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'
+ ,'UseLimit'
+ ,'T40'
+ ,'T20'
+ ,'Used'
+ ,'CorrectAnalysis'
+ ,'Useful'
+ ,'Outcome')
+ ,1:154))
> y <- array(NA,dim=c(8,154),dimnames=list(c('Weeks','UseLimit','T40','T20','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 = '1'
> 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
Weeks UseLimit T40 T20 Used CorrectAnalysis Useful Outcome
1 4 1 1 0 0 0 0 1
2 4 0 2 0 0 0 0 0
3 4 0 2 0 0 0 0 0
4 4 0 2 0 0 0 0 0
5 4 0 2 0 0 0 0 0
6 4 1 2 0 0 0 1 1
7 4 0 2 0 0 0 0 0
8 4 0 1 0 0 0 0 0
9 4 0 2 0 0 0 0 1
10 4 1 2 0 0 0 0 0
11 4 1 1 0 0 0 0 0
12 4 0 2 0 0 0 0 0
13 4 0 2 0 1 0 1 0
14 4 1 1 0 0 0 0 0
15 4 0 2 0 1 0 1 1
16 4 0 1 0 1 0 1 1
17 4 1 1 0 1 1 1 0
18 4 1 1 0 0 0 0 0
19 4 0 2 0 0 0 0 1
20 4 0 1 0 1 1 1 1
21 4 1 2 0 0 0 1 0
22 4 1 2 0 1 0 1 1
23 4 0 2 0 0 0 1 1
24 4 1 2 0 0 0 1 1
25 4 0 1 0 1 0 0 1
26 4 0 2 0 1 0 1 0
27 4 1 2 0 0 0 0 1
28 4 0 2 0 1 0 0 0
29 4 0 2 0 0 0 0 1
30 4 0 2 0 0 0 1 0
31 4 0 2 0 0 0 0 0
32 4 1 2 0 0 0 0 0
33 4 1 2 0 0 0 1 0
34 4 0 1 0 0 0 0 1
35 4 0 2 0 0 0 0 0
36 4 0 2 0 0 0 0 0
37 4 1 1 0 1 0 1 0
38 4 0 2 0 1 0 0 1
39 4 0 2 0 0 0 1 1
40 4 0 1 0 0 0 1 0
41 4 0 2 0 1 1 1 1
42 4 0 2 0 1 0 0 1
43 4 1 2 0 0 0 1 1
44 4 1 1 0 0 0 0 0
45 4 0 2 0 0 0 1 0
46 4 0 2 0 0 0 1 1
47 4 0 2 0 0 0 0 0
48 4 0 2 0 0 0 0 1
49 4 0 2 0 0 0 1 1
50 4 0 2 0 0 0 0 0
51 4 0 1 0 1 0 0 0
52 4 1 1 0 1 1 1 0
53 4 0 2 0 0 0 0 1
54 4 0 2 0 1 1 0 0
55 4 0 2 0 0 0 0 0
56 4 0 1 0 1 0 0 1
57 4 0 2 0 1 0 1 1
58 4 0 2 0 0 0 0 1
59 4 0 2 0 0 0 0 1
60 4 1 1 0 1 1 1 1
61 4 1 1 0 0 0 0 1
62 4 0 2 0 1 0 1 0
63 4 0 2 0 0 0 0 0
64 4 1 1 0 0 0 0 1
65 4 0 2 0 0 0 0 0
66 4 0 2 0 0 0 0 0
67 4 0 1 0 1 1 1 0
68 4 1 2 0 0 0 0 0
69 4 0 2 0 0 0 0 1
70 4 0 2 0 1 0 0 0
71 4 0 2 0 0 0 0 0
72 4 0 2 0 0 0 0 1
73 4 0 2 0 1 0 0 1
74 4 1 2 0 1 0 0 0
75 4 0 2 0 0 0 0 1
76 4 0 1 0 0 0 1 1
77 4 0 2 0 0 0 0 1
78 4 0 2 0 1 0 1 1
79 4 0 1 0 1 1 0 1
80 4 0 1 0 0 0 1 0
81 4 0 2 0 0 0 0 0
82 4 1 2 0 1 0 0 1
83 4 0 2 0 0 0 0 0
84 4 0 2 0 1 1 0 0
85 4 0 2 0 0 0 1 1
86 4 1 2 0 0 0 0 0
87 2 1 0 2 0 0 0 1
88 2 1 0 1 1 0 0 1
89 2 0 0 2 0 0 0 0
90 2 0 0 2 0 0 0 1
91 2 0 0 2 0 0 1 0
92 2 1 0 1 0 0 0 0
93 2 1 0 2 0 0 1 0
94 2 0 0 2 0 0 0 0
95 2 0 0 1 0 0 0 0
96 2 0 0 2 0 0 0 1
97 2 1 0 1 0 0 0 0
98 2 0 0 2 0 0 0 0
99 2 1 0 2 0 0 0 0
100 2 0 0 2 0 0 0 1
101 2 1 0 2 0 0 0 1
102 2 0 0 2 0 0 0 0
103 2 0 0 2 0 0 0 0
104 2 0 0 2 0 0 0 0
105 2 0 0 1 1 0 0 0
106 2 0 0 2 0 0 0 0
107 2 0 0 2 0 0 0 0
108 2 1 0 1 1 0 0 0
109 2 0 0 2 0 0 0 0
110 2 1 0 2 0 0 0 0
111 2 1 0 1 1 0 1 0
112 2 0 0 1 0 0 0 0
113 2 0 0 2 1 0 0 0
114 2 1 0 1 1 0 0 0
115 2 1 0 2 0 0 0 0
116 2 0 0 2 0 0 0 0
117 2 1 0 2 0 0 0 1
118 2 1 0 2 0 0 0 0
119 2 0 0 2 0 0 0 0
120 2 0 0 2 0 0 0 1
121 2 1 0 2 0 0 0 0
122 2 0 0 2 0 0 0 0
123 2 1 0 1 1 0 0 0
124 2 0 0 2 1 0 1 1
125 2 0 0 2 0 0 0 1
126 2 0 0 1 0 0 0 0
127 2 0 0 2 0 0 1 0
128 2 0 0 2 0 0 0 1
129 2 0 0 2 0 0 0 0
130 2 0 0 2 0 0 0 1
131 2 1 0 2 0 0 0 0
132 2 1 0 2 0 0 0 1
133 2 1 0 2 1 0 0 0
134 2 0 0 2 0 0 0 0
135 2 0 0 2 0 0 0 0
136 2 0 0 2 0 0 0 0
137 2 1 0 2 1 0 1 1
138 2 1 0 1 1 0 1 1
