R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(12
+ ,24
+ ,14
+ ,8
+ ,25
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+ ,17
+ ,6
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+ ,11
+ ,22
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+ ,9
+ ,5
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+ ,9
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+ ,8
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+ ,5
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+ ,13
+ ,6
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+ ,9
+ ,31
+ ,9
+ ,7
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+ ,38
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+ ,8
+ ,28
+ ,14
+ ,10
+ ,22
+ ,13
+ ,16
+ ,31
+ ,16)
+ ,dim=c(3
+ ,159)
+ ,dimnames=list(c('ParCritism'
+ ,'ParConcern'
+ ,'ParDoubt')
+ ,1:159))
> y <- array(NA,dim=c(3,159),dimnames=list(c('ParCritism','ParConcern','ParDoubt'),1:159))
> 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
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
ParCritism ParConcern ParDoubt
1 12 24 14
2 8 25 11
3 8 17 6
4 8 18 12
5 9 18 8
6 7 16 10
7 4 20 10
8 11 16 11
9 7 18 16
10 7 17 11
11 12 23 13
12 10 30 12
13 10 23 8
14 8 18 12
15 8 15 11
16 4 12 4
17 9 21 9
18 8 15 8
19 7 20 8
20 11 31 14
21 9 27 15
22 11 34 16
23 13 21 9
24 8 31 14
25 8 19 11
26 9 16 8
27 6 20 9
28 9 21 9
29 9 22 9
30 6 17 9
31 6 24 10
32 16 25 16
33 5 26 11
34 7 25 8
35 9 17 9
36 6 32 16
37 6 33 11
38 5 13 16
39 12 32 12
40 7 25 12
41 10 29 14
42 9 22 9
43 8 18 10
44 5 17 9
45 8 20 10
46 8 15 12
47 10 20 14
48 6 33 14
49 8 29 10
50 7 23 14
51 4 26 16
52 8 18 9
53 8 20 10
54 4 11 6
55 20 28 8
56 8 26 13
57 8 22 10
58 6 17 8
59 4 12 7
60 8 14 15
61 9 17 9
62 6 21 10
63 7 19 12
64 9 18 13
65 5 10 10
66 5 29 11
67 8 31 8
68 8 19 9
69 6 9 13
70 8 20 11
71 7 28 8
72 7 19 9
73 9 30 9
74 11 29 15
75 6 26 9
76 8 23 10
77 6 13 14
78 9 21 12
79 8 19 12
80 6 28 11
81 10 23 14
82 8 18 6
83 8 21 12
84 10 20 8
85 5 23 14
86 7 21 11
87 5 21 10
88 8 15 14
89 14 28 12
90 7 19 10
91 8 26 14
92 6 10 5
93 5 16 11
94 6 22 10
95 10 19 9
96 12 31 10
97 9 31 16
98 12 29 13
99 7 19 9
100 8 22 10
101 10 23 10
102 6 15 7
103 10 20 9
104 10 18 8
105 10 23 14
106 5 25 14
107 7 21 8
108 10 24 9
109 11 25 14
110 6 17 14
111 7 13 8
112 12 28 8
113 11 21 8
114 11 25 7
115 11 9 6
116 5 16 8
117 8 19 6
118 6 17 11
119 9 25 14
120 4 20 11
121 4 29 11
122 7 14 11
123 11 22 14
124 6 15 8
125 7 19 20
126 8 20 11
127 4 15 8
128 8 20 11
129 9 18 10
130 8 33 14
131 11 22 11
132 8 16 9
133 5 17 9
134 4 16 8
135 8 21 10
136 10 26 13
137 6 18 13
138 9 18 12
139 9 17 8
140 13 22 13
141 9 30 14
142 10 30 12
143 20 24 14
144 5 21 15
145 11 21 13
146 6 29 16
147 9 31 9
148 7 20 9
149 9 16 9
150 10 22 8
151 9 20 7
152 8 28 16
153 7 38 11
154 6 22 9
155 13 20 11
156 6 17 9
157 8 28 14
158 10 22 13
159 16 31 16
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) ParConcern ParDoubt
4.81033 0.14009 0.03602
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.2690 -1.5797 -0.1123 1.4217 11.3234
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.81033 0.96646 4.977 1.69e-06 ***
ParConcern 0.14009 0.03916 3.577 0.000463 ***
ParDoubt 0.03602 0.08003 0.450 0.653302
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.588 on 156 degrees of freedom
Multiple R-squared: 0.09782, Adjusted R-squared: 0.08625
F-statistic: 8.457 on 2 and 156 DF, p-value: 0.0003259
> 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,] 0.2877247302 0.575449460 0.7122753
[2,] 0.6100838845 0.779832231 0.3899161
