R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(44
+ ,39.3
+ ,43.6
+ ,40.3
+ ,44.4
+ ,32.4
+ ,44.3
+ ,32.7
+ ,43
+ ,34.5
+ ,42.2
+ ,32.4
+ ,41.4
+ ,33.1
+ ,42.1
+ ,34.9
+ ,41.6
+ ,34.1
+ ,43
+ ,31.9
+ ,42.8
+ ,32.7
+ ,41.5
+ ,32.5
+ ,40.2
+ ,27.2
+ ,41.4
+ ,24.3
+ ,41.4
+ ,24
+ ,41.7
+ ,24.7
+ ,41.4
+ ,25.6
+ ,42.9
+ ,30.1
+ ,43
+ ,32.1
+ ,43.3
+ ,32.3
+ ,44.6
+ ,31
+ ,48.1
+ ,32.2
+ ,49.3
+ ,33.2
+ ,51.9
+ ,35.2
+ ,51.5
+ ,34.2
+ ,50.8
+ ,31
+ ,50.2
+ ,34.1
+ ,50.4
+ ,37.8
+ ,51.4
+ ,40.6
+ ,49.2
+ ,37.5
+ ,49.7
+ ,31.8
+ ,51
+ ,32.4
+ ,48.8
+ ,34.6
+ ,47.2
+ ,35.6
+ ,47.7
+ ,37
+ ,50
+ ,33.8
+ ,52.3
+ ,36.2
+ ,54
+ ,36.6
+ ,55.2
+ ,37.8
+ ,58.6
+ ,39.8
+ ,60.1
+ ,39.7
+ ,64.9
+ ,42.8
+ ,65.6
+ ,43.4
+ ,64
+ ,47.8
+ ,61.6
+ ,46.3
+ ,57.1
+ ,48.6
+ ,51
+ ,53.1
+ ,49.9
+ ,52.7
+ ,48.5
+ ,59
+ ,49.9
+ ,53.9
+ ,51.7
+ ,49.7
+ ,51.3
+ ,54.3
+ ,53.2
+ ,55.9
+ ,59
+ ,63.9
+ ,57
+ ,64
+ ,57.7
+ ,60.7
+ ,59.4
+ ,67.8
+ ,58.8
+ ,70.5
+ ,55.9
+ ,76.6
+ ,53.8
+ ,76.2
+ ,54.2
+ ,71.8
+ ,54.2
+ ,67.8
+ ,56.7
+ ,69.7
+ ,59.8
+ ,76.7
+ ,60.7
+ ,74.2
+ ,59.7
+ ,75.8
+ ,60.2
+ ,84.3
+ ,61.3
+ ,84.9
+ ,59.8
+ ,84.4
+ ,61.2
+ ,89.4
+ ,59.3
+ ,88.5
+ ,59.4
+ ,76.5
+ ,63.1
+ ,71.4
+ ,68
+ ,72.1
+ ,69.4
+ ,75.8
+ ,70.2
+ ,66.6
+ ,72.6
+ ,71.7
+ ,72.1
+ ,75.4
+ ,69.7
+ ,80.9
+ ,71.5
+ ,80.7
+ ,75.7
+ ,85
+ ,76
+ ,91.5
+ ,76.4
+ ,87.7
+ ,83.8
+ ,95.3
+ ,86.2
+ ,102.4
+ ,88.5
+ ,114.2
+ ,95.9
+ ,111.7
+ ,103.1
+ ,113.7
+ ,113.5
+ ,118.8
+ ,115.7
+ ,129
+ ,113.1
+ ,136.4
+ ,112.7
+ ,155
+ ,121.9
+ ,166
+ ,120.3
+ ,168.7
+ ,108.7
+ ,145.5
+ ,102.8
+ ,127.3
+ ,83.4
+ ,91.5
+ ,79.4
+ ,69
+ ,77.8
+ ,54
+ ,85.7
+ ,56.3
+ ,83.2
+ ,54.2
+ ,82
+ ,59.3
+ ,86.9
+ ,63.4
+ ,95.7
+ ,73.3
+ ,97.9
+ ,86.7
+ ,89.3
+ ,81.3
+ ,91.5
+ ,89.6
+ ,86.8
+ ,85.3
+ ,91
+ ,92.4
+ ,93.8
+ ,96.8
+ ,96.8
+ ,93.6
+ ,95.7
+ ,97.6
+ ,91.4
+ ,94.2
+ ,88.7
+ ,99.9
+ ,88.2
+ ,106.4
+ ,87.7
+ ,96
+ ,89.5
+ ,94.9
+ ,95.6
+ ,94.8
+ ,100.5
+ ,95.9
+ ,106.3
+ ,96.2
+ ,112
+ ,103.1
+ ,117.7
+ ,106.9
+ ,125
+ ,114.2
+ ,132.4
+ ,118.2
+ ,138.1
+ ,123.9
+ ,134.7
+ ,137.1
+ ,136.7
+ ,146.2
+ ,134.3
+ ,136.4
+ ,131.6
+ ,133.2
+ ,129.8
+ ,135.9
+ ,131.9
+ ,127.1
+ ,129.8
+ ,128.5
+ ,119.4
+ ,126.6
+ ,116.7
+ ,132.6
+ ,112.8
+ ,130.9
+ ,116
+ ,134.1
+ ,117.5
+ ,141.1
+ ,118.8
+ ,147
+ ,118.7
+ ,141.3
+ ,116.3
+ ,129.6
+ ,115.2
+ ,113.3
+ ,131.7
+ ,120.5
+ ,133.7
+ ,131.2
+ ,132.5
+ ,132.1
+ ,126.9
+ ,128.3)
+ ,dim=c(2
+ ,145)
+ ,dimnames=list(c('Levensmiddelen'
+ ,'Grondstoffen')
+ ,1:145))
> y <- array(NA,dim=c(2,145),dimnames=list(c('Levensmiddelen','Grondstoffen'),1:145))
> 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 = '2'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) 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
Grondstoffen Levensmiddelen
1 39.3 44.0
2 40.3 43.6
3 32.4 44.4
4 32.7 44.3
5 34.5 43.0
6 32.4 42.2
7 33.1 41.4
8 34.9 42.1
9 34.1 41.6
10 31.9 43.0
11 32.7 42.8
12 32.5 41.5
13 27.2 40.2
14 24.3 41.4
15 24.0 41.4
16 24.7 41.7
17 25.6 41.4
18 30.1 42.9
19 32.1 43.0
20 32.3 43.3
21 31.0 44.6
22 32.2 48.1
23 33.2 49.3
24 35.2 51.9
25 34.2 51.5
26 31.0 50.8
27 34.1 50.2
28 37.8 50.4
29 40.6 51.4
30 37.5 49.2
31 31.8 49.7
32 32.4 51.0
33 34.6 48.8
34 35.6 47.2
35 37.0 47.7
36 33.8 50.0
37 36.2 52.3
38 36.6 54.0
39 37.8 55.2
40 39.8 58.6
41 39.7 60.1
42 42.8 64.9
43 43.4 65.6
44 47.8 64.0
45 46.3 61.6
46 48.6 57.1
47 53.1 51.0
48 52.7 49.9
49 59.0 48.5
50 53.9 49.9
51 49.7 51.7
52 54.3 51.3
53 55.9 53.2
54 63.9 59.0
55 64.0 57.0
56 60.7 57.7
57 67.8 59.4
58 70.5 58.8
59 76.6 55.9
60 76.2 53.8
61 71.8 54.2