139 2 0 0 1 0 0 0 0
140 2 0 0 2 0 0 0 0
141 2 0 0 2 1 1 0 1
142 2 0 0 1 1 0 0 1
143 2 1 0 2 0 0 0 0
144 2 0 0 2 0 0 1 1
145 2 0 0 2 0 0 1 0
146 2 0 0 1 0 0 0 1
147 2 0 0 1 1 0 0 0
148 2 0 0 1 0 0 0 0
149 2 1 0 2 0 0 0 0
150 2 0 0 2 0 0 1 1
151 2 0 0 2 0 0 0 1
152 2 1 0 2 1 1 0 0
153 2 1 0 2 1 1 1 0
154 2 1 0 2 1 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) UseLimit T40 T20
3.12216 0.02406 0.45780 -0.61593
Used CorrectAnalysis Useful Outcome
-0.13095 0.29243 0.06352 0.04666
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.55289 -0.08443 0.02606 0.10970 0.55099
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.12216 0.08874 35.183 < 2e-16 ***
UseLimit 0.02406 0.04148 0.580 0.562819
T40 0.45780 0.04644 9.857 < 2e-16 ***
T20 -0.61593 0.04663 -13.208 < 2e-16 ***
Used -0.13095 0.04869 -2.690 0.007989 **
CorrectAnalysis 0.29243 0.07920 3.692 0.000314 ***
Useful 0.06352 0.04541 1.399 0.164014
Outcome 0.04666 0.03949 1.182 0.239253
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2331 on 146 degrees of freedom
Multiple R-squared: 0.9478, Adjusted R-squared: 0.9453
F-statistic: 378.5 on 7 and 146 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,] 9.035063e-48 1.807013e-47 1.000000e+00
[2,] 3.206263e-59 6.412525e-59 1.000000e+00
[3,] 2.795072e-84 5.590144e-84 1.000000e+00
[4,] 8.932841e-88 1.786568e-87 1.000000e+00
[5,] 3.476542e-102 6.953084e-102 1.000000e+00
[6,] 0.000000e+00 0.000000e+00 1.000000e+00
[7,] 1.321777e-141 2.643554e-141 1.000000e+00
[8,] 5.632960e-147 1.126592e-146 1.000000e+00
[9,] 2.651778e-160 5.303556e-160 1.000000e+00
[10,] 1.054769e-183 2.109538e-183 1.000000e+00
[11,] 1.713055e-215 3.426109e-215 1.000000e+00
[12,] 9.810491e-207 1.962098e-206 1.000000e+00
[13,] 1.003432e-217 2.006865e-217 1.000000e+00
[14,] 3.131727e-235 6.263453e-235 1.000000e+00
[15,] 9.336325e-253 1.867265e-252 1.000000e+00
[16,] 5.300311e-293 1.060062e-292 1.000000e+00
[17,] 9.031758e-281 1.806352e-280 1.000000e+00
[18,] 1.367809e-290 2.735619e-290 1.000000e+00
[19,] 4.426121e-310 8.852241e-310 1.000000e+00
[20,] 0.000000e+00 0.000000e+00 1.000000e+00
[21,] 0.000000e+00 0.000000e+00 1.000000e+00
[22,] 0.000000e+00 0.000000e+00 1.000000e+00
[23,] 0.000000e+00 0.000000e+00 1.000000e+00
[24,] 0.000000e+00 0.000000e+00 1.000000e+00
[25,] 0.000000e+00 0.000000e+00 1.000000e+00
[26,] 0.000000e+00 0.000000e+00 1.000000e+00
[27,] 0.000000e+00 0.000000e+00 1.000000e+00
[28,] 0.000000e+00 0.000000e+00 1.000000e+00
[29,] 0.000000e+00 0.000000e+00 1.000000e+00
[30,] 0.000000e+00 0.000000e+00 1.000000e+00
[31,] 0.000000e+00 0.000000e+00 1.000000e+00
[32,] 0.000000e+00 0.000000e+00 1.000000e+00
[33,] 0.000000e+00 0.000000e+00 1.000000e+00
[34,] 0.000000e+00 0.000000e+00 1.000000e+00
[35,] 0.000000e+00 0.000000e+00 1.000000e+00
[36,] 0.000000e+00 0.000000e+00 1.000000e+00
[37,] 0.000000e+00 0.000000e+00 1.000000e+00
[38,] 0.000000e+00 0.000000e+00 1.000000e+00
[39,] 0.000000e+00 0.000000e+00 1.000000e+00
[40,] 0.000000e+00 0.000000e+00 1.000000e+00
[41,] 0.000000e+00 0.000000e+00 1.000000e+00
