R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(12
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+ ,dim=c(7
+ ,162)
+ ,dimnames=list(c('CESDS'
+ ,'PCL'
+ ,'PCC'
+ ,'PBS'
+ ,'PBSA'
+ ,'ATC'
+ ,'ATS')
+ ,1:162))
> y <- array(NA,dim=c(7,162),dimnames=list(c('CESDS','PCL','PCC','PBS','PBSA','ATC','ATS'),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 = '1'
> #'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
CESDS PCL PCC PBS PBSA ATC ATS
1 12 13 12 53 32 41 38
2 11 16 11 86 51 39 32
3 14 19 15 66 42 30 35
4 12 15 6 67 41 31 33
5 21 14 13 76 46 34 37
6 12 13 10 78 47 35 29
7 22 19 12 53 37 39 31
8 11 15 14 80 49 34 36
9 10 14 12 74 45 36 35
10 13 15 6 76 47 37 38
11 10 16 10 79 49 38 31
12 8 16 12 54 33 36 34
13 15 16 12 67 42 38 35
14 14 16 11 54 33 39 38
15 10 17 15 87 53 33 37
16 14 15 12 58 36 32 33
17 14 15 10 75 45 36 32
18 11 20 12 88 54 38 38
19 10 18 11 64 41 39 38
20 13 16 12 57 36 32 32
21 7 16 11 66 41 32 33
22 14 16 12 68 44 31 31
23 12 19 13 54 33 39 38
24 14 16 11 56 37 37 39
25 11 17 9 86 52 39 32
26 9 17 13 80 47 41 32
27 11 16 10 76 43 36 35
28 15 15 14 69 44 33 37
29 14 16 12 78 45 33 33
30 13 14 10 67 44 34 33
31 9 15 12 80 49 31 28
32 15 12 8 54 33 27 32
33 10 14 10 71 43 37 31
34 11 16 12 84 54 34 37
35 13 14 12 74 42 34 30
36 8 7 7 71 44 32 33
37 20 10 6 63 37 29 31
38 12 14 12 71 43 36 33
39 10 16 10 76 46 29 31
40 10 16 10 69 42 35 33
41 9 16 10 74 45 37 32
42 14 14 12 75 44 34 33
43 8 20 15 54 33 38 32
44 14 14 10 52 31 35 33
45 11 14 10 69 42 38 28
46 13 11 12 68 40 37 35
47 9 14 13 65 43 38 39
48 11 15 11 75 46 33 34
49 15 16 11 74 42 36 38
50 11 14 12 75 45 38 32
51 10 16 14 72 44 32 38
52 14 14 10 67 40 32 30
53 18 12 12 63 37 32 33
54 14 16 13 62 46 34 38
55 11 9 5 63 36 32 32
56 12 14 6 76 47 37 32
57 13 16 12 74 45 39 34
58 9 16 12 67 42 29 34
59 10 15 11 73 43 37 36
60 15 16 10 70 43 35 34
61 20 12 7 53 32 30 28
62 12 16 12 77 45 38 34
63 12 16 14 77 45 34 35
64 14 14 11 52 31 31 35
65 13 16 12 54 33 34 31
66 11 17 13 80 49 35 37
67 17 18 14 66 42 36 35
68 12 18 11 73 41 30 27
69 13 12 12 63 38 39 40
70 14 16 12 69 42 35 37
71 13 10 8 67 44 38 36
72 15 14 11 54 33 31 38
73 13 18 14 81 48 34 39
74 10 18 14 69 40 38 41
75 11 16 12 84 50 34 27
76 19 17 9 80 49 39 30
77 13 16 13 70 43 37 37
78 17 16 11 69 44 34 31
79 13 13 12 77 47 28 31
80 9 16 12 54 33 37 27
81 11 16 12 79 46 33 36
82 10 20 12 30 0 37 38
83 9 16 12 71 45 35 37
84 12 15 12 73 43 37 33
85 12 15 11 72 44 32 34
86 13 16 10 77 47 33 31
87 13 14 9 75 45 38 39
88 12 16 12 69 42 33 34
89 15 16 12 54 33 29 32
90 22 15 12 70 43 33 33
91 13 12 9 73 46 31 36
92 15 17 15 54 33 36 32
93 13 16 12 77 46 35 41
94 15 15 12 82 48 32 28
95 10 13 12 80 47 29 30
96 11 16 10 80 47 39 36
97 16 16 13 69 43 37 35
98 11 16 9 78 46 35 31
99 11 16 12 81 48 37 34
100 10 14 10 76 46 32 36
101 10 16 14 76 45 38 36
102 16 16 11 73 45 37 35
103 12 20 15 85 52 36 37
104 11 15 11 66 42 32 28
105 16 16 11 79 47 33 39
106 19 13 12 68 41 40 32
107 11 17 12 76 47 38 35
108 16 16 12 71 43 41 39
109 15 16 11 54 33 36 35
110 24 12 7 46 30 43 42
111 14 16 12 82 49 30 34
112 15 16 14 74 44 31 33
113 11 17 11 88 55 32 41
114 15 13 11 38 11 32 33
115 12 12 10 76 47 37 34
116 10 18 13 86 53 37 32
117 14 14 13 54 33 33 40
118 13 14 8 70 44 34 40
119 9 13 11 69 42 33 35
120 15 16 12 90 55 38 36
121 15 13 11 54 33 33 37
122 14 16 13 76 46 31 27
123 11 13 12 89 54 38 39
124 8 16 14 76 47 37 38
125 11 15 13 73 45 33 31
126 11 16 15 79 47 31 33
127 8 15 10 90 55 39 32
128 10 17 11 74 44 44 39
129 11 15 9 81 53 33 36
130 13 12 11 72 44 35 33
131 11 16 10 71 42 32 33
