R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(1
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+ ,2)
+ ,dim=c(8
+ ,160)
+ ,dimnames=list(c('X1'
+ ,'X2'
+ ,'X3'
+ ,'X4'
+ ,'X5'
+ ,'X6'
+ ,'X7'
+ ,'X8')
+ ,1:160))
> y <- array(NA,dim=c(8,160),dimnames=list(c('X1','X2','X3','X4','X5','X6','X7','X8'),1:160))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
X1 X2 X3 X4 X5 X6 X7 X8
1 1 1 4 4 3 1 21 2
2 4 1 3 3 1 1 21 1
3 5 2 2 3 2 1 24 1
4 2 1 4 5 4 2 21 1
5 1 1 3 4 1 2 21 2
6 1 1 5 4 2 22 2 2
7 1 3 5 3 2 22 1 1
8 1 5 5 1 1 20 2 1
9 1 3 3 1 1 21 1 1
10 1 4 4 3 21 2 3 2
11 5 5 5 2 21 1 1 1
12 3 3 3 1 22 2 1 1
13 3 4 1 1 22 1 1 1
14 4 4 2 1 23 2 2 1
15 4 4 2 1 23 2 4 2
16 4 4 5 2 21 2 1 1
17 3 5 2 2 24 1 1 1
18 5 3 2 1 23 1 1 1
19 4 5 2 2 21 1 2 1
20 3 3 2 1 23 1 3 1
21 3 4 3 2 32 2 1 1
22 3 4 3 1 21 2 1 2
23 3 4 3 2 21 1 1 1
24 4 3 1 2 21 1 1 2
25 5 5 1 1 21 2 1 1
26 4 4 4 2 21 2 2 4
27 5 5 1 1 20 2 1 1
28 3 2 3 1 24 2 1 1
29 4 5 2 1 22 2 1 1
30 2 4 1 2 22 2 1 1
31 5 5 2 2 21 1 1 1
32 2 4 1 2 21 1 1 1
33 3 5 2 1 21 2 1 1
34 3 3 1 2 21 1 1 1
35 3 3 1 1 23 2 1 1
36 3 4 1 1 23 2 1 1
37 4 4 1 2 21 2 1 1
38 3 5 3 1 20 1 1 1
39 5 5 3 2 21 2 1 1
40 2 3 1 1 20 1 1 1
41 3 5 3 2 21 1 1 1
42 3 5 1 1 22 2 4 1
43 4 4 2 2 21 1 1 1
44 5 5 3 1 22 2 1 1
45 3 3 2 1 22 2 4 3
46 3 4 1 2 22 1 2 2
47 3 5 1 1 22 1 2 1
48 3 3 1 1 21 2 1 1
49 3 5 1 1 21 2 1 1
50 3 4 1 2 21 1 2 2
51 3 3 3 1 23 2 1 1
52 4 5 2 2 23 2 1 1
53 3 4 2 2 23 1 1 1
54 2 5 2 1 22 1 1 1
55 3 3 4 1 24 2 1 1
56 3 5 3 1 23 2 1 1
57 3 4 3 2 21 2 2 1
58 4 5 3 1 22 1 1 1
59 2 4 1 2 22 1 1 1
60 5 5 3 2 21 1 1 1
61 4 5 1 1 21 1 2 2
62 2 5 1 1 21 1 1 1
63 2 4 1 2 21 1 2 1
64 3 5 1 1 20 2 4 1
65 4 4 3 2 22 2 1 1
66 5 5 3 2 22 2 1 1
67 3 4 1 1 22 2 1 1
68 3 3 1 1 22 1 2 1
69 4 4 2 1 21 2 3 2
70 3 3 2 1 23 2 1 1
71 4 5 1 2 21 1 1 1
72 3 4 4 1 22 1 1 1
73 3 4 3 2 23 1 1 1
74 4 4 2 1 21 2 1 1
75 2 4 1 1 24 1 1 1
76 2 4 1 1 24 1 1 1
77 4 3 3 2 20 1 4 1
78 2 4 2 2 21 2 1 1
79 4 3 1 1 22 1 1 1
80 3 4 3 1 20 2 2 1
81 5 5 3 1 21 1 1 1
82 3 4 1 1 21 2 1 1
83 3 3 2 1 21 2 1 2
84 4 4 1 1 22 2 1 1
85 3 4 2 2 22 2 1 1
86 3 4 2 1 22 1 1 1
87 4 5 2 1 21 2 1 1
88 3 4 1 1 22 2 1 1
89 3 5 2 2 21 1 2 3
90 3 4 1 1 21 2 1 1
91 2 2 1 1 21 2 1 1
92 4 5 3 2 22 1 1 1
93 3 5 2 2 22 1 1 2
94 5 5 3 2 22 2 1 1
95 3 4 3 1 22 2 2 1
96 5 5 4 1 21 2 1 1
97 4 5 1 2 21 1 5 1
98 3 4 4 1 20 2 1 1
99 3 4 1 1 21 1 1 1
100 5 5 1 1 21 2 1 1
101 4 4 3 2 23 1 2 1
102 3 5 3 1 23 1 1 2
103 5 5 3 1 22 2 3 3
104 4 4 4 1 25 2 1 1
105 2 4 2 2 21 1 1 1
106 5 5 1 2 21 2 1 1
107 4 4 2 1 22 2 3 1
108 2 4 1 1 21 1 1 1
109 2 2 2 1 22 1 1 1
110 5 3 1 21 1 1 1 3
111 4 5 2 22 1 1 1 3
112 4 2 1 21 2 1 1 4
113 2 1 2 21 1 1 1 2
114 5 1 1 23 1 2 1 4
115 4 2 2 22 1 2 1 3
116 4 3 1 1 4 1 3 4
117 1 1 23 2 4 3 3 4
118 3 1 22 1 1 1 2 5
119 1 1 20 2 1 1 4 3
120 1 1 25 1 1 1 3 1
121 1 1 2 1 1 3 5 1
122 1 22 2 4 3 3 3 3
123 1 22 1 1 2 4 5 3
124 1 22 1 1 1 3 3 1
125 1 22 2 1 1 4 4 3
126 2 2 1 1 4 4 2 1
127 21 1 3 1 3 4 3 2
128 23 2 1 1 3 4 2 2
129 21 2 1 2 4 4 2 2
130 21 2 1 3 4 5 5 2
131 20 2 4 1 3 4 4 2
132 21 1 4 1 5 5 1 1
133 24 2 1 1 3 3 1 1
134 23 2 2 3 4 4 3 1
135 22 2 1 1 4 4 3 2
136 21 2 2 2 2 4 2 1
137 22 1 1 1 4 4 1 1
138 21 1 3 1 2 4 1 1
139 21 2 2 1 4 5 4 2
140 21 2 2 1 4 5 3 1
141 22 2 1 1 4 5 3 1
142 20 2 1 1 3 5 3 2
143 21 1 2 1 5 5 3 2
144 21 2 1 1 4 4 3 1
145 22 2 2 3 3 4 2 1
146 21 2 2 2 3 4 1 1
147 23 1 1 1 4 4 2 2
148 23 1 1 1 5 5 1 2
149 24 2 2 1 3 4 2 2
150 32 2 1 1 3 5 1 1
151 22 2 2 1 2 4 4 1
152 22 1 1 2 3 5 1 1
153 20 2 1 1 5 5 1 1
154 21 2 1 1 3 5 1 1
155 23 2 1 1 3 4 3 2
156 21 1 1 1 4 4 2 2
157 21 1 1 1 2 4 1 1
158 23 2 1 2 4 5 1 1
159 24 1 1 1 3 4 3 1
160 22 2 1 1 4 4 3 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X2 X3 X4 X5 X6