62 67.8 54.2
63 69.7 56.7
64 76.7 59.8
65 74.2 60.7
66 75.8 59.7
67 84.3 60.2
68 84.9 61.3
69 84.4 59.8
70 89.4 61.2
71 88.5 59.3
72 76.5 59.4
73 71.4 63.1
74 72.1 68.0
75 75.8 69.4
76 66.6 70.2
77 71.7 72.6
78 75.4 72.1
79 80.9 69.7
80 80.7 71.5
81 85.0 75.7
82 91.5 76.0
83 87.7 76.4
84 95.3 83.8
85 102.4 86.2
86 114.2 88.5
87 111.7 95.9
88 113.7 103.1
89 118.8 113.5
90 129.0 115.7
91 136.4 113.1
92 155.0 112.7
93 166.0 121.9
94 168.7 120.3
95 145.5 108.7
96 127.3 102.8
97 91.5 83.4
98 69.0 79.4
99 54.0 77.8
100 56.3 85.7
101 54.2 83.2
102 59.3 82.0
103 63.4 86.9
104 73.3 95.7
105 86.7 97.9
106 81.3 89.3
107 89.6 91.5
108 85.3 86.8
109 92.4 91.0
110 96.8 93.8
111 93.6 96.8
112 97.6 95.7
113 94.2 91.4
114 99.9 88.7
115 106.4 88.2
116 96.0 87.7
117 94.9 89.5
118 94.8 95.6
119 95.9 100.5
120 96.2 106.3
121 103.1 112.0
122 106.9 117.7
123 114.2 125.0
124 118.2 132.4
125 123.9 138.1
126 137.1 134.7
127 146.2 136.7
128 136.4 134.3
129 133.2 131.6
130 135.9 129.8
131 127.1 131.9
132 128.5 129.8
133 126.6 119.4
134 132.6 116.7
135 130.9 112.8
136 134.1 116.0
137 141.1 117.5
138 147.0 118.8
139 141.3 118.7
140 129.6 116.3
141 113.3 115.2
142 120.5 131.7
143 131.2 133.7
144 132.1 132.5
145 128.3 126.9
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Levensmiddelen
-12.841 1.171
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-31.226 -11.291 -2.773 10.086 40.653
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -12.84093 3.44655 -3.726 0.00028 ***
Levensmiddelen 1.17114 0.04181 28.010 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 15 on 143 degrees of freedom
Multiple R-squared: 0.8458, Adjusted R-squared: 0.8448
F-statistic: 784.5 on 1 and 143 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,] 2.464379e-02 4.928759e-02 0.975356205
[2,] 8.665539e-03 1.733108e-02 0.991334461
[3,] 1.933877e-03 3.867754e-03 0.998066123
[4,] 4.020812e-04 8.041624e-04 0.999597919
[5,] 7.617314e-05 1.523463e-04 0.999923827
[6,] 2.127788e-05 4.255576e-05 0.999978722
[7,] 4.456498e-06 8.912997e-06 0.999995544
[8,] 7.972472e-07 1.594494e-06 0.999999203
[9,] 3.544546e-07 7.089091e-07 0.999999646
[10,] 8.032976e-07 1.606595e-06 0.999999197
[11,] 8.561061e-07 1.712212e-06 0.999999144
[12,] 6.037875e-07 1.207575e-06 0.999999396
[13,] 2.292494e-07 4.584987e-07 0.999999771
[14,] 7.031413e-08 1.406283e-07 0.999999930
[15,] 1.703698e-08 3.407397e-08 0.999999983
[16,] 4.163706e-09 8.327411e-09 0.999999996
[17,] 2.353889e-09 4.707777e-09 0.999999998
[18,] 2.858333e-09 5.716666e-09 0.999999997
[19,] 1.157387e-09 2.314775e-09 0.999999999
[20,] 3.448332e-10 6.896663e-10 1.000000000
[21,] 1.012485e-10 2.024970e-10 1.000000000
[22,] 4.530644e-11 9.061287e-11 1.000000000
[23,] 1.175042e-11 2.350084e-11 1.000000000
[24,] 4.051344e-12 8.102688e-12 1.000000000
[25,] 2.168115e-12 4.336230e-12 1.000000000
[26,] 6.620815e-13 1.324163e-12 1.000000000
[27,] 2.640349e-13 5.280699e-13 1.000000000
[28,] 1.037748e-13 2.075496e-13 1.000000000
[29,] 2.708642e-14 5.417285e-14 1.000000000
[30,] 7.763272e-15 1.552654e-14 1.000000000
[31,] 2.659018e-15 5.318036e-15 1.000000000
[32,] 7.786627e-16 1.557325e-15 1.000000000
[33,] 2.157539e-16 4.315078e-16 1.000000000
[34,] 6.329182e-17 1.265836e-16 1.000000000
[35,] 1.878887e-17 3.757774e-17 1.000000000
[36,] 6.012090e-18 1.202418e-17 1.000000000
[37,] 2.118503e-18 4.237006e-18 1.000000000
[38,] 8.331919e-19 1.666384e-18 1.000000000
[39,] 3.510875e-19 7.021751e-19 1.000000000
[40,] 3.742458e-19 7.484916e-19 1.000000000
[41,] 2.867657e-19 5.735314e-19 1.000000000