[3,] 0.6051798089 0.789640382 0.3948202
[4,] 0.5871371976 0.825725605 0.4128628
[5,] 0.4844942603 0.968988521 0.5155057
[6,] 0.5093085992 0.981382802 0.4906914
[7,] 0.4101561009 0.820312202 0.5898439
[8,] 0.3378213129 0.675642626 0.6621787
[9,] 0.2553215412 0.510643082 0.7446785
[10,] 0.1875111877 0.375022375 0.8124888
[11,] 0.1677887227 0.335577445 0.8322113
[12,] 0.1213101961 0.242620392 0.8786898
[13,] 0.0900123378 0.180024676 0.9099877
[14,] 0.0659864892 0.131972978 0.9340135
[15,] 0.0444402824 0.088880565 0.9555597
[16,] 0.0349444816 0.069888963 0.9650555
[17,] 0.0234620777 0.046924155 0.9765379
[18,] 0.0795226391 0.159045278 0.9204774
[19,] 0.0874673047 0.174934609 0.9125327
[20,] 0.0629426094 0.125885219 0.9370574
[21,] 0.0500521742 0.100104348 0.9499478
[22,] 0.0495963270 0.099192654 0.9504037
[23,] 0.0354554177 0.070910835 0.9645446
[24,] 0.0246125522 0.049225104 0.9753874
[25,] 0.0211195592 0.042239118 0.9788804
[26,] 0.0258611898 0.051722380 0.9741388
[27,] 0.1364028119 0.272805624 0.8635972
[28,] 0.2040920178 0.408184036 0.7959080
[29,] 0.1719335944 0.343867189 0.8280664
[30,] 0.1464293932 0.292858786 0.8535706
[31,] 0.2324089131 0.464817826 0.7675911
[32,] 0.2459854715 0.491970943 0.7540145
[33,] 0.3204283530 0.640856706 0.6795716
[34,] 0.3216619773 0.643323955 0.6783380
[35,] 0.2972956846 0.594591369 0.7027043
[36,] 0.2540735161 0.508147032 0.7459265
[37,] 0.2175171858 0.435034372 0.7824828
[38,] 0.1803138182 0.360627636 0.8196862
[39,] 0.1821049834 0.364209967 0.8178950
[40,] 0.1492429748 0.298485950 0.8507570
[41,] 0.1214043973 0.242808795 0.8785956
[42,] 0.1051863268 0.210372654 0.8948137
[43,] 0.1381016135 0.276203227 0.8618984
[44,] 0.1146127641 0.229225528 0.8853872
[45,] 0.1027352800 0.205470560 0.8972647
[46,] 0.1910503025 0.382100605 0.8089497
[47,] 0.1591942230 0.318388446 0.8408058
[48,] 0.1307486109 0.261497222 0.8692514
[49,] 0.1347777022 0.269555404 0.8652223
[50,] 0.8368907292 0.326218542 0.1631093
[51,] 0.8094137244 0.381172551 0.1905863
[52,] 0.7756969382 0.448606124 0.2243031
[53,] 0.7514206078 0.497158784 0.2485794
[54,] 0.7542880719 0.491423856 0.2457119
[55,] 0.7198481179 0.560303764 0.2801519
[56,] 0.6921134649 0.615773070 0.3078865
[57,] 0.6770970507 0.645805899 0.3229029
[58,] 0.6386250642 0.722749872 0.3613749
[59,] 0.6041717590 0.791656482 0.3958282
[60,] 0.5739594226 0.852081155 0.4260406
[61,] 0.6467632043 0.706473591 0.3532368
[62,] 0.6154235246 0.769152951 0.3845765
[63,] 0.5707588538 0.858482292 0.4292411
[64,] 0.5266648271 0.946670346 0.4733352
[65,] 0.4805270655 0.961054131 0.5194729
[66,] 0.4608856407 0.921771281 0.5391144
[67,] 0.4192994404 0.838598881 0.5807006
[68,] 0.3758689695 0.751737939 0.6241310
[69,] 0.3493053251 0.698610650 0.6506947
[70,] 0.3543411947 0.708682389 0.6456588
[71,] 0.3136297099 0.627259420 0.6863703
[72,] 0.2809418534 0.561883707 0.7190581
[73,] 0.2473470125 0.494694025 0.7526530
[74,] 0.2125874514 0.425174903 0.7874125
[75,] 0.2279199570 0.455839914 0.7720800
[76,] 0.2050959925 0.410191985 0.7949040
[77,] 0.1754213748 0.350842750 0.8245786
[78,] 0.1475221366 0.295044273 0.8524779
[79,] 0.1385865987 0.277173197 0.8614134
[80,] 0.1607096424 0.321419285 0.8392904
[81,] 0.1393191880 0.278638376 0.8606808
[82,] 0.1510646982 0.302129396 0.8489353
[83,] 0.1269566812 0.253913362 0.8730433
[84,] 0.1928811770 0.385762354 0.8071188
[85,] 0.1660925383 0.332185077 0.8339075
[86,] 0.1427710810 0.285542162 0.8572289
[87,] 0.1190313311 0.238062662 0.8809687
[88,] 0.1170145400 0.234029080 0.8829855
[89,] 0.1123608998 0.224721800 0.8876391
