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(11
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+ ,16)
+ ,dim=c(5
+ ,162)
+ ,dimnames=list(c('Month'
+ ,'Doubts'
+ ,'Expectations'
+ ,'Criticism'
+ ,'Depression')
+ ,1:162))
> y <- array(NA,dim=c(5,162),dimnames=list(c('Month','Doubts','Expectations','Criticism','Depression'),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 = '5'
> #'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
Depression Month Doubts Expectations Criticism
1 12 11 14 11 12
2 11 11 11 7 8
3 14 11 6 17 8
4 12 11 12 10 8
5 21 11 8 12 9
6 12 11 10 12 7
7 22 11 10 11 4
8 11 11 11 11 11
9 10 11 16 12 7
10 13 11 11 13 7
11 10 11 13 14 12
12 8 11 12 16 10
13 15 11 8 11 10
14 14 11 12 10 8
15 10 11 11 11 8
16 14 11 4 15 4
17 14 11 9 9 9
18 11 11 8 11 8
19 10 11 8 17 7
20 13 11 14 17 11
21 7 11 15 11 9
22 14 11 16 18 11
23 12 11 9 14 13
24 14 11 14 10 8
25 11 11 11 11 8
26 9 11 8 15 9
27 11 11 9 15 6
28 15 11 9 13 9
29 14 11 9 16 9
30 13 11 9 13 6
31 9 11 10 9 6
32 15 11 16 18 16
33 10 11 11 18 5
34 11 11 8 12 7
35 13 11 9 17 9
36 8 11 16 9 6
37 20 11 11 9 6
38 12 11 16 12 5
39 10 11 12 18 12
40 10 11 12 12 7
41 9 11 14 18 10
42 14 11 9 14 9
43 8 11 10 15 8
44 14 11 9 16 5
45 11 11 10 10 8
46 13 11 12 11 8
47 9 11 14 14 10
48 11 11 14 9 6
49 15 11 10 12 8
50 11 11 14 17 7
51 10 11 16 5 4
52 14 11 9 12 8
53 18 11 10 12 8
54 14 11 6 6 4
55 11 11 8 24 20
56 12 11 13 12 8
57 13 11 10 12 8
58 9 11 8 14 6
59 10 11 7 7 4
60 15 11 15 13 8
61 20 11 9 12 9
62 12 11 10 13 6
63 12 11 12 14 7
64 14 11 13 8 9
65 13 11 10 11 5
66 11 11 11 9 5
67 17 11 8 11 8
68 12 11 9 13 8
69 13 11 13 10 6
70 14 11 11 11 8
71 13 11 8 12 7
72 15 11 9 9 7
73 13 11 9 15 9
74 10 11 15 18 11
75 11 11 9 15 6
76 19 11 10 12 8
77 13 11 14 13 6
78 17 11 12 14 9
79 13 11 12 10 8
80 9 11 11 13 6
81 11 11 14 13 10
82 10 11 6 11 8
83 9 11 12 13 8
84 12 11 8 16 10
85 12 11 14 8 5
86 13 11 11 16 7
87 13 11 10 11 5
88 12 11 14 9 8
89 15 11 12 16 14
90 22 11 10 12 7
91 13 11 14 14 8
92 15 11 5 8 6
93 13 11 11 9 5
94 15 11 10 15 6
95 10 11 9 11 10
96 11 11 10 21 12
97 16 11 16 14 9
98 11 11 13 18 12
99 11 11 9 12 7
100 10 11 10 13 8
101 10 11 10 15 10
102 16 11 7 12 6
103 12 11 9 19 10
104 11 11 8 15 10
105 16 11 14 11 10
106 19 11 14 11 5
107 11 11 8 10 7
108 16 11 9 13 10
109 15 11 14 15 11
110 24 11 14 12 6
111 14 11 8 12 7
112 15 11 8 16 12
113 11 11 8 9 11
114 15 11 7 18 11
115 12 11 6 8 11
116 10 11 8 13 5
117 14 11 6 17 8
118 13 11 11 9 6
119 9 11 14 15 9
120 15 11 11 8 4
121 15 11 11 7 4
122 14 11 11 12 7
123 11 11 14 14 11
124 8 11 8 6 6
125 11 11 20 8 7
126 11 11 11 17 8
127 8 11 8 10 4
128 10 11 11 11 8
129 11 11 10 14 9
130 13 11 14 11 8
131 11 11 11 13 11
132 20 11 9 12 8
133 10 11 9 11 5
134 15 11 8 9 4
135 12 11 10 12 8
136 14 11 13 20 10
137 23 11 13 12 6
138 14 11 12 13 9
139 16 11 8 12 9
140 11 11 13 12 13
141 12 11 14 9 9
142 10 11 12 15 10
143 14 11 14 24 20
144 12 11 15 7 5
145 12 11 13 17 11
146 11 11 16 11 6
147 12 11 9 17 9
148 13 11 9 11 7
149 11 11 9 12 9
150 19 11 8 14 10
151 12 11 7 11 9
152 17 11 16 16 8
153 9 11 11 21 7
154 12 11 9 14 6
155 19 11 11 20 13
156 18 11 9 13 6
157 15 11 14 11 8
158 14 11 13 15 10
159 11 11 16 19 16
160 11 11 9 11 18
161 12 16 11 14 11
162 8 12 11 11 7
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Month Doubts Expectations Criticism
17.77691 -0.32976 -0.07540 -0.01160 -0.03884
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.5414 -2.0199 -0.6249 1.4365 11.2782