[42,] 1.652528e-18 3.305057e-18 1.000000000
[43,] 1.397654e-15 2.795308e-15 1.000000000
[44,] 1.186943e-13 2.373886e-13 1.000000000
[45,] 8.940470e-11 1.788094e-10 1.000000000
[46,] 6.994371e-10 1.398874e-09 0.999999999
[47,] 1.036047e-09 2.072094e-09 0.999999999
[48,] 3.768133e-09 7.536266e-09 0.999999996
[49,] 1.113446e-08 2.226892e-08 0.999999989
[50,] 5.338781e-08 1.067756e-07 0.999999947
[51,] 2.242100e-07 4.484201e-07 0.999999776
[52,] 3.565347e-07 7.130694e-07 0.999999643
[53,] 1.063019e-06 2.126038e-06 0.999998937
[54,] 3.824712e-06 7.649423e-06 0.999996175
[55,] 4.485854e-05 8.971708e-05 0.999955141
[56,] 3.456215e-04 6.912431e-04 0.999654378
[57,] 8.726807e-04 1.745361e-03 0.999127319
[58,] 1.287083e-03 2.574166e-03 0.998712917
[59,] 1.609611e-03 3.219223e-03 0.998390389
[60,] 2.315247e-03 4.630494e-03 0.997684753
[61,] 2.415952e-03 4.831905e-03 0.997584048
[62,] 2.850079e-03 5.700158e-03 0.997149921
[63,] 5.497318e-03 1.099464e-02 0.994502682
[64,] 8.686667e-03 1.737333e-02 0.991313333
[65,] 1.420152e-02 2.840304e-02 0.985798478
[66,] 2.615950e-02 5.231901e-02 0.973840497
[67,] 5.036604e-02 1.007321e-01 0.949633961
[68,] 5.110685e-02 1.022137e-01 0.948893153
[69,] 4.087919e-02 8.175837e-02 0.959120814
[70,] 3.250533e-02 6.501065e-02 0.967494675
[71,] 2.552444e-02 5.104888e-02 0.974475559
[72,] 2.304844e-02 4.609689e-02 0.976951557
[73,] 1.963644e-02 3.927289e-02 0.980363557
[74,] 1.521816e-02 3.043633e-02 0.984781835
[75,] 1.200338e-02 2.400677e-02 0.987996617
[76,] 9.160352e-03 1.832070e-02 0.990839648
[77,] 6.999396e-03 1.399879e-02 0.993000604
[78,] 5.929555e-03 1.185911e-02 0.994070445
[79,] 4.673389e-03 9.346778e-03 0.995326611
[80,] 3.762539e-03 7.525078e-03 0.996237461
[81,] 3.235986e-03 6.471972e-03 0.996764014
[82,] 3.935616e-03 7.871231e-03 0.996064384
[83,] 3.602991e-03 7.205982e-03 0.996397009
[84,] 3.517676e-03 7.035352e-03 0.996482324
[85,] 4.230279e-03 8.460557e-03 0.995769721
[86,] 3.673845e-03 7.347689e-03 0.996326155
[87,] 3.441205e-03 6.882411e-03 0.996558795
[88,] 1.056147e-02 2.112293e-02 0.989438533
[89,] 2.934766e-02 5.869531e-02 0.970652343
[90,] 1.159223e-01 2.318446e-01 0.884077714
[91,] 2.348103e-01 4.696206e-01 0.765189694
[92,] 2.992184e-01 5.984368e-01 0.700781606
[93,] 2.923363e-01 5.846727e-01 0.707663652
[94,] 2.929041e-01 5.858083e-01 0.707095857
[95,] 4.034672e-01 8.069344e-01 0.596532802
[96,] 6.454011e-01 7.091978e-01 0.354598877
[97,] 8.293341e-01 3.413317e-01 0.170665861
[98,] 9.027993e-01 1.944013e-01 0.097200669
[99,] 9.605719e-01 7.885622e-02 0.039428111
[100,] 9.880339e-01 2.393220e-02 0.011966102
[101,] 9.910029e-01 1.799428e-02 0.008997139
[102,] 9.916467e-01 1.670669e-02 0.008353346
[103,] 9.895617e-01 2.087664e-02 0.010438320
[104,] 9.870956e-01 2.580870e-02 0.012904351
[105,] 9.827012e-01 3.459765e-02 0.017298824
[106,] 9.762762e-01 4.744758e-02 0.023723791
[107,] 9.736502e-01 5.269957e-02 0.026349783
[108,] 9.656757e-01 6.864855e-02 0.034324274
[109,] 9.557664e-01 8.846723e-02 0.044233615
[110,] 9.394180e-01 1.211640e-01 0.060581979
[111,] 9.287689e-01 1.424621e-01 0.071231059
[112,] 9.044645e-01 1.910709e-01 0.095535460
[113,] 8.762169e-01 2.475661e-01 0.123783056
[114,] 8.623875e-01 2.752250e-01 0.137612489
[115,] 8.794610e-01 2.410780e-01 0.120539018
[116,] 9.422698e-01 1.154604e-01 0.057730218
[117,] 9.777318e-01 4.453649e-02 0.022268247
[118,] 9.941964e-01 1.160720e-02 0.005803602
[119,] 9.973986e-01 5.202890e-03 0.002601445
[120,] 9.984146e-01 3.170852e-03 0.001585426
[121,] 9.983947e-01 3.210575e-03 0.001605287
