R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(7
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+ ,1)
+ ,dim=c(9
+ ,164)
+ ,dimnames=list(c('Q1_2'
+ ,'Q1_3'
+ ,'Q1_5'
+ ,'Q1_7'
+ ,'Q1_8'
+ ,'Q1_12'
+ ,'Q1_16'
+ ,'Q1_22'
+ ,'GENDER')
+ ,1:164))
> y <- array(NA,dim=c(9,164),dimnames=list(c('Q1_2','Q1_3','Q1_5','Q1_7','Q1_8','Q1_12','Q1_16','Q1_22','GENDER'),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 = '4'
> #'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
> 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
Q1_7 Q1_2 Q1_3 Q1_5 Q1_8 Q1_12 Q1_16 Q1_22 GENDER
1 7 7 7 1 7 1 7 7 1
2 5 5 6 1 5 1 5 5 1
3 5 6 6 2 6 1 4 5 1
4 5 4 5 2 6 2 5 6 2
5 5 5 6 2 6 2 5 6 1
6 7 6 7 1 5 1 6 7 1
7 7 7 7 1 7 1 7 6 2
8 5 6 7 1 6 1 5 7 1
9 3 6 7 1 7 2 7 7 1
10 6 6 6 1 6 1 5 6 1
11 7 5 4 1 7 1 4 7 2
12 6 5 6 1 7 1 6 7 2
13 5 4 6 1 6 1 4 5 1
14 3 6 7 1 6 1 6 6 1
15 7 6 6 1 7 1 7 7 1
16 5 5 6 2 6 3 6 6 2
17 7 3 4 1 7 1 4 7 1
18 7 7 7 1 7 1 6 7 2
19 7 3 7 1 7 1 6 7 2
20 6 5 6 2 7 2 6 6 1
21 5 3 3 1 5 1 4 4 2
22 7 5 7 1 7 NA 5 7 1
23 4 2 5 1 5 1 2 6 1
24 7 6 7 1 6 1 6 7 2
25 7 3 6 1 7 2 5 7 1
26 7 6 5 1 6 1 6 5 1
27 7 6 5 1 6 1 6 5 1
28 3 5 6 1 6 1 5 7 1
29 7 5 5 1 6 1 5 6 2
30 5 7 6 1 6 1 5 6 1
31 7 6 6 1 6 1 6 6 2
32 5 5 5 1 5 1 6 6 1
33 5 5 4 4 3 6 5 1 1
34 4 4 5 3 3 3 4 5 2
35 5 4 4 1 5 1 6 7 2
36 6 6 6 2 6 2 5 5 2
37 7 5 6 1 7 1 5 7 1
38 5 5 7 1 7 1 5 5 2
39 7 7 7 1 7 1 7 7 2
40 7 5 7 1 6 1 5 6 1
41 6 5 7 1 7 1 5 7 1
42 6 6 5 1 7 1 7 6 1
43 7 5 6 2 6 2 5 6 1
44 7 6 6 1 6 2 7 5 2
45 6 7 3 1 5 1 6 6 2
46 6 5 6 4 6 4 3 6 1
47 4 5 5 1 6 2 4 5 2
48 7 5 4 3 7 3 6 7 2
49 5 6 6 2 6 2 5 6 1
50 6 2 6 3 7 2 4 7 2
51 5 4 6 2 6 2 4 5 1
52 3 4 5 1 5 1 6 5 1
53 7 6 6 2 7 1 5 7 1
54 6 3 5 1 4 1 4 3 1
55 6 6 7 1 7 1 6 6 2
56 5 6 6 1 5 2 5 6 1
57 5 5 6 1 6 1 5 5 1
58 7 6 7 1 7 1 6 6 1
59 7 1 4 1 7 1 6 6 2
60 7 5 3 2 7 1 6 7 2
61 6 7 4 1 7 1 5 7 1
62 6 4 4 3 6 1 5 6 1
63 7 5 5 1 6 1 5 5 1
64 7 6 4 1 6 1 5 4 2
65 5 4 6 4 4 4 4 5 1
66 7 6 7 1 6 1 5 6 2
67 6 6 6 1 6 2 6 6 2
68 5 5 6 1 7 1 6 7 2
69 6 5 6 1 7 1 5 6 1
70 5 3 6 1 7 2 5 7 1
71 5 5 7 1 7 1 5 7 1
72 6 6 6 1 7 1 6 7 2
73 6 5 6 1 6 2 6 6 2
74 6 6 6 1 5 3 6 5 1
75 7 6 7 1 7 2 6 7 1
76 6 4 5 2 5 2 4 5 1
77 5 4 4 2 5 2 4 5 1
78 7 6 7 1 7 2 5 6 2
79 7 7 7 1 7 1 6 7 2
80 6 4 6 1 2 1 3 3 2
81 7 5 7 1 6 1 7 4 2