132 20 10 11 66 40 28 32
133 10 16 8 77 46 40 37
134 15 12 11 65 40 27 30
135 12 14 12 74 46 37 38
136 14 15 12 82 53 32 29
137 23 13 9 54 33 28 22
138 14 15 11 63 42 34 35
139 16 11 10 54 35 30 35
140 11 12 8 64 40 35 34
141 12 8 9 69 41 31 35
142 10 16 8 54 33 32 34
143 14 15 9 84 51 30 34
144 12 17 15 86 53 30 35
145 12 16 11 77 46 31 23
146 11 10 8 89 55 40 31
147 12 18 13 76 47 32 27
148 13 13 12 60 38 36 36
149 11 16 12 75 46 32 31
150 19 13 9 73 46 35 32
151 12 10 7 85 53 38 39
152 17 15 13 79 47 42 37
153 9 16 9 71 41 34 38
154 12 16 6 72 44 35 39
155 19 14 8 69 43 35 34
156 18 10 8 78 51 33 31
157 15 17 15 54 33 36 32
158 14 13 6 69 43 32 37
159 11 15 9 81 53 33 36
160 9 16 11 84 51 34 32
161 18 12 8 84 50 32 35
162 16 13 8 69 46 34 36
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) PCL PCC PBS PBSA ATC
25.70538 -0.24077 -0.02191 -0.20575 0.20150 -0.03240
ATS
-0.05725
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.1986 -1.6676 -0.4623 1.2658 8.8653
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 25.70538 3.36503 7.639 2.12e-12 ***
PCL -0.24077 0.12814 -1.879 0.06212 .
PCC -0.02191 0.13142 -0.167 0.86784
PBS -0.20575 0.06961 -2.956 0.00361 **
PBSA 0.20150 0.10189 1.978 0.04974 *
ATC -0.03240 0.07693 -0.421 0.67422
ATS -0.05725 0.07153 -0.800 0.42477
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.982 on 155 degrees of freedom
Multiple R-squared: 0.1463, Adjusted R-squared: 0.1132
F-statistic: 4.426 on 6 and 155 DF, p-value: 0.0003682
> 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,] 0.9833152 0.03336951 0.01668475
[2,] 0.9801164 0.03976728 0.01988364
[3,] 0.9772045 0.04559108 0.02279554
[4,] 0.9584909 0.08301826 0.04150913
[5,] 0.9442979 0.11140424 0.05570212
[6,] 0.9313269 0.13734619 0.06867309
[7,] 0.8955067 0.20898667 0.10449334
[8,] 0.8978590 0.20428204 0.10214102
[9,] 0.8542100 0.29157994 0.14578997
[10,] 0.8883016 0.22339688 0.11169844
[11,] 0.8512850 0.29743002 0.14871501
[12,] 0.9275105 0.14497891 0.07248945
[13,] 0.9051735 0.18965303 0.09482652
[14,] 0.8774919 0.24501615 0.12250808
[15,] 0.8510094 0.29798123 0.14899062
[16,] 0.8065329 0.38693427 0.19346713
[17,] 0.7627028 0.47459437 0.23729718
[18,] 0.7653699 0.46926028 0.23463014
[19,] 0.7153579 0.56928412 0.28464206
[20,] 0.7536359 0.49272821 0.24636411
[21,] 0.7197412 0.56051753 0.28025877
[22,] 0.7241733 0.55165345 0.27582673
[23,] 0.6885726 0.62285490 0.31142745
[24,] 0.6639173 0.67216540 0.33608270
[25,] 0.6367076 0.72658480 0.36329240
[26,] 0.6053814 0.78923726 0.39461863
[27,] 0.6988694 0.60226114 0.30113057
[28,] 0.8656494 0.26870128 0.13435064
[29,] 0.8343074 0.33138512 0.16569256
[30,] 0.8227383 0.35452350 0.17726175
[31,] 0.8110710 0.37785810 0.18892905
[32,] 0.8076463 0.38470731 0.19235365
[33,] 0.7866805 0.42663893 0.21331946
[34,] 0.8381212 0.32375766 0.16187883
[35,] 0.8062709 0.38745825 0.19372913
[36,] 0.7832698 0.43346043 0.21673021
[37,] 0.7451781 0.50964374 0.25482187
[38,] 0.7941831 0.41163385 0.20581693
[39,] 0.7642516 0.47149684 0.23574842
[40,] 0.7689871 0.46202583 0.23101292
[41,] 0.7370065 0.52598701 0.26299351
[42,] 0.7248309 0.55033816 0.27516908
[43,] 0.6870509 0.62589825 0.31294912
[44,] 0.7309152 0.53816967 0.26908484
[45,] 0.6950749 0.60985021 0.30492511
[46,] 0.7058719 0.58825625 0.29412813
[47,] 0.6696743 0.66065140 0.33032570
[48,] 0.6336471 0.73270571 0.36635285
[49,] 0.6811647 0.63767058 0.31883529
[50,] 0.6601278 0.67974435 0.33987218
[51,] 0.6456412 0.70871767 0.35435884
[52,] 0.7125272 0.57494554 0.28747277
[53,] 0.6724396 0.65512084 0.32756042
[54,] 0.6286883 0.74262342 0.37131171