31.7133 -0.8884 -0.9113 -0.9343 -0.8577 -0.7398
X7 X8
-0.8258 -0.2065
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-19.6558 -1.8393 0.1612 2.1229 11.2132
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 31.71334 1.39493 22.735 < 2e-16 ***
X2 -0.88838 0.09680 -9.178 2.99e-16 ***
X3 -0.91126 0.09799 -9.299 < 2e-16 ***
X4 -0.93427 0.09203 -10.151 < 2e-16 ***
X5 -0.85770 0.04569 -18.771 < 2e-16 ***
X6 -0.73977 0.11220 -6.593 6.68e-10 ***
X7 -0.82577 0.09138 -9.037 6.93e-16 ***
X8 -0.20655 0.48264 -0.428 0.669
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.784 on 152 degrees of freedom
Multiple R-squared: 0.78, Adjusted R-squared: 0.7699
F-statistic: 76.99 on 7 and 152 DF, p-value: < 2.2e-16
> 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,] 1.554640e-01 3.109279e-01 0.84453605
[2,] 1.109129e-01 2.218258e-01 0.88908710
[3,] 4.958616e-02 9.917231e-02 0.95041384
[4,] 2.084049e-02 4.168097e-02 0.97915951
[5,] 1.079522e-02 2.159045e-02 0.98920478
[6,] 4.197091e-03 8.394182e-03 0.99580291
[7,] 1.540236e-03 3.080473e-03 0.99845976
[8,] 7.252664e-04 1.450533e-03 0.99927473
[9,] 3.927656e-04 7.855313e-04 0.99960723
[10,] 2.665503e-04 5.331006e-04 0.99973345
[11,] 1.432016e-04 2.864032e-04 0.99985680
[12,] 5.155327e-05 1.031065e-04 0.99994845
[13,] 1.898798e-05 3.797596e-05 0.99998101
[14,] 1.279628e-05 2.559256e-05 0.99998720
[15,] 6.170403e-06 1.234081e-05 0.99999383
[16,] 6.915606e-06 1.383121e-05 0.99999308
[17,] 3.022339e-06 6.044677e-06 0.99999698
[18,] 1.105292e-06 2.210585e-06 0.99999889
[19,] 3.686335e-07 7.372670e-07 0.99999963
[20,] 3.012198e-07 6.024397e-07 0.99999970
[21,] 1.814300e-07 3.628599e-07 0.99999982
[22,] 1.430032e-07 2.860064e-07 0.99999986
[23,] 7.041163e-08 1.408233e-07 0.99999993
[24,] 2.398306e-08 4.796613e-08 0.99999998
[25,] 9.112796e-09 1.822559e-08 0.99999999
[26,] 3.548080e-09 7.096160e-09 1.00000000
[27,] 1.476685e-09 2.953370e-09 1.00000000
[28,] 7.370060e-10 1.474012e-09 1.00000000
[29,] 5.173984e-10 1.034797e-09 1.00000000
[30,] 3.680181e-10 7.360362e-10 1.00000000
[31,] 1.483818e-10 2.967635e-10 1.00000000
[32,] 6.519231e-11 1.303846e-10 1.00000000
[33,] 2.460610e-11 4.921220e-11 1.00000000
[34,] 1.228724e-11 2.457448e-11 1.00000000
[35,] 3.860121e-12 7.720243e-12 1.00000000
[36,] 1.218310e-12 2.436620e-12 1.00000000
[37,] 5.249362e-13 1.049872e-12 1.00000000
[38,] 1.983200e-13 3.966400e-13 1.00000000
[39,] 7.909320e-14 1.581864e-13 1.00000000
[40,] 2.343536e-14 4.687072e-14 1.00000000
[41,] 7.630469e-15 1.526094e-14 1.00000000
[42,] 2.427593e-15 4.855187e-15 1.00000000
[43,] 7.409349e-16 1.481870e-15 1.00000000
[44,] 1.004121e-15 2.008243e-15 1.00000000
[45,] 3.068403e-16 6.136806e-16 1.00000000
[46,] 1.118661e-16 2.237323e-16 1.00000000
[47,] 3.274133e-17 6.548267e-17 1.00000000
[48,] 1.047314e-17 2.094628e-17 1.00000000
[49,] 5.517589e-18 1.103518e-17 1.00000000
[50,] 4.661580e-18 9.323159e-18 1.00000000
[51,] 1.483186e-18 2.966372e-18 1.00000000
[52,] 1.484498e-18 2.968995e-18 1.00000000
[53,] 7.116702e-19 1.423340e-18 1.00000000
[54,] 2.235436e-19 4.470871e-19 1.00000000
[55,] 7.646588e-20 1.529318e-19 1.00000000
[56,] 5.347750e-20 1.069550e-19 1.00000000
[57,] 1.714612e-20 3.429225e-20 1.00000000
[58,] 4.533004e-21 9.066009e-21 1.00000000
[59,] 1.456653e-21 2.913306e-21 1.00000000
[60,] 4.459718e-22 8.919437e-22 1.00000000
[61,] 1.474717e-22 2.949435e-22 1.00000000
[62,] 5.410336e-23 1.082067e-22 1.00000000
[63,] 1.648978e-23 3.297956e-23 1.00000000
[64,] 5.359330e-24 1.071866e-23 1.00000000
[65,] 3.034530e-24 6.069060e-24 1.00000000
[66,] 1.597853e-24 3.195706e-24 1.00000000
[67,] 7.140206e-25 1.428041e-24 1.00000000
