R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(2
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+ ,5)
+ ,dim=c(7
+ ,162)
+ ,dimnames=list(c('X_1t'
+ ,'X_2t'
+ ,'X_3t'
+ ,'X_4t'
+ ,'X_5t'
+ ,'X_6t'
+ ,'Y_t')
+ ,1:162))
> y <- array(NA,dim=c(7,162),dimnames=list(c('X_1t','X_2t','X_3t','X_4t','X_5t','X_6t','Y_t'),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 = '7'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '7'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, 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
Y_t X_1t X_2t X_3t X_4t X_5t X_6t
1 3 2 7 41 38 14 12
2 5 2 5 39 32 18 11
3 4 2 5 30 35 11 14
4 4 1 5 31 33 12 12
5 5 2 8 34 37 16 21
6 5 2 6 35 29 18 12
7 2 2 5 39 31 14 22
8 5 2 6 34 36 14 11
9 4 2 5 36 35 15 10
10 4 2 4 37 38 15 13
11 5 1 6 38 31 17 10
12 3 2 5 36 34 19 8
13 5 1 5 38 35 10 15
14 3 2 6 39 38 16 14
15 5 2 7 33 37 18 10
16 3 1 6 32 33 14 14
17 4 1 7 36 32 14 14
18 5 2 6 38 38 17 11
19 4 1 8 39 38 14 10
20 3 2 7 32 32 16 13
21 4 1 5 32 33 18 7
22 4 2 5 31 31 11 14
23 3 2 7 39 38 14 12
24 3 2 7 37 39 12 14
25 4 1 5 39 32 17 11
26 5 2 4 41 32 9 9
27 4 1 10 36 35 16 11
28 4 2 6 33 37 14 15
29 4 2 5 33 33 15 14
30 4 1 5 34 33 11 13
31 4 2 5 31 28 16 9
32 3 1 5 27 32 13 15
33 4 2 6 37 31 17 10
34 5 2 5 34 37 15 11
35 4 1 5 34 30 14 13
36 4 1 5 32 33 16 8
37 3 1 5 29 31 9 20
38 4 1 5 36 33 15 12
39 4 2 5 29 31 17 10
40 4 1 5 35 33 13 10
41 5 1 5 37 32 15 9
42 4 2 7 34 33 16 14
43 3 1 5 38 32 16 8
44 3 1 6 35 33 12 14
45 4 2 7 38 28 12 11
46 4 2 7 37 35 11 13
47 4 2 5 38 39 15 9
48 5 2 5 33 34 15 11
49 4 2 4 36 38 17 15
50 5 1 5 38 32 13 11
51 4 2 4 32 38 16 10
52 4 1 5 32 30 14 14
53 4 1 5 32 33 11 18
54 4 2 7 34 38 12 14
55 4 1 5 32 32 12 11
56 5 2 5 37 32 15 12
57 4 2 6 39 34 16 13
58 4 2 4 29 34 15 9
59 4 1 6 37 36 12 10
60 4 2 6 35 34 12 15
61 3 1 5 30 28 8 20
62 4 1 7 38 34 13 12
63 5 2 6 34 35 11 12
64 1 2 8 31 35 14 14
65 3 2 7 34 31 15 13
66 5 1 5 35 37 10 11
67 4 2 6 36 35 11 17
68 4 1 6 30 27 12 12
69 3 2 5 39 40 15 13
70 4 1 5 35 37 15 14
71 4 1 5 38 36 14 13
72 3 2 5 31 38 16 15
73 5 2 4 34 39 15 13
74 4 1 6 38 41 15 10
75 5 1 6 34 27 13 11
76 4 2 6 39 30 12 19
77 4 2 6 37 37 17 13
78 4 2 7 34 31 13 17
79 4 1 5 28 31 15 13
80 3 1 7 37 27 13 9
81 5 1 6 33 36 15 11
82 NA 1 5 37 38 16 10
83 5 2 5 35 37 15 9
84 4 1 4 37 33 16 12
85 4 2 8 32 34 15 12
86 5 2 8 33 31 14 13
87 4 1 5 38 39 15 13
88 4 2 5 33 34 14 12
89 3 2 6 29 32 13 15
90 4 2 4 33 33 7 22
91 4 2 5 31 36 17 13
92 3 2 5 36 32 13 15
93 5 2 5 35 41 15 13
94 5 2 5 32 28 14 15
95 5 2 6 29 30 13 10
96 4 2 6 39 36 16 11
97 4 2 5 37 35 12 16
98 4 2 6 35 31 14 11
99 4 1 5 37 34 17 11
100 4 1 7 32 36 15 10
101 4 2 5 38 36 17 10
102 4 1 6 37 35 12 16
103 5 2 6 36 37 16 12
104 4 1 6 32 28 11 11
105 4 2 4 33 39 15 16
106 3 1 5 40 32 9 19
107 5 2 5 38 35 16 11
108 4 1 7 41 39 15 16
109 3 1 6 36 35 10 15
110 2 2 9 43 42 10 24
111 5 2 6 30 34 15 14
112 4 2 6 31 33 11 15
113 5 2 5 32 41 13 11
114 1 1 6 32 33 14 15
115 5 2 5 37 34 18 12
116 5 1 8 37 32 16 10
117 3 2 7 33 40 14 14
118 4 2 5 34 40 14 13
119 5 2 7 33 35 14 9
120 5 2 6 38 36 14 15
121 3 2 6 33 37 12 15
122 4 2 9 31 27 14 14
123 5 2 7 38 39 15 11
124 4 2 6 37 38 15 8
125 4 2 5 33 31 15 11
126 4 2 5 31 33 13 11
127 5 1 6 39 32 17 8
128 4 2 6 44 39 17 10
129 5 2 7 33 36 19 11
130 4 2 5 35 33 15 13
131 4 1 5 32 33 13 11
132 4 1 5 28 32 9 20
133 4 2 6 40 37 15 10
134 3 1 4 27 30 15 15
135 4 1 5 37 38 15 12
136 5 2 7 32 29 16 14
137 3 1 5 28 22 11 23
138 4 1 7 34 35 14 14
139 3 2 7 30 35 11 16
140 4 2 6 35 34 15 11
141 3 1 5 31 35 13 12
142 3 2 8 32 34 15 10
143 5 1 5 30 34 16 14
144 5 2 5 30 35 14 12
145 5 1 5 31 23 15 12
146 5 2 6 40 31 16 11
147 5 2 4 32 27 16 12
148 4 1 5 36 36 11 13
149 4 1 5 32 31 12 11
150 4 1 7 35 32 9 19