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.77691 7.01238 2.535 0.0122 *
Month -0.32976 0.62584 -0.527 0.5990
Doubts -0.07540 0.09084 -0.830 0.4078
Expectations -0.01160 0.08821 -0.131 0.8956
Criticism -0.03884 0.10894 -0.356 0.7220
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.169 on 157 degrees of freedom
Multiple R-squared: 0.008946, Adjusted R-squared: -0.0163
F-statistic: 0.3543 on 4 and 157 DF, p-value: 0.8407
> 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,] 0.97677099 0.04645802 0.02322901
[2,] 0.95529308 0.08941385 0.04470692
[3,] 0.92134905 0.15730190 0.07865095
[4,] 0.87244988 0.25510024 0.12755012
[5,] 0.84895617 0.30208765 0.15104383
[6,] 0.78201087 0.43597826 0.21798913
[7,] 0.70511770 0.58976461 0.29488230
[8,] 0.72514056 0.54971888 0.27485944
[9,] 0.78160843 0.43678314 0.21839157
[10,] 0.71836904 0.56326192 0.28163096
[11,] 0.73064583 0.53870834 0.26935417
[12,] 0.70691434 0.58617132 0.29308566
[13,] 0.73382733 0.53234533 0.26617267
[14,] 0.77042504 0.45914991 0.22957496
[15,] 0.81490065 0.37019870 0.18509935
[16,] 0.76461263 0.47077473 0.23538737
[17,] 0.72626527 0.54746946 0.27373473
[18,] 0.68725485 0.62549031 0.31274515
[19,] 0.71345897 0.57308206 0.28654103
[20,] 0.68303508 0.63392985 0.31696492
[21,] 0.65139875 0.69720250 0.34860125
[22,] 0.60360998 0.79278004 0.39639002
[23,] 0.54348226 0.91303549 0.45651774
[24,] 0.59356428 0.81287144 0.40643572
[25,] 0.62750922 0.74498156 0.37249078
[26,] 0.60001839 0.79996322 0.39998161
[27,] 0.56514177 0.86971646 0.43485823
[28,] 0.50741848 0.98516305 0.49258152
[29,] 0.50939653 0.98120693 0.49060347
[30,] 0.74091168 0.51817665 0.25908832
[31,] 0.69877133 0.60245735 0.30122867
[32,] 0.66999065 0.66001870 0.33000935
[33,] 0.64735530 0.70528941 0.35264470
[34,] 0.62961951 0.74076097 0.37038049
[35,] 0.58423806 0.83152387 0.41576194
[36,] 0.63388920 0.73222160 0.36611080
[37,] 0.59488525 0.81022950 0.40511475
[38,] 0.56241234 0.87517532 0.43758766
[39,] 0.51312534 0.97374932 0.48687466
[40,] 0.50316147 0.99367705 0.49683853
[41,] 0.46065069 0.92130137 0.53934931
[42,] 0.43536479 0.87072959 0.56463521
[43,] 0.39429617 0.78859233 0.60570383
[44,] 0.36949122 0.73898245 0.63050878
[45,] 0.32741956 0.65483912 0.67258044
[46,] 0.40809058 0.81618116 0.59190942
[47,] 0.36370881 0.72741762 0.63629119
[48,] 0.32766626 0.65533253 0.67233374
[49,] 0.28674434 0.57348868 0.71325566
[50,] 0.24654568 0.49309135 0.75345432
[51,] 0.27536719 0.55073439 0.72463281
[52,] 0.29227225 0.58454451 0.70772775
[53,] 0.29685003 0.59370006 0.70314997
[54,] 0.47565050 0.95130101 0.52434950
[55,] 0.43202398 0.86404796 0.56797602
[56,] 0.38914649 0.77829299 0.61085351
[57,] 0.35217060 0.70434120 0.64782940
[58,] 0.30978901 0.61957801 0.69021099
[59,] 0.28428339 0.56856677 0.71571661
[60,] 0.30039377 0.60078754 0.69960623
[61,] 0.26471695 0.52943390 0.73528305
[62,] 0.23010855 0.46021710 0.76989145
[63,] 0.20063088 0.40126175 0.79936912
[64,] 0.16959588 0.33919176 0.83040412
[65,] 0.14982205 0.29964410 0.85017795
[66,] 0.12433684 0.24867368 0.87566316
[67,] 0.11310029 0.22620058 0.88689971
[68,] 0.10001081 0.20002161 0.89998919
[69,] 0.16842595 0.33685190 0.83157405
[70,] 0.14616179 0.29232359 0.85383821
[71,] 0.17433025 0.34866051 0.82566975
[72,] 0.14628562 0.29257125 0.85371438
[73,] 0.16042324 0.32084647 0.83957676
[74,] 0.14030042 0.28060084 0.85969958
[75,] 0.14849223 0.29698446 0.85150777
[76,] 0.16058312 0.32116625 0.83941688
[77,] 0.13657658 0.27315316 0.86342342
