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)
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+ ,dim=c(8
+ ,154)
+ ,dimnames=list(c('Weeks'
+ ,'Uselimits'
+ ,'T20'
+ ,'T40'
+ ,'Used'
+ ,'CorrectAnalysis'
+ ,'Useful'
+ ,'Outcome
')
+ ,1:154))
> y <- array(NA,dim=c(8,154),dimnames=list(c('Weeks','Uselimits','T20','T40','Used','CorrectAnalysis','Useful','Outcome
'),1:154))
> 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'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '6'
> #'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, 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
CorrectAnalysis Weeks Uselimits T20 T40 Used Useful Outcome\r
1 13 4 5 0 9 11 15 17
2 13 4 6 0 10 11 15 18
3 13 4 6 0 10 11 15 18
4 13 4 6 0 10 11 15 18
5 13 4 6 0 10 11 15 18
6 13 4 5 0 10 11 16 17
7 13 4 6 0 10 11 15 18
8 13 4 6 0 9 11 15 18
9 13 4 6 0 10 11 15 17
10 13 4 5 0 10 11 15 18
11 13 4 5 0 9 11 15 18
12 13 4 6 0 10 11 15 18
13 13 4 6 0 10 12 16 18
14 13 4 5 0 9 11 15 18
15 13 4 6 0 10 12 16 17
16 13 4 6 0 9 12 16 17
17 14 4 5 0 9 12 16 18
18 13 4 5 0 9 11 15 18
19 13 4 6 0 10 11 15 17
20 14 4 6 0 9 12 16 17
21 13 4 5 0 10 11 16 18
22 13 4 5 0 10 12 16 17
23 13 4 6 0 10 11 16 17
24 13 4 5 0 10 11 16 17
25 13 4 6 0 9 12 15 17
26 13 4 6 0 10 12 16 18
27 13 4 5 0 10 11 15 17
28 13 4 6 0 10 12 15 18
29 13 4 6 0 10 11 15 17
30 13 4 6 0 10 11 16 18
31 13 4 6 0 10 11 15 18
32 13 4 5 0 10 11 15 18
33 13 4 5 0 10 11 16 18
34 13 4 6 0 9 11 15 17
35 13 4 6 0 10 11 15 18
36 13 4 6 0 10 11 15 18
37 13 4 5 0 9 12 16 18
38 13 4 6 0 10 12 15 17
39 13 4 6 0 10 11 16 17
40 13 4 6 0 9 11 16 18
41 14 4 6 0 10 12 16 17
42 13 4 6 0 10 12 15 17
43 13 4 5 0 10 11 16 17
44 13 4 5 0 9 11 15 18
45 13 4 6 0 10 11 16 18
46 13 4 6 0 10 11 16 17
47 13 4 6 0 10 11 15 18
48 13 4 6 0 10 11 15 17
49 13 4 6 0 10 11 16 17
50 13 4 6 0 10 11 15 18
51 13 4 6 0 9 12 15 18
52 14 4 5 0 9 12 16 18
53 13 4 6 0 10 11 15 17
54 14 4 6 0 10 12 15 18
55 13 4 6 0 10 11 15 18
56 13 4 6 0 9 12 15 17
57 13 4 6 0 10 12 16 17
58 13 4 6 0 10 11 15 17
59 13 4 6 0 10 11 15 17
60 14 4 5 0 9 12 16 17
61 13 4 5 0 9 11 15 17
62 13 4 6 0 10 12 16 18
63 13 4 6 0 10 11 15 18
64 13 4 5 0 9 11 15 17
65 13 4 6 0 10 11 15 18
66 13 4 6 0 10 11 15 18
67 14 4 6 0 9 12 16 18
68 13 4 5 0 10 11 15 18
69 13 4 6 0 10 11 15 17
70 13 4 6 0 10 12 15 18
71 13 4 6 0 10 11 15 18
72 13 4 6 0 10 11 15 17
73 13 4 6 0 10 12 15 17
74 13 4 5 0 10 12 15 18
75 13 4 6 0 10 11 15 17
76 13 4 6 0 9 11 16 17
77 13 4 6 0 10 11 15 17
78 13 4 6 0 10 12 16 17
79 14 4 6 0 9 12 15 17
80 13 4 6 0 9 11 16 18
81 13 4 6 0 10 11 15 18
82 13 4 5 0 10 12 15 17
83 13 4 6 0 10 11 15 18
84 14 4 6 0 10 12 15 18
85 13 4 6 0 10 11 16 17
86 13 4 5 0 10 11 15 18
87 13 2 5 8 0 11 15 17
88 13 2 5 7 0 12 15 17
89 13 2 6 8 0 11 15 18
90 13 2 6 8 0 11 15 17
91 13 2 6 8 0 11 16 18
92 13 2 5 7 0 11 15 18
93 13 2 5 8 0 11 16 18
94 13 2 6 8 0 11 15 18
95 13 2 6 7 0 11 15 18
96 13 2 6 8 0 11 15 17
97 13 2 5 7 0 11 15 18
98 13 2 6 8 0 11 15 18
99 13 2 5 8 0 11 15 18
100 13 2 6 8 0 11 15 17
101 13 2 5 8 0 11 15 17
102 13 2 6 8 0 11 15 18
103 13 2 6 8 0 11 15 18
104 13 2 6 8 0 11 15 18
105 13 2 6 7 0 12 15 18
106 13 2 6 8 0 11 15 18
107 13 2 6 8 0 11 15 18
108 13 2 5 7 0 12 15 18
109 13 2 6 8 0 11 15 18
110 13 2 5 8 0 11 15 18
111 13 2 5 7 0 12 16 18
112 13 2 6 7 0 11 15 18
113 13 2 6 8 0 12 15 18
114 13 2 5 7 0 12 15 18
115 13 2 5 8 0 11 15 18
116 13 2 6 8 0 11 15 18
117 13 2 5 8 0 11 15 17
118 13 2 5 8 0 11 15 18
119 13 2 6 8 0 11 15 18
120 13 2 6 8 0 11 15 17
121 13 2 5 8 0 11 15 18