[55,] 0.5855462 0.82890767 0.41445384
[56,] 0.5494331 0.90113382 0.45056691
[57,] 0.5028906 0.99421873 0.49710937
[58,] 0.5528696 0.89426082 0.44713041
[59,] 0.5059601 0.98807977 0.49403989
[60,] 0.4630693 0.92613864 0.53693068
[61,] 0.4266915 0.85338307 0.57330846
[62,] 0.4022975 0.80459495 0.59770252
[63,] 0.3582417 0.71648334 0.64175833
[64,] 0.3381505 0.67630105 0.66184947
[65,] 0.3092084 0.61841683 0.69079158
[66,] 0.2716652 0.54333042 0.72833479
[67,] 0.4861604 0.97232086 0.51383957
[68,] 0.4419373 0.88387467 0.55806267
[69,] 0.4643530 0.92870590 0.53564705
[70,] 0.4193659 0.83873186 0.58063407
[71,] 0.5374587 0.92508265 0.46254133
[72,] 0.4925073 0.98501459 0.50749270
[73,] 0.4552158 0.91043168 0.54478416
[74,] 0.4882175 0.97643504 0.51178248
[75,] 0.4458543 0.89170851 0.55414574
[76,] 0.4050728 0.81014556 0.59492722
[77,] 0.3616429 0.72328574 0.63835713
[78,] 0.3213542 0.64270833 0.67864583
[79,] 0.2851962 0.57039239 0.71480380
[80,] 0.2490845 0.49816900 0.75091550
[81,] 0.5862938 0.82741235 0.41370617
[82,] 0.5436126 0.91277479 0.45638740
[83,] 0.5047249 0.99055029 0.49527515
[84,] 0.4701956 0.94039123 0.52980439
[85,] 0.4692068 0.93841362 0.53079319
[86,] 0.4582053 0.91641054 0.54179473
[87,] 0.4121543 0.82430860 0.58784570
[88,] 0.4113162 0.82263234 0.58868383
[89,] 0.3713242 0.74264846 0.62867577
[90,] 0.3284726 0.65694520 0.67152740
[91,] 0.3185677 0.63713535 0.68143233
[92,] 0.2932974 0.58659472 0.70670264
[93,] 0.3038755 0.60775095 0.69612452
[94,] 0.2818375 0.56367493 0.71816254
[95,] 0.2961038 0.59220760 0.70389620
[96,] 0.3681508 0.73630151 0.63184925
[97,] 0.4556382 0.91127631 0.54436184
[98,] 0.4117624 0.82352477 0.58823762
[99,] 0.4479326 0.89586517 0.55206742
[100,] 0.4027395 0.80547906 0.59726047
[101,] 0.7560805 0.48783893 0.24391947
[102,] 0.7370666 0.52586673 0.26293337
[103,] 0.7312079 0.53758415 0.26879208
[104,] 0.6974065 0.60518695 0.30259347
[105,] 0.6567903 0.68641941 0.34320971
[106,] 0.6213996 0.75720076 0.37860038
[107,] 0.5727056 0.85458873 0.42729437
[108,] 0.5239053 0.95218948 0.47609474
[109,] 0.4740609 0.94812180 0.52593910
[110,] 0.5455175 0.90896496 0.45448248
[111,] 0.6359189 0.72816225 0.36408112
[112,] 0.5832948 0.83341049 0.41670525
[113,] 0.5351289 0.92974219 0.46487109
[114,] 0.4811027 0.96220546 0.51889727
[115,] 0.4829316 0.96586320 0.51706840
[116,] 0.4604061 0.92081217 0.53959391
[117,] 0.4080028 0.81600562 0.59199719
[118,] 0.4256805 0.85136104 0.57431948
[119,] 0.3729927 0.74598532 0.62700734
[120,] 0.3320121 0.66402426 0.66798787
[121,] 0.2914239 0.58284779 0.70857611
[122,] 0.2518940 0.50378795 0.74810603
[123,] 0.2976942 0.59538836 0.70230582
[124,] 0.2556785 0.51135703 0.74432148
[125,] 0.2077993 0.41559855 0.79220073
[126,] 0.1648971 0.32979430 0.83510285
[127,] 0.1286618 0.25732368 0.87133816
[128,] 0.3019807 0.60396139 0.69801931
[129,] 0.2453553 0.49071052 0.75464474
[130,] 0.1968270 0.39365404 0.80317298
[131,] 0.2031607 0.40632134 0.79683933
[132,] 0.2226848 0.44536966 0.77731517
[133,] 0.2618144 0.52362884 0.73818558
[134,] 0.2201009 0.44020187 0.77989906
[135,] 0.1759081 0.35181619 0.82409190
[136,] 0.1367050 0.27340991 0.86329504
[137,] 0.4733829 0.94676570 0.52661715
[138,] 0.3896317 0.77926344 0.61036828
[139,] 0.3417819 0.68356385 0.65821808
[140,] 0.2641365 0.52827301 0.73586350
[141,] 0.1938262 0.38765234 0.80617383
[142,] 0.4708681 0.94173628 0.52913186
[143,] 0.3513026 0.70260526 0.64869737