82 6 6 6 1 6 1 5 5 1
83 6 6 5 1 6 1 6 6 1
84 7 5 7 1 6 1 6 6 1
85 6 3 6 2 5 2 5 6 2
86 7 7 5 1 6 1 6 6 2
87 7 6 6 1 7 2 6 7 1
88 5 4 5 4 5 3 4 7 1
89 3 4 7 3 7 2 6 7 1
90 6 5 6 2 6 2 5 7 1
91 6 3 2 1 5 1 4 2 1
92 5 7 5 1 6 1 7 5 2
93 6 6 7 1 7 3 6 6 1
94 6 6 7 1 7 1 6 6 1
95 6 4 7 2 6 1 4 6 1
96 7 5 7 1 7 1 5 7 1
97 6 6 6 1 6 1 6 5 2
98 6 5 5 2 5 1 5 5 1
99 6 6 6 1 5 1 4 6 2
100 7 6 6 3 6 2 7 6 2
101 6 4 5 1 6 1 6 7 1
102 5 5 7 1 6 1 5 4 2
103 5 6 5 2 6 2 6 6 1
104 6 5 6 1 6 1 6 6 1
105 6 5 5 1 5 1 5 5 2
106 6 4 5 2 5 3 5 5 1
107 5 4 5 2 5 2 5 5 2
108 6 6 5 1 7 2 5 6 1
109 4 5 7 1 7 1 7 7 1
110 6 6 6 1 6 1 6 6 1
111 7 5 7 1 7 1 7 7 1
112 7 6 6 1 7 2 6 7 1
113 5 5 5 1 4 1 5 5 1
114 5 4 5 2 5 2 4 6 1
115 7 6 7 1 7 1 6 7 1
116 3 4 6 1 7 2 4 7 2
117 7 5 5 2 7 2 3 7 1
118 5 5 7 2 6 4 5 7 1
119 7 6 4 1 5 2 5 5 2
120 5 3 3 2 7 1 5 7 1
121 3 5 7 2 NA NA 5 7 1
122 6 4 5 2 6 2 5 6 1
123 5 5 6 2 6 1 5 5 1
124 4 5 4 4 3 3 3 5 1
125 7 7 7 1 7 1 7 7 1
126 6 5 7 2 6 1 6 7 2
127 7 7 5 1 7 1 6 6 1
128 2 5 7 1 6 2 4 6 1
129 5 4 3 1 5 1 4 6 2
130 6 6 6 1 6 1 6 6 2
131 6 4 5 3 6 2 4 6 1
132 6 4 5 2 7 2 6 6 2
133 2 4 6 1 6 7 2 5 2
134 6 4 5 1 7 1 5 6 2
135 7 6 6 1 6 2 5 7 2
136 4 6 6 2 6 3 6 6 1
137 7 5 7 5 7 3 5 7 1
138 7 3 5 1 7 4 4 7 2
139 6 6 7 1 7 1 6 6 2
140 6 5 6 2 7 2 6 6 1
141 2 4 6 1 6 2 5 7 1
142 7 5 7 2 7 2 5 5 1
143 7 2 7 1 7 2 2 5 1
144 5 5 5 1 6 1 6 6 2
145 5 7 7 1 7 5 6 7 1
146 6 4 5 1 6 1 5 5 1
147 5 4 6 2 7 3 6 7 2
148 6 7 7 1 6 2 7 5 2
149 6 6 6 1 5 1 5 6 2
150 6 5 5 2 4 3 5 5 2
151 5 5 6 1 7 2 5 7 1
152 6 5 7 1 7 2 6 7 1
153 7 7 6 1 5 1 7 5 1
154 7 6 7 1 7 1 7 7 2
155 6 6 7 1 6 1 6 6 2
156 6 5 6 2 5 1 5 6 2
157 6 2 6 2 6 2 6 6 1
158 7 4 4 4 7 4 4 7 1
159 6 6 7 1 7 3 6 6 1
160 5 5 6 1 6 1 6 5 1
161 5 5 4 1 5 1 4 5 2
162 5 5 5 1 6 1 5 5 1
163 4 4 6 1 5 1 5 7 1
164 4 4 5 5 6 4 5 7 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Q1_2 Q1_3 Q1_5 Q1_8 Q1_12
3.06529 0.16418 -0.07652 0.24340 0.41984 -0.30356
Q1_16 Q1_22 GENDER
0.08575 -0.16102 0.30632
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.1890 -0.5600 0.1780 0.7797 2.0283
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.06529 0.81606 3.756 0.000245 ***
Q1_2 0.16418 0.08583 1.913 0.057629 .
Q1_3 -0.07652 0.08853 -0.864 0.388735
Q1_5 0.24340 0.12718 1.914 0.057510 .