[90,] 0.1051619438 0.210323888 0.8948381
[91,] 0.1023209334 0.204641867 0.8976791
[92,] 0.0846828377 0.169365675 0.9153172
[93,] 0.0843091564 0.168618313 0.9156908
[94,] 0.0694429701 0.138885940 0.9305570
[95,] 0.0553718878 0.110743776 0.9446281
[96,] 0.0472172222 0.094434444 0.9527828
[97,] 0.0391228534 0.078245707 0.9608771
[98,] 0.0348469113 0.069693823 0.9651531
[99,] 0.0325003461 0.065000692 0.9674997
[100,] 0.0266970167 0.053394033 0.9733030
[101,] 0.0361698989 0.072339798 0.9638301
[102,] 0.0290224572 0.058044914 0.9709775
[103,] 0.0237524626 0.047504925 0.9762475
[104,] 0.0211996109 0.042399222 0.9788004
[105,] 0.0183336399 0.036667280 0.9816664
[106,] 0.0135642322 0.027128464 0.9864358
[107,] 0.0148539585 0.029707917 0.9851460
[108,] 0.0158659823 0.031731965 0.9841340
[109,] 0.0161080532 0.032216106 0.9838919
[110,] 0.0294834873 0.058966975 0.9705165
[111,] 0.0267347783 0.053469557 0.9732652
[112,] 0.0204538379 0.040907676 0.9795462
[113,] 0.0170001030 0.034000206 0.9829999
[114,] 0.0123297048 0.024659410 0.9876703
[115,] 0.0185569244 0.037113849 0.9814431
[116,] 0.0400288407 0.080057681 0.9599712
[117,] 0.0303382450 0.060676490 0.9696618
[118,] 0.0273246111 0.054649222 0.9726754
[119,] 0.0213042889 0.042608578 0.9786957
[120,] 0.0232503988 0.046500798 0.9767496
[121,] 0.0168475744 0.033695149 0.9831524
[122,] 0.0199359101 0.039871820 0.9800641
[123,] 0.0143598476 0.028719695 0.9856402
[124,] 0.0103528891 0.020705778 0.9896471
[125,] 0.0087051042 0.017410208 0.9912949
[126,] 0.0077727815 0.015545563 0.9922272
[127,] 0.0051916118 0.010383224 0.9948084
[128,] 0.0051890530 0.010378106 0.9948109
[129,] 0.0074057825 0.014811565 0.9925942
[130,] 0.0049670462 0.009934092 0.9950330
[131,] 0.0032218275 0.006443655 0.9967782
[132,] 0.0039949058 0.007989812 0.9960051
[133,] 0.0026383774 0.005276755 0.9973616
[134,] 0.0016388944 0.003277789 0.9983611
[135,] 0.0020335111 0.004067022 0.9979665
[136,] 0.0012241912 0.002448382 0.9987758
[137,] 0.0006985798 0.001397160 0.9993014
[138,] 0.1559104765 0.311820953 0.8440895
[139,] 0.2459954834 0.491990967 0.7540045
[140,] 0.2042112212 0.408422442 0.7957888
[141,] 0.3121559517 0.624311903 0.6878440
[142,] 0.2478151569 0.495630314 0.7521848
[143,] 0.1938573189 0.387714638 0.8061427
[144,] 0.1323946209 0.264789242 0.8676054
[145,] 0.1115292229 0.223058446 0.8884708
[146,] 0.0980939736 0.196187947 0.9019060
[147,] 0.2228180875 0.445636175 0.7771819
[148,] 0.1394939346 0.278987869 0.8605061
> postscript(file="/var/www/html/freestat/rcomp/tmp/1bnn01290714981.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2mfm31290714981.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3mfm31290714981.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4mfm31290714981.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5wom61290714981.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 = 159
Frequency = 1
1 2 3 4 5 6
3.323364036 -0.708675453 0.592097737 0.235916334 1.379979407 -0.411878542
7 8 9 10 11 12
-3.972225717 3.552105690 -0.908146738 -0.587981104 3.499466598 0.554874811
13 14 15 16 17 18
1.679545439 0.235916334 0.692192483 -2.635436759 0.923703258 0.800239788
19 20 21 22 23 24
-0.900194180 1.342756481 -0.132912113 0.850464564 4.923703258 -1.657243519
25 26 27 28 29 30
0.131845309 1.660152994 -1.936209948 0.923703258 0.783616464 -1.515949568
31 32 33 34 35 36
-2.532572891 7.111245707 -3.848762246 -1.600628148 1.484050432 -3.869361849
37 38 39 40 41 42
-3.829369802 -2.207712770 2.274701224 -1.744691221 0.622930068 0.783616464
43 44 45 46 47 48
