R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(9
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+ ,10
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+ ,16
+ ,19
+ ,16
+ ,17
+ ,20)
+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('month'
+ ,'ConcernoverMistakes'
+ ,'Doubtsaboutactions'
+ ,'ParentalExpectations'
+ ,'ParentalCriticism'
+ ,'PersonalStandards'
+ ,'Organization
')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('month','ConcernoverMistakes','Doubtsaboutactions','ParentalExpectations','ParentalCriticism','PersonalStandards','Organization
'),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 = '6'
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
PersonalStandards month ConcernoverMistakes Doubtsaboutactions
1 24 9 24 14
2 25 9 25 11
3 30 9 17 6
4 19 9 18 12
5 22 9 18 8
6 22 9 16 10
7 25 10 20 10
8 23 10 16 11
9 17 10 18 16
10 21 10 17 11
11 19 10 23 13
12 19 10 30 12
13 15 10 23 8
14 16 10 18 12
15 23 10 15 11
16 27 10 12 4
17 22 10 21 9
18 14 10 15 8
19 22 10 20 8
20 23 10 31 14
21 23 10 27 15
22 21 10 34 16
23 19 10 21 9
24 18 10 31 14
25 20 10 19 11
26 23 10 16 8
27 25 10 20 9
28 19 10 21 9
29 24 10 22 9
30 22 10 17 9
31 25 10 24 10
32 26 10 25 16
33 29 10 26 11
34 32 10 25 8
35 25 10 17 9
36 29 10 32 16
37 28 10 33 11
38 17 10 13 16
39 28 10 32 12
40 29 10 25 12
41 26 10 29 14
42 25 10 22 9
43 14 10 18 10
44 25 10 17 9
45 26 10 20 10
46 20 10 15 12
47 18 10 20 14
48 32 10 33 14
49 25 10 29 10
50 25 10 23 14
51 23 10 26 16
52 21 10 18 9
53 20 10 20 10
54 15 10 11 6
55 30 10 28 8
56 24 10 26 13
57 26 10 22 10
58 24 10 17 8
59 22 10 12 7
60 14 10 14 15
61 24 10 17 9
62 24 10 21 10
63 24 10 19 12
64 24 10 18 13
65 19 10 10 10
66 31 10 29 11
67 22 10 31 8
68 27 10 19 9
69 19 10 9 13
70 25 10 20 11
71 20 10 28 8
72 21 10 19 9
73 27 10 30 9
74 23 10 29 15
75 25 10 26 9
76 20 10 23 10
77 21 10 13 14
78 22 10 21 12
79 23 10 19 12
80 25 10 28 11
81 25 10 23 14
82 17 10 18 6
83 19 10 21 12
84 25 10 20 8
85 19 10 23 14
86 20 10 21 11
87 26 10 21 10
88 23 10 15 14
89 27 10 28 12
90 17 10 19 10
91 17 10 26 14
92 19 10 10 5
93 17 10 16 11
94 22 10 22 10
95 21 10 19 9
96 32 10 31 10
97 21 10 31 16
98 21 10 29 13
99 18 10 19 9
100 18 10 22 10
101 23 10 23 10
102 19 10 15 7
103 20 10 20 9
104 21 10 18 8
105 20 10 23 14
106 17 10 25 14
107 18 10 21 8
108 19 10 24 9
109 22 10 25 14
110 15 10 17 14
111 14 10 13 8
112 18 10 28 8
113 24 10 21 8
114 35 10 25 7
115 29 10 9 6
116 21 10 16 8
117 25 10 19 6
118 20 10 17 11
119 22 10 25 14
120 13 10 20 11
121 26 10 29 11
122 17 10 14 11
123 25 10 22 14
124 20 10 15 8
125 19 10 19 20
126 21 10 20 11
127 22 10 15 8
128 24 10 20 11
129 21 10 18 10
130 26 10 33 14
131 24 10 22 11
132 16 10 16 9
133 23 10 17 9
134 18 10 16 8
135 16 10 21 10
136 26 10 26 13
137 19 10 18 13
138 21 10 18 12
139 21 10 17 8
140 22 10 22 13
141 23 10 30 14
142 29 10 30 12
143 21 10 24 14
144 21 10 21 15
145 23 10 21 13
146 27 10 29 16
147 25 10 31 9
148 21 10 20 9
149 10 10 16 9
150 20 10 22 8
151 26 10 20 7
152 24 10 28 16
153 29 10 38 11
154 19 10 22 9
155 24 10 20 11
156 19 10 17 9
157 24 10 28 14
158 22 10 22 13
159 17 10 31 16
ParentalExpectations ParentalCriticism Organization\r
1 11 12 26
2 7 8 23
3 17 8 25
4 10 8 23
5 12 9 19
6 12 7 29
7 11 4 25
8 11 11 21
9 12 7 22
10 13 7 25
11 14 12 24
12 16 10 18
13 11 10 22
14 10 8 15
15 11 8 22
16 15 4 28
17 9 9 20
18 11 8 12
19 17 7 24
20 17 11 20
21 11 9 21
22 18 11 20
23 14 13 21
24 10 8 23
25 11 8 28
26 15 9 24
27 15 6 24
28 13 9 24
29 16 9 23
30 13 6 23
31 9 6 29
32 18 16 24
33 18 5 18
34 12 7 25
35 17 9 21
36 9 6 26
37 9 6 22
38 12 5 22
39 18 12 22
40 12 7 23