Q1_8 0.41984 0.11988 3.502 0.000605 ***
Q1_12 -0.30356 0.10329 -2.939 0.003805 **
Q1_16 0.08575 0.10405 0.824 0.411153
Q1_22 -0.16102 0.10677 -1.508 0.133601
GENDER 0.30632 0.17618 1.739 0.084093 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.064 on 153 degrees of freedom
(2 observations deleted due to missingness)
Multiple R-squared: 0.2034, Adjusted R-squared: 0.1617
F-statistic: 4.882 on 8 and 153 DF, p-value: 2.212e-05
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.32791786 0.65583572 0.67208214
[2,] 0.54882386 0.90235229 0.45117614
[3,] 0.90380092 0.19239816 0.09619908
[4,] 0.84540690 0.30918620 0.15459310
[5,] 0.84119796 0.31760408 0.15880204
[6,] 0.82543521 0.34912958 0.17456479
[7,] 0.78873253 0.42253494 0.21126747
[8,] 0.76843134 0.46313732 0.23156866
[9,] 0.74699559 0.50600882 0.25300441
[10,] 0.70269730 0.59460541 0.29730270
[11,] 0.63884902 0.72230197 0.36115098
[12,] 0.57539560 0.84920880 0.42460440
[13,] 0.78987970 0.42024059 0.21012030
[14,] 0.81479867 0.37040267 0.18520133
[15,] 0.80466381 0.39067237 0.19533619
[16,] 0.95602238 0.08795524 0.04397762
[17,] 0.94592473 0.10815055 0.05407527
[18,] 0.93081912 0.13836175 0.06918088
[19,] 0.91472784 0.17054431 0.08527216
[20,] 0.89997010 0.20005979 0.10002990
[21,] 0.93899762 0.12200477 0.06100238
[22,] 0.92152883 0.15694234 0.07847117
[23,] 0.91637585 0.16724829 0.08362415
[24,] 0.89125234 0.21749532 0.10874766
[25,] 0.89103879 0.21792241 0.10896121
[26,] 0.89707745 0.20584511 0.10292255
[27,] 0.87116970 0.25766060 0.12883030
[28,] 0.90826656 0.18346689 0.09173344
[29,] 0.88434234 0.23131532 0.11565766
[30,] 0.86646764 0.26706473 0.13353236
[31,] 0.88604540 0.22790920 0.11395460
[32,] 0.87257862 0.25484276 0.12742138
[33,] 0.84441253 0.31117495 0.15558747
[34,] 0.82107557 0.35784887 0.17892443
[35,] 0.85626456 0.28747087 0.14373544
[36,] 0.83121053 0.33757893 0.16878947
[37,] 0.81258911 0.37482178 0.18741089
[38,] 0.78124953 0.43750094 0.21875047
[39,] 0.74763880 0.50472240 0.25236120
[40,] 0.86118833 0.27762335 0.13881167
[41,] 0.83909503 0.32180994 0.16090497
[42,] 0.86557225 0.26885550 0.13442775
[43,] 0.84191919 0.31616163 0.15808081
[44,] 0.81107220 0.37785561 0.18892780
[45,] 0.79159344 0.41681312 0.20840656
[46,] 0.77402248 0.45195503 0.22597752
[47,] 0.76476009 0.47047982 0.23523991
[48,] 0.73249324 0.53501352 0.26750676
[49,] 0.69607924 0.60784153 0.30392076
[50,] 0.65615071 0.68769859 0.34384929
[51,] 0.66289029 0.67421943 0.33710971
[52,] 0.62824196 0.74351607 0.37175804
[53,] 0.58906982 0.82186037 0.41093018
[54,] 0.58569401 0.82861198 0.41430599
[55,] 0.53854598 0.92290803 0.46145402
[56,] 0.56101980 0.87796040 0.43898020
[57,] 0.51345281 0.97309438 0.48654719
[58,] 0.47040716 0.94081433 0.52959284
[59,] 0.45127018 0.90254036 0.54872982
[60,] 0.41227388 0.82454776 0.58772612
[61,] 0.37020941 0.74041881 0.62979059
[62,] 0.36243931 0.72487862 0.63756069
[63,] 0.36674551 0.73349102 0.63325449
[64,] 0.34856897 0.69713795 0.65143103
[65,] 0.30784730 0.61569459 0.69215270
[66,] 0.28866763 0.57733526 0.71133237
[67,] 0.25603021 0.51206043 0.74396979
[68,] 0.30755201 0.61510402 0.69244799
[69,] 0.28343627 0.56687255 0.71656373
[70,] 0.24494625 0.48989251 0.75505375
[71,] 0.20948231 0.41896462 0.79051769
[72,] 0.22734798 0.45469595 0.77265202
[73,] 0.22120532 0.44241064 0.77879468
[74,] 0.19488812 0.38977623 0.80511188
[75,] 0.19377093 0.38754186 0.80622907
[76,] 0.16306382 0.32612765 0.83693618
[77,] 0.38823738 0.77647476 0.61176262
[78,] 0.35491886 0.70983771 0.64508114
[79,] 0.31628663 0.63257326 0.68371337
[80,] 0.37298573 0.74597146 0.62701427
[81,] 0.33144735 0.66289471 0.66855265
[82,] 0.29183563 0.58367127 0.70816437
[83,] 0.26103429 0.52206858 0.73896571
[84,] 0.26649281 0.53298563 0.73350719
[85,] 0.23224634 0.46449269 0.76775366
[86,] 0.20170979 0.40341957 0.79829021
[87,] 0.17586295 0.35172589 0.82413705
[88,] 0.15506974 0.31013948 0.84493026
[89,] 0.13575049 0.27150097 0.86424951
[90,] 0.14021303 0.28042607 0.85978697
[91,] 0.13428394 0.26856787 0.86571606
[92,] 0.11173865 0.22347730 0.88826135
[93,] 0.09160055 0.18320111 0.90839945
[94,] 0.09237029 0.18474058 0.90762971
[95,] 0.07701651 0.15403302 0.92298349
[96,] 0.06103166 0.12206332 0.93896834