[42,] 0.000000e+00 0.000000e+00 1.000000e+00
[43,] 0.000000e+00 0.000000e+00 1.000000e+00
[44,] 0.000000e+00 0.000000e+00 1.000000e+00
[45,] 0.000000e+00 0.000000e+00 1.000000e+00
[46,] 0.000000e+00 0.000000e+00 1.000000e+00
[47,] 0.000000e+00 0.000000e+00 1.000000e+00
[48,] 0.000000e+00 0.000000e+00 1.000000e+00
[49,] 0.000000e+00 0.000000e+00 1.000000e+00
[50,] 0.000000e+00 0.000000e+00 1.000000e+00
[51,] 0.000000e+00 0.000000e+00 1.000000e+00
[52,] 0.000000e+00 0.000000e+00 1.000000e+00
[53,] 0.000000e+00 0.000000e+00 1.000000e+00
[54,] 0.000000e+00 0.000000e+00 1.000000e+00
[55,] 0.000000e+00 0.000000e+00 1.000000e+00
[56,] 0.000000e+00 0.000000e+00 1.000000e+00
[57,] 0.000000e+00 0.000000e+00 1.000000e+00
[58,] 0.000000e+00 0.000000e+00 1.000000e+00
[59,] 0.000000e+00 0.000000e+00 1.000000e+00
[60,] 0.000000e+00 0.000000e+00 1.000000e+00
[61,] 0.000000e+00 0.000000e+00 1.000000e+00
[62,] 0.000000e+00 0.000000e+00 1.000000e+00
[63,] 0.000000e+00 0.000000e+00 1.000000e+00
[64,] 0.000000e+00 0.000000e+00 1.000000e+00
[65,] 0.000000e+00 0.000000e+00 1.000000e+00
[66,] 0.000000e+00 0.000000e+00 1.000000e+00
[67,] 0.000000e+00 0.000000e+00 1.000000e+00
[68,] 0.000000e+00 0.000000e+00 1.000000e+00
[69,] 0.000000e+00 0.000000e+00 1.000000e+00
[70,] 1.000000e+00 1.503792e-69 7.518959e-70
[71,] 9.999868e-01 2.643691e-05 1.321845e-05
[72,] 9.768569e-01 4.628614e-02 2.314307e-02
[73,] 1.000000e+00 2.068515e-62 1.034257e-62
[74,] 1.273462e-61 2.546925e-61 1.000000e+00
[75,] 1.000000e+00 4.830362e-68 2.415181e-68
[76,] 1.000000e+00 1.725567e-18 8.627835e-19
[77,] 1.603087e-23 3.206174e-23 1.000000e+00
[78,] 1.000000e+00 0.000000e+00 0.000000e+00
[79,] 1.000000e+00 0.000000e+00 0.000000e+00
[80,] 1.000000e+00 0.000000e+00 0.000000e+00
[81,] 1.000000e+00 0.000000e+00 0.000000e+00
[82,] 1.000000e+00 0.000000e+00 0.000000e+00
[83,] 1.000000e+00 0.000000e+00 0.000000e+00
[84,] 1.000000e+00 0.000000e+00 0.000000e+00
[85,] 1.000000e+00 0.000000e+00 0.000000e+00
[86,] 1.000000e+00 0.000000e+00 0.000000e+00
[87,] 1.000000e+00 0.000000e+00 0.000000e+00
[88,] 1.000000e+00 0.000000e+00 0.000000e+00
[89,] 1.000000e+00 0.000000e+00 0.000000e+00
[90,] 1.000000e+00 0.000000e+00 0.000000e+00
[91,] 1.000000e+00 0.000000e+00 0.000000e+00
[92,] 1.000000e+00 0.000000e+00 0.000000e+00
[93,] 1.000000e+00 0.000000e+00 0.000000e+00
[94,] 1.000000e+00 0.000000e+00 0.000000e+00
[95,] 1.000000e+00 0.000000e+00 0.000000e+00
[96,] 1.000000e+00 0.000000e+00 0.000000e+00
[97,] 1.000000e+00 0.000000e+00 0.000000e+00
[98,] 1.000000e+00 0.000000e+00 0.000000e+00
[99,] 1.000000e+00 0.000000e+00 0.000000e+00
[100,] 1.000000e+00 0.000000e+00 0.000000e+00
[101,] 1.000000e+00 0.000000e+00 0.000000e+00
[102,] 1.000000e+00 0.000000e+00 0.000000e+00
[103,] 1.000000e+00 0.000000e+00 0.000000e+00
[104,] 1.000000e+00 0.000000e+00 0.000000e+00
[105,] 1.000000e+00 0.000000e+00 0.000000e+00
[106,] 1.000000e+00 0.000000e+00 0.000000e+00
[107,] 1.000000e+00 0.000000e+00 0.000000e+00
[108,] 1.000000e+00 0.000000e+00 0.000000e+00
[109,] 1.000000e+00 0.000000e+00 0.000000e+00