[122,] 9.971239e-01 5.752111e-03 0.002876056
[123,] 9.980919e-01 3.816252e-03 0.001908126
[124,] 9.969107e-01 6.178606e-03 0.003089303
[125,] 9.942503e-01 1.149942e-02 0.005749710
[126,] 9.908343e-01 1.833143e-02 0.009165714
[127,] 9.833826e-01 3.323476e-02 0.016617380
[128,] 9.700022e-01 5.999553e-02 0.029997767
[129,] 9.523646e-01 9.527071e-02 0.047635356
[130,] 9.185436e-01 1.629127e-01 0.081456360
[131,] 8.706713e-01 2.586573e-01 0.129328656
[132,] 7.975135e-01 4.049731e-01 0.202486536
[133,] 7.468187e-01 5.063625e-01 0.253181251
[134,] 8.391249e-01 3.217501e-01 0.160875051
[135,] 9.232220e-01 1.535559e-01 0.076777966
[136,] 9.409504e-01 1.180992e-01 0.059049583
> postscript(file="/var/wessaorg/rcomp/tmp/13dfq1353078444.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/2bgyy1353078444.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3om4r1353078444.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4covn1353078444.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5twq61353078444.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 145
Frequency = 1
1 2 3 4 5 6
0.61089926 2.07935413 -6.75755562 -6.34044190 -3.01796357 -4.18105383
7 8 9 10 11 12
-2.54414409 -1.56394011 -1.77837152 -5.61796357 -4.58373613 -3.26125781
13 14 15 16 17 18
-7.03877948 -11.34414409 -11.64414409 -11.29548524 -10.04414409 -7.30084985
19 20 21 22 23 24
-5.41796357 -5.56930472 -8.39178305 -11.29076317 -11.69612778 -12.74108443
25 26 27 28 29 30
-13.27262956 -15.65283354 -11.85015124 -8.38437867 -6.75551585 -7.27901406
31 32 33 34 35 36
-13.56458265 -14.48706098 -9.71055919 -6.83673971 -6.02230830 -11.91592380
37 38 39 40 41 42
-12.20953930 -13.80047250 -14.00583711 -15.98770351 -17.84440928 -20.36586772
43 44 45 46 47 48
-20.58566374 -14.31184426 -13.00111504 -5.43099775 6.21293902 7.10118992
49 50 51 52 53 54
15.04078196 8.30118992 1.99314300 7.06159787 6.43643724 7.64384162
55 56 57 58 59 60
10.08611597 5.96631995 11.07538675 14.47806905 23.97436686 26.03375493
61 62 63 64 65 66
21.16530006 17.16530006 16.13745712 19.50693188 15.95290842 18.72404559
67 68 69 70 71 72
26.63847701 25.95022611 27.20693188 30.56733983 31.89250046 19.77538675
73 74 75 76 77 78
10.34217920 5.30360703 7.36401499 -2.77289475 -0.48362397 3.80194461
79 80 81 82 83 84
12.11267384 9.80462692 9.18585078 15.33450963 11.06605476 9.99963966
85 86 87 88 89 90
14.28891043 23.39529493 12.22887983 5.79669216 -1.28313447 6.34036375
91 92 93 94 95 96
16.78532040 35.85377527 36.07931326 40.65313274 31.03832398 19.74803332
97 98 99 100 101 102
6.66809453 -11.14735677 -24.27353729 -31.22552098 -30.39767804 -23.89231343
103 104 105 106 107 108
-25.53088559 -25.93689274 -15.11339452 -10.44161481 -4.71811660 -3.51377187
109 110 111 112 113 114
-1.33254801 -0.21173210 -6.92514363 -1.63689274 -0.00100288 8.86106749
115 116 117 118 119 120
15.94663608 6.13220467 2.92415775 -4.31977902 -8.95835118 -15.45094680
121 122 123 124 125 126
-15.22642870 -18.10191060 -19.35121199 -24.01762709 -24.99310899 -7.81124259
127 128 129 130 131 132
-1.05351695 -8.04278772 -8.08071735 -3.27267043 -14.53205850 -10.67267043
133 134 135 136 137 138
-0.39284380 8.76922657 11.63666156 11.08902259 16.33231683 20.70983850
139 140 141 142 143 144
15.12695222 6.23768144 -8.77406766 -20.89783107 -12.54010542 -10.23474081
145
-7.47637262
> postscript(file="/var/wessaorg/rcomp/tmp/6uzwr1353078444.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 = 145
Frequency = 1
lag(myerror, k = 1) myerror
0 0.61089926 NA
1 2.07935413 0.61089926