[78,] 0.11565611 0.23131223 0.88434389
[79,] 0.09707615 0.19415230 0.90292385
[80,] 0.07916507 0.15833015 0.92083493
[81,] 0.06463384 0.12926768 0.93536616
[82,] 0.05938537 0.11877074 0.94061463
[83,] 0.22811065 0.45622131 0.77188935
[84,] 0.19861579 0.39723157 0.80138421
[85,] 0.17857293 0.35714586 0.82142707
[86,] 0.15008337 0.30016673 0.84991663
[87,] 0.13514643 0.27029286 0.86485357
[88,] 0.13270637 0.26541274 0.86729363
[89,] 0.11694198 0.23388396 0.88305802
[90,] 0.12282896 0.24565793 0.87717104
[91,] 0.10790526 0.21581051 0.89209474
[92,] 0.09616661 0.19233322 0.90383339
[93,] 0.09436768 0.18873536 0.90563232
[94,] 0.09179625 0.18359250 0.90820375
[95,] 0.08631426 0.17262852 0.91368574
[96,] 0.07184473 0.14368947 0.92815527
[97,] 0.06292826 0.12585651 0.93707174
[98,] 0.06366081 0.12732161 0.93633919
[99,] 0.10933693 0.21867386 0.89066307
[100,] 0.09714930 0.19429859 0.90285070
[101,] 0.09432752 0.18865504 0.90567248
[102,] 0.08461809 0.16923618 0.91538191
[103,] 0.48052041 0.96104082 0.51947959
[104,] 0.43465385 0.86930770 0.56534615
[105,] 0.40355762 0.80711525 0.59644238
[106,] 0.36936441 0.73872883 0.63063559
[107,] 0.33632888 0.67265777 0.66367112
[108,] 0.29486861 0.58973723 0.70513139
[109,] 0.29638780 0.59277561 0.70361220
[110,] 0.25472276 0.50944552 0.74527724
[111,] 0.21517938 0.43035876 0.78482062
[112,] 0.23106037 0.46212075 0.76893963
[113,] 0.20878007 0.41756014 0.79121993
[114,] 0.19189673 0.38379346 0.80810327
[115,] 0.16137364 0.32274728 0.83862636
[116,] 0.13811896 0.27623791 0.86188104
[117,] 0.16846588 0.33693177 0.83153412
[118,] 0.14008717 0.28017434 0.85991283
[119,] 0.12593043 0.25186086 0.87406957
[120,] 0.18157271 0.36314543 0.81842729
[121,] 0.17753509 0.35507017 0.82246491
[122,] 0.16140187 0.32280374 0.83859813
[123,] 0.12839126 0.25678251 0.87160874
[124,] 0.11089837 0.22179675 0.88910163
[125,] 0.21163011 0.42326022 0.78836989
[126,] 0.22232112 0.44464223 0.77767888
[127,] 0.18402656 0.36805312 0.81597344
[128,] 0.15179751 0.30359502 0.84820249
[129,] 0.11927564 0.23855128 0.88072436
[130,] 0.55243318 0.89513365 0.44756682
[131,] 0.49145504 0.98291007 0.50854496
[132,] 0.47395060 0.94790119 0.52604940
[133,] 0.41552681 0.83105363 0.58447319
[134,] 0.34534214 0.69068429 0.65465786
[135,] 0.33625587 0.67251174 0.66374413
[136,] 0.27261120 0.54522240 0.72738880
[137,] 0.21097861 0.42195723 0.78902139
[138,] 0.16542114 0.33084228 0.83457886
[139,] 0.13136523 0.26273047 0.86863477
[140,] 0.09978364 0.19956728 0.90021636
[141,] 0.06605700 0.13211401 0.93394300
[142,] 0.05133075 0.10266150 0.94866925
[143,] 0.09465331 0.18930662 0.90534669
[144,] 0.05806349 0.11612698 0.94193651
[145,] 0.05355952 0.10711904 0.94644048
[146,] 0.16632792 0.33265583 0.83367208
[147,] 0.21078976 0.42157952 0.78921024
> postscript(file="/var/www/html/freestat/rcomp/tmp/16hmf1290547088.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2zq301290547088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3zq301290547088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4zq301290547088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5zq301290547088.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
-0.50034036 -1.92828845 0.81069225 -0.81808949 7.94233753 -0.98453183
7 8 9 10 11 12
8.88735942 -1.76538263 -2.53212043 0.10246909 -2.54094519 -4.67082221
13 14 15 16 17 18
1.96957509 1.18191051 -2.88189237 0.48134410 0.98294237 -2.10809807
19 20 21 22 23 24
-3.07734053 0.53041719 -5.54144819 1.69282001 -0.80371622 1.33271431