[97,] 0.09877947 0.19755893 0.90122053
[98,] 0.07880204 0.15760409 0.92119796
[99,] 0.07679166 0.15358333 0.92320834
[100,] 0.07593006 0.15186012 0.92406994
[101,] 0.06107966 0.12215932 0.93892034
[102,] 0.04854379 0.09708757 0.95145621
[103,] 0.04586102 0.09172204 0.95413898
[104,] 0.15081503 0.30163006 0.84918497
[105,] 0.15545492 0.31090983 0.84454508
[106,] 0.13495817 0.26991634 0.86504183
[107,] 0.15197461 0.30394922 0.84802539
[108,] 0.14643228 0.29286456 0.85356772
[109,] 0.12654344 0.25308689 0.87345656
[110,] 0.12176709 0.24353418 0.87823291
[111,] 0.09903850 0.19807700 0.90096150
[112,] 0.08760763 0.17521526 0.91239237
[113,] 0.06715300 0.13430600 0.93284700
[114,] 0.05440487 0.10880973 0.94559513
[115,] 0.25698789 0.51397579 0.74301211
[116,] 0.21463425 0.42926849 0.78536575
[117,] 0.17392044 0.34784087 0.82607956
[118,] 0.13963461 0.27926922 0.86036539
[119,] 0.11161428 0.22322855 0.88838572
[120,] 0.34338955 0.68677911 0.65661045
[121,] 0.29061307 0.58122614 0.70938693
[122,] 0.34220448 0.68440896 0.65779552
[123,] 0.38652335 0.77304671 0.61347665
[124,] 0.34425647 0.68851294 0.65574353
[125,] 0.46660297 0.93320593 0.53339703
[126,] 0.42248137 0.84496275 0.57751863
[127,] 0.35000892 0.70001785 0.64999108
[128,] 0.72394711 0.55210579 0.27605289
[129,] 0.65480771 0.69038459 0.34519229
[130,] 0.63244724 0.73510552 0.36755276
[131,] 0.62885766 0.74228468 0.37114234
[132,] 0.55764896 0.88470208 0.44235104
[133,] 0.48816415 0.97632831 0.51183585
[134,] 0.57166793 0.85666414 0.42833207
[135,] 0.56806871 0.86386259 0.43193129
[136,] 0.48413002 0.96826003 0.51586998
[137,] 0.36516552 0.73033105 0.63483448
[138,] 0.25867919 0.51735839 0.74132081
[139,] 0.15401326 0.30802651 0.84598674
[140,] 0.75277167 0.49445666 0.24722833
[141,] 0.56611344 0.86777311 0.43388656
> postscript(file="/var/www/rcomp/tmp/1qly61290552679.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$resid)
Warning message:
In x[, 1] - mysum$resid :
longer object length is not a multiple of shorter object length
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2qly61290552679.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3jcg91290552679.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4jcg91290552679.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5jcg91290552679.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 162
Frequency = 1
1 2 3 4 5 6
0.663013715 -0.396024354 -1.137692147 -0.813360989 -0.594686839 1.752615828
7 8 9 10 11 12
0.195669337 -0.581475085 -2.869252196 0.180980792 0.712710796 -0.305736898
13 14 15 16 17 18
-0.565940105 -2.828243055 0.750664347 -0.683201069 1.347385901 0.442437308
19 20 21 23 24 25
1.099137953 -0.100274503 -0.678843620 -0.561758677 1.026451757 1.718245510
26 27 28 29 30 31
0.857688292 0.857688292 -2.493824452 0.962306642 -0.983194370 0.788907633
32 33 34 35 36 37
-0.397277664 0.434116264 -0.568957136 -0.454932219 -0.226206378 1.086336260
38 39 40 41 42 43
-1.465503184 0.356688932 1.421680482 0.162860789 -0.486879777 1.405313161
44 45 46 47 48 49
0.845698590 -0.185001826 0.697124329 -1.809405650 0.661528461 -0.758862000
50 51 52 53 54 55
0.175040825 -0.505782897 -2.394122098 0.678759379 1.039349913 -0.554407126
56 57 58 59 60 61
-0.095620992 -0.815863642 0.751917657 1.036895094 0.221287796 -0.395063120
62 63 64 65 66 67
-0.130521383 1.107611829 0.399567762 0.454210095 0.951180538 0.092466561
68 69 70 71 72 73
-1.305736898 -0.074683335 -0.281754490 -0.837139211 -0.469912059 0.256641723
74 75 76 77 78 79
0.961169965 1.216496180 0.837531862 -0.238992667 0.834900178 0.442437308
80 81 82 83 84 85
1.570801449 0.621819757 0.019961197 0.018707887 1.335932106 0.847177989
86 87 88 89 90 91
0.548207943 1.139971651 -0.023673459 -2.941956937 0.566332756 0.228917444
92 93 94 95 96 97
-1.698560028 0.359035513 -0.248082343 0.428202299 1.162860789 -0.372111962
98 99 100 101 102 103
0.284049397 0.380243673 0.519914746 0.508077805 -1.206683491 -0.921134905
104 105 106 107 108 109
0.259407577 0.221126334 1.055342414 -0.554541296 -0.011824097 -2.008635963
110 111 112 113 114 115
0.095232416 0.991364037 1.139971651 -0.052709595 -0.001448543 0.912937252
116 117 118 119 120 122
-2.666506057 1.241465691 0.249975141 1.283985573 -1.058288724 0.492963794