41 18 10 30
42 14 9 23
43 15 8 17
44 16 5 23
45 10 8 23
46 11 8 25
47 14 10 24
48 9 6 24
49 12 8 23
50 17 7 21
51 5 4 24
52 12 8 24
53 12 8 28
54 6 4 16
55 24 20 20
56 12 8 29
57 12 8 27
58 14 6 22
59 7 4 28
60 13 8 16
61 12 9 25
62 13 6 24
63 14 7 28
64 8 9 24
65 11 5 23
66 9 5 30
67 11 8 24
68 13 8 21
69 10 6 25
70 11 8 25
71 12 7 22
72 9 7 23
73 15 9 26
74 18 11 23
75 15 6 25
76 12 8 21
77 13 6 25
78 14 9 24
79 10 8 29
80 13 6 22
81 13 10 27
82 11 8 26
83 13 8 22
84 16 10 24
85 8 5 27
86 16 7 24
87 11 5 24
88 9 8 29
89 16 14 22
90 12 7 21
91 14 8 24
92 8 6 24
93 9 5 23
94 15 6 20
95 11 10 27
96 21 12 26
97 14 9 25
98 18 12 21
99 12 7 21
100 13 8 19
101 15 10 21
102 12 6 21
103 19 10 16
104 15 10 22
105 11 10 29
106 11 5 15
107 10 7 17
108 13 10 15
109 15 11 21
110 12 6 21
111 12 7 19
112 16 12 24
113 9 11 20
114 18 11 17
115 8 11 23
116 13 5 24
117 17 8 14
118 9 6 19
119 15 9 24
120 8 4 13
121 7 4 22
122 12 7 16
123 14 11 19
124 6 6 25
125 8 7 25
126 17 8 23
127 10 4 24
128 11 8 26
129 14 9 26
130 11 8 25
131 13 11 18
132 12 8 21
133 11 5 26
134 9 4 23
135 12 8 23
136 20 10 22
137 12 6 20
138 13 9 13
139 12 9 24
140 12 13 15
141 9 9 14
142 15 10 22
143 24 20 10
144 7 5 24
145 17 11 22
146 11 6 24
147 17 9 19
148 11 7 20
149 12 9 13
150 14 10 20
151 11 9 22
152 16 8 24
153 21 7 29
154 14 6 12
155 20 13 20
156 13 6 21
157 11 8 24
158 15 10 22
159 19 16 20
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) month ConcernoverMistakes
22.450345 -1.499330 0.330887
Doubtsaboutactions ParentalExpectations ParentalCriticism
-0.356681 0.198030 0.006132
`Organization\r`
0.393046
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.5436 -2.3215 0.0417 2.0743 11.4538
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.450345 14.596982 1.538 0.12612
month -1.499330 1.442617 -1.039 0.30031
ConcernoverMistakes 0.330887 0.055592 5.952 1.76e-08 ***
Doubtsaboutactions -0.356681 0.107249 -3.326 0.00111 **
ParentalExpectations 0.198030 0.101714 1.947 0.05339 .
ParentalCriticism 0.006132 0.129654 0.047 0.96234
`Organization\r` 0.393046 0.072189 5.445 2.04e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.408 on 152 degrees of freedom
Multiple R-squared: 0.3715, Adjusted R-squared: 0.3467
F-statistic: 14.98 on 6 and 152 DF, p-value: 2.024e-13
> 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,] 0.16771489 0.33542978 0.8322851
[2,] 0.47621099 0.95242198 0.5237890
[3,] 0.40577612 0.81155225 0.5942239
[4,] 0.83064315 0.33871370 0.1693569
[5,] 0.75348023 0.49303954 0.2465198
[6,] 0.73548793 0.52902414 0.2645121
[7,] 0.65377440 0.69245120 0.3462256
[8,] 0.59336664 0.81326673 0.4066334
[9,] 0.57694920 0.84610160 0.4230508
[10,] 0.50026696 0.99946608 0.4997330
[11,] 0.49653597 0.99307193 0.5034640
[12,] 0.49273799 0.98547599 0.5072620
[13,] 0.42290067 0.84580134 0.5770993
[14,] 0.36224280 0.72448560 0.6377572
[15,] 0.38040301 0.76080602 0.6195970
[16,] 0.34926013 0.69852026 0.6507399
[17,] 0.28830006 0.57660012 0.7116999
[18,] 0.24764433 0.49528865 0.7523557
[19,] 0.23927925 0.47855849 0.7607208
[20,] 0.19792421 0.39584842 0.8020758
[21,] 0.15417241 0.30834481 0.8458276
[22,] 0.12600524 0.25201047 0.8739948
[23,] 0.16102607 0.32205213 0.8389739
[24,] 0.26983064 0.53966127 0.7301694
[25,] 0.54834851 0.90330298 0.4516515
[26,] 0.52712914 0.94574173 0.4728709
[27,] 0.60426159 0.79147681 0.3957384
[28,] 0.59969999 0.80060002 0.4003000
[29,] 0.55294325 0.89411350 0.4470567
[30,] 0.52071143 0.95857714 0.4792886
[31,] 0.61443759 0.77112481 0.3855624
[32,] 0.57974371 0.84051258 0.4202563
[33,] 0.53628142 0.92743715 0.4637186
[34,] 0.62438002 0.75123996 0.3756200