2 -6.75755562 2.07935413
3 -6.34044190 -6.75755562
4 -3.01796357 -6.34044190
5 -4.18105383 -3.01796357
6 -2.54414409 -4.18105383
7 -1.56394011 -2.54414409
8 -1.77837152 -1.56394011
9 -5.61796357 -1.77837152
10 -4.58373613 -5.61796357
11 -3.26125781 -4.58373613
12 -7.03877948 -3.26125781
13 -11.34414409 -7.03877948
14 -11.64414409 -11.34414409
15 -11.29548524 -11.64414409
16 -10.04414409 -11.29548524
17 -7.30084985 -10.04414409
18 -5.41796357 -7.30084985
19 -5.56930472 -5.41796357
20 -8.39178305 -5.56930472
21 -11.29076317 -8.39178305
22 -11.69612778 -11.29076317
23 -12.74108443 -11.69612778
24 -13.27262956 -12.74108443
25 -15.65283354 -13.27262956
26 -11.85015124 -15.65283354
27 -8.38437867 -11.85015124
28 -6.75551585 -8.38437867
29 -7.27901406 -6.75551585
30 -13.56458265 -7.27901406
31 -14.48706098 -13.56458265
32 -9.71055919 -14.48706098
33 -6.83673971 -9.71055919
34 -6.02230830 -6.83673971
35 -11.91592380 -6.02230830
36 -12.20953930 -11.91592380
37 -13.80047250 -12.20953930
38 -14.00583711 -13.80047250
39 -15.98770351 -14.00583711
40 -17.84440928 -15.98770351
41 -20.36586772 -17.84440928
42 -20.58566374 -20.36586772
43 -14.31184426 -20.58566374
44 -13.00111504 -14.31184426
45 -5.43099775 -13.00111504
46 6.21293902 -5.43099775
47 7.10118992 6.21293902
48 15.04078196 7.10118992
49 8.30118992 15.04078196
50 1.99314300 8.30118992
51 7.06159787 1.99314300
52 6.43643724 7.06159787
53 7.64384162 6.43643724
54 10.08611597 7.64384162
55 5.96631995 10.08611597
56 11.07538675 5.96631995
57 14.47806905 11.07538675
58 23.97436686 14.47806905
59 26.03375493 23.97436686
60 21.16530006 26.03375493
61 17.16530006 21.16530006
62 16.13745712 17.16530006
63 19.50693188 16.13745712
64 15.95290842 19.50693188
65 18.72404559 15.95290842
66 26.63847701 18.72404559
67 25.95022611 26.63847701
68 27.20693188 25.95022611
69 30.56733983 27.20693188
70 31.89250046 30.56733983
71 19.77538675 31.89250046
72 10.34217920 19.77538675
73 5.30360703 10.34217920
74 7.36401499 5.30360703
75 -2.77289475 7.36401499
76 -0.48362397 -2.77289475
77 3.80194461 -0.48362397
78 12.11267384 3.80194461
79 9.80462692 12.11267384
80 9.18585078 9.80462692
81 15.33450963 9.18585078
82 11.06605476 15.33450963
83 9.99963966 11.06605476
84 14.28891043 9.99963966
85 23.39529493 14.28891043
86 12.22887983 23.39529493
87 5.79669216 12.22887983
88 -1.28313447 5.79669216
89 6.34036375 -1.28313447
90 16.78532040 6.34036375
91 35.85377527 16.78532040
92 36.07931326 35.85377527
93 40.65313274 36.07931326
94 31.03832398 40.65313274
95 19.74803332 31.03832398
96 6.66809453 19.74803332
97 -11.14735677 6.66809453
98 -24.27353729 -11.14735677
99 -31.22552098 -24.27353729
100 -30.39767804 -31.22552098
101 -23.89231343 -30.39767804
102 -25.53088559 -23.89231343
103 -25.93689274 -25.53088559
104 -15.11339452 -25.93689274
105 -10.44161481 -15.11339452
106 -4.71811660 -10.44161481
107 -3.51377187 -4.71811660
108 -1.33254801 -3.51377187
109 -0.21173210 -1.33254801
110 -6.92514363 -0.21173210
111 -1.63689274 -6.92514363
112 -0.00100288 -1.63689274
113 8.86106749 -0.00100288
114 15.94663608 8.86106749
115 6.13220467 15.94663608
116 2.92415775 6.13220467
117 -4.31977902 2.92415775
118 -8.95835118 -4.31977902
119 -15.45094680 -8.95835118
120 -15.22642870 -15.45094680
121 -18.10191060 -15.22642870
122 -19.35121199 -18.10191060
123 -24.01762709 -19.35121199
124 -24.99310899 -24.01762709
125 -7.81124259 -24.99310899
126 -1.05351695 -7.81124259
127 -8.04278772 -1.05351695
128 -8.08071735 -8.04278772
129 -3.27267043 -8.08071735
130 -14.53205850 -3.27267043
