8nujb1290499179.png",intern=TRUE))
character(0)
> try(system("convert tmp/9nujb1290499179.ps tmp/9nujb1290499179.png",intern=TRUE))
character(0)
> try(system("convert tmp/10nujb1290499179.ps tmp/10nujb1290499179.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.126 2.733 7.337