[35,] 0.59512318 0.80975363 0.4048768
[36,] 0.63009427 0.73981146 0.3699057
[37,] 0.57886695 0.84226609 0.4211330
[38,] 0.56530286 0.86939427 0.4346971
[39,] 0.68091467 0.63817066 0.3190853
[40,] 0.63746549 0.72506902 0.3625345
[41,] 0.62363294 0.75273411 0.3763671
[42,] 0.58396652 0.83206696 0.4160335
[43,] 0.53925765 0.92148470 0.4607423
[44,] 0.56489085 0.87021830 0.4351091
[45,] 0.52700228 0.94599544 0.4729977
[46,] 0.59021603 0.81956794 0.4097840
[47,] 0.55295456 0.89409088 0.4470454
[48,] 0.51468462 0.97063077 0.4853154
[49,] 0.48384700 0.96769399 0.5161530
[50,] 0.43680002 0.87360005 0.5632000
[51,] 0.39552301 0.79104602 0.6044770
[52,] 0.36678563 0.73357126 0.6332144
[53,] 0.32732595 0.65465190 0.6726740
[54,] 0.28726970 0.57453939 0.7127303
[55,] 0.32790454 0.65580907 0.6720955
[56,] 0.28712450 0.57424900 0.7128755
[57,] 0.30139502 0.60279005 0.6986050
[58,] 0.35842445 0.71684891 0.6415755
[59,] 0.43759874 0.87519748 0.5624013
[60,] 0.40005351 0.80010701 0.5999465
[61,] 0.38555617 0.77111234 0.6144438
[62,] 0.44979259 0.89958518 0.5502074
[63,] 0.40391055 0.80782110 0.5960895
[64,] 0.36243747 0.72487494 0.6375625
[65,] 0.32738432 0.65476863 0.6726157
[66,] 0.29579562 0.59159125 0.7042044
[67,] 0.27163644 0.54327287 0.7283636
[68,] 0.24954790 0.49909581 0.7504521
[69,] 0.21446500 0.42893000 0.7855350
[70,] 0.18282753 0.36565506 0.8171725
[71,] 0.15719170 0.31438340 0.8428083
[72,] 0.13769955 0.27539910 0.8623005
[73,] 0.22131305 0.44262611 0.7786869
[74,] 0.20327996 0.40655991 0.7967200
[75,] 0.17628176 0.35256352 0.8237182
[76,] 0.17686720 0.35373440 0.8231328
[77,] 0.17269126 0.34538253 0.8273087
[78,] 0.17979878 0.35959757 0.8202012
[79,] 0.16863029 0.33726059 0.8313697
[80,] 0.15861980 0.31723960 0.8413802
[81,] 0.16228863 0.32457725 0.8377114
[82,] 0.23483617 0.46967235 0.7651638
[83,] 0.20139934 0.40279868 0.7986007
[84,] 0.18475720 0.36951440 0.8152428
[85,] 0.15599789 0.31199579 0.8440021
[86,] 0.14097332 0.28194664 0.8590267
[87,] 0.14432965 0.28865930 0.8556703
[88,] 0.14577416 0.29154832 0.8542258
[89,] 0.14181193 0.28362385 0.8581881
[90,] 0.13459831 0.26919662 0.8654017
[91,] 0.12941649 0.25883297 0.8705835
[92,] 0.10574605 0.21149211 0.8942539
[93,] 0.08759527 0.17519054 0.9124047
[94,] 0.07016834 0.14033668 0.9298317
[95,] 0.05644097 0.11288195 0.9435590
[96,] 0.05965099 0.11930197 0.9403490
[97,] 0.04872863 0.09745727 0.9512714
[98,] 0.04258511 0.08517022 0.9574149
[99,] 0.03694512 0.07389025 0.9630549
[100,] 0.02809448 0.05618896 0.9719055
[101,] 0.02761203 0.05522407 0.9723880
[102,] 0.03476231 0.06952462 0.9652377
[103,] 0.14803191 0.29606383 0.8519681
[104,] 0.13611495 0.27222990 0.8638850
[105,] 0.54910356 0.90179287 0.4508964
[106,] 0.85096246 0.29807509 0.1490375
[107,] 0.81754678 0.36490645 0.1824532
[108,] 0.87251335 0.25497330 0.1274866
[109,] 0.84745489 0.30509022 0.1525451
[110,] 0.81875423 0.36249154 0.1812458
[111,] 0.85218284 0.29563432 0.1478172
[112,] 0.82884302 0.34231395 0.1711570
[113,] 0.78841726 0.42316549 0.2115827
[114,] 0.81978161 0.36043677 0.1802184
[115,] 0.77700405 0.44599191 0.2229960
[116,] 0.73743720 0.52512559 0.2625628
[117,] 0.68854908 0.62290184 0.3114509
[118,] 0.65156836 0.69686329 0.3484316
[119,] 0.60927369 0.78145263 0.3907263
[120,] 0.55029231 0.89941537 0.4497077
[121,] 0.48701467 0.97402935 0.5129853
[122,] 0.48471272 0.96942545 0.5152873
[123,] 0.48259711 0.96519422 0.5174029
[124,] 0.43205686 0.86411373 0.5679431
[125,] 0.38380813 0.76761625 0.6161919
[126,] 0.53144857 0.93710287 0.4685514
[127,] 0.49216882 0.98433763 0.5078312
[128,] 0.41869256 0.83738513 0.5813074
[129,] 0.42816753 0.85633505 0.5718325