122 13 2 6 8 0 11 15 18
123 13 2 5 7 0 12 15 18
124 13 2 6 8 0 12 16 17
125 13 2 6 8 0 11 15 17
126 13 2 6 7 0 11 15 18
127 13 2 6 8 0 11 16 18
128 13 2 6 8 0 11 15 17
129 13 2 6 8 0 11 15 18
130 13 2 6 8 0 11 15 17
131 13 2 5 8 0 11 15 18
132 13 2 5 8 0 11 15 17
133 13 2 5 8 0 12 15 18
134 13 2 6 8 0 11 15 18
135 13 2 6 8 0 11 15 18
136 13 2 6 8 0 11 15 18
137 13 2 5 8 0 12 16 17
138 13 2 5 7 0 12 16 17
139 13 2 6 7 0 11 15 18
140 13 2 6 8 0 11 15 18
141 14 2 6 8 0 12 15 17
142 13 2 6 7 0 12 15 17
143 13 2 5 8 0 11 15 18
144 13 2 6 8 0 11 16 17
145 13 2 6 8 0 11 16 18
146 13 2 6 7 0 11 15 17
147 13 2 6 7 0 12 15 18
148 13 2 6 7 0 11 15 18
149 13 2 5 8 0 11 15 18
150 13 2 6 8 0 11 16 17
151 13 2 6 8 0 11 15 17
152 14 2 5 8 0 12 15 18
153 14 2 5 8 0 12 16 18
154 13 2 5 8 0 12 15 18
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Weeks Uselimits T20 T40
4.786535 1.389725 0.008643 0.157197 -0.156530
Used Useful `Outcome\\r`
0.263764 0.040381 0.035707
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.43388 -0.11807 -0.01663 0.02136 0.75439
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.786535 1.690339 2.832 0.005284 **
Weeks 1.389725 0.379864 3.658 0.000354 ***
Uselimits 0.008643 0.041496 0.208 0.835293
T20 0.157197 0.067800 2.319 0.021808 *
T40 -0.156530 0.058477 -2.677 0.008284 **
Used 0.263764 0.044810 5.886 2.6e-08 ***
Useful 0.040381 0.045562 0.886 0.376919
`Outcome\\r` 0.035707 0.039538 0.903 0.367957
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2329 on 146 degrees of freedom
Multiple R-squared: 0.284, Adjusted R-squared: 0.2497
F-statistic: 8.274 on 7 and 146 DF, p-value: 1.741e-08
> 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,] 8.991926e-45 1.798385e-44 1.000000000
[2,] 5.893747e-57 1.178749e-56 1.000000000
[3,] 4.275484e-72 8.550968e-72 1.000000000
[4,] 3.754406e-85 7.508813e-85 1.000000000
[5,] 4.092217e-99 8.184433e-99 1.000000000
[6,] 0.000000e+00 0.000000e+00 1.000000000
[7,] 5.523334e-01 8.953332e-01 0.447666618
[8,] 4.988084e-01 9.976169e-01 0.501191566
[9,] 4.590339e-01 9.180677e-01 0.540966136
[10,] 8.982567e-01 2.034866e-01 0.101743287
[11,] 8.583044e-01 2.833911e-01 0.141695557
[12,] 8.473366e-01 3.053268e-01 0.152663402
[13,] 7.976022e-01 4.047956e-01 0.202397825
[14,] 7.415485e-01 5.169030e-01 0.258451476
[15,] 7.445445e-01 5.109111e-01 0.255455549
[16,] 7.466036e-01 5.067928e-01 0.253396381
[17,] 7.091238e-01 5.817525e-01 0.290876245
[18,] 6.616193e-01 6.767614e-01 0.338380689
[19,] 6.103462e-01 7.793075e-01 0.389653767
[20,] 5.525277e-01 8.949447e-01 0.447472327
[21,] 4.910989e-01 9.821978e-01 0.508901124
[22,] 4.308112e-01 8.616223e-01 0.569188827
[23,] 3.730625e-01 7.461250e-01 0.626937483
[24,] 3.256478e-01 6.512956e-01 0.674352213
[25,] 2.741575e-01 5.483150e-01 0.725842476
[26,] 2.271280e-01 4.542561e-01 0.772871966
[27,] 3.078687e-01 6.157374e-01 0.692131291
[28,] 2.686946e-01 5.373892e-01 0.731305399
[29,] 2.228095e-01 4.456190e-01 0.777190522
[30,] 2.155303e-01 4.310606e-01 0.784469710
[31,] 7.569087e-01 4.861826e-01 0.243091321
[32,] 7.325202e-01 5.349596e-01 0.267479777
[33,] 6.871915e-01 6.256170e-01 0.312808480
[34,] 6.517965e-01 6.964071e-01 0.348203537
[35,] 6.020677e-01 7.958646e-01 0.397932286
[36,] 5.516443e-01 8.967114e-01 0.448355677
[37,] 5.001761e-01 9.996477e-01 0.499823866
[38,] 4.488655e-01 8.977309e-01 0.551134526
[39,] 3.991408e-01 7.982816e-01 0.600859218
[40,] 3.505868e-01 7.011736e-01 0.649413185
[41,] 4.301764e-01 8.603527e-01 0.569823646
[42,] 7.010427e-01 5.979147e-01 0.298957334
[43,] 6.593232e-01 6.813537e-01 0.340676837