[110,] 1.000000e+00 0.000000e+00 0.000000e+00
[111,] 1.000000e+00 0.000000e+00 0.000000e+00
[112,] 1.000000e+00 0.000000e+00 0.000000e+00
[113,] 1.000000e+00 0.000000e+00 0.000000e+00
[114,] 1.000000e+00 0.000000e+00 0.000000e+00
[115,] 1.000000e+00 2.092885e-315 1.046443e-315
[116,] 1.000000e+00 3.687667e-296 1.843834e-296
[117,] 1.000000e+00 1.509910e-285 7.549552e-286
[118,] 1.000000e+00 3.487746e-297 1.743873e-297
[119,] 1.000000e+00 7.563962e-257 3.781981e-257
[120,] 1.000000e+00 1.199428e-238 5.997139e-239
[121,] 1.000000e+00 7.710083e-222 3.855041e-222
[122,] 1.000000e+00 4.786859e-210 2.393429e-210
[123,] 1.000000e+00 6.373233e-218 3.186616e-218
[124,] 1.000000e+00 7.009818e-186 3.504909e-186
[125,] 1.000000e+00 2.371215e-162 1.185608e-162
[126,] 1.000000e+00 3.601867e-149 1.800933e-149
[127,] 1.000000e+00 1.276336e-144 6.381679e-145
[128,] 1.000000e+00 0.000000e+00 0.000000e+00
[129,] 1.000000e+00 1.100399e-103 5.501994e-104
[130,] 1.000000e+00 1.514512e-89 7.572560e-90
[131,] 1.000000e+00 4.407366e-85 2.203683e-85
[132,] 1.000000e+00 1.273573e-59 6.367866e-60
[133,] 1.000000e+00 6.259757e-48 3.129879e-48
> postscript(file="/var/wessaorg/rcomp/tmp/111pp1355473774.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/24kgi1355473774.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3q1r41355473774.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4cp3x1355473774.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5svbr1355473774.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
0.3493153214 -0.0377648940 -0.0377648940 -0.0377648940 -0.0377648940
6 7 8 9 10
-0.1720089111 -0.0377648940 0.4200381657 -0.0844277287 -0.0618249037
11 12 13 14 15
0.3959781560 -0.0377648940 0.0296616644 0.3959781560 -0.0170011703
16 17 18 19 20
0.4408018895 0.1709768413 0.3959781560 -0.0844277287 0.1483740163
21 22 23 24 25
-0.1253460764 -0.0410611800 -0.1479489014 -0.1720089111 0.5043230622
26 27 28 29 30
0.0296616644 -0.1084877384 0.0931828371 -0.0844277287 -0.1012860667
31 32 33 34 35
-0.0377648940 -0.0618249037 -0.1253460764 0.3733753311 -0.0377648940
36 37 38 39 40
-0.0377648940 0.4634047145 0.0465200024 -0.1479489014 0.3565169930
41 42 43 44 45
-0.3094290434 0.0465200024 -0.1720089111 0.3959781560 -0.1012860667
46 47 48 49 50
-0.1479489014 -0.0377648940 -0.0844277287 -0.1479489014 -0.0377648940
51 52 53 54 55
0.5509858968 0.1709768413 -0.0844277287 -0.1992450361 -0.0377648940
56 57 58 59 60
0.5043230622 -0.0170011703 -0.0844277287 -0.0844277287 0.1243140066
61 62 63 64 65
0.3493153214 0.0296616644 -0.0377648940 0.3493153214 -0.0377648940
66 67 68 69 70
-0.0377648940 0.1950368510 -0.0618249037 -0.0844277287 0.0931828371
71 72 73 74 75
-0.0377648940 -0.0844277287 0.0465200024 0.0691228274 -0.0844277287
76 77 78 79 80
0.3098541584 -0.0844277287 -0.0170011703 0.2118951890 0.3565169930
81 82 83 84 85
-0.0377648940 0.0224599927 -0.0377648940 -0.1992450361 -0.1479489014
86 87 88 89 90
-0.0618249037 0.0389737632 -0.4460061967 0.1096966076 0.0630337729
91 92 93 94 95