25 26 27 28 29 30
-1.88189237 -4.02286541 -2.06397325 2.02933845 1.06413551 -0.08717129
31 32 33 34 35 36
-4.05816547 2.88700290 -2.91720896 -2.13533563 0.07573453 -4.60575407
37 38 39 40 41 42
7.01723643 -0.60979358 -2.56995101 -2.83372803 -3.49682037 1.04093747
43 44 45 46 47 48
-4.91089819 0.90878920 -1.96889329 0.19350953 -3.54321645 -1.75655787
49 50 51 52 53 54
2.05430475 -1.62492913 -2.72982330 0.97890285 5.05430475 0.52775671
55 56 57 58 59 60
-1.49127187 -0.71948955 0.05430475 -4.15097417 -3.38524236 2.44291327
61 62 63 64 65 66
7.01773943 -1.01176939 -0.81052999 1.27295095 -0.07380401 -2.02160015
67 68 69 70 71 72
3.89190193 -1.00949813 0.17963925 1.11810763 -0.13533563 1.90526921
73 74 75 76 77 78
0.05253649 -2.38258189 -2.06397325 6.05430475 0.28983821 4.26714317
79 80 81 82 83 84
0.18191051 -3.93636749 -1.55481547 -3.25890187 -3.78329243 -0.97242981
85 86 87 88 89 90
-0.80699347 0.13726615 -0.07380401 -0.67888471 2.48452410 9.01546817
91 92 93 94 95 96
0.37911039 1.55322601 -0.02160015 2.01142865 -2.95502301 -1.68595775
97 98 99 100 101 102
3.56875077 -1.49454911 -2.05993373 -2.93409623 -2.83322503 2.75042589
103 104 105 106 107 108
-0.86223085 -1.98402883 3.42198649 6.22780360 -2.15853367 3.06817503
109 110 111 112 113 114
2.50721915 11.27823919 0.86466437 2.10524335 -2.01478637 2.01420291
115 116 117 118 119 120
-1.17718919 -3.20140977 0.81069225 0.01723643 -3.57045401 1.92796426
121 122 123 124 125 126
1.91636524 1.09087007 -1.50437987 -5.24376633 -1.27690891 -1.81229825
127 128 129 130 131 132
-5.27504340 -2.88189237 -1.88366063 0.34431333 -1.74218459 6.97890285
133 134 135 136 137 138
-3.14920591 1.71335758 -0.94569525 1.45097577 10.20283729 1.25554415
139 140 141 142 143 144
2.94233753 -1.52530666 -0.64004813 -2.68242123 1.96113954 -0.74319059
145 146 147 148 149 150
-0.54498471 -1.58255603 -0.92426547 -0.07153275 -1.98226057 6.00437215
151 152 153 154 155 156
-1.14466339 4.55311223 -3.80473875 -1.07557227 6.41668170 4.91282871
157 158 159 160 161 162
2.34431333 1.39298067 -1.10139808 -1.64433038 0.91819458 -4.59097292
> postscript(file="/var/www/html/freestat/rcomp/tmp/6rz2l1290547088.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 -0.50034036 NA
1 -1.92828845 -0.50034036
2 0.81069225 -1.92828845
3 -0.81808949 0.81069225
4 7.94233753 -0.81808949
5 -0.98453183 7.94233753
6 8.88735942 -0.98453183
7 -1.76538263 8.88735942
8 -2.53212043 -1.76538263
9 0.10246909 -2.53212043
10 -2.54094519 0.10246909
11 -4.67082221 -2.54094519
12 1.96957509 -4.67082221
13 1.18191051 1.96957509
14 -2.88189237 1.18191051
15 0.48134410 -2.88189237
16 0.98294237 0.48134410
17 -2.10809807 0.98294237
18 -3.07734053 -2.10809807
19 0.53041719 -3.07734053
20 -5.54144819 0.53041719
21 1.69282001 -5.54144819
22 -0.80371622 1.69282001
23 1.33271431 -0.80371622
24 -1.88189237 1.33271431
25 -4.02286541 -1.88189237
26 -2.06397325 -4.02286541
27 2.02933845 -2.06397325
28 1.06413551 2.02933845
29 -0.08717129 1.06413551
30 -4.05816547 -0.08717129
31 2.88700290 -4.05816547
32 -2.91720896 2.88700290
33 -2.13533563 -2.91720896
34 0.07573453 -2.13533563
35 -4.60575407 0.07573453
36 7.01723643 -4.60575407
37 -0.60979358 7.01723643
38 -2.56995101 -0.60979358
39 -2.83372803 -2.56995101
40 -3.49682037 -2.83372803
41 1.04093747 -3.49682037
42 -4.91089819 1.04093747
43 0.90878920 -4.91089819
44 -1.96889329 0.90878920
45 0.19350953 -1.96889329
46 -3.54321645 0.19350953
47 -1.75655787 -3.54321645
48 2.05430475 -1.75655787
49 -1.62492913 2.05430475