[68,] 4.327639e-25 8.655278e-25 1.00000000
[69,] 1.756519e-25 3.513038e-25 1.00000000
[70,] 5.426688e-26 1.085338e-25 1.00000000
[71,] 3.350409e-26 6.700818e-26 1.00000000
[72,] 1.111042e-26 2.222084e-26 1.00000000
[73,] 3.954233e-27 7.908466e-27 1.00000000
[74,] 1.505416e-27 3.010832e-27 1.00000000
[75,] 4.164093e-28 8.328186e-28 1.00000000
[76,] 1.097522e-28 2.195045e-28 1.00000000
[77,] 2.814269e-29 5.628538e-29 1.00000000
[78,] 9.353825e-30 1.870765e-29 1.00000000
[79,] 2.658517e-30 5.317035e-30 1.00000000
[80,] 9.899620e-31 1.979924e-30 1.00000000
[81,] 1.269972e-30 2.539945e-30 1.00000000
[82,] 3.813532e-31 7.627064e-31 1.00000000
[83,] 1.009213e-31 2.018426e-31 1.00000000
[84,] 6.122688e-32 1.224538e-31 1.00000000
[85,] 1.793451e-32 3.586902e-32 1.00000000
[86,] 6.053038e-33 1.210608e-32 1.00000000
[87,] 4.631611e-33 9.263221e-33 1.00000000
[88,] 1.875827e-33 3.751655e-33 1.00000000
[89,] 4.227938e-34 8.455876e-34 1.00000000
[90,] 3.112709e-34 6.225419e-34 1.00000000
[91,] 1.329532e-34 2.659063e-34 1.00000000
[92,] 5.051855e-35 1.010371e-34 1.00000000
[93,] 3.661855e-35 7.323711e-35 1.00000000
[94,] 8.249757e-36 1.649951e-35 1.00000000
[95,] 3.794352e-36 7.588703e-36 1.00000000
[96,] 3.212369e-36 6.424738e-36 1.00000000
[97,] 8.927409e-37 1.785482e-36 1.00000000
[98,] 4.101090e-37 8.202181e-37 1.00000000
[99,] 9.705248e-38 1.941050e-37 1.00000000
[100,] 1.215083e-37 2.430165e-37 1.00000000
[101,] 1.041906e-37 2.083813e-37 1.00000000
[102,] 2.962296e-38 5.924592e-38 1.00000000
[103,] 1.082788e-38 2.165577e-38 1.00000000
[104,] 1.236352e-38 2.472704e-38 1.00000000
[105,] 1.362577e-38 2.725153e-38 1.00000000
[106,] 2.102021e-38 4.204042e-38 1.00000000
[107,] 5.917407e-38 1.183481e-37 1.00000000
[108,] 2.335368e-38 4.670737e-38 1.00000000
[109,] 1.699872e-38 3.399744e-38 1.00000000
[110,] 3.466243e-39 6.932485e-39 1.00000000
[111,] 5.461703e-31 1.092341e-30 1.00000000
[112,] 4.071054e-28 8.142108e-28 1.00000000
[113,] 1.681965e-28 3.363929e-28 1.00000000
[114,] 5.595615e-29 1.119123e-28 1.00000000
[115,] 1.927657e-29 3.855314e-29 1.00000000
[116,] 6.159810e-09 1.231962e-08 0.99999999
[117,] 1.409128e-01 2.818257e-01 0.85908716
[118,] 6.309128e-01 7.381745e-01 0.36908724
[119,] 7.968616e-01 4.062768e-01 0.20313840
[120,] 8.802904e-01 2.394192e-01 0.11970962
[121,] 9.017249e-01 1.965501e-01 0.09827507
[122,] 9.164808e-01 1.670383e-01 0.08351917
[123,] 9.184180e-01 1.631639e-01 0.08158195
[124,] 9.395434e-01 1.209132e-01 0.06045661
[125,] 9.267545e-01 1.464910e-01 0.07324549
[126,] 9.085114e-01 1.829772e-01 0.09148862
[127,] 8.772144e-01 2.455712e-01 0.12278562
[128,] 8.416975e-01 3.166050e-01 0.15830251
[129,] 8.042233e-01 3.915534e-01 0.19577669
[130,] 7.571472e-01 4.857056e-01 0.24285280
[131,] 6.960614e-01 6.078771e-01 0.30393857
[132,] 7.518969e-01 4.962063e-01 0.24810313
[133,] 6.797924e-01 6.404153e-01 0.32020763
[134,] 5.872048e-01 8.255904e-01 0.41279519
[135,] 5.068549e-01 9.862903e-01 0.49314514
[136,] 4.022262e-01 8.044523e-01 0.59777383
[137,] 3.055965e-01 6.111931e-01 0.69440346
[138,] 2.025357e-01 4.050714e-01 0.79746432
[139,] 1.662394e-01 3.324788e-01 0.83376060
> postscript(file="/var/www/html/freestat/rcomp/tmp/1sy2r1291309082.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2sy2r1291309082.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3k8kc1291309082.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4k8kc1291309082.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5k8kc1291309082.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 = 160
Frequency = 1
1 2 3 4 5
-1.375648960 -2.143119155 2.169025752 1.949537413 -3.262535659
6 7 8 9 10
-1.476669339 -1.666497971 -3.269732804 -6.955013560 1.669639637