[44,] 9.475304e-01 1.049391e-01 0.052469562
[45,] 9.328011e-01 1.343978e-01 0.067198907
[46,] 9.612015e-01 7.759710e-02 0.038798549
[47,] 9.608454e-01 7.830922e-02 0.039154610
[48,] 9.504103e-01 9.917937e-02 0.049589685
[49,] 9.378469e-01 1.243061e-01 0.062153067
[50,] 9.823656e-01 3.526879e-02 0.017634393
[51,] 9.796658e-01 4.066843e-02 0.020334214
[52,] 9.812269e-01 3.754629e-02 0.018773144
[53,] 9.750305e-01 4.993902e-02 0.024969511
[54,] 9.739697e-01 5.206063e-02 0.026030313
[55,] 9.658799e-01 6.824023e-02 0.034120117
[56,] 9.558213e-01 8.835742e-02 0.044178709
[57,] 9.835133e-01 3.297331e-02 0.016486654
[58,] 9.779496e-01 4.410090e-02 0.022050449
[59,] 9.713953e-01 5.720935e-02 0.028604676
[60,] 9.721837e-01 5.563254e-02 0.027816272
[61,] 9.636559e-01 7.268820e-02 0.036344098
[62,] 9.537002e-01 9.259956e-02 0.046299779
[63,] 9.532316e-01 9.353687e-02 0.046768435
[64,] 9.564051e-01 8.718987e-02 0.043594934
[65,] 9.447984e-01 1.104032e-01 0.055201604
[66,] 9.428792e-01 1.142416e-01 0.057120794
[67,] 9.286201e-01 1.427597e-01 0.071379865
[68,] 9.346838e-01 1.306323e-01 0.065316157
[69,] 9.839702e-01 3.205961e-02 0.016029805
[70,] 9.800563e-01 3.988733e-02 0.019943664
[71,] 9.746383e-01 5.072341e-02 0.025361703
[72,] 9.806368e-01 3.872648e-02 0.019363240
[73,] 9.787859e-01 4.242825e-02 0.021214123
[74,] 9.980798e-01 3.840490e-03 0.001920245
[75,] 9.971721e-01 5.655837e-03 0.002827918
[76,] 9.958918e-01 8.216377e-03 0.004108188
[77,] 9.941021e-01 1.179583e-02 0.005897913
[78,] 9.920758e-01 1.584831e-02 0.007924157
[79,] 9.889565e-01 2.208709e-02 0.011043545
[80,] 9.847468e-01 3.050633e-02 0.015253167
[81,] 9.794112e-01 4.117751e-02 0.020588755
[82,] 9.749280e-01 5.014406e-02 0.025072030
[83,] 9.666798e-01 6.664043e-02 0.033320213
[84,] 9.561895e-01 8.762104e-02 0.043810518
[85,] 9.468636e-01 1.062729e-01 0.053136442
[86,] 9.317005e-01 1.365989e-01 0.068299452
[87,] 9.197239e-01 1.605523e-01 0.080276148
[88,] 8.989071e-01 2.021859e-01 0.101092942
[89,] 8.741146e-01 2.517707e-01 0.125885361
[90,] 8.455327e-01 3.089347e-01 0.154467332
[91,] 8.127447e-01 3.745106e-01 0.187255313
[92,] 7.758857e-01 4.482285e-01 0.224114251
[93,] 7.350834e-01 5.298333e-01 0.264916628
[94,] 6.906625e-01 6.186750e-01 0.309337521
[95,] 6.604521e-01 6.790959e-01 0.339547947
[96,] 6.111543e-01 7.776915e-01 0.388845750
[97,] 5.598492e-01 8.803015e-01 0.440150760
[98,] 5.206753e-01 9.586493e-01 0.479324663
[99,] 4.675849e-01 9.351698e-01 0.532415075
[100,] 4.138453e-01 8.276906e-01 0.586154689
[101,] 3.761258e-01 7.522515e-01 0.623874228
[102,] 3.398021e-01 6.796043e-01 0.660197873
[103,] 3.871091e-01 7.742182e-01 0.612890889
[104,] 3.512369e-01 7.024738e-01 0.648763114
[105,] 2.999937e-01 5.999873e-01 0.700006327
[106,] 2.541315e-01 5.082629e-01 0.745868547
[107,] 2.106759e-01 4.213517e-01 0.789324128
[108,] 1.710331e-01 3.420661e-01 0.828966930
[109,] 1.379845e-01 2.759691e-01 0.862015452
[110,] 1.079359e-01 2.158717e-01 0.892064138
[111,] 8.266694e-02 1.653339e-01 0.917333059
[112,] 6.312687e-02 1.262537e-01 0.936873126
[113,] 5.275081e-02 1.055016e-01 0.947249185
[114,] 6.742503e-02 1.348501e-01 0.932574969
[115,] 4.927693e-02 9.855385e-02 0.950723074
[116,] 3.923076e-02 7.846151e-02 0.960769244
[117,] 2.780694e-02 5.561388e-02 0.972193062
[118,] 1.890541e-02 3.781083e-02 0.981094585
[119,] 1.284483e-02 2.568967e-02 0.987155165
[120,] 8.264031e-03 1.652806e-02 0.991735969
[121,] 5.062650e-03 1.012530e-02 0.994937350
[122,] 3.067285e-03 6.134570e-03 0.996932715
[123,] 6.080312e-03 1.216062e-02 0.993919688