131 -10.67267043 -14.53205850
132 -0.39284380 -10.67267043
133 8.76922657 -0.39284380
134 11.63666156 8.76922657
135 11.08902259 11.63666156
136 16.33231683 11.08902259
137 20.70983850 16.33231683
138 15.12695222 20.70983850
139 6.23768144 15.12695222
140 -8.77406766 6.23768144
141 -20.89783107 -8.77406766
142 -12.54010542 -20.89783107
143 -10.23474081 -12.54010542
144 -7.47637262 -10.23474081
145 NA -7.47637262
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.07935413 0.61089926
[2,] -6.75755562 2.07935413
[3,] -6.34044190 -6.75755562
[4,] -3.01796357 -6.34044190
[5,] -4.18105383 -3.01796357
[6,] -2.54414409 -4.18105383
[7,] -1.56394011 -2.54414409
[8,] -1.77837152 -1.56394011
[9,] -5.61796357 -1.77837152
[10,] -4.58373613 -5.61796357
[11,] -3.26125781 -4.58373613
[12,] -7.03877948 -3.26125781
[13,] -11.34414409 -7.03877948
[14,] -11.64414409 -11.34414409
[15,] -11.29548524 -11.64414409
[16,] -10.04414409 -11.29548524
[17,] -7.30084985 -10.04414409
[18,] -5.41796357 -7.30084985
[19,] -5.56930472 -5.41796357
[20,] -8.39178305 -5.56930472
[21,] -11.29076317 -8.39178305
[22,] -11.69612778 -11.29076317
[23,] -12.74108443 -11.69612778
[24,] -13.27262956 -12.74108443
[25,] -15.65283354 -13.27262956
[26,] -11.85015124 -15.65283354
[27,] -8.38437867 -11.85015124
[28,] -6.75551585 -8.38437867
[29,] -7.27901406 -6.75551585
[30,] -13.56458265 -7.27901406
[31,] -14.48706098 -13.56458265
[32,] -9.71055919 -14.48706098
[33,] -6.83673971 -9.71055919
[34,] -6.02230830 -6.83673971
[35,] -11.91592380 -6.02230830
[36,] -12.20953930 -11.91592380
[37,] -13.80047250 -12.20953930
[38,] -14.00583711 -13.80047250
[39,] -15.98770351 -14.00583711
[40,] -17.84440928 -15.98770351
[41,] -20.36586772 -17.84440928
[42,] -20.58566374 -20.36586772
[43,] -14.31184426 -20.58566374
[44,] -13.00111504 -14.31184426
[45,] -5.43099775 -13.00111504
[46,] 6.21293902 -5.43099775
[47,] 7.10118992 6.21293902
[48,] 15.04078196 7.10118992
[49,] 8.30118992 15.04078196
[50,] 1.99314300 8.30118992
[51,] 7.06159787 1.99314300
[52,] 6.43643724 7.06159787
[53,] 7.64384162 6.43643724
[54,] 10.08611597 7.64384162
[55,] 5.96631995 10.08611597
[56,] 11.07538675 5.96631995
[57,] 14.47806905 11.07538675
[58,] 23.97436686 14.47806905
[59,] 26.03375493 23.97436686
[60,] 21.16530006 26.03375493
[61,] 17.16530006 21.16530006
[62,] 16.13745712 17.16530006
[63,] 19.50693188 16.13745712
[64,] 15.95290842 19.50693188
[65,] 18.72404559 15.95290842
[66,] 26.63847701 18.72404559
[67,] 25.95022611 26.63847701
[68,] 27.20693188 25.95022611
[69,] 30.56733983 27.20693188
[70,] 31.89250046 30.56733983
[71,] 19.77538675 31.89250046
[72,] 10.34217920 19.77538675
[73,] 5.30360703 10.34217920
[74,] 7.36401499 5.30360703
[75,] -2.77289475 7.36401499
[76,] -0.48362397 -2.77289475
[77,] 3.80194461 -0.48362397
[78,] 12.11267384 3.80194461
[79,] 9.80462692 12.11267384
[80,] 9.18585078 9.80462692
[81,] 15.33450963 9.18585078
[82,] 11.06605476 15.33450963
[83,] 9.99963966 11.06605476
[84,] 14.28891043 9.99963966
[85,] 23.39529493 14.28891043
[86,] 12.22887983 23.39529493
[87,] 5.79669216 12.22887983
[88,] -1.28313447 5.79669216
[89,] 6.34036375 -1.28313447
[90,] 16.78532040 6.34036375
[91,] 35.85377527 16.78532040
[92,] 36.07931326 35.85377527
[93,] 40.65313274 36.07931326
[94,] 31.03832398 40.65313274
[95,] 19.74803332 31.03832398
[96,] 6.66809453 19.74803332
[97,] -11.14735677 6.66809453
[98,] -24.27353729 -11.14735677
[99,] -31.22552098 -24.27353729
[100,] -30.39767804 -31.22552098
[101,] -23.89231343 -30.39767804
[102,] -25.53088559 -23.89231343