> postscript(file="/var/wessaorg/rcomp/tmp/19kpp1322167162.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/28io11322167162.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3l2wq1322167162.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4bmfx1322167162.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5i5jf1322167162.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
-2.35202835 -0.09868999 1.28989497 -1.54517740 8.53781686 -0.98424390
7 8 9 10 11 12
7.61955017 -1.03826089 -2.74377895 0.57819159 -2.24749378 -6.01645481
13 14 15 16 17 18
1.96681851 0.28782632 -0.87572493 -0.22558284 1.48719378 1.00436776
19 20 21 22 23 24
-2.78515443 -1.24780935 -6.36822898 0.31377173 -0.94604793 -0.11424487
25 26 27 28 29 30
-0.10323474 -2.17779616 -0.49153396 1.73084222 2.34907654 -1.20564015
31 32 33 34 35 36
-3.63724443 -0.47325979 -3.19842018 -0.96857276 0.50970332 -7.19855186
37 38 39 40 41 42
5.05468501 -1.07251658 -2.55183736 -2.87718350 -3.44538593 1.48418409
43 44 45 46 47 48
-5.03734387 -0.63994911 -2.54775716 -0.66067778 -4.87684158 -1.67511407
49 50 51 52 53 54
3.49211256 -1.64496564 -2.38628545 0.36383987 3.87935577 -0.80391647
55 56 57 58 59 60
-3.85203950 -1.00605819 0.77771891 -4.38203158 -2.23800791 2.18430951
61 62 63 64 65 66
4.36880367 0.36257336 0.33402935 -0.63315237 -1.25299478 -0.48897673
67 68 69 70 71 72
4.22162032 0.14529627 -0.69462167 1.39561328 -1.91119661 0.54708002
73 74 75 76 77 78
2.26304937 -1.34983571 -0.73501658 7.15227775 0.48656636 3.59481896
79 80 81 82 83 84
-0.25849439 -5.38477906 -0.47493769 -1.08035692 -3.79739847 -0.38784142
85 86 87 88 89 90
-0.92175935 0.58201091 0.69004214 -0.84092669 0.64224954 8.86530206
91 92 93 94 95 96
-0.80305113 1.17554081 1.46459179 3.00816423 -2.66608550 -0.32009984
97 98 99 100 101 102
3.16632197 -0.96783727 -0.45133555 -2.64994722 -1.68487424 3.54250722
103 104 105 106 107 108
1.73379256 -3.09673738 4.47339083 5.54482232 -0.94817037 3.91450676
109 110 111 112 113 114
1.01888594 8.55420700 2.32610819 2.70658548 0.03597930 1.19356101
115 116 117 118 119 120
-1.28548592 -1.04114656 -0.22981514 -0.23147187 -4.52790043 4.13678725
121 122 123 124 125 126
0.31386297 1.34969482 -0.41803474 -4.00579189 -2.01304020 -0.84726577
127 128 129 130 131 132
-3.34438062 -1.35367626 -1.78045780 -0.60411899 -1.56288149 5.20179899
133 134 135 136 137 138
-1.69001418 0.33069720 -0.74114439 1.05788562 7.24935394 -0.24846675
139 140 141 142 143 144
0.19571035 -3.45258249 -2.63886501 -4.23367897 2.02811306 0.70682945
145 146 147 148 149 150
-0.71735033 -1.82264696 -0.33786677 -1.39729180 -1.61657649 5.33833650
151 152 153 154 155 156
-0.87137886 5.45354153 -3.03224939 -0.40708285 5.45320363 3.49330230
157 158 159 160 161 162
1.17554081 0.24315878 -1.78045780 -2.67219530 5.60744513 1.69000949
> postscript(file="/var/wessaorg/rcomp/tmp/635mx1322167162.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 -2.35202835 NA
1 -0.09868999 -2.35202835
2 1.28989497 -0.09868999
3 -1.54517740 1.28989497
4 8.53781686 -1.54517740
5 -0.98424390 8.53781686
6 7.61955017 -0.98424390
7 -1.03826089 7.61955017
8 -2.74377895 -1.03826089
9 0.57819159 -2.74377895
10 -2.24749378 0.57819159
11 -6.01645481 -2.24749378
12 1.96681851 -6.01645481
13 0.28782632 1.96681851
14 -0.87572493 0.28782632
15 -0.22558284 -0.87572493
16 1.48719378 -0.22558284
17 1.00436776 1.48719378
18 -2.78515443 1.00436776
19 -1.24780935 -2.78515443
20 -6.36822898 -1.24780935
21 0.31377173 -6.36822898
22 -0.94604793 0.31377173
23 -0.11424487 -0.94604793
24 -0.10323474 -0.11424487
25 -2.17779616 -0.10323474
26 -0.49153396 -2.17779616
27 1.73084222 -0.49153396
28 2.34907654 1.73084222
29 -1.20564015 2.34907654
30 -3.63724443 -1.20564015
31 -0.47325979 -3.63724443
32 -3.19842018 -0.47325979