[124,] 3.847261e-03 7.694521e-03 0.996152739
[125,] 2.430701e-03 4.861402e-03 0.997569299
[126,] 1.575970e-03 3.151940e-03 0.998424030
[127,] 2.878698e-03 5.757397e-03 0.997121302
[128,] 2.313758e-03 4.627516e-03 0.997686242
[129,] 1.803268e-03 3.606536e-03 0.998196732
[130,] 8.144158e-04 1.628832e-03 0.999185584
[131,] 1.260926e-02 2.521853e-02 0.987390736
[132,] 9.378334e-03 1.875667e-02 0.990621666
[133,] 4.354695e-03 8.709390e-03 0.995645305
> postscript(file="/var/wessaorg/rcomp/tmp/1nyyd1355752085.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/28qqa1355752085.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/33ugp1355752085.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4fuwl1355752085.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5leka1355752085.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 = 154
Frequency = 1
1 2 3 4 5 6
-0.09402503 0.01815461 0.01815461 0.01815461 0.01815461 0.02212414
7 8 9 10 11 12
0.01815461 -0.13837554 0.05386193 0.02679780 -0.12973235 0.01815461
13 14 15 16 17 18
-0.28599077 -0.12973235 -0.25028345 -0.40681360 0.56612227 -0.12973235
19 20 21 22 23 24
0.05386193 0.59318640 -0.01358318 -0.24164026 0.01348096 0.02212414
25 26 27 28 29 30
-0.36643262 -0.28599077 0.06250512 -0.24560979 0.05386193 -0.02222636
31 32 33 34 35 36
0.01815461 0.02679780 -0.01358318 -0.10266822 0.01815461 0.01815461
37 38 39 40 41 42
-0.43387773 -0.20990247 0.01348096 -0.17875652 0.74971655 -0.20990247
43 44 45 46 47 48
0.02212414 -0.12973235 -0.02222636 0.01348096 0.01815461 0.05386193
49 50 51 52 53 54
0.01348096 0.01815461 -0.40213994 0.56612227 0.05386193 0.75439021
55 56 57 58 59 60
0.01815461 -0.36643262 -0.25028345 0.05386193 0.05386193 0.60182959
61 62 63 64 65 66
-0.09402503 -0.28599077 0.01815461 -0.09402503 0.01815461 0.01815461
67 68 69 70 71 72
0.55747908 0.02679780 0.05386193 -0.24560979 0.01815461 0.05386193
73 74 75 76 77 78
-0.20990247 -0.23696660 0.05386193 -0.14304920 0.05386193 -0.25028345
79 80 81 82 83 84
0.63356738 -0.17875652 0.01815461 -0.20125928 0.01815461 0.75439021
85 86 87 88 89 90
0.01348096 0.02679780 0.01907486 -0.08749232 -0.02527564 0.01043168
91 92 93 94 95 96
-0.06565662 0.14056476 -0.05701343 -0.02527564 0.13192158 0.01043168
97 98 99 100 101 102
0.14056476 -0.02527564 -0.01663246 0.01043168 0.01907486 -0.02527564
103 104 105 106 107 108
-0.02527564 -0.02527564 -0.13184283 -0.02527564 -0.02527564 -0.12319964
109 110 111 112 113 114
-0.02527564 -0.01663246 -0.16358062 0.13192158 -0.28904005 -0.12319964
115 116 117 118 119 120
-0.01663246 -0.02527564 0.01907486 -0.01663246 -0.02527564 0.01043168
121 122 123 124 125 126
-0.01663246 -0.02527564 -0.12319964 -0.29371370 0.01043168 0.13192158
127 128 129 130 131 132
-0.06565662 0.01043168 -0.02527564 0.01043168 -0.01663246 0.01907486
133 134 135 136 137 138
-0.28039686 -0.02527564 -0.02527564 -0.02527564 -0.28507052 -0.12787330
139 140 141 142 143 144
0.13192158 -0.02527564 0.74666727 -0.09613551 -0.01663246 -0.02994930
145 146 147 148 149 150
-0.06565662 0.16762890 -0.13184283 0.13192158 -0.01663246 -0.02994930
151 152 153 154
0.01043168 0.71960314 0.67922217 -0.28039686
> postscript(file="/var/wessaorg/rcomp/tmp/610t11355752085.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 = 154
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.09402503 NA
1 0.01815461 -0.09402503
2 0.01815461 0.01815461
3 0.01815461 0.01815461
4 0.01815461 0.01815461
5 0.02212414 0.01815461
6 0.01815461 0.02212414
7 -0.13837554 0.01815461
8 0.05386193 -0.13837554