[103,] -25.93689274 -25.53088559
[104,] -15.11339452 -25.93689274
[105,] -10.44161481 -15.11339452
[106,] -4.71811660 -10.44161481
[107,] -3.51377187 -4.71811660
[108,] -1.33254801 -3.51377187
[109,] -0.21173210 -1.33254801
[110,] -6.92514363 -0.21173210
[111,] -1.63689274 -6.92514363
[112,] -0.00100288 -1.63689274
[113,] 8.86106749 -0.00100288
[114,] 15.94663608 8.86106749
[115,] 6.13220467 15.94663608
[116,] 2.92415775 6.13220467
[117,] -4.31977902 2.92415775
[118,] -8.95835118 -4.31977902
[119,] -15.45094680 -8.95835118
[120,] -15.22642870 -15.45094680
[121,] -18.10191060 -15.22642870
[122,] -19.35121199 -18.10191060
[123,] -24.01762709 -19.35121199
[124,] -24.99310899 -24.01762709
[125,] -7.81124259 -24.99310899
[126,] -1.05351695 -7.81124259
[127,] -8.04278772 -1.05351695
[128,] -8.08071735 -8.04278772
[129,] -3.27267043 -8.08071735
[130,] -14.53205850 -3.27267043
[131,] -10.67267043 -14.53205850
[132,] -0.39284380 -10.67267043
[133,] 8.76922657 -0.39284380
[134,] 11.63666156 8.76922657
[135,] 11.08902259 11.63666156
[136,] 16.33231683 11.08902259
[137,] 20.70983850 16.33231683
[138,] 15.12695222 20.70983850
[139,] 6.23768144 15.12695222
[140,] -8.77406766 6.23768144
[141,] -20.89783107 -8.77406766
[142,] -12.54010542 -20.89783107
[143,] -10.23474081 -12.54010542
[144,] -7.47637262 -10.23474081
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.07935413 0.61089926
2 -6.75755562 2.07935413
3 -6.34044190 -6.75755562
4 -3.01796357 -6.34044190
5 -4.18105383 -3.01796357
6 -2.54414409 -4.18105383
7 -1.56394011 -2.54414409
8 -1.77837152 -1.56394011
9 -5.61796357 -1.77837152
10 -4.58373613 -5.61796357
11 -3.26125781 -4.58373613
12 -7.03877948 -3.26125781
13 -11.34414409 -7.03877948
14 -11.64414409 -11.34414409
15 -11.29548524 -11.64414409
16 -10.04414409 -11.29548524
17 -7.30084985 -10.04414409
18 -5.41796357 -7.30084985
19 -5.56930472 -5.41796357
20 -8.39178305 -5.56930472
21 -11.29076317 -8.39178305
22 -11.69612778 -11.29076317
23 -12.74108443 -11.69612778
24 -13.27262956 -12.74108443
25 -15.65283354 -13.27262956
26 -11.85015124 -15.65283354
27 -8.38437867 -11.85015124
28 -6.75551585 -8.38437867
29 -7.27901406 -6.75551585
30 -13.56458265 -7.27901406
31 -14.48706098 -13.56458265
32 -9.71055919 -14.48706098
33 -6.83673971 -9.71055919
34 -6.02230830 -6.83673971
35 -11.91592380 -6.02230830
36 -12.20953930 -11.91592380
37 -13.80047250 -12.20953930
38 -14.00583711 -13.80047250
39 -15.98770351 -14.00583711
40 -17.84440928 -15.98770351
41 -20.36586772 -17.84440928
42 -20.58566374 -20.36586772
43 -14.31184426 -20.58566374
44 -13.00111504 -14.31184426
45 -5.43099775 -13.00111504
46 6.21293902 -5.43099775
47 7.10118992 6.21293902
48 15.04078196 7.10118992
49 8.30118992 15.04078196
50 1.99314300 8.30118992
51 7.06159787 1.99314300
52 6.43643724 7.06159787
53 7.64384162 6.43643724
54 10.08611597 7.64384162
55 5.96631995 10.08611597
56 11.07538675 5.96631995
57 14.47806905 11.07538675
58 23.97436686 14.47806905
59 26.03375493 23.97436686
60 21.16530006 26.03375493
61 17.16530006 21.16530006
62 16.13745712 17.16530006
63 19.50693188 16.13745712
64 15.95290842 19.50693188
65 18.72404559 15.95290842
66 26.63847701 18.72404559
67 25.95022611 26.63847701
68 27.20693188 25.95022611
69 30.56733983 27.20693188
70 31.89250046 30.56733983
71 19.77538675 31.89250046
72 10.34217920 19.77538675
73 5.30360703 10.34217920
74 7.36401499 5.30360703
75 -2.77289475 7.36401499
76 -0.48362397 -2.77289475
77 3.80194461 -0.48362397
78 12.11267384 3.80194461
79 9.80462692 12.11267384