151 5 2 6 38 39 16 12
152 5 2 7 42 37 13 17
153 4 1 10 34 38 16 9
154 4 2 6 35 39 12 12
155 4 2 8 35 34 9 19
156 5 2 4 33 31 13 18
157 3 2 5 36 32 13 15
158 4 2 6 32 37 14 14
159 5 2 7 33 36 19 11
160 5 2 7 34 32 13 9
161 5 2 6 32 35 12 18
162 5 2 6 34 36 13 16
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X_1t X_2t X_3t X_4t X_5t
4.86568 0.24504 -0.07269 0.01418 -0.01670 0.01578
X_6t
-0.07144
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.85016 -0.40256 0.02114 0.64157 1.60924
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.86568 0.94774 5.134 8.45e-07 ***
X_1t 0.24504 0.13113 1.869 0.06357 .
X_2t -0.07269 0.05224 -1.391 0.16612
X_3t 0.01418 0.01927 0.736 0.46282
X_4t -0.01670 0.01871 -0.893 0.37350
X_5t 0.01578 0.03153 0.500 0.61756
X_6t -0.07144 0.02285 -3.126 0.00212 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7562 on 154 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.1314, Adjusted R-squared: 0.09759
F-statistic: 3.884 on 6 and 154 DF, p-value: 0.001223
> 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,] 0.2320060 0.46401203 0.76799399
[2,] 0.1503966 0.30079311 0.84960345
[3,] 0.9517027 0.09659463 0.04829731
[4,] 0.9782621 0.04347571 0.02173786
[5,] 0.9758496 0.04830085 0.02415043
[6,] 0.9601425 0.07971501 0.03985751
[7,] 0.9898323 0.02033535 0.01016768
[8,] 0.9845533 0.03089343 0.01544672
[9,] 0.9833915 0.03321702 0.01660851
[10,] 0.9774813 0.04503749 0.02251874
[11,] 0.9869046 0.02619083 0.01309541
[12,] 0.9833090 0.03338193 0.01669096
[13,] 0.9746224 0.05075512 0.02537756
[14,] 0.9786798 0.04264036 0.02132018
[15,] 0.9768345 0.04633097 0.02316548
[16,] 0.9664369 0.06712630 0.03356315
[17,] 0.9705648 0.05887032 0.02943516
[18,] 0.9585453 0.08290946 0.04145473
[19,] 0.9431633 0.11367349 0.05683675
[20,] 0.9229988 0.15400250 0.07700125
[21,] 0.8979374 0.20412510 0.10206255
[22,] 0.8785186 0.24296276 0.12148138
[23,] 0.8787414 0.24251713 0.12125857
[24,] 0.8521927 0.29561462 0.14780731
[25,] 0.8571080 0.28578407 0.14289204
[26,] 0.8215388 0.35692232 0.17846116
[27,] 0.7876152 0.42476961 0.21238480
[28,] 0.7513556 0.49728889 0.24864444
[29,] 0.7043173 0.59136545 0.29568272
[30,] 0.6613315 0.67733701 0.33866850
[31,] 0.6105665 0.77886695 0.38943348
[32,] 0.6017140 0.79657207 0.39828604
[33,] 0.5494306 0.90113889 0.45056944
[34,] 0.6617734 0.67645317 0.33822659
[35,] 0.6621525 0.67569508 0.33784754
[36,] 0.6175499 0.76490021 0.38245010
[37,] 0.5661750 0.86765002 0.43382501
[38,] 0.5277938 0.94441247 0.47220624
[39,] 0.5315094 0.93698124 0.46849062
[40,] 0.4822663 0.96453270 0.51773365
[41,] 0.5055437 0.98891257 0.49445629
[42,] 0.4651126 0.93022524 0.53488738
[43,] 0.4170594 0.83411889 0.58294055
[44,] 0.3870759 0.77415184 0.61292408
[45,] 0.3408081 0.68161619 0.65919191
[46,] 0.2960912 0.59218247 0.70390876
[47,] 0.2986803 0.59736063 0.70131968
[48,] 0.2600001 0.52000026 0.73999987
[49,] 0.2336162 0.46723240 0.76638380
[50,] 0.1977940 0.39558806 0.80220597
[51,] 0.1657469 0.33149371 0.83425315
[52,] 0.1443314 0.28866273 0.85566864
[53,] 0.1183125 0.23662495 0.88168753
[54,] 0.1284633 0.25692652 0.87153674
[55,] 0.6365809 0.72683821 0.36341911
[56,] 0.6730538 0.65389233 0.32694616
[57,] 0.7029618 0.59407644 0.29703822
[58,] 0.6653803 0.66923934 0.33461967
[59,] 0.6237686 0.75246275 0.37623138
[60,] 0.6900234 0.61995326 0.30997663
[61,] 0.6482814 0.70343714 0.35171857
[62,] 0.6037760 0.79244801 0.39622401
[63,] 0.6342244 0.73155118 0.36577559
[64,] 0.6433206 0.71335873 0.35667937
[65,] 0.5997181 0.80056384 0.40028192
[66,] 0.6235165 0.75296696 0.37648348
[67,] 0.5831209 0.83375828 0.41687914
[68,] 0.5475501 0.90489986 0.45244993
[69,] 0.5102251 0.97954972 0.48977486
[70,] 0.4657626 0.93152529 0.53423735
[71,] 0.5351427 0.92971455 0.46485728