0.0461754349 -0.5302910931 0.0221154252 0.1096966076 -0.5062310834
96 97 98 99 100
0.0630337729 -0.5302910931 0.1096966076 0.0856365979 0.0630337729
101 102 103 104 105
0.0389737632 0.1096966076 0.1096966076 0.1096966076 -0.3752833523
106 107 108 109 110
0.1096966076 0.1096966076 -0.3993433620 0.1096966076 0.0856365979
111 112 113 114 115
-0.4628645347 -0.5062310834 0.2406443387 -0.3993433620 0.0856365979
116 117 118 119 120
0.1096966076 0.0389737632 0.0856365979 0.1096966076 0.0630337729
121 122 123 124 125
0.0856365979 0.1096966076 -0.3993433620 0.1304603314 0.0630337729
126 127 128 129 130
-0.5062310834 0.0461754349 0.0630337729 0.1096966076 0.0630337729
131 132 133 134 135
0.0856365979 0.0389737632 0.2165843290 0.1096966076 0.1096966076
136 137 138 139 140
0.1096966076 0.1064003217 -0.5095273694 -0.5062310834 0.1096966076
141 142 143 144 145
-0.0984463691 -0.4219461870 0.0856365979 -0.0004873997 0.0461754349
146 147 148 149 150
-0.5528939181 -0.3752833523 -0.5062310834 0.0856365979 -0.0004873997
151 152 153 154
0.0630337729 -0.0758435441 -0.1393647168 0.2165843290
> postscript(file="/var/wessaorg/rcomp/tmp/6l8qd1355473774.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 0.3493153214 NA
1 -0.0377648940 0.3493153214
2 -0.0377648940 -0.0377648940
3 -0.0377648940 -0.0377648940
4 -0.0377648940 -0.0377648940
5 -0.1720089111 -0.0377648940
6 -0.0377648940 -0.1720089111
7 0.4200381657 -0.0377648940
8 -0.0844277287 0.4200381657
9 -0.0618249037 -0.0844277287
10 0.3959781560 -0.0618249037
11 -0.0377648940 0.3959781560
12 0.0296616644 -0.0377648940
13 0.3959781560 0.0296616644
14 -0.0170011703 0.3959781560
15 0.4408018895 -0.0170011703
16 0.1709768413 0.4408018895
17 0.3959781560 0.1709768413
18 -0.0844277287 0.3959781560
19 0.1483740163 -0.0844277287
20 -0.1253460764 0.1483740163
21 -0.0410611800 -0.1253460764
22 -0.1479489014 -0.0410611800
23 -0.1720089111 -0.1479489014
24 0.5043230622 -0.1720089111
25 0.0296616644 0.5043230622
26 -0.1084877384 0.0296616644
27 0.0931828371 -0.1084877384
28 -0.0844277287 0.0931828371
29 -0.1012860667 -0.0844277287
30 -0.0377648940 -0.1012860667
31 -0.0618249037 -0.0377648940
32 -0.1253460764 -0.0618249037
33 0.3733753311 -0.1253460764
34 -0.0377648940 0.3733753311
35 -0.0377648940 -0.0377648940
36 0.4634047145 -0.0377648940
37 0.0465200024 0.4634047145
38 -0.1479489014 0.0465200024
39 0.3565169930 -0.1479489014
40 -0.3094290434 0.3565169930
41 0.0465200024 -0.3094290434
42 -0.1720089111 0.0465200024
43 0.3959781560 -0.1720089111
44 -0.1012860667 0.3959781560
45 -0.1479489014 -0.1012860667
46 -0.0377648940 -0.1479489014
47 -0.0844277287 -0.0377648940
48 -0.1479489014 -0.0844277287
49 -0.0377648940 -0.1479489014
50 0.5509858968 -0.0377648940
51 0.1709768413 0.5509858968
52 -0.0844277287 0.1709768413
53 -0.1992450361 -0.0844277287
54 -0.0377648940 -0.1992450361
55 0.5043230622 -0.0377648940
56 -0.0170011703 0.5043230622
57 -0.0844277287 -0.0170011703
58 -0.0844277287 -0.0844277287
59 0.1243140066 -0.0844277287
60 0.3493153214 0.1243140066
61 0.0296616644 0.3493153214
62 -0.0377648940 0.0296616644