[130,] 0.35658334 0.71316669 0.6434167
[131,] 0.32035944 0.64071888 0.6796406
[132,] 0.28591318 0.57182637 0.7140868
[133,] 0.33650441 0.67300882 0.6634956
[134,] 0.47809411 0.95618823 0.5219059
[135,] 0.40099275 0.80198550 0.5990072
[136,] 0.31988414 0.63976829 0.6801159
[137,] 0.30794447 0.61588895 0.6920555
[138,] 0.25474242 0.50948484 0.7452576
[139,] 0.15909156 0.31818312 0.8409084
[140,] 0.30410696 0.60821391 0.6958930
> postscript(file="/var/wessaorg/rcomp/tmp/14oq61322182280.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2g3uq1322182280.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/33syy1322182280.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4f3811322182280.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/57off1322182280.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 159
Frequency = 1
1 2 3 4 5 6
-0.37524384 1.21961360 4.31691182 -2.70158580 0.04167893 -2.50137472
7 8 9 10 11 12
2.46301590 3.67250046 -1.77241462 -0.60209952 -3.70970798 -4.40812214
13 14 15 16 17 18
-8.10066624 -1.05789204 3.62873963 2.99876951 1.10607342 -2.51084836
19 20 21 22 23 24
-2.06388010 -1.01590110 1.47173023 -3.49323099 -3.30165368 -5.79042791
25 26 27 28 29 30
-3.05308234 0.64346444 1.69499409 -4.25822995 0.20983737 0.47676191
31 32 33 34 35 36
-0.04891937 2.88190947 6.19334748 6.87878992 3.45233417 4.62320572
37 38 39 40 41 42
3.08109533 -0.10571356 1.94959574 6.09160514 -1.47647966 1.60589803
43 44 45 46 47 48
-5.54749680 2.88880342 4.42260730 -0.19371587 -3.34810030 7.36504744
49 50 51 52 53 54
0.04856169 3.26268108 2.19900634 -1.06140548 -3.93868093 -1.45816206
55 56 57 58 59 60
3.39526959 -1.24700661 1.79259015 2.31509605 0.65305525 -1.65143652
61 62 63 64 65 66
1.87030373 1.11684857 0.71564022 4.15130752 0.55184662 4.26641257
67 68 69 70 71 72
-4.52159004 5.58881352 1.35858379 2.79516699 -4.93473519 -0.39902372
73 74 75 76 77 78
-0.41836659 -1.37461249 -0.68337474 -2.18002397 1.79762499 -0.38621713
79 80 81 82 83 84
0.10858352 0.94341012 1.67813182 -6.71950933 -2.39596328 1.11575276
85 86 87 88 89 90
-3.30105411 -3.12669385 3.51904171 2.34352481 2.65694028 -3.85034261
91 92 93 94 95 96
-6.32115863 -1.03664565 -2.68073499 -0.03791724 -2.38566390 3.40084782
97 98 99 100 101 102
-3.66141063 -3.30801605 -3.20702366 -3.26107605 0.21362007 -1.59070440
103 104 105 106 107 108
-0.97729304 -1.23835145 -3.71189856 -1.84037333 -2.25723643 -1.71961445
109 110 111 112 113 114
-0.02756266 -3.75571150 -4.79229037 -8.54360996 2.73712739 11.45376109
115 116 117 118 119 120
10.01330569 -0.93594495 4.47796769 1.55442737 -1.19443423 -3.86966589
121 122 123 124 125 126
2.81296983 0.12600210 4.94922035 -0.61802346 0.93640710 -1.60692393
127 128 129 130 131 132
0.99516573 1.40212147 -1.89300860 0.56367630 3.47025305 -4.22049450
133 134 135 136 137 138
0.69981850 -2.74464564 -6.30434057 1.90780442 -0.05023425 4.12797552
139 140 141 142 143 144
-1.09333180 3.54851704 3.26976679 4.21772613 1.78935991 1.09456825
145 146 147 148 149 150
1.15019900 4.00589776 -0.39399585 0.05316496 -7.08226286 -2.57777896
151 152 153 154 155 156
3.54144686 0.33436837 -2.70715576 -0.05220384 1.94745920 -1.73714706
157 158 159
0.61115790 0.22150492 -6.72926210
> postscript(file="/var/wessaorg/rcomp/tmp/6srdb1322182280.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.37524384 NA
1 1.21961360 -0.37524384
2 4.31691182 1.21961360
3 -2.70158580 4.31691182
4 0.04167893 -2.70158580
5 -2.50137472 0.04167893
6 2.46301590 -2.50137472
7 3.67250046 2.46301590
8 -1.77241462 3.67250046
9 -0.60209952 -1.77241462
10 -3.70970798 -0.60209952
11 -4.40812214 -3.70970798
12 -8.10066624 -4.40812214