0.307947871 -2.515949568 0.027774283 0.656176715 1.883711211 -3.937417106
49 50 51 52 53 54
-1.233006859 -1.536549170 -5.028841087 0.343963639 0.027774283 -2.567381501
55 56 57 58 59 60
10.979111471 -0.920793783 -0.252399304 -1.479933799 -2.743484063 0.688216205
61 62 63 64 65 66
1.484050432 -2.112312510 -0.904170459 1.199900566 -1.571357780 -4.269022627
67 68 69 70 71 72
-1.441148910 0.203876845 -0.539318291 -0.008241485 -2.020888529 -0.796123155
73 74 75 76 77 78
-0.337077885 1.586914300 -2.776730710 -0.392486097 -1.135681234 0.815655954
79 80 81 82 83 84
0.095829541 -3.128935834 1.463450830 0.452010943 -0.184344046 2.099805820
85 86 87 88 89 90
-3.536549170 -1.148328278 -3.112312510 0.584145179 4.835048398 -0.832138923
91 92 93 94 95 96
-0.956809551 -0.391278940 -2.447894310 -2.252399304 2.203876845 2.486819553
97 98 99 100 101 102
-0.729275055 2.658945836 -0.796123155 -0.252399304 1.607513903 -1.163744444
103 104 105 106 107 108
2.063790052 2.379979407 1.463450830 -3.816722757 -1.040280974 1.503442877
109 110 111 112 113 114
2.183277243 -1.696028408 0.080413375 2.979111471 2.959719026 2.435387620
115 116 117 118 119 120
4.712792086 -2.339847006 0.311924150 -1.587981104 0.183277243 -4.008241485
121 122 123 124 125 126
-5.269022627 -0.167720723 2.603537624 -1.199760212 -1.192296604 -0.008241485
127 128 129 130 131 132
-3.199760212 -0.008241485 1.307947871 -1.937417106 2.711584928 0.624137226
133 134 135 136 137 138
-2.515949568 -3.339847006 -0.112312510 1.079206217 -1.800099434 1.235916334
139 140 141 142 143 144
1.520066201 4.639553392 -0.517156725 0.554874811 11.323364036 -3.292391351
145 146 147 148 149 150
2.779640185 -3.449101468 -0.477164678 -0.936209948 1.624137226 1.819632232
151 152 153 154 155 156
1.135821588 -1.309014674 -3.529803770 -2.216383536 4.991758515 -1.515949568
157 158 159
-1.236983138 1.639553392 6.270724945
> postscript(file="/var/www/html/freestat/rcomp/tmp/6wom61290714981.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 3.323364036 NA
1 -0.708675453 3.323364036
2 0.592097737 -0.708675453
3 0.235916334 0.592097737
4 1.379979407 0.235916334
5 -0.411878542 1.379979407
6 -3.972225717 -0.411878542
7 3.552105690 -3.972225717
8 -0.908146738 3.552105690
9 -0.587981104 -0.908146738
10 3.499466598 -0.587981104
11 0.554874811 3.499466598
12 1.679545439 0.554874811
13 0.235916334 1.679545439
14 0.692192483 0.235916334
15 -2.635436759 0.692192483
16 0.923703258 -2.635436759
17 0.800239788 0.923703258
18 -0.900194180 0.800239788
19 1.342756481 -0.900194180
20 -0.132912113 1.342756481
21 0.850464564 -0.132912113
22 4.923703258 0.850464564
23 -1.657243519 4.923703258
24 0.131845309 -1.657243519
25 1.660152994 0.131845309
26 -1.936209948 1.660152994
27 0.923703258 -1.936209948
28 0.783616464 0.923703258
29 -1.515949568 0.783616464
30 -2.532572891 -1.515949568
31 7.111245707 -2.532572891
32 -3.848762246 7.111245707
33 -1.600628148 -3.848762246
34 1.484050432 -1.600628148
35 -3.869361849 1.484050432
36 -3.829369802 -3.869361849
37 -2.207712770 -3.829369802
38 2.274701224 -2.207712770
39 -1.744691221 2.274701224
40 0.622930068 -1.744691221
41 0.783616464 0.622930068
42 0.307947871 0.783616464
43 -2.515949568 0.307947871
44 0.027774283 -2.515949568
45 0.656176715 0.027774283
46 1.883711211 0.656176715
47 -3.937417106 1.883711211
48 -1.233006859 -3.937417106
49 -1.536549170 -1.233006859
50 -5.028841087 -1.536549170
51 0.343963639 -5.028841087
52 0.027774283 0.343963639
53 -2.567381501 0.027774283
54 10.979111471 -2.567381501
55 -0.920793783 10.979111471
56 -0.252399304 -0.920793783