11 12 13 14 15
3.937145218 -0.998929795 -2.672831293 1.661666827 3.519763148
16 17 18 19 20
2.788532884 1.776471313 0.207744124 1.029148612 -0.140707590
21 22 23 24 25
9.400707479 -0.761700261 -0.773749061 -2.278095305 0.097618318
26 27 28 29 30
3.322694217 -0.760080630 -0.171912346 0.866574182 -1.998795833
31 32 33 34 35
1.203374470 -3.596262893 -0.991124766 -3.484643340 -1.963744679
36 37 38 39 40
-1.075364232 -0.856494781 -1.677334910 2.854399498 -6.276609636
41 42 43 44 45
0.114631386 1.432639695 -0.685005977 2.777831099 0.980231788
46 47 48 49 50
-0.706241767 -0.958676704 -3.679142575 -1.902381682 -1.563940715
51 52 53 54 55
-0.141230847 2.658540478 0.030391919 -1.873193930 1.627725017
56 57 58 59 60
1.635530047 0.791793195 1.038062986 -2.738563945 2.114631386
61 62 63 64 65
-0.609827616 -3.642149794 -2.770488750 -0.282758201 1.823718000
66 67 68 69 70
3.712098446 -1.933063180 -2.735437597 0.978591109 -1.052487763
71 72 73 74 75
-0.707882447 0.060939455 0.941648835 -0.879505212 -1.957433397
76 77 78 79 80
-1.957433397 0.957493973 -1.945237864 -2.561211740 -1.000173101
81 82 83 84 85
1.180364038 -2.790762128 -2.561337624 -0.933063180 -0.087538916
86 87 88 89 90
-1.761574377 0.008875234 -1.933063180 0.442244683 -2.790762128
91 92 93 94 95
-5.567523022 1.972330334 0.267621453 3.712098446 0.715224795
96 97 98 99 100
2.831389067 2.595214125 -0.914690328 -3.530530241 0.097618318
101 102 103 104 105
2.767422978 1.102309969 4.842475455 4.373804412 -2.685005977
106 107 108 109 110
1.031885666 1.629742022 -4.530530241 -4.538335270 -0.474446621
111 112 113 114 115
2.147838536 -1.298580084 -4.546498634 0.563643329 0.222465309
116 117 118 119 120
-15.728600412 1.956094423 -2.561289018 -4.211083287 -1.827936268
121 122 123 124 125
-19.655760828 2.279976278 0.099534769 -3.562576648 -0.672681406
126 127 128 129 130
-17.843094769 2.265661847 2.505754318 2.297720614 6.449078503
131 132 133 134 135
3.891073352 3.773988450 1.733664027 6.762470986 3.189227409
136 137 138 139 140
1.287031599 0.442750641 -0.450133422 4.666026581 3.633704403
141 142 143 144 145
3.722447487 1.071296574 3.809570939 1.982679374 4.078997895
146 147 148 149 150
1.318956404 2.475072820 3.246765737 4.417011234 11.213200253
151 152 153 154 155
3.004312537 1.259087154 0.928598149 0.213200253 3.331528461
156 157 158 159 160
0.475072820 -2.272647255 4.005166549 3.236599979 2.982679374
> postscript(file="/var/www/html/freestat/rcomp/tmp/6vhjx1291309082.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 = 160
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.375648960 NA
1 -2.143119155 -1.375648960
2 2.169025752 -2.143119155
3 1.949537413 2.169025752
4 -3.262535659 1.949537413
5 -1.476669339 -3.262535659
6 -1.666497971 -1.476669339
7 -3.269732804 -1.666497971
8 -6.955013560 -3.269732804
9 1.669639637 -6.955013560
10 3.937145218 1.669639637
11 -0.998929795 3.937145218
12 -2.672831293 -0.998929795
13 1.661666827 -2.672831293
14 3.519763148 1.661666827
15 2.788532884 3.519763148
16 1.776471313 2.788532884
17 0.207744124 1.776471313
18 1.029148612 0.207744124
19 -0.140707590 1.029148612
20 9.400707479 -0.140707590
21 -0.761700261 9.400707479
22 -0.773749061 -0.761700261
23 -2.278095305 -0.773749061
24 0.097618318 -2.278095305
25 3.322694217 0.097618318
26 -0.760080630 3.322694217
27 -0.171912346 -0.760080630
28 0.866574182 -0.171912346
29 -1.998795833 0.866574182
30 1.203374470 -1.998795833
31 -3.596262893 1.203374470
32 -0.991124766 -3.596262893
33 -3.484643340 -0.991124766
34 -1.963744679 -3.484643340
35 -1.075364232 -1.963744679
36 -0.856494781 -1.075364232
37 -1.677334910 -0.856494781
38 2.854399498 -1.677334910
39 -6.276609636 2.854399498