[72,] 0.5756919 0.84861618 0.42430809
[73,] 0.5563154 0.88736915 0.44368458
[74,] 0.5144546 0.97109079 0.48554539
[75,] 0.4757751 0.95155023 0.52422489
[76,] 0.5146437 0.97071259 0.48535629
[77,] 0.4680929 0.93618586 0.53190707
[78,] 0.4282796 0.85655918 0.57172041
[79,] 0.4577774 0.91555479 0.54222260
[80,] 0.4336899 0.86737982 0.56631009
[81,] 0.4012075 0.80241509 0.59879245
[82,] 0.4628864 0.92577287 0.53711356
[83,] 0.4768615 0.95372310 0.52313845
[84,] 0.4888453 0.97769061 0.51115469
[85,] 0.4801481 0.96029618 0.51985191
[86,] 0.4506051 0.90121028 0.54939486
[87,] 0.4039535 0.80790693 0.59604653
[88,] 0.3735387 0.74707739 0.62646130
[89,] 0.3353730 0.67074602 0.66462699
[90,] 0.2927762 0.58555242 0.70722379
[91,] 0.2844270 0.56885392 0.71557304
[92,] 0.2541680 0.50833607 0.74583197
[93,] 0.2496151 0.49923015 0.75038492
[94,] 0.2131078 0.42621569 0.78689215
[95,] 0.1806595 0.36131896 0.81934052
[96,] 0.1639031 0.32780621 0.83609689
[97,] 0.1468868 0.29377355 0.85311323
[98,] 0.1248851 0.24977013 0.87511493
[99,] 0.1152183 0.23043668 0.88478166
[100,] 0.1643620 0.32872397 0.83563802
[101,] 0.1779018 0.35580357 0.82209822
[102,] 0.1472970 0.29459400 0.85270300
[103,] 0.1684695 0.33693901 0.83153050
[104,] 0.7970895 0.40582098 0.20291049
[105,] 0.7697712 0.46045765 0.23022882
[106,] 0.7654101 0.46917988 0.23458994
[107,] 0.8045397 0.39092060 0.19546030
[108,] 0.7668833 0.46623343 0.23311671
[109,] 0.7850763 0.42984733 0.21492366
[110,] 0.7791931 0.44161386 0.22080693
[111,] 0.8123944 0.37521119 0.18760560
[112,] 0.7789361 0.44212780 0.22106390
[113,] 0.7686961 0.46260771 0.23130386
[114,] 0.7335852 0.53282968 0.26641484
[115,] 0.6992210 0.60155794 0.30077897
[116,] 0.6478407 0.70431852 0.35215926
[117,] 0.6232893 0.75342150 0.37671075
[118,] 0.6445334 0.71093318 0.35546659
[119,] 0.6037652 0.79246958 0.39623479
[120,] 0.5785217 0.84295659 0.42147829
[121,] 0.5182972 0.96340568 0.48170284
[122,] 0.5063307 0.98733860 0.49366930
[123,] 0.5139382 0.97212354 0.48606177
[124,] 0.5634850 0.87303000 0.43651500
[125,] 0.5285460 0.94290793 0.47145396
[126,] 0.5005943 0.99881139 0.49940570
[127,] 0.5856153 0.82876947 0.41438473
[128,] 0.5241956 0.95160887 0.47580443
[129,] 0.5489480 0.90210394 0.45105197
[130,] 0.5035563 0.99288745 0.49644372
[131,] 0.6093522 0.78129565 0.39064783
[132,] 0.8092199 0.38156018 0.19078009
[133,] 0.7678114 0.46437721 0.23218860
[134,] 0.7334856 0.53302878 0.26651439
[135,] 0.6747745 0.65045095 0.32522547
[136,] 0.5855346 0.82893081 0.41446540
[137,] 0.4913600 0.98272005 0.50863997
[138,] 0.3880243 0.77604857 0.61197571
[139,] 0.2956821 0.59136420 0.70431790
[140,] 0.2262347 0.45246935 0.77376533
[141,] 0.1545527 0.30910542 0.84544729
[142,] 0.2732383 0.54647655 0.72676172
[143,] 0.1419475 0.28389497 0.85805252
> postscript(file="/var/wessaorg/rcomp/tmp/1dc8u1383232324.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$resid)
Warning message:
In x[, 1] - mysum$resid :
longer object length is not a multiple of shorter object length
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/258141383232324.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3det31383232324.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4nkkm1383232324.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5a14j1383232324.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 161
Frequency = 1
1 2 3 4 5 6
-1.157447270 0.490809395 -0.006705322 0.032099279 1.609238912 0.641571102
7 8 9 10 11 12
-1.676913345 0.764296641 -0.440668403 -0.263116829 0.750355699 -1.663357294
13 14 15 16 17 18
1.212107195 -1.090439969 0.733312277 -0.798063485 0.201201648 0.693636839
19 20 21 22 23 24
0.045758097 -1.090111746 -0.433954375 -0.087675164 -1.129085160 -0.909587478
25 26 27 28 29 30