13 -1.05789204 -8.10066624
14 3.62873963 -1.05789204
15 2.99876951 3.62873963
16 1.10607342 2.99876951
17 -2.51084836 1.10607342
18 -2.06388010 -2.51084836
19 -1.01590110 -2.06388010
20 1.47173023 -1.01590110
21 -3.49323099 1.47173023
22 -3.30165368 -3.49323099
23 -5.79042791 -3.30165368
24 -3.05308234 -5.79042791
25 0.64346444 -3.05308234
26 1.69499409 0.64346444
27 -4.25822995 1.69499409
28 0.20983737 -4.25822995
29 0.47676191 0.20983737
30 -0.04891937 0.47676191
31 2.88190947 -0.04891937
32 6.19334748 2.88190947
33 6.87878992 6.19334748
34 3.45233417 6.87878992
35 4.62320572 3.45233417
36 3.08109533 4.62320572
37 -0.10571356 3.08109533
38 1.94959574 -0.10571356
39 6.09160514 1.94959574
40 -1.47647966 6.09160514
41 1.60589803 -1.47647966
42 -5.54749680 1.60589803
43 2.88880342 -5.54749680
44 4.42260730 2.88880342
45 -0.19371587 4.42260730
46 -3.34810030 -0.19371587
47 7.36504744 -3.34810030
48 0.04856169 7.36504744
49 3.26268108 0.04856169
50 2.19900634 3.26268108
51 -1.06140548 2.19900634
52 -3.93868093 -1.06140548
53 -1.45816206 -3.93868093
54 3.39526959 -1.45816206
55 -1.24700661 3.39526959
56 1.79259015 -1.24700661
57 2.31509605 1.79259015
58 0.65305525 2.31509605
59 -1.65143652 0.65305525
60 1.87030373 -1.65143652
61 1.11684857 1.87030373
62 0.71564022 1.11684857
63 4.15130752 0.71564022
64 0.55184662 4.15130752
65 4.26641257 0.55184662
66 -4.52159004 4.26641257
67 5.58881352 -4.52159004
68 1.35858379 5.58881352
69 2.79516699 1.35858379
70 -4.93473519 2.79516699
71 -0.39902372 -4.93473519
72 -0.41836659 -0.39902372
73 -1.37461249 -0.41836659
74 -0.68337474 -1.37461249
75 -2.18002397 -0.68337474
76 1.79762499 -2.18002397
77 -0.38621713 1.79762499
78 0.10858352 -0.38621713
79 0.94341012 0.10858352
80 1.67813182 0.94341012
81 -6.71950933 1.67813182
82 -2.39596328 -6.71950933
83 1.11575276 -2.39596328
84 -3.30105411 1.11575276
85 -3.12669385 -3.30105411
86 3.51904171 -3.12669385
87 2.34352481 3.51904171
88 2.65694028 2.34352481
89 -3.85034261 2.65694028
90 -6.32115863 -3.85034261
91 -1.03664565 -6.32115863
92 -2.68073499 -1.03664565
93 -0.03791724 -2.68073499
94 -2.38566390 -0.03791724
95 3.40084782 -2.38566390
96 -3.66141063 3.40084782
97 -3.30801605 -3.66141063
98 -3.20702366 -3.30801605
99 -3.26107605 -3.20702366
100 0.21362007 -3.26107605
101 -1.59070440 0.21362007
102 -0.97729304 -1.59070440
103 -1.23835145 -0.97729304
104 -3.71189856 -1.23835145
105 -1.84037333 -3.71189856
106 -2.25723643 -1.84037333
107 -1.71961445 -2.25723643
108 -0.02756266 -1.71961445
109 -3.75571150 -0.02756266
110 -4.79229037 -3.75571150
111 -8.54360996 -4.79229037
112 2.73712739 -8.54360996
113 11.45376109 2.73712739
114 10.01330569 11.45376109
115 -0.93594495 10.01330569
116 4.47796769 -0.93594495
117 1.55442737 4.47796769
118 -1.19443423 1.55442737
119 -3.86966589 -1.19443423
120 2.81296983 -3.86966589
121 0.12600210 2.81296983
122 4.94922035 0.12600210
123 -0.61802346 4.94922035
124 0.93640710 -0.61802346
125 -1.60692393 0.93640710
126 0.99516573 -1.60692393
127 1.40212147 0.99516573
128 -1.89300860 1.40212147
129 0.56367630 -1.89300860
130 3.47025305 0.56367630
131 -4.22049450 3.47025305
132 0.69981850 -4.22049450
133 -2.74464564 0.69981850
134 -6.30434057 -2.74464564
135 1.90780442 -6.30434057
136 -0.05023425 1.90780442
137 4.12797552 -0.05023425
138 -1.09333180 4.12797552
139 3.54851704 -1.09333180
140 3.26976679 3.54851704
141 4.21772613 3.26976679
142 1.78935991 4.21772613
143 1.09456825 1.78935991
144 1.15019900 1.09456825
145 4.00589776 1.15019900
146 -0.39399585 4.00589776
147 0.05316496 -0.39399585
148 -7.08226286 0.05316496
149 -2.57777896 -7.08226286