40 0.114631386 -6.276609636
41 1.432639695 0.114631386
42 -0.685005977 1.432639695
43 2.777831099 -0.685005977
44 0.980231788 2.777831099
45 -0.706241767 0.980231788
46 -0.958676704 -0.706241767
47 -3.679142575 -0.958676704
48 -1.902381682 -3.679142575
49 -1.563940715 -1.902381682
50 -0.141230847 -1.563940715
51 2.658540478 -0.141230847
52 0.030391919 2.658540478
53 -1.873193930 0.030391919
54 1.627725017 -1.873193930
55 1.635530047 1.627725017
56 0.791793195 1.635530047
57 1.038062986 0.791793195
58 -2.738563945 1.038062986
59 2.114631386 -2.738563945
60 -0.609827616 2.114631386
61 -3.642149794 -0.609827616
62 -2.770488750 -3.642149794
63 -0.282758201 -2.770488750
64 1.823718000 -0.282758201
65 3.712098446 1.823718000
66 -1.933063180 3.712098446
67 -2.735437597 -1.933063180
68 0.978591109 -2.735437597
69 -1.052487763 0.978591109
70 -0.707882447 -1.052487763
71 0.060939455 -0.707882447
72 0.941648835 0.060939455
73 -0.879505212 0.941648835
74 -1.957433397 -0.879505212
75 -1.957433397 -1.957433397
76 0.957493973 -1.957433397
77 -1.945237864 0.957493973
78 -2.561211740 -1.945237864
79 -1.000173101 -2.561211740
80 1.180364038 -1.000173101
81 -2.790762128 1.180364038
82 -2.561337624 -2.790762128
83 -0.933063180 -2.561337624
84 -0.087538916 -0.933063180
85 -1.761574377 -0.087538916
86 0.008875234 -1.761574377
87 -1.933063180 0.008875234
88 0.442244683 -1.933063180
89 -2.790762128 0.442244683
90 -5.567523022 -2.790762128
91 1.972330334 -5.567523022
92 0.267621453 1.972330334
93 3.712098446 0.267621453
94 0.715224795 3.712098446
95 2.831389067 0.715224795
96 2.595214125 2.831389067
97 -0.914690328 2.595214125
98 -3.530530241 -0.914690328
99 0.097618318 -3.530530241
100 2.767422978 0.097618318
101 1.102309969 2.767422978
102 4.842475455 1.102309969
103 4.373804412 4.842475455
104 -2.685005977 4.373804412
105 1.031885666 -2.685005977
106 1.629742022 1.031885666
107 -4.530530241 1.629742022
108 -4.538335270 -4.530530241
109 -0.474446621 -4.538335270
110 2.147838536 -0.474446621
111 -1.298580084 2.147838536
112 -4.546498634 -1.298580084
113 0.563643329 -4.546498634
114 0.222465309 0.563643329
115 -15.728600412 0.222465309
116 1.956094423 -15.728600412
117 -2.561289018 1.956094423
118 -4.211083287 -2.561289018
119 -1.827936268 -4.211083287
120 -19.655760828 -1.827936268
121 2.279976278 -19.655760828
122 0.099534769 2.279976278
123 -3.562576648 0.099534769
124 -0.672681406 -3.562576648
125 -17.843094769 -0.672681406
126 2.265661847 -17.843094769
127 2.505754318 2.265661847
128 2.297720614 2.505754318
129 6.449078503 2.297720614
130 3.891073352 6.449078503
131 3.773988450 3.891073352
132 1.733664027 3.773988450
133 6.762470986 1.733664027
134 3.189227409 6.762470986
135 1.287031599 3.189227409
136 0.442750641 1.287031599
137 -0.450133422 0.442750641
138 4.666026581 -0.450133422
139 3.633704403 4.666026581
140 3.722447487 3.633704403
141 1.071296574 3.722447487
142 3.809570939 1.071296574
143 1.982679374 3.809570939
144 4.078997895 1.982679374
145 1.318956404 4.078997895
146 2.475072820 1.318956404
147 3.246765737 2.475072820
148 4.417011234 3.246765737
149 11.213200253 4.417011234
150 3.004312537 11.213200253
151 1.259087154 3.004312537
152 0.928598149 1.259087154
153 0.213200253 0.928598149
154 3.331528461 0.213200253
155 0.475072820 3.331528461
156 -2.272647255 0.475072820
157 4.005166549 -2.272647255
158 3.236599979 4.005166549
159 2.982679374 3.236599979
160 NA 2.982679374
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.143119155 -1.375648960
[2,] 2.169025752 -2.143119155
[3,] 1.949537413 2.169025752
[4,] -3.262535659 1.949537413
[5,] -1.476669339 -3.262535659
[6,] -1.666497971 -1.476669339
[7,] -3.269732804 -1.666497971