-0.248372180 0.388865607 0.223471982 0.080945010 -0.145749515 0.076775303
31 32 33 34 35 36
-0.573862695 -0.729322770 -0.480505013 0.692530630 -0.020646264 -0.330958528
37 38 39 40 41 42
-0.354062794 -0.086135971 -0.439743125 -0.183286657 0.668658189 -0.030334130
43 44 45 46 47 48
-1.432742054 -0.809053332 -0.321765285 -0.032042135 -0.473684254 0.656620094
49 50 51 52 53 54
-0.137604033 0.828915511 -0.422315802 0.079158375 0.462350059 0.116258490
55 56 57 58 59 60
-0.070221502 0.637944010 -0.228671286 -0.502227295 -0.073093968 0.034044628
61 62 63 64 65 66
-0.402558781 0.079125533 0.866371950 -2.850156705 -1.119394394 1.002274636
67 68 69 70 71 72
0.195222487 0.018783702 -1.185397995 0.137718929 0.022812698 -0.978235551
73 74 75 76 77 78
0.796123532 -0.051119015 0.874840295 0.196301738 -0.165994244 0.197929041
79 80 81 83 84 85
0.065360602 -1.237901378 1.007742804 0.535464516 -0.188780235 -0.039696674
86 87 88 89 90 91
0.983249869 0.057127629 -0.256160718 -0.930040096 0.479317440 -0.170291662
92 93 94 95 96 97
-1.101994029 0.888023421 0.872164744 0.679352864 -0.338161950 0.021135695
98 99 100 101 102 103
-0.333370398 -0.186615676 0.023167879 -0.483886633 0.338864011 0.792520940
104 105 106 107 108 109
-0.048547081 0.024632175 -0.564799729 0.586635358 0.374285152 -0.686844146
110 111 112 113 114 115
-1.053230506 0.986177395 0.089848309 0.819234844 -2.726620956 0.624008427
116 117 118 119 120 121
0.942383708 -0.867719379 -0.098716063 0.691581990 0.993342539 -0.887501672
122 123 124 125 126 127
0.088952270 0.814573903 -0.474956376 -0.393471497 -0.300161676 0.609986782
128 129 130 131 132 133
-0.446194822 0.772280951 -0.245554154 -0.069300964 0.676815457 -0.391311679
134 135 136 137 138 139
-0.866957031 -0.016831042 0.931239192 -0.307382241 0.279655348 -0.718447164
140 141 142 143 144 145
-0.299055466 -0.950282986 -1.182581732 1.142755954 0.803079643 0.817797334
146 147 148 149 150 151
0.564171011 0.536900092 0.098504784 -0.086918699 0.651478644 0.797553224
152 153 154 155 156 157
1.184663784 0.159040623 -0.096796976 0.512517820 1.065492976 -1.101994029
158 159 160 161 162
0.023683535 0.772280951 0.643086004 1.307612576 1.137285946
> postscript(file="/var/wessaorg/rcomp/tmp/69du41383232324.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 = 161
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.157447270 NA
1 0.490809395 -1.157447270
2 -0.006705322 0.490809395
3 0.032099279 -0.006705322
4 1.609238912 0.032099279
5 0.641571102 1.609238912
6 -1.676913345 0.641571102
7 0.764296641 -1.676913345
8 -0.440668403 0.764296641
9 -0.263116829 -0.440668403
10 0.750355699 -0.263116829
11 -1.663357294 0.750355699
12 1.212107195 -1.663357294
13 -1.090439969 1.212107195
14 0.733312277 -1.090439969
15 -0.798063485 0.733312277
16 0.201201648 -0.798063485
17 0.693636839 0.201201648
18 0.045758097 0.693636839
19 -1.090111746 0.045758097
20 -0.433954375 -1.090111746
21 -0.087675164 -0.433954375
22 -1.129085160 -0.087675164
23 -0.909587478 -1.129085160
24 -0.248372180 -0.909587478
25 0.388865607 -0.248372180
26 0.223471982 0.388865607
27 0.080945010 0.223471982
28 -0.145749515 0.080945010
29 0.076775303 -0.145749515
30 -0.573862695 0.076775303
31 -0.729322770 -0.573862695
32 -0.480505013 -0.729322770
33 0.692530630 -0.480505013
34 -0.020646264 0.692530630
35 -0.330958528 -0.020646264
36 -0.354062794 -0.330958528
37 -0.086135971 -0.354062794
38 -0.439743125 -0.086135971
39 -0.183286657 -0.439743125
40 0.668658189 -0.183286657
41 -0.030334130 0.668658189
42 -1.432742054 -0.030334130
43 -0.809053332 -1.432742054
44 -0.321765285 -0.809053332
45 -0.032042135 -0.321765285
46 -0.473684254 -0.032042135
47 0.656620094 -0.473684254
48 -0.137604033 0.656620094
49 0.828915511 -0.137604033
50 -0.422315802 0.828915511
51 0.079158375 -0.422315802