80 9.18585078 9.80462692
81 15.33450963 9.18585078
82 11.06605476 15.33450963
83 9.99963966 11.06605476
84 14.28891043 9.99963966
85 23.39529493 14.28891043
86 12.22887983 23.39529493
87 5.79669216 12.22887983
88 -1.28313447 5.79669216
89 6.34036375 -1.28313447
90 16.78532040 6.34036375
91 35.85377527 16.78532040
92 36.07931326 35.85377527
93 40.65313274 36.07931326
94 31.03832398 40.65313274
95 19.74803332 31.03832398
96 6.66809453 19.74803332
97 -11.14735677 6.66809453
98 -24.27353729 -11.14735677
99 -31.22552098 -24.27353729
100 -30.39767804 -31.22552098
101 -23.89231343 -30.39767804
102 -25.53088559 -23.89231343
103 -25.93689274 -25.53088559
104 -15.11339452 -25.93689274
105 -10.44161481 -15.11339452
106 -4.71811660 -10.44161481
107 -3.51377187 -4.71811660
108 -1.33254801 -3.51377187
109 -0.21173210 -1.33254801
110 -6.92514363 -0.21173210
111 -1.63689274 -6.92514363
112 -0.00100288 -1.63689274
113 8.86106749 -0.00100288
114 15.94663608 8.86106749
115 6.13220467 15.94663608
116 2.92415775 6.13220467
117 -4.31977902 2.92415775
118 -8.95835118 -4.31977902
119 -15.45094680 -8.95835118
120 -15.22642870 -15.45094680
121 -18.10191060 -15.22642870
122 -19.35121199 -18.10191060
123 -24.01762709 -19.35121199
124 -24.99310899 -24.01762709
125 -7.81124259 -24.99310899
126 -1.05351695 -7.81124259
127 -8.04278772 -1.05351695
128 -8.08071735 -8.04278772
129 -3.27267043 -8.08071735
130 -14.53205850 -3.27267043
131 -10.67267043 -14.53205850
132 -0.39284380 -10.67267043
133 8.76922657 -0.39284380
134 11.63666156 8.76922657
135 11.08902259 11.63666156
136 16.33231683 11.08902259
137 20.70983850 16.33231683
138 15.12695222 20.70983850
139 6.23768144 15.12695222
140 -8.77406766 6.23768144
141 -20.89783107 -8.77406766
142 -12.54010542 -20.89783107
143 -10.23474081 -12.54010542
144 -7.47637262 -10.23474081
> 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/7jmhc1353078444.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/8yols1353078444.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/9g1r31353078444.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/100jo11353078444.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11kcjh1353078444.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12q8hd1353078444.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13m1j71353078444.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14wywc1353078445.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/15uc521353078445.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/16gld81353078445.tab")
+ }
>
> try(system("convert tmp/13dfq1353078444.ps tmp/13dfq1353078444.png",intern=TRUE))
character(0)
> try(system("convert tmp/2bgyy1353078444.ps tmp/2bgyy1353078444.png",intern=TRUE))
character(0)
> try(system("convert tmp/3om4r1353078444.ps tmp/3om4r1353078444.png",intern=TRUE))
character(0)
> try(system("convert tmp/4covn1353078444.ps tmp/4covn1353078444.png",intern=TRUE))
character(0)
> try(system("convert tmp/5twq61353078444.ps tmp/5twq61353078444.png",intern=TRUE))
character(0)
> try(system("convert tmp/6uzwr1353078444.ps tmp/6uzwr1353078444.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jmhc1353078444.ps tmp/7jmhc1353078444.png",intern=TRUE))
character(0)
> try(system("convert tmp/8yols1353078444.ps tmp/8yols1353078444.png",intern=TRUE))
character(0)
> try(system("convert tmp/9g1r31353078444.ps tmp/9g1r31353078444.png",intern=TRUE))
character(0)
> try(system("convert tmp/100jo11353078444.ps tmp/100jo11353078444.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
9.071 1.441 10.518