[8,] -6.955013560 -3.269732804
[9,] 1.669639637 -6.955013560
[10,] 3.937145218 1.669639637
[11,] -0.998929795 3.937145218
[12,] -2.672831293 -0.998929795
[13,] 1.661666827 -2.672831293
[14,] 3.519763148 1.661666827
[15,] 2.788532884 3.519763148
[16,] 1.776471313 2.788532884
[17,] 0.207744124 1.776471313
[18,] 1.029148612 0.207744124
[19,] -0.140707590 1.029148612
[20,] 9.400707479 -0.140707590
[21,] -0.761700261 9.400707479
[22,] -0.773749061 -0.761700261
[23,] -2.278095305 -0.773749061
[24,] 0.097618318 -2.278095305
[25,] 3.322694217 0.097618318
[26,] -0.760080630 3.322694217
[27,] -0.171912346 -0.760080630
[28,] 0.866574182 -0.171912346
[29,] -1.998795833 0.866574182
[30,] 1.203374470 -1.998795833
[31,] -3.596262893 1.203374470
[32,] -0.991124766 -3.596262893
[33,] -3.484643340 -0.991124766
[34,] -1.963744679 -3.484643340
[35,] -1.075364232 -1.963744679
[36,] -0.856494781 -1.075364232
[37,] -1.677334910 -0.856494781
[38,] 2.854399498 -1.677334910
[39,] -6.276609636 2.854399498
[40,] 0.114631386 -6.276609636
[41,] 1.432639695 0.114631386
[42,] -0.685005977 1.432639695
[43,] 2.777831099 -0.685005977
[44,] 0.980231788 2.777831099
[45,] -0.706241767 0.980231788
[46,] -0.958676704 -0.706241767
[47,] -3.679142575 -0.958676704
[48,] -1.902381682 -3.679142575
[49,] -1.563940715 -1.902381682
[50,] -0.141230847 -1.563940715
[51,] 2.658540478 -0.141230847
[52,] 0.030391919 2.658540478
[53,] -1.873193930 0.030391919
[54,] 1.627725017 -1.873193930
[55,] 1.635530047 1.627725017
[56,] 0.791793195 1.635530047
[57,] 1.038062986 0.791793195
[58,] -2.738563945 1.038062986
[59,] 2.114631386 -2.738563945
[60,] -0.609827616 2.114631386
[61,] -3.642149794 -0.609827616
[62,] -2.770488750 -3.642149794
[63,] -0.282758201 -2.770488750
[64,] 1.823718000 -0.282758201
[65,] 3.712098446 1.823718000
[66,] -1.933063180 3.712098446
[67,] -2.735437597 -1.933063180
[68,] 0.978591109 -2.735437597
[69,] -1.052487763 0.978591109
[70,] -0.707882447 -1.052487763
[71,] 0.060939455 -0.707882447
[72,] 0.941648835 0.060939455
[73,] -0.879505212 0.941648835
[74,] -1.957433397 -0.879505212
[75,] -1.957433397 -1.957433397
[76,] 0.957493973 -1.957433397
[77,] -1.945237864 0.957493973
[78,] -2.561211740 -1.945237864
[79,] -1.000173101 -2.561211740
[80,] 1.180364038 -1.000173101
[81,] -2.790762128 1.180364038
[82,] -2.561337624 -2.790762128
[83,] -0.933063180 -2.561337624
[84,] -0.087538916 -0.933063180
[85,] -1.761574377 -0.087538916
[86,] 0.008875234 -1.761574377
[87,] -1.933063180 0.008875234
[88,] 0.442244683 -1.933063180
[89,] -2.790762128 0.442244683
[90,] -5.567523022 -2.790762128
[91,] 1.972330334 -5.567523022
[92,] 0.267621453 1.972330334
[93,] 3.712098446 0.267621453
[94,] 0.715224795 3.712098446
[95,] 2.831389067 0.715224795
[96,] 2.595214125 2.831389067
[97,] -0.914690328 2.595214125
[98,] -3.530530241 -0.914690328
[99,] 0.097618318 -3.530530241
[100,] 2.767422978 0.097618318
[101,] 1.102309969 2.767422978
[102,] 4.842475455 1.102309969
[103,] 4.373804412 4.842475455
[104,] -2.685005977 4.373804412
[105,] 1.031885666 -2.685005977
[106,] 1.629742022 1.031885666
[107,] -4.530530241 1.629742022
[108,] -4.538335270 -4.530530241
[109,] -0.474446621 -4.538335270
[110,] 2.147838536 -0.474446621
[111,] -1.298580084 2.147838536
[112,] -4.546498634 -1.298580084
[113,] 0.563643329 -4.546498634
[114,] 0.222465309 0.563643329
[115,] -15.728600412 0.222465309
[116,] 1.956094423 -15.728600412
[117,] -2.561289018 1.956094423
[118,] -4.211083287 -2.561289018
[119,] -1.827936268 -4.211083287
[120,] -19.655760828 -1.827936268
[121,] 2.279976278 -19.655760828
[122,] 0.099534769 2.279976278
[123,] -3.562576648 0.099534769
[124,] -0.672681406 -3.562576648