57 -1.479933799 -0.252399304
58 -2.743484063 -1.479933799
59 0.688216205 -2.743484063
60 1.484050432 0.688216205
61 -2.112312510 1.484050432
62 -0.904170459 -2.112312510
63 1.199900566 -0.904170459
64 -1.571357780 1.199900566
65 -4.269022627 -1.571357780
66 -1.441148910 -4.269022627
67 0.203876845 -1.441148910
68 -0.539318291 0.203876845
69 -0.008241485 -0.539318291
70 -2.020888529 -0.008241485
71 -0.796123155 -2.020888529
72 -0.337077885 -0.796123155
73 1.586914300 -0.337077885
74 -2.776730710 1.586914300
75 -0.392486097 -2.776730710
76 -1.135681234 -0.392486097
77 0.815655954 -1.135681234
78 0.095829541 0.815655954
79 -3.128935834 0.095829541
80 1.463450830 -3.128935834
81 0.452010943 1.463450830
82 -0.184344046 0.452010943
83 2.099805820 -0.184344046
84 -3.536549170 2.099805820
85 -1.148328278 -3.536549170
86 -3.112312510 -1.148328278
87 0.584145179 -3.112312510
88 4.835048398 0.584145179
89 -0.832138923 4.835048398
90 -0.956809551 -0.832138923
91 -0.391278940 -0.956809551
92 -2.447894310 -0.391278940
93 -2.252399304 -2.447894310
94 2.203876845 -2.252399304
95 2.486819553 2.203876845
96 -0.729275055 2.486819553
97 2.658945836 -0.729275055
98 -0.796123155 2.658945836
99 -0.252399304 -0.796123155
100 1.607513903 -0.252399304
101 -1.163744444 1.607513903
102 2.063790052 -1.163744444
103 2.379979407 2.063790052
104 1.463450830 2.379979407
105 -3.816722757 1.463450830
106 -1.040280974 -3.816722757
107 1.503442877 -1.040280974
108 2.183277243 1.503442877
109 -1.696028408 2.183277243
110 0.080413375 -1.696028408
111 2.979111471 0.080413375
112 2.959719026 2.979111471
113 2.435387620 2.959719026
114 4.712792086 2.435387620
115 -2.339847006 4.712792086
116 0.311924150 -2.339847006
117 -1.587981104 0.311924150
118 0.183277243 -1.587981104
119 -4.008241485 0.183277243
120 -5.269022627 -4.008241485
121 -0.167720723 -5.269022627
122 2.603537624 -0.167720723
123 -1.199760212 2.603537624
124 -1.192296604 -1.199760212
125 -0.008241485 -1.192296604
126 -3.199760212 -0.008241485
127 -0.008241485 -3.199760212
128 1.307947871 -0.008241485
129 -1.937417106 1.307947871
130 2.711584928 -1.937417106
131 0.624137226 2.711584928
132 -2.515949568 0.624137226
133 -3.339847006 -2.515949568
134 -0.112312510 -3.339847006
135 1.079206217 -0.112312510
136 -1.800099434 1.079206217
137 1.235916334 -1.800099434
138 1.520066201 1.235916334
139 4.639553392 1.520066201
140 -0.517156725 4.639553392
141 0.554874811 -0.517156725
142 11.323364036 0.554874811
143 -3.292391351 11.323364036
144 2.779640185 -3.292391351
145 -3.449101468 2.779640185
146 -0.477164678 -3.449101468
147 -0.936209948 -0.477164678
148 1.624137226 -0.936209948
149 1.819632232 1.624137226
150 1.135821588 1.819632232
151 -1.309014674 1.135821588
152 -3.529803770 -1.309014674
153 -2.216383536 -3.529803770
154 4.991758515 -2.216383536
155 -1.515949568 4.991758515
156 -1.236983138 -1.515949568
157 1.639553392 -1.236983138
158 6.270724945 1.639553392
159 NA 6.270724945
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.708675453 3.323364036
[2,] 0.592097737 -0.708675453
[3,] 0.235916334 0.592097737
[4,] 1.379979407 0.235916334
[5,] -0.411878542 1.379979407
[6,] -3.972225717 -0.411878542
[7,] 3.552105690 -3.972225717
[8,] -0.908146738 3.552105690
[9,] -0.587981104 -0.908146738
[10,] 3.499466598 -0.587981104
[11,] 0.554874811 3.499466598
[12,] 1.679545439 0.554874811
[13,] 0.235916334 1.679545439
[14,] 0.692192483 0.235916334
[15,] -2.635436759 0.692192483
[16,] 0.923703258 -2.635436759
[17,] 0.800239788 0.923703258
[18,] -0.900194180 0.800239788