63 0.3493153214 -0.0377648940
64 -0.0377648940 0.3493153214
65 -0.0377648940 -0.0377648940
66 0.1950368510 -0.0377648940
67 -0.0618249037 0.1950368510
68 -0.0844277287 -0.0618249037
69 0.0931828371 -0.0844277287
70 -0.0377648940 0.0931828371
71 -0.0844277287 -0.0377648940
72 0.0465200024 -0.0844277287
73 0.0691228274 0.0465200024
74 -0.0844277287 0.0691228274
75 0.3098541584 -0.0844277287
76 -0.0844277287 0.3098541584
77 -0.0170011703 -0.0844277287
78 0.2118951890 -0.0170011703
79 0.3565169930 0.2118951890
80 -0.0377648940 0.3565169930
81 0.0224599927 -0.0377648940
82 -0.0377648940 0.0224599927
83 -0.1992450361 -0.0377648940
84 -0.1479489014 -0.1992450361
85 -0.0618249037 -0.1479489014
86 0.0389737632 -0.0618249037
87 -0.4460061967 0.0389737632
88 0.1096966076 -0.4460061967
89 0.0630337729 0.1096966076
90 0.0461754349 0.0630337729
91 -0.5302910931 0.0461754349
92 0.0221154252 -0.5302910931
93 0.1096966076 0.0221154252
94 -0.5062310834 0.1096966076
95 0.0630337729 -0.5062310834
96 -0.5302910931 0.0630337729
97 0.1096966076 -0.5302910931
98 0.0856365979 0.1096966076
99 0.0630337729 0.0856365979
100 0.0389737632 0.0630337729
101 0.1096966076 0.0389737632
102 0.1096966076 0.1096966076
103 0.1096966076 0.1096966076
104 -0.3752833523 0.1096966076
105 0.1096966076 -0.3752833523
106 0.1096966076 0.1096966076
107 -0.3993433620 0.1096966076
108 0.1096966076 -0.3993433620
109 0.0856365979 0.1096966076
110 -0.4628645347 0.0856365979
111 -0.5062310834 -0.4628645347
112 0.2406443387 -0.5062310834
113 -0.3993433620 0.2406443387
114 0.0856365979 -0.3993433620
115 0.1096966076 0.0856365979
116 0.0389737632 0.1096966076
117 0.0856365979 0.0389737632
118 0.1096966076 0.0856365979
119 0.0630337729 0.1096966076
120 0.0856365979 0.0630337729
121 0.1096966076 0.0856365979
122 -0.3993433620 0.1096966076
123 0.1304603314 -0.3993433620
124 0.0630337729 0.1304603314
125 -0.5062310834 0.0630337729
126 0.0461754349 -0.5062310834
127 0.0630337729 0.0461754349
128 0.1096966076 0.0630337729
129 0.0630337729 0.1096966076
130 0.0856365979 0.0630337729
131 0.0389737632 0.0856365979
132 0.2165843290 0.0389737632
133 0.1096966076 0.2165843290
134 0.1096966076 0.1096966076
135 0.1096966076 0.1096966076
136 0.1064003217 0.1096966076
137 -0.5095273694 0.1064003217
138 -0.5062310834 -0.5095273694
139 0.1096966076 -0.5062310834
140 -0.0984463691 0.1096966076
141 -0.4219461870 -0.0984463691
142 0.0856365979 -0.4219461870
143 -0.0004873997 0.0856365979
144 0.0461754349 -0.0004873997
145 -0.5528939181 0.0461754349
146 -0.3752833523 -0.5528939181
147 -0.5062310834 -0.3752833523
148 0.0856365979 -0.5062310834
149 -0.0004873997 0.0856365979
150 0.0630337729 -0.0004873997
151 -0.0758435441 0.0630337729
152 -0.1393647168 -0.0758435441
153 0.2165843290 -0.1393647168
154 NA 0.2165843290
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.0377648940 0.3493153214
[2,] -0.0377648940 -0.0377648940
[3,] -0.0377648940 -0.0377648940
[4,] -0.0377648940 -0.0377648940
[5,] -0.1720089111 -0.0377648940
[6,] -0.0377648940 -0.1720089111