52 0.462350059 0.079158375
53 0.116258490 0.462350059
54 -0.070221502 0.116258490
55 0.637944010 -0.070221502
56 -0.228671286 0.637944010
57 -0.502227295 -0.228671286
58 -0.073093968 -0.502227295
59 0.034044628 -0.073093968
60 -0.402558781 0.034044628
61 0.079125533 -0.402558781
62 0.866371950 0.079125533
63 -2.850156705 0.866371950
64 -1.119394394 -2.850156705
65 1.002274636 -1.119394394
66 0.195222487 1.002274636
67 0.018783702 0.195222487
68 -1.185397995 0.018783702
69 0.137718929 -1.185397995
70 0.022812698 0.137718929
71 -0.978235551 0.022812698
72 0.796123532 -0.978235551
73 -0.051119015 0.796123532
74 0.874840295 -0.051119015
75 0.196301738 0.874840295
76 -0.165994244 0.196301738
77 0.197929041 -0.165994244
78 0.065360602 0.197929041
79 -1.237901378 0.065360602
80 1.007742804 -1.237901378
81 0.535464516 1.007742804
82 -0.188780235 0.535464516
83 -0.039696674 -0.188780235
84 0.983249869 -0.039696674
85 0.057127629 0.983249869
86 -0.256160718 0.057127629
87 -0.930040096 -0.256160718
88 0.479317440 -0.930040096
89 -0.170291662 0.479317440
90 -1.101994029 -0.170291662
91 0.888023421 -1.101994029
92 0.872164744 0.888023421
93 0.679352864 0.872164744
94 -0.338161950 0.679352864
95 0.021135695 -0.338161950
96 -0.333370398 0.021135695
97 -0.186615676 -0.333370398
98 0.023167879 -0.186615676
99 -0.483886633 0.023167879
100 0.338864011 -0.483886633
101 0.792520940 0.338864011
102 -0.048547081 0.792520940
103 0.024632175 -0.048547081
104 -0.564799729 0.024632175
105 0.586635358 -0.564799729
106 0.374285152 0.586635358
107 -0.686844146 0.374285152
108 -1.053230506 -0.686844146
109 0.986177395 -1.053230506
110 0.089848309 0.986177395
111 0.819234844 0.089848309
112 -2.726620956 0.819234844
113 0.624008427 -2.726620956
114 0.942383708 0.624008427
115 -0.867719379 0.942383708
116 -0.098716063 -0.867719379
117 0.691581990 -0.098716063
118 0.993342539 0.691581990
119 -0.887501672 0.993342539
120 0.088952270 -0.887501672
121 0.814573903 0.088952270
122 -0.474956376 0.814573903
123 -0.393471497 -0.474956376
124 -0.300161676 -0.393471497
125 0.609986782 -0.300161676
126 -0.446194822 0.609986782
127 0.772280951 -0.446194822
128 -0.245554154 0.772280951
129 -0.069300964 -0.245554154
130 0.676815457 -0.069300964
131 -0.391311679 0.676815457
132 -0.866957031 -0.391311679
133 -0.016831042 -0.866957031
134 0.931239192 -0.016831042
135 -0.307382241 0.931239192
136 0.279655348 -0.307382241
137 -0.718447164 0.279655348
138 -0.299055466 -0.718447164
139 -0.950282986 -0.299055466
140 -1.182581732 -0.950282986
141 1.142755954 -1.182581732
142 0.803079643 1.142755954
143 0.817797334 0.803079643
144 0.564171011 0.817797334
145 0.536900092 0.564171011
146 0.098504784 0.536900092
147 -0.086918699 0.098504784
148 0.651478644 -0.086918699
149 0.797553224 0.651478644
150 1.184663784 0.797553224
151 0.159040623 1.184663784
152 -0.096796976 0.159040623
153 0.512517820 -0.096796976
154 1.065492976 0.512517820
155 -1.101994029 1.065492976
156 0.023683535 -1.101994029
157 0.772280951 0.023683535
158 0.643086004 0.772280951
159 1.307612576 0.643086004
160 1.137285946 1.307612576
161 NA 1.137285946
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.490809395 -1.157447270
[2,] -0.006705322 0.490809395
[3,] 0.032099279 -0.006705322
[4,] 1.609238912 0.032099279
[5,] 0.641571102 1.609238912
[6,] -1.676913345 0.641571102
[7,] 0.764296641 -1.676913345
[8,] -0.440668403 0.764296641
[9,] -0.263116829 -0.440668403
[10,] 0.750355699 -0.263116829
[11,] -1.663357294 0.750355699
[12,] 1.212107195 -1.663357294
[13,] -1.090439969 1.212107195
[14,] 0.733312277 -1.090439969
[15,] -0.798063485 0.733312277
[16,] 0.201201648 -0.798063485
[17,] 0.693636839 0.201201648
[18,] 0.045758097 0.693636839
[19,] -1.090111746 0.045758097