123 124 125 126 127 128
-1.059265362 -0.660985388 0.663013715 -0.052774802 0.434693438 -3.189012215
129 130 131 132 133 134
-0.520979591 -0.211092367 0.335310450 -0.318948653 -1.879414568 -0.293357485
135 136 137 138 139 140
1.339234532 -1.541051448 0.796371766 2.028262431 -0.554407126 -0.100274503
141 142 143 144 145 146
-3.026090363 0.900978807 1.894151138 -1.123441734 -0.037002197 0.271786990
147 148 149 150 151 152
-0.777845601 -0.241952042 0.294495297 1.004681759 -0.610104812 0.380671341
153 154 155 156 157 158
1.104128572 0.520864093 -0.134567838 0.215268738 0.812090269 1.363682364
159 160 161 162 163 164
0.359035513 -0.901612018 -0.769649818 -0.892388171 -0.909810003 -1.469103915
> postscript(file="/var/www/rcomp/tmp/6c3fu1290552679.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 = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 0.663013715 NA
1 -0.396024354 0.663013715
2 -1.137692147 -0.396024354
3 -0.813360989 -1.137692147
4 -0.594686839 -0.813360989
5 1.752615828 -0.594686839
6 0.195669337 1.752615828
7 -0.581475085 0.195669337
8 -2.869252196 -0.581475085
9 0.180980792 -2.869252196
10 0.712710796 0.180980792
11 -0.305736898 0.712710796
12 -0.565940105 -0.305736898
13 -2.828243055 -0.565940105
14 0.750664347 -2.828243055
15 -0.683201069 0.750664347
16 1.347385901 -0.683201069
17 0.442437308 1.347385901
18 1.099137953 0.442437308
19 -0.100274503 1.099137953
20 -0.678843620 -0.100274503
21 -0.561758677 -0.678843620
22 1.026451757 -0.561758677
23 1.718245510 1.026451757
24 0.857688292 1.718245510
25 0.857688292 0.857688292
26 -2.493824452 0.857688292
27 0.962306642 -2.493824452
28 -0.983194370 0.962306642
29 0.788907633 -0.983194370
30 -0.397277664 0.788907633
31 0.434116264 -0.397277664
32 -0.568957136 0.434116264
33 -0.454932219 -0.568957136
34 -0.226206378 -0.454932219
35 1.086336260 -0.226206378
36 -1.465503184 1.086336260
37 0.356688932 -1.465503184
38 1.421680482 0.356688932
39 0.162860789 1.421680482
40 -0.486879777 0.162860789
41 1.405313161 -0.486879777
42 0.845698590 1.405313161
43 -0.185001826 0.845698590
44 0.697124329 -0.185001826
45 -1.809405650 0.697124329
46 0.661528461 -1.809405650
47 -0.758862000 0.661528461
48 0.175040825 -0.758862000
49 -0.505782897 0.175040825
50 -2.394122098 -0.505782897
51 0.678759379 -2.394122098
52 1.039349913 0.678759379
53 -0.554407126 1.039349913
54 -0.095620992 -0.554407126
55 -0.815863642 -0.095620992
56 0.751917657 -0.815863642
57 1.036895094 0.751917657
58 0.221287796 1.036895094
59 -0.395063120 0.221287796
60 -0.130521383 -0.395063120
61 1.107611829 -0.130521383
62 0.399567762 1.107611829
63 0.454210095 0.399567762
64 0.951180538 0.454210095
65 0.092466561 0.951180538
66 -1.305736898 0.092466561
67 -0.074683335 -1.305736898
68 -0.281754490 -0.074683335
69 -0.837139211 -0.281754490
70 -0.469912059 -0.837139211
71 0.256641723 -0.469912059
72 0.961169965 0.256641723
73 1.216496180 0.961169965
74 0.837531862 1.216496180
75 -0.238992667 0.837531862
76 0.834900178 -0.238992667
77 0.442437308 0.834900178
78 1.570801449 0.442437308
79 0.621819757 1.570801449
80 0.019961197 0.621819757
81 0.018707887 0.019961197
82 1.335932106 0.018707887
83 0.847177989 1.335932106
84 0.548207943 0.847177989
85 1.139971651 0.548207943
86 -0.023673459 1.139971651
87 -2.941956937 -0.023673459
88 0.566332756 -2.941956937
89 0.228917444 0.566332756
90 -1.698560028 0.228917444
91 0.359035513 -1.698560028
92 -0.248082343 0.359035513
93 0.428202299 -0.248082343
94 1.162860789 0.428202299
95 -0.372111962 1.162860789
96 0.284049397 -0.372111962
97 0.380243673 0.284049397
98 0.519914746 0.380243673
99 0.508077805 0.519914746
100 -1.206683491 0.508077805
101 -0.921134905 -1.206683491
102 0.259407577 -0.921134905
103 0.221126334 0.259407577
104 1.055342414 0.221126334
105 -0.554541296 1.055342414
106 -0.011824097 -0.554541296
107 -2.008635963 -0.011824097
108 0.095232416 -2.008635963
109 0.991364037 0.095232416
110 1.139971651 0.991364037
111 -0.052709595 1.139971651
112 -0.001448543 -0.052709595
113 0.912937252 -0.001448543
114 -2.666506057 0.912937252
115 1.241465691 -2.666506057
116 0.249975141 1.241465691
117 1.283985573 0.249975141
118 -1.058288724 1.283985573
119 0.492963794 -1.058288724
120 -1.059265362 0.492963794