33 -0.96857276 -3.19842018
34 0.50970332 -0.96857276
35 -7.19855186 0.50970332
36 5.05468501 -7.19855186
37 -1.07251658 5.05468501
38 -2.55183736 -1.07251658
39 -2.87718350 -2.55183736
40 -3.44538593 -2.87718350
41 1.48418409 -3.44538593
42 -5.03734387 1.48418409
43 -0.63994911 -5.03734387
44 -2.54775716 -0.63994911
45 -0.66067778 -2.54775716
46 -4.87684158 -0.66067778
47 -1.67511407 -4.87684158
48 3.49211256 -1.67511407
49 -1.64496564 3.49211256
50 -2.38628545 -1.64496564
51 0.36383987 -2.38628545
52 3.87935577 0.36383987
53 -0.80391647 3.87935577
54 -3.85203950 -0.80391647
55 -1.00605819 -3.85203950
56 0.77771891 -1.00605819
57 -4.38203158 0.77771891
58 -2.23800791 -4.38203158
59 2.18430951 -2.23800791
60 4.36880367 2.18430951
61 0.36257336 4.36880367
62 0.33402935 0.36257336
63 -0.63315237 0.33402935
64 -1.25299478 -0.63315237
65 -0.48897673 -1.25299478
66 4.22162032 -0.48897673
67 0.14529627 4.22162032
68 -0.69462167 0.14529627
69 1.39561328 -0.69462167
70 -1.91119661 1.39561328
71 0.54708002 -1.91119661
72 2.26304937 0.54708002
73 -1.34983571 2.26304937
74 -0.73501658 -1.34983571
75 7.15227775 -0.73501658
76 0.48656636 7.15227775
77 3.59481896 0.48656636
78 -0.25849439 3.59481896
79 -5.38477906 -0.25849439
80 -0.47493769 -5.38477906
81 -1.08035692 -0.47493769
82 -3.79739847 -1.08035692
83 -0.38784142 -3.79739847
84 -0.92175935 -0.38784142
85 0.58201091 -0.92175935
86 0.69004214 0.58201091
87 -0.84092669 0.69004214
88 0.64224954 -0.84092669
89 8.86530206 0.64224954
90 -0.80305113 8.86530206
91 1.17554081 -0.80305113
92 1.46459179 1.17554081
93 3.00816423 1.46459179
94 -2.66608550 3.00816423
95 -0.32009984 -2.66608550
96 3.16632197 -0.32009984
97 -0.96783727 3.16632197
98 -0.45133555 -0.96783727
99 -2.64994722 -0.45133555
100 -1.68487424 -2.64994722
101 3.54250722 -1.68487424
102 1.73379256 3.54250722
103 -3.09673738 1.73379256
104 4.47339083 -3.09673738
105 5.54482232 4.47339083
106 -0.94817037 5.54482232
107 3.91450676 -0.94817037
108 1.01888594 3.91450676
109 8.55420700 1.01888594
110 2.32610819 8.55420700
111 2.70658548 2.32610819
112 0.03597930 2.70658548
113 1.19356101 0.03597930
114 -1.28548592 1.19356101
115 -1.04114656 -1.28548592
116 -0.22981514 -1.04114656
117 -0.23147187 -0.22981514
118 -4.52790043 -0.23147187
119 4.13678725 -4.52790043
120 0.31386297 4.13678725
121 1.34969482 0.31386297
122 -0.41803474 1.34969482
123 -4.00579189 -0.41803474
124 -2.01304020 -4.00579189
125 -0.84726577 -2.01304020
126 -3.34438062 -0.84726577
127 -1.35367626 -3.34438062
128 -1.78045780 -1.35367626
129 -0.60411899 -1.78045780
130 -1.56288149 -0.60411899
131 5.20179899 -1.56288149
132 -1.69001418 5.20179899
133 0.33069720 -1.69001418
134 -0.74114439 0.33069720
135 1.05788562 -0.74114439
136 7.24935394 1.05788562
137 -0.24846675 7.24935394
138 0.19571035 -0.24846675
139 -3.45258249 0.19571035
140 -2.63886501 -3.45258249
141 -4.23367897 -2.63886501
142 2.02811306 -4.23367897
143 0.70682945 2.02811306
144 -0.71735033 0.70682945
145 -1.82264696 -0.71735033
146 -0.33786677 -1.82264696
147 -1.39729180 -0.33786677
148 -1.61657649 -1.39729180
149 5.33833650 -1.61657649
150 -0.87137886 5.33833650
151 5.45354153 -0.87137886
152 -3.03224939 5.45354153
153 -0.40708285 -3.03224939
154 5.45320363 -0.40708285
155 3.49330230 5.45320363
156 1.17554081 3.49330230
157 0.24315878 1.17554081
158 -1.78045780 0.24315878
159 -2.67219530 -1.78045780
160 5.60744513 -2.67219530
161 1.69000949 5.60744513
162 NA 1.69000949
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.09868999 -2.35202835
[2,] 1.28989497 -0.09868999
[3,] -1.54517740 1.28989497
[4,] 8.53781686 -1.54517740