50 -2.72982330 -1.62492913
51 0.97890285 -2.72982330
52 5.05430475 0.97890285
53 0.52775671 5.05430475
54 -1.49127187 0.52775671
55 -0.71948955 -1.49127187
56 0.05430475 -0.71948955
57 -4.15097417 0.05430475
58 -3.38524236 -4.15097417
59 2.44291327 -3.38524236
60 7.01773943 2.44291327
61 -1.01176939 7.01773943
62 -0.81052999 -1.01176939
63 1.27295095 -0.81052999
64 -0.07380401 1.27295095
65 -2.02160015 -0.07380401
66 3.89190193 -2.02160015
67 -1.00949813 3.89190193
68 0.17963925 -1.00949813
69 1.11810763 0.17963925
70 -0.13533563 1.11810763
71 1.90526921 -0.13533563
72 0.05253649 1.90526921
73 -2.38258189 0.05253649
74 -2.06397325 -2.38258189
75 6.05430475 -2.06397325
76 0.28983821 6.05430475
77 4.26714317 0.28983821
78 0.18191051 4.26714317
79 -3.93636749 0.18191051
80 -1.55481547 -3.93636749
81 -3.25890187 -1.55481547
82 -3.78329243 -3.25890187
83 -0.97242981 -3.78329243
84 -0.80699347 -0.97242981
85 0.13726615 -0.80699347
86 -0.07380401 0.13726615
87 -0.67888471 -0.07380401
88 2.48452410 -0.67888471
89 9.01546817 2.48452410
90 0.37911039 9.01546817
91 1.55322601 0.37911039
92 -0.02160015 1.55322601
93 2.01142865 -0.02160015
94 -2.95502301 2.01142865
95 -1.68595775 -2.95502301
96 3.56875077 -1.68595775
97 -1.49454911 3.56875077
98 -2.05993373 -1.49454911
99 -2.93409623 -2.05993373
100 -2.83322503 -2.93409623
101 2.75042589 -2.83322503
102 -0.86223085 2.75042589
103 -1.98402883 -0.86223085
104 3.42198649 -1.98402883
105 6.22780360 3.42198649
106 -2.15853367 6.22780360
107 3.06817503 -2.15853367
108 2.50721915 3.06817503
109 11.27823919 2.50721915
110 0.86466437 11.27823919
111 2.10524335 0.86466437
112 -2.01478637 2.10524335
113 2.01420291 -2.01478637
114 -1.17718919 2.01420291
115 -3.20140977 -1.17718919
116 0.81069225 -3.20140977
117 0.01723643 0.81069225
118 -3.57045401 0.01723643
119 1.92796426 -3.57045401
120 1.91636524 1.92796426
121 1.09087007 1.91636524
122 -1.50437987 1.09087007
123 -5.24376633 -1.50437987
124 -1.27690891 -5.24376633
125 -1.81229825 -1.27690891
126 -5.27504340 -1.81229825
127 -2.88189237 -5.27504340
128 -1.88366063 -2.88189237
129 0.34431333 -1.88366063
130 -1.74218459 0.34431333
131 6.97890285 -1.74218459
132 -3.14920591 6.97890285
133 1.71335758 -3.14920591
134 -0.94569525 1.71335758
135 1.45097577 -0.94569525
136 10.20283729 1.45097577
137 1.25554415 10.20283729
138 2.94233753 1.25554415
139 -1.52530666 2.94233753
140 -0.64004813 -1.52530666
141 -2.68242123 -0.64004813
142 1.96113954 -2.68242123
143 -0.74319059 1.96113954
144 -0.54498471 -0.74319059
145 -1.58255603 -0.54498471
146 -0.92426547 -1.58255603
147 -0.07153275 -0.92426547
148 -1.98226057 -0.07153275
149 6.00437215 -1.98226057
150 -1.14466339 6.00437215
151 4.55311223 -1.14466339
152 -3.80473875 4.55311223
153 -1.07557227 -3.80473875
154 6.41668170 -1.07557227
155 4.91282871 6.41668170
156 2.34431333 4.91282871
157 1.39298067 2.34431333
158 -1.10139808 1.39298067
159 -1.64433038 -1.10139808
160 0.91819458 -1.64433038
161 -4.59097292 0.91819458
162 NA -4.59097292
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.92828845 -0.50034036
[2,] 0.81069225 -1.92828845
[3,] -0.81808949 0.81069225
[4,] 7.94233753 -0.81808949
[5,] -0.98453183 7.94233753
[6,] 8.88735942 -0.98453183
[7,] -1.76538263 8.88735942
[8,] -2.53212043 -1.76538263
[9,] 0.10246909 -2.53212043
[10,] -2.54094519 0.10246909
[11,] -4.67082221 -2.54094519
[12,] 1.96957509 -4.67082221
[13,] 1.18191051 1.96957509
[14,] -2.88189237 1.18191051
[15,] 0.48134410 -2.88189237