121 -0.660985388 -1.059265362
122 0.663013715 -0.660985388
123 -0.052774802 0.663013715
124 0.434693438 -0.052774802
125 -3.189012215 0.434693438
126 -0.520979591 -3.189012215
127 -0.211092367 -0.520979591
128 0.335310450 -0.211092367
129 -0.318948653 0.335310450
130 -1.879414568 -0.318948653
131 -0.293357485 -1.879414568
132 1.339234532 -0.293357485
133 -1.541051448 1.339234532
134 0.796371766 -1.541051448
135 2.028262431 0.796371766
136 -0.554407126 2.028262431
137 -0.100274503 -0.554407126
138 -3.026090363 -0.100274503
139 0.900978807 -3.026090363
140 1.894151138 0.900978807
141 -1.123441734 1.894151138
142 -0.037002197 -1.123441734
143 0.271786990 -0.037002197
144 -0.777845601 0.271786990
145 -0.241952042 -0.777845601
146 0.294495297 -0.241952042
147 1.004681759 0.294495297
148 -0.610104812 1.004681759
149 0.380671341 -0.610104812
150 1.104128572 0.380671341
151 0.520864093 1.104128572
152 -0.134567838 0.520864093
153 0.215268738 -0.134567838
154 0.812090269 0.215268738
155 1.363682364 0.812090269
156 0.359035513 1.363682364
157 -0.901612018 0.359035513
158 -0.769649818 -0.901612018
159 -0.892388171 -0.769649818
160 -0.909810003 -0.892388171
161 -1.469103915 -0.909810003
162 NA -1.469103915
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.396024354 0.663013715
[2,] -1.137692147 -0.396024354
[3,] -0.813360989 -1.137692147
[4,] -0.594686839 -0.813360989
[5,] 1.752615828 -0.594686839
[6,] 0.195669337 1.752615828
[7,] -0.581475085 0.195669337
[8,] -2.869252196 -0.581475085
[9,] 0.180980792 -2.869252196
[10,] 0.712710796 0.180980792
[11,] -0.305736898 0.712710796
[12,] -0.565940105 -0.305736898
[13,] -2.828243055 -0.565940105
[14,] 0.750664347 -2.828243055
[15,] -0.683201069 0.750664347
[16,] 1.347385901 -0.683201069
[17,] 0.442437308 1.347385901
[18,] 1.099137953 0.442437308
[19,] -0.100274503 1.099137953
[20,] -0.678843620 -0.100274503
[21,] -0.561758677 -0.678843620
[22,] 1.026451757 -0.561758677
[23,] 1.718245510 1.026451757
[24,] 0.857688292 1.718245510
[25,] 0.857688292 0.857688292
[26,] -2.493824452 0.857688292
[27,] 0.962306642 -2.493824452
[28,] -0.983194370 0.962306642
[29,] 0.788907633 -0.983194370
[30,] -0.397277664 0.788907633
[31,] 0.434116264 -0.397277664
[32,] -0.568957136 0.434116264
[33,] -0.454932219 -0.568957136
[34,] -0.226206378 -0.454932219
[35,] 1.086336260 -0.226206378
[36,] -1.465503184 1.086336260
[37,] 0.356688932 -1.465503184
[38,] 1.421680482 0.356688932
[39,] 0.162860789 1.421680482
[40,] -0.486879777 0.162860789
[41,] 1.405313161 -0.486879777
[42,] 0.845698590 1.405313161
[43,] -0.185001826 0.845698590
[44,] 0.697124329 -0.185001826
[45,] -1.809405650 0.697124329
[46,] 0.661528461 -1.809405650
[47,] -0.758862000 0.661528461
[48,] 0.175040825 -0.758862000
[49,] -0.505782897 0.175040825
[50,] -2.394122098 -0.505782897
[51,] 0.678759379 -2.394122098
[52,] 1.039349913 0.678759379
[53,] -0.554407126 1.039349913
[54,] -0.095620992 -0.554407126
[55,] -0.815863642 -0.095620992
[56,] 0.751917657 -0.815863642
[57,] 1.036895094 0.751917657
[58,] 0.221287796 1.036895094
[59,] -0.395063120 0.221287796
[60,] -0.130521383 -0.395063120
[61,] 1.107611829 -0.130521383
[62,] 0.399567762 1.107611829
[63,] 0.454210095 0.399567762
[64,] 0.951180538 0.454210095
[65,] 0.092466561 0.951180538
[66,] -1.305736898 0.092466561
[67,] -0.074683335 -1.305736898
[68,] -0.281754490 -0.074683335
[69,] -0.837139211 -0.281754490
[70,] -0.469912059 -0.837139211
[71,] 0.256641723 -0.469912059
[72,] 0.961169965 0.256641723
[73,] 1.216496180 0.961169965
[74,] 0.837531862 1.216496180
[75,] -0.238992667 0.837531862
[76,] 0.834900178 -0.238992667
[77,] 0.442437308 0.834900178
[78,] 1.570801449 0.442437308
[79,] 0.621819757 1.570801449
[80,] 0.019961197 0.621819757
[81,] 0.018707887 0.019961197
[82,] 1.335932106 0.018707887
[83,] 0.847177989 1.335932106
[84,] 0.548207943 0.847177989
[85,] 1.139971651 0.548207943
[86,] -0.023673459 1.139971651
[87,] -2.941956937 -0.023673459
[88,] 0.566332756 -2.941956937
[89,] 0.228917444 0.566332756
[90,] -1.698560028 0.228917444
[91,] 0.359035513 -1.698560028
[92,] -0.248082343 0.359035513
[93,] 0.428202299 -0.248082343
[94,] 1.162860789 0.428202299
[95,] -0.372111962 1.162860789
[96,] 0.284049397 -0.372111962
[97,] 0.380243673 0.284049397