[19,] 1.342756481 -0.900194180
[20,] -0.132912113 1.342756481
[21,] 0.850464564 -0.132912113
[22,] 4.923703258 0.850464564
[23,] -1.657243519 4.923703258
[24,] 0.131845309 -1.657243519
[25,] 1.660152994 0.131845309
[26,] -1.936209948 1.660152994
[27,] 0.923703258 -1.936209948
[28,] 0.783616464 0.923703258
[29,] -1.515949568 0.783616464
[30,] -2.532572891 -1.515949568
[31,] 7.111245707 -2.532572891
[32,] -3.848762246 7.111245707
[33,] -1.600628148 -3.848762246
[34,] 1.484050432 -1.600628148
[35,] -3.869361849 1.484050432
[36,] -3.829369802 -3.869361849
[37,] -2.207712770 -3.829369802
[38,] 2.274701224 -2.207712770
[39,] -1.744691221 2.274701224
[40,] 0.622930068 -1.744691221
[41,] 0.783616464 0.622930068
[42,] 0.307947871 0.783616464
[43,] -2.515949568 0.307947871
[44,] 0.027774283 -2.515949568
[45,] 0.656176715 0.027774283
[46,] 1.883711211 0.656176715
[47,] -3.937417106 1.883711211
[48,] -1.233006859 -3.937417106
[49,] -1.536549170 -1.233006859
[50,] -5.028841087 -1.536549170
[51,] 0.343963639 -5.028841087
[52,] 0.027774283 0.343963639
[53,] -2.567381501 0.027774283
[54,] 10.979111471 -2.567381501
[55,] -0.920793783 10.979111471
[56,] -0.252399304 -0.920793783
[57,] -1.479933799 -0.252399304
[58,] -2.743484063 -1.479933799
[59,] 0.688216205 -2.743484063
[60,] 1.484050432 0.688216205
[61,] -2.112312510 1.484050432
[62,] -0.904170459 -2.112312510
[63,] 1.199900566 -0.904170459
[64,] -1.571357780 1.199900566
[65,] -4.269022627 -1.571357780
[66,] -1.441148910 -4.269022627
[67,] 0.203876845 -1.441148910
[68,] -0.539318291 0.203876845
[69,] -0.008241485 -0.539318291
[70,] -2.020888529 -0.008241485
[71,] -0.796123155 -2.020888529
[72,] -0.337077885 -0.796123155
[73,] 1.586914300 -0.337077885
[74,] -2.776730710 1.586914300
[75,] -0.392486097 -2.776730710
[76,] -1.135681234 -0.392486097
[77,] 0.815655954 -1.135681234
[78,] 0.095829541 0.815655954
[79,] -3.128935834 0.095829541
[80,] 1.463450830 -3.128935834
[81,] 0.452010943 1.463450830
[82,] -0.184344046 0.452010943
[83,] 2.099805820 -0.184344046
[84,] -3.536549170 2.099805820
[85,] -1.148328278 -3.536549170
[86,] -3.112312510 -1.148328278
[87,] 0.584145179 -3.112312510
[88,] 4.835048398 0.584145179
[89,] -0.832138923 4.835048398
[90,] -0.956809551 -0.832138923
[91,] -0.391278940 -0.956809551
[92,] -2.447894310 -0.391278940
[93,] -2.252399304 -2.447894310
[94,] 2.203876845 -2.252399304
[95,] 2.486819553 2.203876845
[96,] -0.729275055 2.486819553
[97,] 2.658945836 -0.729275055
[98,] -0.796123155 2.658945836
[99,] -0.252399304 -0.796123155
[100,] 1.607513903 -0.252399304
[101,] -1.163744444 1.607513903
[102,] 2.063790052 -1.163744444
[103,] 2.379979407 2.063790052
[104,] 1.463450830 2.379979407
[105,] -3.816722757 1.463450830
[106,] -1.040280974 -3.816722757
[107,] 1.503442877 -1.040280974
[108,] 2.183277243 1.503442877
[109,] -1.696028408 2.183277243
[110,] 0.080413375 -1.696028408
[111,] 2.979111471 0.080413375
[112,] 2.959719026 2.979111471
[113,] 2.435387620 2.959719026
[114,] 4.712792086 2.435387620
[115,] -2.339847006 4.712792086
[116,] 0.311924150 -2.339847006
[117,] -1.587981104 0.311924150
[118,] 0.183277243 -1.587981104
[119,] -4.008241485 0.183277243
[120,] -5.269022627 -4.008241485
[121,] -0.167720723 -5.269022627
[122,] 2.603537624 -0.167720723
[123,] -1.199760212 2.603537624
[124,] -1.192296604 -1.199760212
[125,] -0.008241485 -1.192296604
[126,] -3.199760212 -0.008241485
[127,] -0.008241485 -3.199760212
[128,] 1.307947871 -0.008241485
[129,] -1.937417106 1.307947871
[130,] 2.711584928 -1.937417106
[131,] 0.624137226 2.711584928