[16,] 0.98294237 0.48134410
[17,] -2.10809807 0.98294237
[18,] -3.07734053 -2.10809807
[19,] 0.53041719 -3.07734053
[20,] -5.54144819 0.53041719
[21,] 1.69282001 -5.54144819
[22,] -0.80371622 1.69282001
[23,] 1.33271431 -0.80371622
[24,] -1.88189237 1.33271431
[25,] -4.02286541 -1.88189237
[26,] -2.06397325 -4.02286541
[27,] 2.02933845 -2.06397325
[28,] 1.06413551 2.02933845
[29,] -0.08717129 1.06413551
[30,] -4.05816547 -0.08717129
[31,] 2.88700290 -4.05816547
[32,] -2.91720896 2.88700290
[33,] -2.13533563 -2.91720896
[34,] 0.07573453 -2.13533563
[35,] -4.60575407 0.07573453
[36,] 7.01723643 -4.60575407
[37,] -0.60979358 7.01723643
[38,] -2.56995101 -0.60979358
[39,] -2.83372803 -2.56995101
[40,] -3.49682037 -2.83372803
[41,] 1.04093747 -3.49682037
[42,] -4.91089819 1.04093747
[43,] 0.90878920 -4.91089819
[44,] -1.96889329 0.90878920
[45,] 0.19350953 -1.96889329
[46,] -3.54321645 0.19350953
[47,] -1.75655787 -3.54321645
[48,] 2.05430475 -1.75655787
[49,] -1.62492913 2.05430475
[50,] -2.72982330 -1.62492913
[51,] 0.97890285 -2.72982330
[52,] 5.05430475 0.97890285
[53,] 0.52775671 5.05430475
[54,] -1.49127187 0.52775671
[55,] -0.71948955 -1.49127187
[56,] 0.05430475 -0.71948955
[57,] -4.15097417 0.05430475
[58,] -3.38524236 -4.15097417
[59,] 2.44291327 -3.38524236
[60,] 7.01773943 2.44291327
[61,] -1.01176939 7.01773943
[62,] -0.81052999 -1.01176939
[63,] 1.27295095 -0.81052999
[64,] -0.07380401 1.27295095
[65,] -2.02160015 -0.07380401
[66,] 3.89190193 -2.02160015
[67,] -1.00949813 3.89190193
[68,] 0.17963925 -1.00949813
[69,] 1.11810763 0.17963925
[70,] -0.13533563 1.11810763
[71,] 1.90526921 -0.13533563
[72,] 0.05253649 1.90526921
[73,] -2.38258189 0.05253649
[74,] -2.06397325 -2.38258189
[75,] 6.05430475 -2.06397325
[76,] 0.28983821 6.05430475
[77,] 4.26714317 0.28983821
[78,] 0.18191051 4.26714317
[79,] -3.93636749 0.18191051
[80,] -1.55481547 -3.93636749
[81,] -3.25890187 -1.55481547
[82,] -3.78329243 -3.25890187
[83,] -0.97242981 -3.78329243
[84,] -0.80699347 -0.97242981
[85,] 0.13726615 -0.80699347
[86,] -0.07380401 0.13726615
[87,] -0.67888471 -0.07380401
[88,] 2.48452410 -0.67888471
[89,] 9.01546817 2.48452410
[90,] 0.37911039 9.01546817
[91,] 1.55322601 0.37911039
[92,] -0.02160015 1.55322601
[93,] 2.01142865 -0.02160015
[94,] -2.95502301 2.01142865
[95,] -1.68595775 -2.95502301
[96,] 3.56875077 -1.68595775
[97,] -1.49454911 3.56875077
[98,] -2.05993373 -1.49454911
[99,] -2.93409623 -2.05993373
[100,] -2.83322503 -2.93409623
[101,] 2.75042589 -2.83322503
[102,] -0.86223085 2.75042589
[103,] -1.98402883 -0.86223085
[104,] 3.42198649 -1.98402883
[105,] 6.22780360 3.42198649
[106,] -2.15853367 6.22780360
[107,] 3.06817503 -2.15853367
[108,] 2.50721915 3.06817503
[109,] 11.27823919 2.50721915
[110,] 0.86466437 11.27823919
[111,] 2.10524335 0.86466437
[112,] -2.01478637 2.10524335
[113,] 2.01420291 -2.01478637
[114,] -1.17718919 2.01420291
[115,] -3.20140977 -1.17718919
[116,] 0.81069225 -3.20140977
[117,] 0.01723643 0.81069225
[118,] -3.57045401 0.01723643
[119,] 1.92796426 -3.57045401
[120,] 1.91636524 1.92796426
[121,] 1.09087007 1.91636524
[122,] -1.50437987 1.09087007
[123,] -5.24376633 -1.50437987
[124,] -1.27690891 -5.24376633
[125,] -1.81229825 -1.27690891
[126,] -5.27504340 -1.81229825
[127,] -2.88189237 -5.27504340
[128,] -1.88366063 -2.88189237
[129,] 0.34431333 -1.88366063
[130,] -1.74218459 0.34431333
[131,] 6.97890285 -1.74218459
[132,] -3.14920591 6.97890285
[133,] 1.71335758 -3.14920591
[134,] -0.94569525 1.71335758