150 3.54144686 -2.57777896
151 0.33436837 3.54144686
152 -2.70715576 0.33436837
153 -0.05220384 -2.70715576
154 1.94745920 -0.05220384
155 -1.73714706 1.94745920
156 0.61115790 -1.73714706
157 0.22150492 0.61115790
158 -6.72926210 0.22150492
159 NA -6.72926210
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.21961360 -0.37524384
[2,] 4.31691182 1.21961360
[3,] -2.70158580 4.31691182
[4,] 0.04167893 -2.70158580
[5,] -2.50137472 0.04167893
[6,] 2.46301590 -2.50137472
[7,] 3.67250046 2.46301590
[8,] -1.77241462 3.67250046
[9,] -0.60209952 -1.77241462
[10,] -3.70970798 -0.60209952
[11,] -4.40812214 -3.70970798
[12,] -8.10066624 -4.40812214
[13,] -1.05789204 -8.10066624
[14,] 3.62873963 -1.05789204
[15,] 2.99876951 3.62873963
[16,] 1.10607342 2.99876951
[17,] -2.51084836 1.10607342
[18,] -2.06388010 -2.51084836
[19,] -1.01590110 -2.06388010
[20,] 1.47173023 -1.01590110
[21,] -3.49323099 1.47173023
[22,] -3.30165368 -3.49323099
[23,] -5.79042791 -3.30165368
[24,] -3.05308234 -5.79042791
[25,] 0.64346444 -3.05308234
[26,] 1.69499409 0.64346444
[27,] -4.25822995 1.69499409
[28,] 0.20983737 -4.25822995
[29,] 0.47676191 0.20983737
[30,] -0.04891937 0.47676191
[31,] 2.88190947 -0.04891937
[32,] 6.19334748 2.88190947
[33,] 6.87878992 6.19334748
[34,] 3.45233417 6.87878992
[35,] 4.62320572 3.45233417
[36,] 3.08109533 4.62320572
[37,] -0.10571356 3.08109533
[38,] 1.94959574 -0.10571356
[39,] 6.09160514 1.94959574
[40,] -1.47647966 6.09160514
[41,] 1.60589803 -1.47647966
[42,] -5.54749680 1.60589803
[43,] 2.88880342 -5.54749680
[44,] 4.42260730 2.88880342
[45,] -0.19371587 4.42260730
[46,] -3.34810030 -0.19371587
[47,] 7.36504744 -3.34810030
[48,] 0.04856169 7.36504744
[49,] 3.26268108 0.04856169
[50,] 2.19900634 3.26268108
[51,] -1.06140548 2.19900634
[52,] -3.93868093 -1.06140548
[53,] -1.45816206 -3.93868093
[54,] 3.39526959 -1.45816206
[55,] -1.24700661 3.39526959
[56,] 1.79259015 -1.24700661
[57,] 2.31509605 1.79259015
[58,] 0.65305525 2.31509605
[59,] -1.65143652 0.65305525
[60,] 1.87030373 -1.65143652
[61,] 1.11684857 1.87030373
[62,] 0.71564022 1.11684857
[63,] 4.15130752 0.71564022
[64,] 0.55184662 4.15130752
[65,] 4.26641257 0.55184662
[66,] -4.52159004 4.26641257
[67,] 5.58881352 -4.52159004
[68,] 1.35858379 5.58881352
[69,] 2.79516699 1.35858379
[70,] -4.93473519 2.79516699
[71,] -0.39902372 -4.93473519
[72,] -0.41836659 -0.39902372
[73,] -1.37461249 -0.41836659
[74,] -0.68337474 -1.37461249
[75,] -2.18002397 -0.68337474
[76,] 1.79762499 -2.18002397
[77,] -0.38621713 1.79762499
[78,] 0.10858352 -0.38621713
[79,] 0.94341012 0.10858352
[80,] 1.67813182 0.94341012
[81,] -6.71950933 1.67813182
[82,] -2.39596328 -6.71950933
[83,] 1.11575276 -2.39596328
[84,] -3.30105411 1.11575276
[85,] -3.12669385 -3.30105411
[86,] 3.51904171 -3.12669385
[87,] 2.34352481 3.51904171
[88,] 2.65694028 2.34352481
[89,] -3.85034261 2.65694028
[90,] -6.32115863 -3.85034261
[91,] -1.03664565 -6.32115863
[92,] -2.68073499 -1.03664565
[93,] -0.03791724 -2.68073499
[94,] -2.38566390 -0.03791724
[95,] 3.40084782 -2.38566390
[96,] -3.66141063 3.40084782
[97,] -3.30801605 -3.66141063
[98,] -3.20702366 -3.30801605
[99,] -3.26107605 -3.20702366
[100,] 0.21362007 -3.26107605
[101,] -1.59070440 0.21362007
[102,] -0.97729304 -1.59070440
[103,] -1.23835145 -0.97729304
[104,] -3.71189856 -1.23835145
[105,] -1.84037333 -3.71189856
[106,] -2.25723643 -1.84037333
[107,] -1.71961445 -2.25723643
[108,] -0.02756266 -1.71961445
[109,] -3.75571150 -0.02756266
[110,] -4.79229037 -3.75571150
[111,] -8.54360996 -4.79229037
[112,] 2.73712739 -8.54360996
[113,] 11.45376109 2.73712739
[114,] 10.01330569 11.45376109