[5,] -0.98424390 8.53781686
[6,] 7.61955017 -0.98424390
[7,] -1.03826089 7.61955017
[8,] -2.74377895 -1.03826089
[9,] 0.57819159 -2.74377895
[10,] -2.24749378 0.57819159
[11,] -6.01645481 -2.24749378
[12,] 1.96681851 -6.01645481
[13,] 0.28782632 1.96681851
[14,] -0.87572493 0.28782632
[15,] -0.22558284 -0.87572493
[16,] 1.48719378 -0.22558284
[17,] 1.00436776 1.48719378
[18,] -2.78515443 1.00436776
[19,] -1.24780935 -2.78515443
[20,] -6.36822898 -1.24780935
[21,] 0.31377173 -6.36822898
[22,] -0.94604793 0.31377173
[23,] -0.11424487 -0.94604793
[24,] -0.10323474 -0.11424487
[25,] -2.17779616 -0.10323474
[26,] -0.49153396 -2.17779616
[27,] 1.73084222 -0.49153396
[28,] 2.34907654 1.73084222
[29,] -1.20564015 2.34907654
[30,] -3.63724443 -1.20564015
[31,] -0.47325979 -3.63724443
[32,] -3.19842018 -0.47325979
[33,] -0.96857276 -3.19842018
[34,] 0.50970332 -0.96857276
[35,] -7.19855186 0.50970332
[36,] 5.05468501 -7.19855186
[37,] -1.07251658 5.05468501
[38,] -2.55183736 -1.07251658
[39,] -2.87718350 -2.55183736
[40,] -3.44538593 -2.87718350
[41,] 1.48418409 -3.44538593
[42,] -5.03734387 1.48418409
[43,] -0.63994911 -5.03734387
[44,] -2.54775716 -0.63994911
[45,] -0.66067778 -2.54775716
[46,] -4.87684158 -0.66067778
[47,] -1.67511407 -4.87684158
[48,] 3.49211256 -1.67511407
[49,] -1.64496564 3.49211256
[50,] -2.38628545 -1.64496564
[51,] 0.36383987 -2.38628545
[52,] 3.87935577 0.36383987
[53,] -0.80391647 3.87935577
[54,] -3.85203950 -0.80391647
[55,] -1.00605819 -3.85203950
[56,] 0.77771891 -1.00605819
[57,] -4.38203158 0.77771891
[58,] -2.23800791 -4.38203158
[59,] 2.18430951 -2.23800791
[60,] 4.36880367 2.18430951
[61,] 0.36257336 4.36880367
[62,] 0.33402935 0.36257336
[63,] -0.63315237 0.33402935
[64,] -1.25299478 -0.63315237
[65,] -0.48897673 -1.25299478
[66,] 4.22162032 -0.48897673
[67,] 0.14529627 4.22162032
[68,] -0.69462167 0.14529627
[69,] 1.39561328 -0.69462167
[70,] -1.91119661 1.39561328
[71,] 0.54708002 -1.91119661
[72,] 2.26304937 0.54708002
[73,] -1.34983571 2.26304937
[74,] -0.73501658 -1.34983571
[75,] 7.15227775 -0.73501658
[76,] 0.48656636 7.15227775
[77,] 3.59481896 0.48656636
[78,] -0.25849439 3.59481896
[79,] -5.38477906 -0.25849439
[80,] -0.47493769 -5.38477906
[81,] -1.08035692 -0.47493769
[82,] -3.79739847 -1.08035692
[83,] -0.38784142 -3.79739847
[84,] -0.92175935 -0.38784142
[85,] 0.58201091 -0.92175935
[86,] 0.69004214 0.58201091
[87,] -0.84092669 0.69004214
[88,] 0.64224954 -0.84092669
[89,] 8.86530206 0.64224954
[90,] -0.80305113 8.86530206
[91,] 1.17554081 -0.80305113
[92,] 1.46459179 1.17554081
[93,] 3.00816423 1.46459179
[94,] -2.66608550 3.00816423
[95,] -0.32009984 -2.66608550
[96,] 3.16632197 -0.32009984
[97,] -0.96783727 3.16632197
[98,] -0.45133555 -0.96783727
[99,] -2.64994722 -0.45133555
[100,] -1.68487424 -2.64994722
[101,] 3.54250722 -1.68487424
[102,] 1.73379256 3.54250722
[103,] -3.09673738 1.73379256
[104,] 4.47339083 -3.09673738
[105,] 5.54482232 4.47339083
[106,] -0.94817037 5.54482232
[107,] 3.91450676 -0.94817037
[108,] 1.01888594 3.91450676
[109,] 8.55420700 1.01888594
[110,] 2.32610819 8.55420700
[111,] 2.70658548 2.32610819
[112,] 0.03597930 2.70658548
[113,] 1.19356101 0.03597930
[114,] -1.28548592 1.19356101
[115,] -1.04114656 -1.28548592
[116,] -0.22981514 -1.04114656
[117,] -0.23147187 -0.22981514
[118,] -4.52790043 -0.23147187
[119,] 4.13678725 -4.52790043
[120,] 0.31386297 4.13678725
[121,] 1.34969482 0.31386297
[122,] -0.41803474 1.34969482
[123,] -4.00579189 -0.41803474
[124,] -2.01304020 -4.00579189
[125,] -0.84726577 -2.01304020
[126,] -3.34438062 -0.84726577