[135,] 1.45097577 -0.94569525
[136,] 10.20283729 1.45097577
[137,] 1.25554415 10.20283729
[138,] 2.94233753 1.25554415
[139,] -1.52530666 2.94233753
[140,] -0.64004813 -1.52530666
[141,] -2.68242123 -0.64004813
[142,] 1.96113954 -2.68242123
[143,] -0.74319059 1.96113954
[144,] -0.54498471 -0.74319059
[145,] -1.58255603 -0.54498471
[146,] -0.92426547 -1.58255603
[147,] -0.07153275 -0.92426547
[148,] -1.98226057 -0.07153275
[149,] 6.00437215 -1.98226057
[150,] -1.14466339 6.00437215
[151,] 4.55311223 -1.14466339
[152,] -3.80473875 4.55311223
[153,] -1.07557227 -3.80473875
[154,] 6.41668170 -1.07557227
[155,] 4.91282871 6.41668170
[156,] 2.34431333 4.91282871
[157,] 1.39298067 2.34431333
[158,] -1.10139808 1.39298067
[159,] -1.64433038 -1.10139808
[160,] 0.91819458 -1.64433038
[161,] -4.59097292 0.91819458
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.92828845 -0.50034036
2 0.81069225 -1.92828845
3 -0.81808949 0.81069225
4 7.94233753 -0.81808949
5 -0.98453183 7.94233753
6 8.88735942 -0.98453183
7 -1.76538263 8.88735942
8 -2.53212043 -1.76538263
9 0.10246909 -2.53212043
10 -2.54094519 0.10246909
11 -4.67082221 -2.54094519
12 1.96957509 -4.67082221
13 1.18191051 1.96957509
14 -2.88189237 1.18191051
15 0.48134410 -2.88189237
16 0.98294237 0.48134410
17 -2.10809807 0.98294237
18 -3.07734053 -2.10809807
19 0.53041719 -3.07734053
20 -5.54144819 0.53041719
21 1.69282001 -5.54144819
22 -0.80371622 1.69282001
23 1.33271431 -0.80371622
24 -1.88189237 1.33271431
25 -4.02286541 -1.88189237
26 -2.06397325 -4.02286541
27 2.02933845 -2.06397325
28 1.06413551 2.02933845
29 -0.08717129 1.06413551
30 -4.05816547 -0.08717129
31 2.88700290 -4.05816547
32 -2.91720896 2.88700290
33 -2.13533563 -2.91720896
34 0.07573453 -2.13533563
35 -4.60575407 0.07573453
36 7.01723643 -4.60575407
37 -0.60979358 7.01723643
38 -2.56995101 -0.60979358
39 -2.83372803 -2.56995101
40 -3.49682037 -2.83372803
41 1.04093747 -3.49682037
42 -4.91089819 1.04093747
43 0.90878920 -4.91089819
44 -1.96889329 0.90878920
45 0.19350953 -1.96889329
46 -3.54321645 0.19350953
47 -1.75655787 -3.54321645
48 2.05430475 -1.75655787
49 -1.62492913 2.05430475
50 -2.72982330 -1.62492913
51 0.97890285 -2.72982330
52 5.05430475 0.97890285
53 0.52775671 5.05430475
54 -1.49127187 0.52775671
55 -0.71948955 -1.49127187
56 0.05430475 -0.71948955
57 -4.15097417 0.05430475
58 -3.38524236 -4.15097417
59 2.44291327 -3.38524236
60 7.01773943 2.44291327
61 -1.01176939 7.01773943
62 -0.81052999 -1.01176939
63 1.27295095 -0.81052999
64 -0.07380401 1.27295095
65 -2.02160015 -0.07380401
66 3.89190193 -2.02160015
67 -1.00949813 3.89190193
68 0.17963925 -1.00949813
69 1.11810763 0.17963925
70 -0.13533563 1.11810763
71 1.90526921 -0.13533563
72 0.05253649 1.90526921
73 -2.38258189 0.05253649
74 -2.06397325 -2.38258189
75 6.05430475 -2.06397325
76 0.28983821 6.05430475
77 4.26714317 0.28983821
78 0.18191051 4.26714317
79 -3.93636749 0.18191051
80 -1.55481547 -3.93636749
81 -3.25890187 -1.55481547
82 -3.78329243 -3.25890187
83 -0.97242981 -3.78329243
84 -0.80699347 -0.97242981
85 0.13726615 -0.80699347
86 -0.07380401 0.13726615
87 -0.67888471 -0.07380401
88 2.48452410 -0.67888471
89 9.01546817 2.48452410
90 0.37911039 9.01546817
91 1.55322601 0.37911039
92 -0.02160015 1.55322601
93 2.01142865 -0.02160015
94 -2.95502301 2.01142865
95 -1.68595775 -2.95502301
96 3.56875077 -1.68595775
97 -1.49454911 3.56875077
98 -2.05993373 -1.49454911
99 -2.93409623 -2.05993373
100 -2.83322503 -2.93409623