[20,] -0.433954375 -1.090111746
[21,] -0.087675164 -0.433954375
[22,] -1.129085160 -0.087675164
[23,] -0.909587478 -1.129085160
[24,] -0.248372180 -0.909587478
[25,] 0.388865607 -0.248372180
[26,] 0.223471982 0.388865607
[27,] 0.080945010 0.223471982
[28,] -0.145749515 0.080945010
[29,] 0.076775303 -0.145749515
[30,] -0.573862695 0.076775303
[31,] -0.729322770 -0.573862695
[32,] -0.480505013 -0.729322770
[33,] 0.692530630 -0.480505013
[34,] -0.020646264 0.692530630
[35,] -0.330958528 -0.020646264
[36,] -0.354062794 -0.330958528
[37,] -0.086135971 -0.354062794
[38,] -0.439743125 -0.086135971
[39,] -0.183286657 -0.439743125
[40,] 0.668658189 -0.183286657
[41,] -0.030334130 0.668658189
[42,] -1.432742054 -0.030334130
[43,] -0.809053332 -1.432742054
[44,] -0.321765285 -0.809053332
[45,] -0.032042135 -0.321765285
[46,] -0.473684254 -0.032042135
[47,] 0.656620094 -0.473684254
[48,] -0.137604033 0.656620094
[49,] 0.828915511 -0.137604033
[50,] -0.422315802 0.828915511
[51,] 0.079158375 -0.422315802
[52,] 0.462350059 0.079158375
[53,] 0.116258490 0.462350059
[54,] -0.070221502 0.116258490
[55,] 0.637944010 -0.070221502
[56,] -0.228671286 0.637944010
[57,] -0.502227295 -0.228671286
[58,] -0.073093968 -0.502227295
[59,] 0.034044628 -0.073093968
[60,] -0.402558781 0.034044628
[61,] 0.079125533 -0.402558781
[62,] 0.866371950 0.079125533
[63,] -2.850156705 0.866371950
[64,] -1.119394394 -2.850156705
[65,] 1.002274636 -1.119394394
[66,] 0.195222487 1.002274636
[67,] 0.018783702 0.195222487
[68,] -1.185397995 0.018783702
[69,] 0.137718929 -1.185397995
[70,] 0.022812698 0.137718929
[71,] -0.978235551 0.022812698
[72,] 0.796123532 -0.978235551
[73,] -0.051119015 0.796123532
[74,] 0.874840295 -0.051119015
[75,] 0.196301738 0.874840295
[76,] -0.165994244 0.196301738
[77,] 0.197929041 -0.165994244
[78,] 0.065360602 0.197929041
[79,] -1.237901378 0.065360602
[80,] 1.007742804 -1.237901378
[81,] 0.535464516 1.007742804
[82,] -0.188780235 0.535464516
[83,] -0.039696674 -0.188780235
[84,] 0.983249869 -0.039696674
[85,] 0.057127629 0.983249869
[86,] -0.256160718 0.057127629
[87,] -0.930040096 -0.256160718
[88,] 0.479317440 -0.930040096
[89,] -0.170291662 0.479317440
[90,] -1.101994029 -0.170291662
[91,] 0.888023421 -1.101994029
[92,] 0.872164744 0.888023421
[93,] 0.679352864 0.872164744
[94,] -0.338161950 0.679352864
[95,] 0.021135695 -0.338161950
[96,] -0.333370398 0.021135695
[97,] -0.186615676 -0.333370398
[98,] 0.023167879 -0.186615676
[99,] -0.483886633 0.023167879
[100,] 0.338864011 -0.483886633
[101,] 0.792520940 0.338864011
[102,] -0.048547081 0.792520940
[103,] 0.024632175 -0.048547081
[104,] -0.564799729 0.024632175
[105,] 0.586635358 -0.564799729
[106,] 0.374285152 0.586635358
[107,] -0.686844146 0.374285152
[108,] -1.053230506 -0.686844146
[109,] 0.986177395 -1.053230506
[110,] 0.089848309 0.986177395
[111,] 0.819234844 0.089848309
[112,] -2.726620956 0.819234844
[113,] 0.624008427 -2.726620956
[114,] 0.942383708 0.624008427
[115,] -0.867719379 0.942383708
[116,] -0.098716063 -0.867719379
[117,] 0.691581990 -0.098716063
[118,] 0.993342539 0.691581990
[119,] -0.887501672 0.993342539
[120,] 0.088952270 -0.887501672
[121,] 0.814573903 0.088952270
[122,] -0.474956376 0.814573903
[123,] -0.393471497 -0.474956376
[124,] -0.300161676 -0.393471497
[125,] 0.609986782 -0.300161676
[126,] -0.446194822 0.609986782
[127,] 0.772280951 -0.446194822
[128,] -0.245554154 0.772280951
[129,] -0.069300964 -0.245554154
[130,] 0.676815457 -0.069300964
[131,] -0.391311679 0.676815457
[132,] -0.866957031 -0.391311679
[133,] -0.016831042 -0.866957031
[134,] 0.931239192 -0.016831042
[135,] -0.307382241 0.931239192
[136,] 0.279655348 -0.307382241
[137,] -0.718447164 0.279655348
[138,] -0.299055466 -0.718447164
[139,] -0.950282986 -0.299055466