[132,] -2.515949568 0.624137226
[133,] -3.339847006 -2.515949568
[134,] -0.112312510 -3.339847006
[135,] 1.079206217 -0.112312510
[136,] -1.800099434 1.079206217
[137,] 1.235916334 -1.800099434
[138,] 1.520066201 1.235916334
[139,] 4.639553392 1.520066201
[140,] -0.517156725 4.639553392
[141,] 0.554874811 -0.517156725
[142,] 11.323364036 0.554874811
[143,] -3.292391351 11.323364036
[144,] 2.779640185 -3.292391351
[145,] -3.449101468 2.779640185
[146,] -0.477164678 -3.449101468
[147,] -0.936209948 -0.477164678
[148,] 1.624137226 -0.936209948
[149,] 1.819632232 1.624137226
[150,] 1.135821588 1.819632232
[151,] -1.309014674 1.135821588
[152,] -3.529803770 -1.309014674
[153,] -2.216383536 -3.529803770
[154,] 4.991758515 -2.216383536
[155,] -1.515949568 4.991758515
[156,] -1.236983138 -1.515949568
[157,] 1.639553392 -1.236983138
[158,] 6.270724945 1.639553392
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.708675453 3.323364036
2 0.592097737 -0.708675453
3 0.235916334 0.592097737
4 1.379979407 0.235916334
5 -0.411878542 1.379979407
6 -3.972225717 -0.411878542
7 3.552105690 -3.972225717
8 -0.908146738 3.552105690
9 -0.587981104 -0.908146738
10 3.499466598 -0.587981104
11 0.554874811 3.499466598
12 1.679545439 0.554874811
13 0.235916334 1.679545439
14 0.692192483 0.235916334
15 -2.635436759 0.692192483
16 0.923703258 -2.635436759
17 0.800239788 0.923703258
18 -0.900194180 0.800239788
19 1.342756481 -0.900194180
20 -0.132912113 1.342756481
21 0.850464564 -0.132912113
22 4.923703258 0.850464564
23 -1.657243519 4.923703258
24 0.131845309 -1.657243519
25 1.660152994 0.131845309
26 -1.936209948 1.660152994
27 0.923703258 -1.936209948
28 0.783616464 0.923703258
29 -1.515949568 0.783616464
30 -2.532572891 -1.515949568
31 7.111245707 -2.532572891
32 -3.848762246 7.111245707
33 -1.600628148 -3.848762246
34 1.484050432 -1.600628148
35 -3.869361849 1.484050432
36 -3.829369802 -3.869361849
37 -2.207712770 -3.829369802
38 2.274701224 -2.207712770
39 -1.744691221 2.274701224
40 0.622930068 -1.744691221
41 0.783616464 0.622930068
42 0.307947871 0.783616464
43 -2.515949568 0.307947871
44 0.027774283 -2.515949568
45 0.656176715 0.027774283
46 1.883711211 0.656176715
47 -3.937417106 1.883711211
48 -1.233006859 -3.937417106
49 -1.536549170 -1.233006859
50 -5.028841087 -1.536549170
51 0.343963639 -5.028841087
52 0.027774283 0.343963639
53 -2.567381501 0.027774283
54 10.979111471 -2.567381501
55 -0.920793783 10.979111471
56 -0.252399304 -0.920793783
57 -1.479933799 -0.252399304
58 -2.743484063 -1.479933799
59 0.688216205 -2.743484063
60 1.484050432 0.688216205
61 -2.112312510 1.484050432
62 -0.904170459 -2.112312510
63 1.199900566 -0.904170459
64 -1.571357780 1.199900566
65 -4.269022627 -1.571357780
66 -1.441148910 -4.269022627
67 0.203876845 -1.441148910
68 -0.539318291 0.203876845
69 -0.008241485 -0.539318291
70 -2.020888529 -0.008241485
71 -0.796123155 -2.020888529
72 -0.337077885 -0.796123155
73 1.586914300 -0.337077885
74 -2.776730710 1.586914300
75 -0.392486097 -2.776730710
76 -1.135681234 -0.392486097
77 0.815655954 -1.135681234
78 0.095829541 0.815655954
79 -3.128935834 0.095829541
80 1.463450830 -3.128935834
81 0.452010943 1.463450830
82 -0.184344046 0.452010943
83 2.099805820 -0.184344046
84 -3.536549170 2.099805820
85 -1.148328278 -3.536549170
86 -3.112312510 -1.148328278
87 0.584145179 -3.112312510
88 4.835048398 0.584145179
89 -0.832138923 4.835048398
90 -0.956809551 -0.832138923
91 -0.391278940 -0.956809551
92 -2.447894310 -0.391278940
93 -2.252399304 -2.447894310
94 2.203876845 -2.252399304
95 2.486819553 2.203876845