[98,] 0.519914746 0.380243673
[99,] 0.508077805 0.519914746
[100,] -1.206683491 0.508077805
[101,] -0.921134905 -1.206683491
[102,] 0.259407577 -0.921134905
[103,] 0.221126334 0.259407577
[104,] 1.055342414 0.221126334
[105,] -0.554541296 1.055342414
[106,] -0.011824097 -0.554541296
[107,] -2.008635963 -0.011824097
[108,] 0.095232416 -2.008635963
[109,] 0.991364037 0.095232416
[110,] 1.139971651 0.991364037
[111,] -0.052709595 1.139971651
[112,] -0.001448543 -0.052709595
[113,] 0.912937252 -0.001448543
[114,] -2.666506057 0.912937252
[115,] 1.241465691 -2.666506057
[116,] 0.249975141 1.241465691
[117,] 1.283985573 0.249975141
[118,] -1.058288724 1.283985573
[119,] 0.492963794 -1.058288724
[120,] -1.059265362 0.492963794
[121,] -0.660985388 -1.059265362
[122,] 0.663013715 -0.660985388
[123,] -0.052774802 0.663013715
[124,] 0.434693438 -0.052774802
[125,] -3.189012215 0.434693438
[126,] -0.520979591 -3.189012215
[127,] -0.211092367 -0.520979591
[128,] 0.335310450 -0.211092367
[129,] -0.318948653 0.335310450
[130,] -1.879414568 -0.318948653
[131,] -0.293357485 -1.879414568
[132,] 1.339234532 -0.293357485
[133,] -1.541051448 1.339234532
[134,] 0.796371766 -1.541051448
[135,] 2.028262431 0.796371766
[136,] -0.554407126 2.028262431
[137,] -0.100274503 -0.554407126
[138,] -3.026090363 -0.100274503
[139,] 0.900978807 -3.026090363
[140,] 1.894151138 0.900978807
[141,] -1.123441734 1.894151138
[142,] -0.037002197 -1.123441734
[143,] 0.271786990 -0.037002197
[144,] -0.777845601 0.271786990
[145,] -0.241952042 -0.777845601
[146,] 0.294495297 -0.241952042
[147,] 1.004681759 0.294495297
[148,] -0.610104812 1.004681759
[149,] 0.380671341 -0.610104812
[150,] 1.104128572 0.380671341
[151,] 0.520864093 1.104128572
[152,] -0.134567838 0.520864093
[153,] 0.215268738 -0.134567838
[154,] 0.812090269 0.215268738
[155,] 1.363682364 0.812090269
[156,] 0.359035513 1.363682364
[157,] -0.901612018 0.359035513
[158,] -0.769649818 -0.901612018
[159,] -0.892388171 -0.769649818
[160,] -0.909810003 -0.892388171
[161,] -1.469103915 -0.909810003
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.396024354 0.663013715
2 -1.137692147 -0.396024354
3 -0.813360989 -1.137692147
4 -0.594686839 -0.813360989
5 1.752615828 -0.594686839
6 0.195669337 1.752615828
7 -0.581475085 0.195669337
8 -2.869252196 -0.581475085
9 0.180980792 -2.869252196
10 0.712710796 0.180980792
11 -0.305736898 0.712710796
12 -0.565940105 -0.305736898
13 -2.828243055 -0.565940105
14 0.750664347 -2.828243055
15 -0.683201069 0.750664347
16 1.347385901 -0.683201069
17 0.442437308 1.347385901
18 1.099137953 0.442437308
19 -0.100274503 1.099137953
20 -0.678843620 -0.100274503
21 -0.561758677 -0.678843620
22 1.026451757 -0.561758677
23 1.718245510 1.026451757
24 0.857688292 1.718245510
25 0.857688292 0.857688292
26 -2.493824452 0.857688292
27 0.962306642 -2.493824452
28 -0.983194370 0.962306642
29 0.788907633 -0.983194370
30 -0.397277664 0.788907633
31 0.434116264 -0.397277664
32 -0.568957136 0.434116264
33 -0.454932219 -0.568957136
34 -0.226206378 -0.454932219
35 1.086336260 -0.226206378
36 -1.465503184 1.086336260
37 0.356688932 -1.465503184
38 1.421680482 0.356688932
39 0.162860789 1.421680482
40 -0.486879777 0.162860789
41 1.405313161 -0.486879777
42 0.845698590 1.405313161
43 -0.185001826 0.845698590
44 0.697124329 -0.185001826
45 -1.809405650 0.697124329
46 0.661528461 -1.809405650
47 -0.758862000 0.661528461
48 0.175040825 -0.758862000
49 -0.505782897 0.175040825
50 -2.394122098 -0.505782897
51 0.678759379 -2.394122098
52 1.039349913 0.678759379
53 -0.554407126 1.039349913
54 -0.095620992 -0.554407126
55 -0.815863642 -0.095620992
56 0.751917657 -0.815863642
57 1.036895094 0.751917657
58 0.221287796 1.036895094
59 -0.395063120 0.221287796
60 -0.130521383 -0.395063120
61 1.107611829 -0.130521383
62 0.399567762 1.107611829
63 0.454210095 0.399567762
64 0.951180538 0.454210095
65 0.092466561 0.951180538
66 -1.305736898 0.092466561
67 -0.074683335 -1.305736898
68 -0.281754490 -0.074683335
69 -0.837139211 -0.281754490
70 -0.469912059 -0.837139211
71 0.256641723 -0.469912059
72 0.961169965 0.256641723
73 1.216496180 0.961169965
74 0.837531862 1.216496180
75 -0.238992667 0.837531862
76 0.834900178 -0.238992667