[7,] 0.4200381657 -0.0377648940
[8,] -0.0844277287 0.4200381657
[9,] -0.0618249037 -0.0844277287
[10,] 0.3959781560 -0.0618249037
[11,] -0.0377648940 0.3959781560
[12,] 0.0296616644 -0.0377648940
[13,] 0.3959781560 0.0296616644
[14,] -0.0170011703 0.3959781560
[15,] 0.4408018895 -0.0170011703
[16,] 0.1709768413 0.4408018895
[17,] 0.3959781560 0.1709768413
[18,] -0.0844277287 0.3959781560
[19,] 0.1483740163 -0.0844277287
[20,] -0.1253460764 0.1483740163
[21,] -0.0410611800 -0.1253460764
[22,] -0.1479489014 -0.0410611800
[23,] -0.1720089111 -0.1479489014
[24,] 0.5043230622 -0.1720089111
[25,] 0.0296616644 0.5043230622
[26,] -0.1084877384 0.0296616644
[27,] 0.0931828371 -0.1084877384
[28,] -0.0844277287 0.0931828371
[29,] -0.1012860667 -0.0844277287
[30,] -0.0377648940 -0.1012860667
[31,] -0.0618249037 -0.0377648940
[32,] -0.1253460764 -0.0618249037
[33,] 0.3733753311 -0.1253460764
[34,] -0.0377648940 0.3733753311
[35,] -0.0377648940 -0.0377648940
[36,] 0.4634047145 -0.0377648940
[37,] 0.0465200024 0.4634047145
[38,] -0.1479489014 0.0465200024
[39,] 0.3565169930 -0.1479489014
[40,] -0.3094290434 0.3565169930
[41,] 0.0465200024 -0.3094290434
[42,] -0.1720089111 0.0465200024
[43,] 0.3959781560 -0.1720089111
[44,] -0.1012860667 0.3959781560
[45,] -0.1479489014 -0.1012860667
[46,] -0.0377648940 -0.1479489014
[47,] -0.0844277287 -0.0377648940
[48,] -0.1479489014 -0.0844277287
[49,] -0.0377648940 -0.1479489014
[50,] 0.5509858968 -0.0377648940
[51,] 0.1709768413 0.5509858968
[52,] -0.0844277287 0.1709768413
[53,] -0.1992450361 -0.0844277287
[54,] -0.0377648940 -0.1992450361
[55,] 0.5043230622 -0.0377648940
[56,] -0.0170011703 0.5043230622
[57,] -0.0844277287 -0.0170011703
[58,] -0.0844277287 -0.0844277287
[59,] 0.1243140066 -0.0844277287
[60,] 0.3493153214 0.1243140066
[61,] 0.0296616644 0.3493153214
[62,] -0.0377648940 0.0296616644
[63,] 0.3493153214 -0.0377648940
[64,] -0.0377648940 0.3493153214
[65,] -0.0377648940 -0.0377648940
[66,] 0.1950368510 -0.0377648940
[67,] -0.0618249037 0.1950368510
[68,] -0.0844277287 -0.0618249037
[69,] 0.0931828371 -0.0844277287
[70,] -0.0377648940 0.0931828371
[71,] -0.0844277287 -0.0377648940
[72,] 0.0465200024 -0.0844277287
[73,] 0.0691228274 0.0465200024
[74,] -0.0844277287 0.0691228274
[75,] 0.3098541584 -0.0844277287
[76,] -0.0844277287 0.3098541584
[77,] -0.0170011703 -0.0844277287
[78,] 0.2118951890 -0.0170011703
[79,] 0.3565169930 0.2118951890
[80,] -0.0377648940 0.3565169930
[81,] 0.0224599927 -0.0377648940
[82,] -0.0377648940 0.0224599927
[83,] -0.1992450361 -0.0377648940
[84,] -0.1479489014 -0.1992450361
[85,] -0.0618249037 -0.1479489014
[86,] 0.0389737632 -0.0618249037
[87,] -0.4460061967 0.0389737632
[88,] 0.1096966076 -0.4460061967
[89,] 0.0630337729 0.1096966076
[90,] 0.0461754349 0.0630337729
[91,] -0.5302910931 0.0461754349
[92,] 0.0221154252 -0.5302910931
[93,] 0.1096966076 0.0221154252
[94,] -0.5062310834 0.1096966076
[95,] 0.0630337729 -0.5062310834
[96,] -0.5302910931 0.0630337729
[97,] 0.1096966076 -0.5302910931
[98,] 0.0856365979 0.1096966076
[99,] 0.0630337729 0.0856365979