96 -0.729275055 2.486819553
97 2.658945836 -0.729275055
98 -0.796123155 2.658945836
99 -0.252399304 -0.796123155
100 1.607513903 -0.252399304
101 -1.163744444 1.607513903
102 2.063790052 -1.163744444
103 2.379979407 2.063790052
104 1.463450830 2.379979407
105 -3.816722757 1.463450830
106 -1.040280974 -3.816722757
107 1.503442877 -1.040280974
108 2.183277243 1.503442877
109 -1.696028408 2.183277243
110 0.080413375 -1.696028408
111 2.979111471 0.080413375
112 2.959719026 2.979111471
113 2.435387620 2.959719026
114 4.712792086 2.435387620
115 -2.339847006 4.712792086
116 0.311924150 -2.339847006
117 -1.587981104 0.311924150
118 0.183277243 -1.587981104
119 -4.008241485 0.183277243
120 -5.269022627 -4.008241485
121 -0.167720723 -5.269022627
122 2.603537624 -0.167720723
123 -1.199760212 2.603537624
124 -1.192296604 -1.199760212
125 -0.008241485 -1.192296604
126 -3.199760212 -0.008241485
127 -0.008241485 -3.199760212
128 1.307947871 -0.008241485
129 -1.937417106 1.307947871
130 2.711584928 -1.937417106
131 0.624137226 2.711584928
132 -2.515949568 0.624137226
133 -3.339847006 -2.515949568
134 -0.112312510 -3.339847006
135 1.079206217 -0.112312510
136 -1.800099434 1.079206217
137 1.235916334 -1.800099434
138 1.520066201 1.235916334
139 4.639553392 1.520066201
140 -0.517156725 4.639553392
141 0.554874811 -0.517156725
142 11.323364036 0.554874811
143 -3.292391351 11.323364036
144 2.779640185 -3.292391351
145 -3.449101468 2.779640185
146 -0.477164678 -3.449101468
147 -0.936209948 -0.477164678
148 1.624137226 -0.936209948
149 1.819632232 1.624137226
150 1.135821588 1.819632232
151 -1.309014674 1.135821588
152 -3.529803770 -1.309014674
153 -2.216383536 -3.529803770
154 4.991758515 -2.216383536
155 -1.515949568 4.991758515
156 -1.236983138 -1.515949568
157 1.639553392 -1.236983138
158 6.270724945 1.639553392
> 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/77x3r1290714981.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/87x3r1290714981.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/90p2u1290714981.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/100p2u1290714981.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11l7ii1290714981.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/126qh61290714981.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/13dqeh1290714981.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/14o0vk1290714981.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/15r0cq1290714981.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/16naaz1290714981.tab")
+ }
>
> try(system("convert tmp/1bnn01290714981.ps tmp/1bnn01290714981.png",intern=TRUE))
character(0)
> try(system("convert tmp/2mfm31290714981.ps tmp/2mfm31290714981.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mfm31290714981.ps tmp/3mfm31290714981.png",intern=TRUE))
character(0)
> try(system("convert tmp/4mfm31290714981.ps tmp/4mfm31290714981.png",intern=TRUE))
character(0)
> try(system("convert tmp/5wom61290714981.ps tmp/5wom61290714981.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wom61290714981.ps tmp/6wom61290714981.png",intern=TRUE))
character(0)
> try(system("convert tmp/77x3r1290714981.ps tmp/77x3r1290714981.png",intern=TRUE))
character(0)
> try(system("convert tmp/87x3r1290714981.ps tmp/87x3r1290714981.png",intern=TRUE))
character(0)
> try(system("convert tmp/90p2u1290714981.ps tmp/90p2u1290714981.png",intern=TRUE))
character(0)
> try(system("convert tmp/100p2u1290714981.ps tmp/100p2u1290714981.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.350 2.643 5.814