9 0.02679780 0.05386193
10 -0.12973235 0.02679780
11 0.01815461 -0.12973235
12 -0.28599077 0.01815461
13 -0.12973235 -0.28599077
14 -0.25028345 -0.12973235
15 -0.40681360 -0.25028345
16 0.56612227 -0.40681360
17 -0.12973235 0.56612227
18 0.05386193 -0.12973235
19 0.59318640 0.05386193
20 -0.01358318 0.59318640
21 -0.24164026 -0.01358318
22 0.01348096 -0.24164026
23 0.02212414 0.01348096
24 -0.36643262 0.02212414
25 -0.28599077 -0.36643262
26 0.06250512 -0.28599077
27 -0.24560979 0.06250512
28 0.05386193 -0.24560979
29 -0.02222636 0.05386193
30 0.01815461 -0.02222636
31 0.02679780 0.01815461
32 -0.01358318 0.02679780
33 -0.10266822 -0.01358318
34 0.01815461 -0.10266822
35 0.01815461 0.01815461
36 -0.43387773 0.01815461
37 -0.20990247 -0.43387773
38 0.01348096 -0.20990247
39 -0.17875652 0.01348096
40 0.74971655 -0.17875652
41 -0.20990247 0.74971655
42 0.02212414 -0.20990247
43 -0.12973235 0.02212414
44 -0.02222636 -0.12973235
45 0.01348096 -0.02222636
46 0.01815461 0.01348096
47 0.05386193 0.01815461
48 0.01348096 0.05386193
49 0.01815461 0.01348096
50 -0.40213994 0.01815461
51 0.56612227 -0.40213994
52 0.05386193 0.56612227
53 0.75439021 0.05386193
54 0.01815461 0.75439021
55 -0.36643262 0.01815461
56 -0.25028345 -0.36643262
57 0.05386193 -0.25028345
58 0.05386193 0.05386193
59 0.60182959 0.05386193
60 -0.09402503 0.60182959
61 -0.28599077 -0.09402503
62 0.01815461 -0.28599077
63 -0.09402503 0.01815461
64 0.01815461 -0.09402503
65 0.01815461 0.01815461
66 0.55747908 0.01815461
67 0.02679780 0.55747908
68 0.05386193 0.02679780
69 -0.24560979 0.05386193
70 0.01815461 -0.24560979
71 0.05386193 0.01815461
72 -0.20990247 0.05386193
73 -0.23696660 -0.20990247
74 0.05386193 -0.23696660
75 -0.14304920 0.05386193
76 0.05386193 -0.14304920
77 -0.25028345 0.05386193
78 0.63356738 -0.25028345
79 -0.17875652 0.63356738
80 0.01815461 -0.17875652
81 -0.20125928 0.01815461
82 0.01815461 -0.20125928
83 0.75439021 0.01815461
84 0.01348096 0.75439021
85 0.02679780 0.01348096
86 0.01907486 0.02679780
87 -0.08749232 0.01907486
88 -0.02527564 -0.08749232
89 0.01043168 -0.02527564
90 -0.06565662 0.01043168
91 0.14056476 -0.06565662
92 -0.05701343 0.14056476
93 -0.02527564 -0.05701343
94 0.13192158 -0.02527564
95 0.01043168 0.13192158
96 0.14056476 0.01043168
97 -0.02527564 0.14056476
98 -0.01663246 -0.02527564
99 0.01043168 -0.01663246
100 0.01907486 0.01043168
101 -0.02527564 0.01907486
102 -0.02527564 -0.02527564
103 -0.02527564 -0.02527564
104 -0.13184283 -0.02527564
105 -0.02527564 -0.13184283
106 -0.02527564 -0.02527564
107 -0.12319964 -0.02527564
108 -0.02527564 -0.12319964
109 -0.01663246 -0.02527564
110 -0.16358062 -0.01663246
111 0.13192158 -0.16358062
112 -0.28904005 0.13192158
113 -0.12319964 -0.28904005
114 -0.01663246 -0.12319964
115 -0.02527564 -0.01663246
116 0.01907486 -0.02527564
117 -0.01663246 0.01907486
118 -0.02527564 -0.01663246
119 0.01043168 -0.02527564
120 -0.01663246 0.01043168
121 -0.02527564 -0.01663246
122 -0.12319964 -0.02527564
123 -0.29371370 -0.12319964
124 0.01043168 -0.29371370
125 0.13192158 0.01043168
126 -0.06565662 0.13192158
127 0.01043168 -0.06565662
128 -0.02527564 0.01043168
129 0.01043168 -0.02527564
130 -0.01663246 0.01043168
131 0.01907486 -0.01663246
132 -0.28039686 0.01907486
133 -0.02527564 -0.28039686
134 -0.02527564 -0.02527564
135 -0.02527564 -0.02527564
136 -0.28507052 -0.02527564
137 -0.12787330 -0.28507052
138 0.13192158 -0.12787330
139 -0.02527564 0.13192158
140 0.74666727 -0.02527564
141 -0.09613551 0.74666727
142 -0.01663246 -0.09613551
143 -0.02994930 -0.01663246