[140,] -1.182581732 -0.950282986
[141,] 1.142755954 -1.182581732
[142,] 0.803079643 1.142755954
[143,] 0.817797334 0.803079643
[144,] 0.564171011 0.817797334
[145,] 0.536900092 0.564171011
[146,] 0.098504784 0.536900092
[147,] -0.086918699 0.098504784
[148,] 0.651478644 -0.086918699
[149,] 0.797553224 0.651478644
[150,] 1.184663784 0.797553224
[151,] 0.159040623 1.184663784
[152,] -0.096796976 0.159040623
[153,] 0.512517820 -0.096796976
[154,] 1.065492976 0.512517820
[155,] -1.101994029 1.065492976
[156,] 0.023683535 -1.101994029
[157,] 0.772280951 0.023683535
[158,] 0.643086004 0.772280951
[159,] 1.307612576 0.643086004
[160,] 1.137285946 1.307612576
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.490809395 -1.157447270
2 -0.006705322 0.490809395
3 0.032099279 -0.006705322
4 1.609238912 0.032099279
5 0.641571102 1.609238912
6 -1.676913345 0.641571102
7 0.764296641 -1.676913345
8 -0.440668403 0.764296641
9 -0.263116829 -0.440668403
10 0.750355699 -0.263116829
11 -1.663357294 0.750355699
12 1.212107195 -1.663357294
13 -1.090439969 1.212107195
14 0.733312277 -1.090439969
15 -0.798063485 0.733312277
16 0.201201648 -0.798063485
17 0.693636839 0.201201648
18 0.045758097 0.693636839
19 -1.090111746 0.045758097
20 -0.433954375 -1.090111746
21 -0.087675164 -0.433954375
22 -1.129085160 -0.087675164
23 -0.909587478 -1.129085160
24 -0.248372180 -0.909587478
25 0.388865607 -0.248372180
26 0.223471982 0.388865607
27 0.080945010 0.223471982
28 -0.145749515 0.080945010
29 0.076775303 -0.145749515
30 -0.573862695 0.076775303
31 -0.729322770 -0.573862695
32 -0.480505013 -0.729322770
33 0.692530630 -0.480505013
34 -0.020646264 0.692530630
35 -0.330958528 -0.020646264
36 -0.354062794 -0.330958528
37 -0.086135971 -0.354062794
38 -0.439743125 -0.086135971
39 -0.183286657 -0.439743125
40 0.668658189 -0.183286657
41 -0.030334130 0.668658189
42 -1.432742054 -0.030334130
43 -0.809053332 -1.432742054
44 -0.321765285 -0.809053332
45 -0.032042135 -0.321765285
46 -0.473684254 -0.032042135
47 0.656620094 -0.473684254
48 -0.137604033 0.656620094
49 0.828915511 -0.137604033
50 -0.422315802 0.828915511
51 0.079158375 -0.422315802
52 0.462350059 0.079158375
53 0.116258490 0.462350059
54 -0.070221502 0.116258490
55 0.637944010 -0.070221502
56 -0.228671286 0.637944010
57 -0.502227295 -0.228671286
58 -0.073093968 -0.502227295
59 0.034044628 -0.073093968
60 -0.402558781 0.034044628
61 0.079125533 -0.402558781
62 0.866371950 0.079125533
63 -2.850156705 0.866371950
64 -1.119394394 -2.850156705
65 1.002274636 -1.119394394
66 0.195222487 1.002274636
67 0.018783702 0.195222487
68 -1.185397995 0.018783702
69 0.137718929 -1.185397995
70 0.022812698 0.137718929
71 -0.978235551 0.022812698
72 0.796123532 -0.978235551
73 -0.051119015 0.796123532
74 0.874840295 -0.051119015
75 0.196301738 0.874840295
76 -0.165994244 0.196301738
77 0.197929041 -0.165994244
78 0.065360602 0.197929041
79 -1.237901378 0.065360602
80 1.007742804 -1.237901378
81 0.535464516 1.007742804
82 -0.188780235 0.535464516
83 -0.039696674 -0.188780235
84 0.983249869 -0.039696674
85 0.057127629 0.983249869
86 -0.256160718 0.057127629
87 -0.930040096 -0.256160718
88 0.479317440 -0.930040096
89 -0.170291662 0.479317440
90 -1.101994029 -0.170291662
91 0.888023421 -1.101994029
92 0.872164744 0.888023421
93 0.679352864 0.872164744
94 -0.338161950 0.679352864
95 0.021135695 -0.338161950
96 -0.333370398 0.021135695
97 -0.186615676 -0.333370398
98 0.023167879 -0.186615676
99 -0.483886633 0.023167879
100 0.338864011 -0.483886633
101 0.792520940 0.338864011
102 -0.048547081 0.792520940
103 0.024632175 -0.048547081
104 -0.564799729 0.024632175
105 0.586635358 -0.564799729
106 0.374285152 0.586635358
107 -0.686844146 0.374285152
108 -1.053230506 -0.686844146