[125,] -17.843094769 -0.672681406
[126,] 2.265661847 -17.843094769
[127,] 2.505754318 2.265661847
[128,] 2.297720614 2.505754318
[129,] 6.449078503 2.297720614
[130,] 3.891073352 6.449078503
[131,] 3.773988450 3.891073352
[132,] 1.733664027 3.773988450
[133,] 6.762470986 1.733664027
[134,] 3.189227409 6.762470986
[135,] 1.287031599 3.189227409
[136,] 0.442750641 1.287031599
[137,] -0.450133422 0.442750641
[138,] 4.666026581 -0.450133422
[139,] 3.633704403 4.666026581
[140,] 3.722447487 3.633704403
[141,] 1.071296574 3.722447487
[142,] 3.809570939 1.071296574
[143,] 1.982679374 3.809570939
[144,] 4.078997895 1.982679374
[145,] 1.318956404 4.078997895
[146,] 2.475072820 1.318956404
[147,] 3.246765737 2.475072820
[148,] 4.417011234 3.246765737
[149,] 11.213200253 4.417011234
[150,] 3.004312537 11.213200253
[151,] 1.259087154 3.004312537
[152,] 0.928598149 1.259087154
[153,] 0.213200253 0.928598149
[154,] 3.331528461 0.213200253
[155,] 0.475072820 3.331528461
[156,] -2.272647255 0.475072820
[157,] 4.005166549 -2.272647255
[158,] 3.236599979 4.005166549
[159,] 2.982679374 3.236599979
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.143119155 -1.375648960
2 2.169025752 -2.143119155
3 1.949537413 2.169025752
4 -3.262535659 1.949537413
5 -1.476669339 -3.262535659
6 -1.666497971 -1.476669339
7 -3.269732804 -1.666497971
8 -6.955013560 -3.269732804
9 1.669639637 -6.955013560
10 3.937145218 1.669639637
11 -0.998929795 3.937145218
12 -2.672831293 -0.998929795
13 1.661666827 -2.672831293
14 3.519763148 1.661666827
15 2.788532884 3.519763148
16 1.776471313 2.788532884
17 0.207744124 1.776471313
18 1.029148612 0.207744124
19 -0.140707590 1.029148612
20 9.400707479 -0.140707590
21 -0.761700261 9.400707479
22 -0.773749061 -0.761700261
23 -2.278095305 -0.773749061
24 0.097618318 -2.278095305
25 3.322694217 0.097618318
26 -0.760080630 3.322694217
27 -0.171912346 -0.760080630
28 0.866574182 -0.171912346
29 -1.998795833 0.866574182
30 1.203374470 -1.998795833
31 -3.596262893 1.203374470
32 -0.991124766 -3.596262893
33 -3.484643340 -0.991124766
34 -1.963744679 -3.484643340
35 -1.075364232 -1.963744679
36 -0.856494781 -1.075364232
37 -1.677334910 -0.856494781
38 2.854399498 -1.677334910
39 -6.276609636 2.854399498
40 0.114631386 -6.276609636
41 1.432639695 0.114631386
42 -0.685005977 1.432639695
43 2.777831099 -0.685005977
44 0.980231788 2.777831099
45 -0.706241767 0.980231788
46 -0.958676704 -0.706241767
47 -3.679142575 -0.958676704
48 -1.902381682 -3.679142575
49 -1.563940715 -1.902381682
50 -0.141230847 -1.563940715
51 2.658540478 -0.141230847
52 0.030391919 2.658540478
53 -1.873193930 0.030391919
54 1.627725017 -1.873193930
55 1.635530047 1.627725017
56 0.791793195 1.635530047
57 1.038062986 0.791793195
58 -2.738563945 1.038062986
59 2.114631386 -2.738563945
60 -0.609827616 2.114631386
61 -3.642149794 -0.609827616
62 -2.770488750 -3.642149794
63 -0.282758201 -2.770488750
64 1.823718000 -0.282758201
65 3.712098446 1.823718000
66 -1.933063180 3.712098446
67 -2.735437597 -1.933063180
68 0.978591109 -2.735437597
69 -1.052487763 0.978591109
70 -0.707882447 -1.052487763
71 0.060939455 -0.707882447
72 0.941648835 0.060939455
73 -0.879505212 0.941648835
74 -1.957433397 -0.879505212
75 -1.957433397 -1.957433397
76 0.957493973 -1.957433397
77 -1.945237864 0.957493973
78 -2.561211740 -1.945237864
79 -1.000173101 -2.561211740
80 1.180364038 -1.000173101
81 -2.790762128 1.180364038
82 -2.561337624 -2.790762128
83 -0.933063180 -2.561337624
84 -0.087538916 -0.933063180
85 -1.761574377 -0.087538916
86 0.008875234 -1.761574377
87 -1.933063180 0.008875234
88 0.442244683 -1.933063180
89 -2.790762128 0.442244683
90 -5.567523022 -2.790762128