144 -0.06565662 -0.02994930
145 0.16762890 -0.06565662
146 -0.13184283 0.16762890
147 0.13192158 -0.13184283
148 -0.01663246 0.13192158
149 -0.02994930 -0.01663246
150 0.01043168 -0.02994930
151 0.71960314 0.01043168
152 0.67922217 0.71960314
153 -0.28039686 0.67922217
154 NA -0.28039686
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.01815461 -0.09402503
[2,] 0.01815461 0.01815461
[3,] 0.01815461 0.01815461
[4,] 0.01815461 0.01815461
[5,] 0.02212414 0.01815461
[6,] 0.01815461 0.02212414
[7,] -0.13837554 0.01815461
[8,] 0.05386193 -0.13837554
[9,] 0.02679780 0.05386193
[10,] -0.12973235 0.02679780
[11,] 0.01815461 -0.12973235
[12,] -0.28599077 0.01815461
[13,] -0.12973235 -0.28599077
[14,] -0.25028345 -0.12973235
[15,] -0.40681360 -0.25028345
[16,] 0.56612227 -0.40681360
[17,] -0.12973235 0.56612227
[18,] 0.05386193 -0.12973235
[19,] 0.59318640 0.05386193
[20,] -0.01358318 0.59318640
[21,] -0.24164026 -0.01358318
[22,] 0.01348096 -0.24164026
[23,] 0.02212414 0.01348096
[24,] -0.36643262 0.02212414
[25,] -0.28599077 -0.36643262
[26,] 0.06250512 -0.28599077
[27,] -0.24560979 0.06250512
[28,] 0.05386193 -0.24560979
[29,] -0.02222636 0.05386193
[30,] 0.01815461 -0.02222636
[31,] 0.02679780 0.01815461
[32,] -0.01358318 0.02679780
[33,] -0.10266822 -0.01358318
[34,] 0.01815461 -0.10266822
[35,] 0.01815461 0.01815461
[36,] -0.43387773 0.01815461
[37,] -0.20990247 -0.43387773
[38,] 0.01348096 -0.20990247
[39,] -0.17875652 0.01348096
[40,] 0.74971655 -0.17875652
[41,] -0.20990247 0.74971655
[42,] 0.02212414 -0.20990247
[43,] -0.12973235 0.02212414
[44,] -0.02222636 -0.12973235
[45,] 0.01348096 -0.02222636
[46,] 0.01815461 0.01348096
[47,] 0.05386193 0.01815461
[48,] 0.01348096 0.05386193
[49,] 0.01815461 0.01348096
[50,] -0.40213994 0.01815461
[51,] 0.56612227 -0.40213994
[52,] 0.05386193 0.56612227
[53,] 0.75439021 0.05386193
[54,] 0.01815461 0.75439021
[55,] -0.36643262 0.01815461
[56,] -0.25028345 -0.36643262
[57,] 0.05386193 -0.25028345
[58,] 0.05386193 0.05386193
[59,] 0.60182959 0.05386193
[60,] -0.09402503 0.60182959
[61,] -0.28599077 -0.09402503
[62,] 0.01815461 -0.28599077
[63,] -0.09402503 0.01815461
[64,] 0.01815461 -0.09402503
[65,] 0.01815461 0.01815461
[66,] 0.55747908 0.01815461
[67,] 0.02679780 0.55747908
[68,] 0.05386193 0.02679780
[69,] -0.24560979 0.05386193
[70,] 0.01815461 -0.24560979
[71,] 0.05386193 0.01815461
[72,] -0.20990247 0.05386193
[73,] -0.23696660 -0.20990247
[74,] 0.05386193 -0.23696660
[75,] -0.14304920 0.05386193
[76,] 0.05386193 -0.14304920
[77,] -0.25028345 0.05386193
[78,] 0.63356738 -0.25028345
[79,] -0.17875652 0.63356738
[80,] 0.01815461 -0.17875652
[81,] -0.20125928 0.01815461
[82,] 0.01815461 -0.20125928
[83,] 0.75439021 0.01815461
[84,] 0.01348096 0.75439021
[85,] 0.02679780 0.01348096
[86,] 0.01907486 0.02679780
[87,] -0.08749232 0.01907486
[88,] -0.02527564 -0.08749232
[89,] 0.01043168 -0.02527564
[90,] -0.06565662 0.01043168
[91,] 0.14056476 -0.06565662
[92,] -0.05701343 0.14056476
[93,] -0.02527564 -0.05701343
[94,] 0.13192158 -0.02527564
[95,] 0.01043168 0.13192158
[96,] 0.14056476 0.01043168
[97,] -0.02527564 0.14056476
[98,] -0.01663246 -0.02527564
[99,] 0.01043168 -0.01663246
[100,] 0.01907486 0.01043168
[101,] -0.02527564 0.01907486
[102,] -0.02527564 -0.02527564
[103,] -0.02527564 -0.02527564
[104,] -0.13184283 -0.02527564
[105,] -0.02527564 -0.13184283
[106,] -0.02527564 -0.02527564
[107,] -0.12319964 -0.02527564
[108,] -0.02527564 -0.12319964
[109,] -0.01663246 -0.02527564
[110,] -0.16358062 -0.01663246
[111,] 0.13192158 -0.16358062