109 0.986177395 -1.053230506
110 0.089848309 0.986177395
111 0.819234844 0.089848309
112 -2.726620956 0.819234844
113 0.624008427 -2.726620956
114 0.942383708 0.624008427
115 -0.867719379 0.942383708
116 -0.098716063 -0.867719379
117 0.691581990 -0.098716063
118 0.993342539 0.691581990
119 -0.887501672 0.993342539
120 0.088952270 -0.887501672
121 0.814573903 0.088952270
122 -0.474956376 0.814573903
123 -0.393471497 -0.474956376
124 -0.300161676 -0.393471497
125 0.609986782 -0.300161676
126 -0.446194822 0.609986782
127 0.772280951 -0.446194822
128 -0.245554154 0.772280951
129 -0.069300964 -0.245554154
130 0.676815457 -0.069300964
131 -0.391311679 0.676815457
132 -0.866957031 -0.391311679
133 -0.016831042 -0.866957031
134 0.931239192 -0.016831042
135 -0.307382241 0.931239192
136 0.279655348 -0.307382241
137 -0.718447164 0.279655348
138 -0.299055466 -0.718447164
139 -0.950282986 -0.299055466
140 -1.182581732 -0.950282986
141 1.142755954 -1.182581732
142 0.803079643 1.142755954
143 0.817797334 0.803079643
144 0.564171011 0.817797334
145 0.536900092 0.564171011
146 0.098504784 0.536900092
147 -0.086918699 0.098504784
148 0.651478644 -0.086918699
149 0.797553224 0.651478644
150 1.184663784 0.797553224
151 0.159040623 1.184663784
152 -0.096796976 0.159040623
153 0.512517820 -0.096796976
154 1.065492976 0.512517820
155 -1.101994029 1.065492976
156 0.023683535 -1.101994029
157 0.772280951 0.023683535
158 0.643086004 0.772280951
159 1.307612576 0.643086004
160 1.137285946 1.307612576
> 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/7nb6l1383232324.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/81xuk1383232324.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/9g4691383232324.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/101eo91383232324.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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11m2rv1383232325.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12y21p1383232325.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> 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, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13awmq1383232325.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/144hni1383232325.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,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/157a021383232325.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,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ 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,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ 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,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ 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/16wk7b1383232325.tab")
+ }
>
> try(system("convert tmp/1dc8u1383232324.ps tmp/1dc8u1383232324.png",intern=TRUE))
character(0)
> try(system("convert tmp/258141383232324.ps tmp/258141383232324.png",intern=TRUE))
character(0)
> try(system("convert tmp/3det31383232324.ps tmp/3det31383232324.png",intern=TRUE))
character(0)
> try(system("convert tmp/4nkkm1383232324.ps tmp/4nkkm1383232324.png",intern=TRUE))
character(0)
> try(system("convert tmp/5a14j1383232324.ps tmp/5a14j1383232324.png",intern=TRUE))
character(0)
> try(system("convert tmp/69du41383232324.ps tmp/69du41383232324.png",intern=TRUE))
character(0)
> try(system("convert tmp/7nb6l1383232324.ps tmp/7nb6l1383232324.png",intern=TRUE))
character(0)
> try(system("convert tmp/81xuk1383232324.ps tmp/81xuk1383232324.png",intern=TRUE))
character(0)
> try(system("convert tmp/9g4691383232324.ps tmp/9g4691383232324.png",intern=TRUE))
character(0)
> try(system("convert tmp/101eo91383232324.ps tmp/101eo91383232324.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
12.144 2.474 14.611