77 0.442437308 0.834900178
78 1.570801449 0.442437308
79 0.621819757 1.570801449
80 0.019961197 0.621819757
81 0.018707887 0.019961197
82 1.335932106 0.018707887
83 0.847177989 1.335932106
84 0.548207943 0.847177989
85 1.139971651 0.548207943
86 -0.023673459 1.139971651
87 -2.941956937 -0.023673459
88 0.566332756 -2.941956937
89 0.228917444 0.566332756
90 -1.698560028 0.228917444
91 0.359035513 -1.698560028
92 -0.248082343 0.359035513
93 0.428202299 -0.248082343
94 1.162860789 0.428202299
95 -0.372111962 1.162860789
96 0.284049397 -0.372111962
97 0.380243673 0.284049397
98 0.519914746 0.380243673
99 0.508077805 0.519914746
100 -1.206683491 0.508077805
101 -0.921134905 -1.206683491
102 0.259407577 -0.921134905
103 0.221126334 0.259407577
104 1.055342414 0.221126334
105 -0.554541296 1.055342414
106 -0.011824097 -0.554541296
107 -2.008635963 -0.011824097
108 0.095232416 -2.008635963
109 0.991364037 0.095232416
110 1.139971651 0.991364037
111 -0.052709595 1.139971651
112 -0.001448543 -0.052709595
113 0.912937252 -0.001448543
114 -2.666506057 0.912937252
115 1.241465691 -2.666506057
116 0.249975141 1.241465691
117 1.283985573 0.249975141
118 -1.058288724 1.283985573
119 0.492963794 -1.058288724
120 -1.059265362 0.492963794
121 -0.660985388 -1.059265362
122 0.663013715 -0.660985388
123 -0.052774802 0.663013715
124 0.434693438 -0.052774802
125 -3.189012215 0.434693438
126 -0.520979591 -3.189012215
127 -0.211092367 -0.520979591
128 0.335310450 -0.211092367
129 -0.318948653 0.335310450
130 -1.879414568 -0.318948653
131 -0.293357485 -1.879414568
132 1.339234532 -0.293357485
133 -1.541051448 1.339234532
134 0.796371766 -1.541051448
135 2.028262431 0.796371766
136 -0.554407126 2.028262431
137 -0.100274503 -0.554407126
138 -3.026090363 -0.100274503
139 0.900978807 -3.026090363
140 1.894151138 0.900978807
141 -1.123441734 1.894151138
142 -0.037002197 -1.123441734
143 0.271786990 -0.037002197
144 -0.777845601 0.271786990
145 -0.241952042 -0.777845601
146 0.294495297 -0.241952042
147 1.004681759 0.294495297
148 -0.610104812 1.004681759
149 0.380671341 -0.610104812
150 1.104128572 0.380671341
151 0.520864093 1.104128572
152 -0.134567838 0.520864093
153 0.215268738 -0.134567838
154 0.812090269 0.215268738
155 1.363682364 0.812090269
156 0.359035513 1.363682364
157 -0.901612018 0.359035513
158 -0.769649818 -0.901612018
159 -0.892388171 -0.769649818
160 -0.909810003 -0.892388171
161 -1.469103915 -0.909810003
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/7muex1290552679.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/rcomp/tmp/8muex1290552679.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/rcomp/tmp/9muex1290552679.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/rcomp/tmp/10fmdi1290552679.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/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11i4u51290552679.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/1245at1290552679.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13ixqk1290552679.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/143xpq1290552679.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/157g5e1290552679.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/16agm21290552679.tab")
+ }
>
> try(system("convert tmp/1qly61290552679.ps tmp/1qly61290552679.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qly61290552679.ps tmp/2qly61290552679.png",intern=TRUE))
character(0)
> try(system("convert tmp/3jcg91290552679.ps tmp/3jcg91290552679.png",intern=TRUE))
character(0)
> try(system("convert tmp/4jcg91290552679.ps tmp/4jcg91290552679.png",intern=TRUE))
character(0)
> try(system("convert tmp/5jcg91290552679.ps tmp/5jcg91290552679.png",intern=TRUE))
character(0)
> try(system("convert tmp/6c3fu1290552679.ps tmp/6c3fu1290552679.png",intern=TRUE))
character(0)
> try(system("convert tmp/7muex1290552679.ps tmp/7muex1290552679.png",intern=TRUE))
character(0)
> try(system("convert tmp/8muex1290552679.ps tmp/8muex1290552679.png",intern=TRUE))
character(0)
> try(system("convert tmp/9muex1290552679.ps tmp/9muex1290552679.png",intern=TRUE))
character(0)
> try(system("convert tmp/10fmdi1290552679.ps tmp/10fmdi1290552679.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.030 2.040 8.093