[112,] -0.28904005 0.13192158
[113,] -0.12319964 -0.28904005
[114,] -0.01663246 -0.12319964
[115,] -0.02527564 -0.01663246
[116,] 0.01907486 -0.02527564
[117,] -0.01663246 0.01907486
[118,] -0.02527564 -0.01663246
[119,] 0.01043168 -0.02527564
[120,] -0.01663246 0.01043168
[121,] -0.02527564 -0.01663246
[122,] -0.12319964 -0.02527564
[123,] -0.29371370 -0.12319964
[124,] 0.01043168 -0.29371370
[125,] 0.13192158 0.01043168
[126,] -0.06565662 0.13192158
[127,] 0.01043168 -0.06565662
[128,] -0.02527564 0.01043168
[129,] 0.01043168 -0.02527564
[130,] -0.01663246 0.01043168
[131,] 0.01907486 -0.01663246
[132,] -0.28039686 0.01907486
[133,] -0.02527564 -0.28039686
[134,] -0.02527564 -0.02527564
[135,] -0.02527564 -0.02527564
[136,] -0.28507052 -0.02527564
[137,] -0.12787330 -0.28507052
[138,] 0.13192158 -0.12787330
[139,] -0.02527564 0.13192158
[140,] 0.74666727 -0.02527564
[141,] -0.09613551 0.74666727
[142,] -0.01663246 -0.09613551
[143,] -0.02994930 -0.01663246
[144,] -0.06565662 -0.02994930
[145,] 0.16762890 -0.06565662
[146,] -0.13184283 0.16762890
[147,] 0.13192158 -0.13184283
[148,] -0.01663246 0.13192158
[149,] -0.02994930 -0.01663246
[150,] 0.01043168 -0.02994930
[151,] 0.71960314 0.01043168
[152,] 0.67922217 0.71960314
[153,] -0.28039686 0.67922217
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.01815461 -0.09402503
2 0.01815461 0.01815461
3 0.01815461 0.01815461
4 0.01815461 0.01815461
5 0.02212414 0.01815461
6 0.01815461 0.02212414
7 -0.13837554 0.01815461
8 0.05386193 -0.13837554
9 0.02679780 0.05386193
10 -0.12973235 0.02679780
11 0.01815461 -0.12973235
12 -0.28599077 0.01815461
13 -0.12973235 -0.28599077
14 -0.25028345 -0.12973235
15 -0.40681360 -0.25028345
16 0.56612227 -0.40681360
17 -0.12973235 0.56612227
18 0.05386193 -0.12973235
19 0.59318640 0.05386193
20 -0.01358318 0.59318640
21 -0.24164026 -0.01358318
22 0.01348096 -0.24164026
23 0.02212414 0.01348096
24 -0.36643262 0.02212414
25 -0.28599077 -0.36643262
26 0.06250512 -0.28599077
27 -0.24560979 0.06250512
28 0.05386193 -0.24560979
29 -0.02222636 0.05386193
30 0.01815461 -0.02222636
31 0.02679780 0.01815461
32 -0.01358318 0.02679780
33 -0.10266822 -0.01358318
34 0.01815461 -0.10266822
35 0.01815461 0.01815461
36 -0.43387773 0.01815461
37 -0.20990247 -0.43387773
38 0.01348096 -0.20990247
39 -0.17875652 0.01348096
40 0.74971655 -0.17875652
41 -0.20990247 0.74971655
42 0.02212414 -0.20990247
43 -0.12973235 0.02212414
44 -0.02222636 -0.12973235
45 0.01348096 -0.02222636
46 0.01815461 0.01348096
47 0.05386193 0.01815461
48 0.01348096 0.05386193
49 0.01815461 0.01348096
50 -0.40213994 0.01815461
51 0.56612227 -0.40213994
52 0.05386193 0.56612227
53 0.75439021 0.05386193
54 0.01815461 0.75439021
55 -0.36643262 0.01815461
56 -0.25028345 -0.36643262
57 0.05386193 -0.25028345
58 0.05386193 0.05386193
59 0.60182959 0.05386193
60 -0.09402503 0.60182959
61 -0.28599077 -0.09402503
62 0.01815461 -0.28599077
63 -0.09402503 0.01815461
64 0.01815461 -0.09402503
65 0.01815461 0.01815461
66 0.55747908 0.01815461
67 0.02679780 0.55747908
68 0.05386193 0.02679780
69 -0.24560979 0.05386193
70 0.01815461 -0.24560979
71 0.05386193 0.01815461
72 -0.20990247 0.05386193
73 -0.23696660 -0.20990247
74 0.05386193 -0.23696660
75 -0.14304920 0.05386193
76 0.05386193 -0.14304920
77 -0.25028345 0.05386193
78 0.63356738 -0.25028345
79 -0.17875652 0.63356738
80 0.01815461 -0.17875652
81 -0.20125928 0.01815461
82 0.01815461 -0.20125928
83 0.75439021 0.01815461
84 0.01348096 0.75439021
85 0.02679780 0.01348096
86 0.01907486 0.02679780