[115,] -0.93594495 10.01330569
[116,] 4.47796769 -0.93594495
[117,] 1.55442737 4.47796769
[118,] -1.19443423 1.55442737
[119,] -3.86966589 -1.19443423
[120,] 2.81296983 -3.86966589
[121,] 0.12600210 2.81296983
[122,] 4.94922035 0.12600210
[123,] -0.61802346 4.94922035
[124,] 0.93640710 -0.61802346
[125,] -1.60692393 0.93640710
[126,] 0.99516573 -1.60692393
[127,] 1.40212147 0.99516573
[128,] -1.89300860 1.40212147
[129,] 0.56367630 -1.89300860
[130,] 3.47025305 0.56367630
[131,] -4.22049450 3.47025305
[132,] 0.69981850 -4.22049450
[133,] -2.74464564 0.69981850
[134,] -6.30434057 -2.74464564
[135,] 1.90780442 -6.30434057
[136,] -0.05023425 1.90780442
[137,] 4.12797552 -0.05023425
[138,] -1.09333180 4.12797552
[139,] 3.54851704 -1.09333180
[140,] 3.26976679 3.54851704
[141,] 4.21772613 3.26976679
[142,] 1.78935991 4.21772613
[143,] 1.09456825 1.78935991
[144,] 1.15019900 1.09456825
[145,] 4.00589776 1.15019900
[146,] -0.39399585 4.00589776
[147,] 0.05316496 -0.39399585
[148,] -7.08226286 0.05316496
[149,] -2.57777896 -7.08226286
[150,] 3.54144686 -2.57777896
[151,] 0.33436837 3.54144686
[152,] -2.70715576 0.33436837
[153,] -0.05220384 -2.70715576
[154,] 1.94745920 -0.05220384
[155,] -1.73714706 1.94745920
[156,] 0.61115790 -1.73714706
[157,] 0.22150492 0.61115790
[158,] -6.72926210 0.22150492
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.21961360 -0.37524384
2 4.31691182 1.21961360
3 -2.70158580 4.31691182
4 0.04167893 -2.70158580
5 -2.50137472 0.04167893
6 2.46301590 -2.50137472
7 3.67250046 2.46301590
8 -1.77241462 3.67250046
9 -0.60209952 -1.77241462
10 -3.70970798 -0.60209952
11 -4.40812214 -3.70970798
12 -8.10066624 -4.40812214
13 -1.05789204 -8.10066624
14 3.62873963 -1.05789204
15 2.99876951 3.62873963
16 1.10607342 2.99876951
17 -2.51084836 1.10607342
18 -2.06388010 -2.51084836
19 -1.01590110 -2.06388010
20 1.47173023 -1.01590110
21 -3.49323099 1.47173023
22 -3.30165368 -3.49323099
23 -5.79042791 -3.30165368
24 -3.05308234 -5.79042791
25 0.64346444 -3.05308234
26 1.69499409 0.64346444
27 -4.25822995 1.69499409
28 0.20983737 -4.25822995
29 0.47676191 0.20983737
30 -0.04891937 0.47676191
31 2.88190947 -0.04891937
32 6.19334748 2.88190947
33 6.87878992 6.19334748
34 3.45233417 6.87878992
35 4.62320572 3.45233417
36 3.08109533 4.62320572
37 -0.10571356 3.08109533
38 1.94959574 -0.10571356
39 6.09160514 1.94959574
40 -1.47647966 6.09160514
41 1.60589803 -1.47647966
42 -5.54749680 1.60589803
43 2.88880342 -5.54749680
44 4.42260730 2.88880342
45 -0.19371587 4.42260730
46 -3.34810030 -0.19371587
47 7.36504744 -3.34810030
48 0.04856169 7.36504744
49 3.26268108 0.04856169
50 2.19900634 3.26268108
51 -1.06140548 2.19900634
52 -3.93868093 -1.06140548
53 -1.45816206 -3.93868093
54 3.39526959 -1.45816206
55 -1.24700661 3.39526959
56 1.79259015 -1.24700661
57 2.31509605 1.79259015
58 0.65305525 2.31509605
59 -1.65143652 0.65305525
60 1.87030373 -1.65143652
61 1.11684857 1.87030373
62 0.71564022 1.11684857
63 4.15130752 0.71564022
64 0.55184662 4.15130752
65 4.26641257 0.55184662
66 -4.52159004 4.26641257
67 5.58881352 -4.52159004
68 1.35858379 5.58881352
69 2.79516699 1.35858379
70 -4.93473519 2.79516699
71 -0.39902372 -4.93473519
72 -0.41836659 -0.39902372
73 -1.37461249 -0.41836659
74 -0.68337474 -1.37461249
75 -2.18002397 -0.68337474
76 1.79762499 -2.18002397
77 -0.38621713 1.79762499
78 0.10858352 -0.38621713
79 0.94341012 0.10858352
80 1.67813182 0.94341012
81 -6.71950933 1.67813182
82 -2.39596328 -6.71950933
83 1.11575276 -2.39596328
84 -3.30105411 1.11575276
85 -3.12669385 -3.30105411
86 3.51904171 -3.12669385
87 2.34352481 3.51904171