[127,] -1.35367626 -3.34438062
[128,] -1.78045780 -1.35367626
[129,] -0.60411899 -1.78045780
[130,] -1.56288149 -0.60411899
[131,] 5.20179899 -1.56288149
[132,] -1.69001418 5.20179899
[133,] 0.33069720 -1.69001418
[134,] -0.74114439 0.33069720
[135,] 1.05788562 -0.74114439
[136,] 7.24935394 1.05788562
[137,] -0.24846675 7.24935394
[138,] 0.19571035 -0.24846675
[139,] -3.45258249 0.19571035
[140,] -2.63886501 -3.45258249
[141,] -4.23367897 -2.63886501
[142,] 2.02811306 -4.23367897
[143,] 0.70682945 2.02811306
[144,] -0.71735033 0.70682945
[145,] -1.82264696 -0.71735033
[146,] -0.33786677 -1.82264696
[147,] -1.39729180 -0.33786677
[148,] -1.61657649 -1.39729180
[149,] 5.33833650 -1.61657649
[150,] -0.87137886 5.33833650
[151,] 5.45354153 -0.87137886
[152,] -3.03224939 5.45354153
[153,] -0.40708285 -3.03224939
[154,] 5.45320363 -0.40708285
[155,] 3.49330230 5.45320363
[156,] 1.17554081 3.49330230
[157,] 0.24315878 1.17554081
[158,] -1.78045780 0.24315878
[159,] -2.67219530 -1.78045780
[160,] 5.60744513 -2.67219530
[161,] 1.69000949 5.60744513
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.09868999 -2.35202835
2 1.28989497 -0.09868999
3 -1.54517740 1.28989497
4 8.53781686 -1.54517740
5 -0.98424390 8.53781686
6 7.61955017 -0.98424390
7 -1.03826089 7.61955017
8 -2.74377895 -1.03826089
9 0.57819159 -2.74377895
10 -2.24749378 0.57819159
11 -6.01645481 -2.24749378
12 1.96681851 -6.01645481
13 0.28782632 1.96681851
14 -0.87572493 0.28782632
15 -0.22558284 -0.87572493
16 1.48719378 -0.22558284
17 1.00436776 1.48719378
18 -2.78515443 1.00436776
19 -1.24780935 -2.78515443
20 -6.36822898 -1.24780935
21 0.31377173 -6.36822898
22 -0.94604793 0.31377173
23 -0.11424487 -0.94604793
24 -0.10323474 -0.11424487
25 -2.17779616 -0.10323474
26 -0.49153396 -2.17779616
27 1.73084222 -0.49153396
28 2.34907654 1.73084222
29 -1.20564015 2.34907654
30 -3.63724443 -1.20564015
31 -0.47325979 -3.63724443
32 -3.19842018 -0.47325979
33 -0.96857276 -3.19842018
34 0.50970332 -0.96857276
35 -7.19855186 0.50970332
36 5.05468501 -7.19855186
37 -1.07251658 5.05468501
38 -2.55183736 -1.07251658
39 -2.87718350 -2.55183736
40 -3.44538593 -2.87718350
41 1.48418409 -3.44538593
42 -5.03734387 1.48418409
43 -0.63994911 -5.03734387
44 -2.54775716 -0.63994911
45 -0.66067778 -2.54775716
46 -4.87684158 -0.66067778
47 -1.67511407 -4.87684158
48 3.49211256 -1.67511407
49 -1.64496564 3.49211256
50 -2.38628545 -1.64496564
51 0.36383987 -2.38628545
52 3.87935577 0.36383987
53 -0.80391647 3.87935577
54 -3.85203950 -0.80391647
55 -1.00605819 -3.85203950
56 0.77771891 -1.00605819
57 -4.38203158 0.77771891
58 -2.23800791 -4.38203158
59 2.18430951 -2.23800791
60 4.36880367 2.18430951
61 0.36257336 4.36880367
62 0.33402935 0.36257336
63 -0.63315237 0.33402935
64 -1.25299478 -0.63315237
65 -0.48897673 -1.25299478
66 4.22162032 -0.48897673
67 0.14529627 4.22162032
68 -0.69462167 0.14529627
69 1.39561328 -0.69462167
70 -1.91119661 1.39561328
71 0.54708002 -1.91119661
72 2.26304937 0.54708002
73 -1.34983571 2.26304937
74 -0.73501658 -1.34983571
75 7.15227775 -0.73501658
76 0.48656636 7.15227775
77 3.59481896 0.48656636
78 -0.25849439 3.59481896
79 -5.38477906 -0.25849439
80 -0.47493769 -5.38477906
81 -1.08035692 -0.47493769
82 -3.79739847 -1.08035692
83 -0.38784142 -3.79739847
84 -0.92175935 -0.38784142
85 0.58201091 -0.92175935
86 0.69004214 0.58201091
87 -0.84092669 0.69004214
88 0.64224954 -0.84092669
89 8.86530206 0.64224954
90 -0.80305113 8.86530206
91 1.17554081 -0.80305113
92 1.46459179 1.17554081
93 3.00816423 1.46459179
94 -2.66608550 3.00816423
95 -0.32009984 -2.66608550
96 3.16632197 -0.32009984