87 -0.08749232 0.01907486
88 -0.02527564 -0.08749232
89 0.01043168 -0.02527564
90 -0.06565662 0.01043168
91 0.14056476 -0.06565662
92 -0.05701343 0.14056476
93 -0.02527564 -0.05701343
94 0.13192158 -0.02527564
95 0.01043168 0.13192158
96 0.14056476 0.01043168
97 -0.02527564 0.14056476
98 -0.01663246 -0.02527564
99 0.01043168 -0.01663246
100 0.01907486 0.01043168
101 -0.02527564 0.01907486
102 -0.02527564 -0.02527564
103 -0.02527564 -0.02527564
104 -0.13184283 -0.02527564
105 -0.02527564 -0.13184283
106 -0.02527564 -0.02527564
107 -0.12319964 -0.02527564
108 -0.02527564 -0.12319964
109 -0.01663246 -0.02527564
110 -0.16358062 -0.01663246
111 0.13192158 -0.16358062
112 -0.28904005 0.13192158
113 -0.12319964 -0.28904005
114 -0.01663246 -0.12319964
115 -0.02527564 -0.01663246
116 0.01907486 -0.02527564
117 -0.01663246 0.01907486
118 -0.02527564 -0.01663246
119 0.01043168 -0.02527564
120 -0.01663246 0.01043168
121 -0.02527564 -0.01663246
122 -0.12319964 -0.02527564
123 -0.29371370 -0.12319964
124 0.01043168 -0.29371370
125 0.13192158 0.01043168
126 -0.06565662 0.13192158
127 0.01043168 -0.06565662
128 -0.02527564 0.01043168
129 0.01043168 -0.02527564
130 -0.01663246 0.01043168
131 0.01907486 -0.01663246
132 -0.28039686 0.01907486
133 -0.02527564 -0.28039686
134 -0.02527564 -0.02527564
135 -0.02527564 -0.02527564
136 -0.28507052 -0.02527564
137 -0.12787330 -0.28507052
138 0.13192158 -0.12787330
139 -0.02527564 0.13192158
140 0.74666727 -0.02527564
141 -0.09613551 0.74666727
142 -0.01663246 -0.09613551
143 -0.02994930 -0.01663246
144 -0.06565662 -0.02994930
145 0.16762890 -0.06565662
146 -0.13184283 0.16762890
147 0.13192158 -0.13184283
148 -0.01663246 0.13192158
149 -0.02994930 -0.01663246
150 0.01043168 -0.02994930
151 0.71960314 0.01043168
152 0.67922217 0.71960314
153 -0.28039686 0.67922217
> 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/7gsoy1355752086.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8l56o1355752086.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9q2iz1355752086.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10z3l11355752086.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11okbs1355752086.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/123ggd1355752086.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/13gjzz1355752086.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/14s5tl1355752086.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/15yj3k1355752086.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/16a8hl1355752086.tab")
+ }
>
> try(system("convert tmp/1nyyd1355752085.ps tmp/1nyyd1355752085.png",intern=TRUE))
character(0)
> try(system("convert tmp/28qqa1355752085.ps tmp/28qqa1355752085.png",intern=TRUE))
character(0)
> try(system("convert tmp/33ugp1355752085.ps tmp/33ugp1355752085.png",intern=TRUE))
character(0)
> try(system("convert tmp/4fuwl1355752085.ps tmp/4fuwl1355752085.png",intern=TRUE))
character(0)
> try(system("convert tmp/5leka1355752085.ps tmp/5leka1355752085.png",intern=TRUE))
character(0)
> try(system("convert tmp/610t11355752085.ps tmp/610t11355752085.png",intern=TRUE))
character(0)
> try(system("convert tmp/7gsoy1355752086.ps tmp/7gsoy1355752086.png",intern=TRUE))
character(0)
> try(system("convert tmp/8l56o1355752086.ps tmp/8l56o1355752086.png",intern=TRUE))
character(0)
> try(system("convert tmp/9q2iz1355752086.ps tmp/9q2iz1355752086.png",intern=TRUE))
character(0)
> try(system("convert tmp/10z3l11355752086.ps tmp/10z3l11355752086.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
9.040 1.426 10.802