91 1.972330334 -5.567523022
92 0.267621453 1.972330334
93 3.712098446 0.267621453
94 0.715224795 3.712098446
95 2.831389067 0.715224795
96 2.595214125 2.831389067
97 -0.914690328 2.595214125
98 -3.530530241 -0.914690328
99 0.097618318 -3.530530241
100 2.767422978 0.097618318
101 1.102309969 2.767422978
102 4.842475455 1.102309969
103 4.373804412 4.842475455
104 -2.685005977 4.373804412
105 1.031885666 -2.685005977
106 1.629742022 1.031885666
107 -4.530530241 1.629742022
108 -4.538335270 -4.530530241
109 -0.474446621 -4.538335270
110 2.147838536 -0.474446621
111 -1.298580084 2.147838536
112 -4.546498634 -1.298580084
113 0.563643329 -4.546498634
114 0.222465309 0.563643329
115 -15.728600412 0.222465309
116 1.956094423 -15.728600412
117 -2.561289018 1.956094423
118 -4.211083287 -2.561289018
119 -1.827936268 -4.211083287
120 -19.655760828 -1.827936268
121 2.279976278 -19.655760828
122 0.099534769 2.279976278
123 -3.562576648 0.099534769
124 -0.672681406 -3.562576648
125 -17.843094769 -0.672681406
126 2.265661847 -17.843094769
127 2.505754318 2.265661847
128 2.297720614 2.505754318
129 6.449078503 2.297720614
130 3.891073352 6.449078503
131 3.773988450 3.891073352
132 1.733664027 3.773988450
133 6.762470986 1.733664027
134 3.189227409 6.762470986
135 1.287031599 3.189227409
136 0.442750641 1.287031599
137 -0.450133422 0.442750641
138 4.666026581 -0.450133422
139 3.633704403 4.666026581
140 3.722447487 3.633704403
141 1.071296574 3.722447487
142 3.809570939 1.071296574
143 1.982679374 3.809570939
144 4.078997895 1.982679374
145 1.318956404 4.078997895
146 2.475072820 1.318956404
147 3.246765737 2.475072820
148 4.417011234 3.246765737
149 11.213200253 4.417011234
150 3.004312537 11.213200253
151 1.259087154 3.004312537
152 0.928598149 1.259087154
153 0.213200253 0.928598149
154 3.331528461 0.213200253
155 0.475072820 3.331528461
156 -2.272647255 0.475072820
157 4.005166549 -2.272647255
158 3.236599979 4.005166549
159 2.982679374 3.236599979
> 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/7oqi01291309082.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8oqi01291309082.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9oqi01291309082.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10gzil1291309082.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11kiy91291309082.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/12niwf1291309082.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/131sun1291309082.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/14nttb1291309082.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/15f2sw1291309082.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/16tc8n1291309082.tab")
+ }
>
> try(system("convert tmp/1sy2r1291309082.ps tmp/1sy2r1291309082.png",intern=TRUE))
character(0)
> try(system("convert tmp/2sy2r1291309082.ps tmp/2sy2r1291309082.png",intern=TRUE))
character(0)
> try(system("convert tmp/3k8kc1291309082.ps tmp/3k8kc1291309082.png",intern=TRUE))
character(0)
> try(system("convert tmp/4k8kc1291309082.ps tmp/4k8kc1291309082.png",intern=TRUE))
character(0)
> try(system("convert tmp/5k8kc1291309082.ps tmp/5k8kc1291309082.png",intern=TRUE))
character(0)
> try(system("convert tmp/6vhjx1291309082.ps tmp/6vhjx1291309082.png",intern=TRUE))
character(0)
> try(system("convert tmp/7oqi01291309082.ps tmp/7oqi01291309082.png",intern=TRUE))
character(0)
> try(system("convert tmp/8oqi01291309082.ps tmp/8oqi01291309082.png",intern=TRUE))
character(0)
> try(system("convert tmp/9oqi01291309082.ps tmp/9oqi01291309082.png",intern=TRUE))
character(0)
> try(system("convert tmp/10gzil1291309082.ps tmp/10gzil1291309082.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.933 2.656 6.294