97 -0.96783727 3.16632197
98 -0.45133555 -0.96783727
99 -2.64994722 -0.45133555
100 -1.68487424 -2.64994722
101 3.54250722 -1.68487424
102 1.73379256 3.54250722
103 -3.09673738 1.73379256
104 4.47339083 -3.09673738
105 5.54482232 4.47339083
106 -0.94817037 5.54482232
107 3.91450676 -0.94817037
108 1.01888594 3.91450676
109 8.55420700 1.01888594
110 2.32610819 8.55420700
111 2.70658548 2.32610819
112 0.03597930 2.70658548
113 1.19356101 0.03597930
114 -1.28548592 1.19356101
115 -1.04114656 -1.28548592
116 -0.22981514 -1.04114656
117 -0.23147187 -0.22981514
118 -4.52790043 -0.23147187
119 4.13678725 -4.52790043
120 0.31386297 4.13678725
121 1.34969482 0.31386297
122 -0.41803474 1.34969482
123 -4.00579189 -0.41803474
124 -2.01304020 -4.00579189
125 -0.84726577 -2.01304020
126 -3.34438062 -0.84726577
127 -1.35367626 -3.34438062
128 -1.78045780 -1.35367626
129 -0.60411899 -1.78045780
130 -1.56288149 -0.60411899
131 5.20179899 -1.56288149
132 -1.69001418 5.20179899
133 0.33069720 -1.69001418
134 -0.74114439 0.33069720
135 1.05788562 -0.74114439
136 7.24935394 1.05788562
137 -0.24846675 7.24935394
138 0.19571035 -0.24846675
139 -3.45258249 0.19571035
140 -2.63886501 -3.45258249
141 -4.23367897 -2.63886501
142 2.02811306 -4.23367897
143 0.70682945 2.02811306
144 -0.71735033 0.70682945
145 -1.82264696 -0.71735033
146 -0.33786677 -1.82264696
147 -1.39729180 -0.33786677
148 -1.61657649 -1.39729180
149 5.33833650 -1.61657649
150 -0.87137886 5.33833650
151 5.45354153 -0.87137886
152 -3.03224939 5.45354153
153 -0.40708285 -3.03224939
154 5.45320363 -0.40708285
155 3.49330230 5.45320363
156 1.17554081 3.49330230
157 0.24315878 1.17554081
158 -1.78045780 0.24315878
159 -2.67219530 -1.78045780
160 5.60744513 -2.67219530
161 1.69000949 5.60744513
> 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/wessaorg/rcomp/tmp/7jhuu1322167162.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8yxu81322167162.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9ikxq1322167162.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/102mrn1322167162.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11ooez1322167162.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/wessaorg/rcomp/tmp/12buwt1322167162.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/wessaorg/rcomp/tmp/13zg821322167163.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/wessaorg/rcomp/tmp/14lv051322167163.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/wessaorg/rcomp/tmp/15cu0b1322167163.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/wessaorg/rcomp/tmp/16ruja1322167163.tab")
+ }
>
> try(system("convert tmp/19kpp1322167162.ps tmp/19kpp1322167162.png",intern=TRUE))
character(0)
> try(system("convert tmp/28io11322167162.ps tmp/28io11322167162.png",intern=TRUE))
character(0)
> try(system("convert tmp/3l2wq1322167162.ps tmp/3l2wq1322167162.png",intern=TRUE))
character(0)
> try(system("convert tmp/4bmfx1322167162.ps tmp/4bmfx1322167162.png",intern=TRUE))
character(0)
> try(system("convert tmp/5i5jf1322167162.ps tmp/5i5jf1322167162.png",intern=TRUE))
character(0)
> try(system("convert tmp/635mx1322167162.ps tmp/635mx1322167162.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jhuu1322167162.ps tmp/7jhuu1322167162.png",intern=TRUE))
character(0)
> try(system("convert tmp/8yxu81322167162.ps tmp/8yxu81322167162.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ikxq1322167162.ps tmp/9ikxq1322167162.png",intern=TRUE))
character(0)
> try(system("convert tmp/102mrn1322167162.ps tmp/102mrn1322167162.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.050 0.481 5.573