88 2.65694028 2.34352481
89 -3.85034261 2.65694028
90 -6.32115863 -3.85034261
91 -1.03664565 -6.32115863
92 -2.68073499 -1.03664565
93 -0.03791724 -2.68073499
94 -2.38566390 -0.03791724
95 3.40084782 -2.38566390
96 -3.66141063 3.40084782
97 -3.30801605 -3.66141063
98 -3.20702366 -3.30801605
99 -3.26107605 -3.20702366
100 0.21362007 -3.26107605
101 -1.59070440 0.21362007
102 -0.97729304 -1.59070440
103 -1.23835145 -0.97729304
104 -3.71189856 -1.23835145
105 -1.84037333 -3.71189856
106 -2.25723643 -1.84037333
107 -1.71961445 -2.25723643
108 -0.02756266 -1.71961445
109 -3.75571150 -0.02756266
110 -4.79229037 -3.75571150
111 -8.54360996 -4.79229037
112 2.73712739 -8.54360996
113 11.45376109 2.73712739
114 10.01330569 11.45376109
115 -0.93594495 10.01330569
116 4.47796769 -0.93594495
117 1.55442737 4.47796769
118 -1.19443423 1.55442737
119 -3.86966589 -1.19443423
120 2.81296983 -3.86966589
121 0.12600210 2.81296983
122 4.94922035 0.12600210
123 -0.61802346 4.94922035
124 0.93640710 -0.61802346
125 -1.60692393 0.93640710
126 0.99516573 -1.60692393
127 1.40212147 0.99516573
128 -1.89300860 1.40212147
129 0.56367630 -1.89300860
130 3.47025305 0.56367630
131 -4.22049450 3.47025305
132 0.69981850 -4.22049450
133 -2.74464564 0.69981850
134 -6.30434057 -2.74464564
135 1.90780442 -6.30434057
136 -0.05023425 1.90780442
137 4.12797552 -0.05023425
138 -1.09333180 4.12797552
139 3.54851704 -1.09333180
140 3.26976679 3.54851704
141 4.21772613 3.26976679
142 1.78935991 4.21772613
143 1.09456825 1.78935991
144 1.15019900 1.09456825
145 4.00589776 1.15019900
146 -0.39399585 4.00589776
147 0.05316496 -0.39399585
148 -7.08226286 0.05316496
149 -2.57777896 -7.08226286
150 3.54144686 -2.57777896
151 0.33436837 3.54144686
152 -2.70715576 0.33436837
153 -0.05220384 -2.70715576
154 1.94745920 -0.05220384
155 -1.73714706 1.94745920
156 0.61115790 -1.73714706
157 0.22150492 0.61115790
158 -6.72926210 0.22150492
> 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/71rm21322182280.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/8ehpi1322182280.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/95fpm1322182280.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/10ijcm1322182280.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11cbkr1322182280.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12fttm1322182280.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13i82a1322182280.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/140fm11322182280.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15784l1322182280.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16pt3v1322182281.tab")
+ }
>
> try(system("convert tmp/14oq61322182280.ps tmp/14oq61322182280.png",intern=TRUE))
character(0)
> try(system("convert tmp/2g3uq1322182280.ps tmp/2g3uq1322182280.png",intern=TRUE))
character(0)
> try(system("convert tmp/33syy1322182280.ps tmp/33syy1322182280.png",intern=TRUE))
character(0)
> try(system("convert tmp/4f3811322182280.ps tmp/4f3811322182280.png",intern=TRUE))
character(0)
> try(system("convert tmp/57off1322182280.ps tmp/57off1322182280.png",intern=TRUE))
character(0)
> try(system("convert tmp/6srdb1322182280.ps tmp/6srdb1322182280.png",intern=TRUE))
character(0)
> try(system("convert tmp/71rm21322182280.ps tmp/71rm21322182280.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ehpi1322182280.ps tmp/8ehpi1322182280.png",intern=TRUE))
character(0)
> try(system("convert tmp/95fpm1322182280.ps tmp/95fpm1322182280.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ijcm1322182280.ps tmp/10ijcm1322182280.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.860 0.638 5.518