101 2.75042589 -2.83322503
102 -0.86223085 2.75042589
103 -1.98402883 -0.86223085
104 3.42198649 -1.98402883
105 6.22780360 3.42198649
106 -2.15853367 6.22780360
107 3.06817503 -2.15853367
108 2.50721915 3.06817503
109 11.27823919 2.50721915
110 0.86466437 11.27823919
111 2.10524335 0.86466437
112 -2.01478637 2.10524335
113 2.01420291 -2.01478637
114 -1.17718919 2.01420291
115 -3.20140977 -1.17718919
116 0.81069225 -3.20140977
117 0.01723643 0.81069225
118 -3.57045401 0.01723643
119 1.92796426 -3.57045401
120 1.91636524 1.92796426
121 1.09087007 1.91636524
122 -1.50437987 1.09087007
123 -5.24376633 -1.50437987
124 -1.27690891 -5.24376633
125 -1.81229825 -1.27690891
126 -5.27504340 -1.81229825
127 -2.88189237 -5.27504340
128 -1.88366063 -2.88189237
129 0.34431333 -1.88366063
130 -1.74218459 0.34431333
131 6.97890285 -1.74218459
132 -3.14920591 6.97890285
133 1.71335758 -3.14920591
134 -0.94569525 1.71335758
135 1.45097577 -0.94569525
136 10.20283729 1.45097577
137 1.25554415 10.20283729
138 2.94233753 1.25554415
139 -1.52530666 2.94233753
140 -0.64004813 -1.52530666
141 -2.68242123 -0.64004813
142 1.96113954 -2.68242123
143 -0.74319059 1.96113954
144 -0.54498471 -0.74319059
145 -1.58255603 -0.54498471
146 -0.92426547 -1.58255603
147 -0.07153275 -0.92426547
148 -1.98226057 -0.07153275
149 6.00437215 -1.98226057
150 -1.14466339 6.00437215
151 4.55311223 -1.14466339
152 -3.80473875 4.55311223
153 -1.07557227 -3.80473875
154 6.41668170 -1.07557227
155 4.91282871 6.41668170
156 2.34431333 4.91282871
157 1.39298067 2.34431333
158 -1.10139808 1.39298067
159 -1.64433038 -1.10139808
160 0.91819458 -1.64433038
161 -4.59097292 0.91819458
> 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/7k91o1290547088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8k91o1290547088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9k91o1290547088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
Warning messages:
1: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
2: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10d0191290547088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11g0hf1290547088.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/12jjyk1290547088.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/1382vw1290547088.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/14jbcz1290547088.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/15mus51290547088.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/160mqe1290547088.tab")
+ }
>
> try(system("convert tmp/16hmf1290547088.ps tmp/16hmf1290547088.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zq301290547088.ps tmp/2zq301290547088.png",intern=TRUE))
character(0)
> try(system("convert tmp/3zq301290547088.ps tmp/3zq301290547088.png",intern=TRUE))
character(0)
> try(system("convert tmp/4zq301290547088.ps tmp/4zq301290547088.png",intern=TRUE))
character(0)
> try(system("convert tmp/5zq301290547088.ps tmp/5zq301290547088.png",intern=TRUE))
character(0)
> try(system("convert tmp/6rz2l1290547088.ps tmp/6rz2l1290547088.png",intern=TRUE))
character(0)
> try(system("convert tmp/7k91o1290547088.ps tmp/7k91o1290547088.png",intern=TRUE))
character(0)
> try(system("convert tmp/8k91o1290547088.ps tmp/8k91o1290547088.png",intern=TRUE))
character(0)
> try(system("convert tmp/9k91o1290547088.ps tmp/9k91o1290547088.png",intern=TRUE))
character(0)
> try(system("convert tmp/10d0191290547088.ps tmp/10d0191290547088.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.647 2.619 6.314