[100,] 0.0389737632 0.0630337729
[101,] 0.1096966076 0.0389737632
[102,] 0.1096966076 0.1096966076
[103,] 0.1096966076 0.1096966076
[104,] -0.3752833523 0.1096966076
[105,] 0.1096966076 -0.3752833523
[106,] 0.1096966076 0.1096966076
[107,] -0.3993433620 0.1096966076
[108,] 0.1096966076 -0.3993433620
[109,] 0.0856365979 0.1096966076
[110,] -0.4628645347 0.0856365979
[111,] -0.5062310834 -0.4628645347
[112,] 0.2406443387 -0.5062310834
[113,] -0.3993433620 0.2406443387
[114,] 0.0856365979 -0.3993433620
[115,] 0.1096966076 0.0856365979
[116,] 0.0389737632 0.1096966076
[117,] 0.0856365979 0.0389737632
[118,] 0.1096966076 0.0856365979
[119,] 0.0630337729 0.1096966076
[120,] 0.0856365979 0.0630337729
[121,] 0.1096966076 0.0856365979
[122,] -0.3993433620 0.1096966076
[123,] 0.1304603314 -0.3993433620
[124,] 0.0630337729 0.1304603314
[125,] -0.5062310834 0.0630337729
[126,] 0.0461754349 -0.5062310834
[127,] 0.0630337729 0.0461754349
[128,] 0.1096966076 0.0630337729
[129,] 0.0630337729 0.1096966076
[130,] 0.0856365979 0.0630337729
[131,] 0.0389737632 0.0856365979
[132,] 0.2165843290 0.0389737632
[133,] 0.1096966076 0.2165843290
[134,] 0.1096966076 0.1096966076
[135,] 0.1096966076 0.1096966076
[136,] 0.1064003217 0.1096966076
[137,] -0.5095273694 0.1064003217
[138,] -0.5062310834 -0.5095273694
[139,] 0.1096966076 -0.5062310834
[140,] -0.0984463691 0.1096966076
[141,] -0.4219461870 -0.0984463691
[142,] 0.0856365979 -0.4219461870
[143,] -0.0004873997 0.0856365979
[144,] 0.0461754349 -0.0004873997
[145,] -0.5528939181 0.0461754349
[146,] -0.3752833523 -0.5528939181
[147,] -0.5062310834 -0.3752833523
[148,] 0.0856365979 -0.5062310834
[149,] -0.0004873997 0.0856365979
[150,] 0.0630337729 -0.0004873997
[151,] -0.0758435441 0.0630337729
[152,] -0.1393647168 -0.0758435441
[153,] 0.2165843290 -0.1393647168
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.0377648940 0.3493153214
2 -0.0377648940 -0.0377648940
3 -0.0377648940 -0.0377648940
4 -0.0377648940 -0.0377648940
5 -0.1720089111 -0.0377648940
6 -0.0377648940 -0.1720089111
7 0.4200381657 -0.0377648940
8 -0.0844277287 0.4200381657
9 -0.0618249037 -0.0844277287
10 0.3959781560 -0.0618249037
11 -0.0377648940 0.3959781560
12 0.0296616644 -0.0377648940
13 0.3959781560 0.0296616644
14 -0.0170011703 0.3959781560
15 0.4408018895 -0.0170011703
16 0.1709768413 0.4408018895
17 0.3959781560 0.1709768413
18 -0.0844277287 0.3959781560
19 0.1483740163 -0.0844277287
20 -0.1253460764 0.1483740163
21 -0.0410611800 -0.1253460764
22 -0.1479489014 -0.0410611800
23 -0.1720089111 -0.1479489014
24 0.5043230622 -0.1720089111
25 0.0296616644 0.5043230622
26 -0.1084877384 0.0296616644
27 0.0931828371 -0.1084877384
28 -0.0844277287 0.0931828371
29 -0.1012860667 -0.0844277287
30 -0.0377648940 -0.1012860667
31 -0.0618249037 -0.0377648940
32 -0.1253460764 -0.0618249037
33 0.3733753311 -0.1253460764
34 -0.0377648940 0.3733753311
35 -0.0377648940 -0.0377648940
36 0.4634047145 -0.0377648940
37 0.0465200024 0.4634047145
38 -0.1479489014 0.0465200024
39 0.3565169930 -0.1479489014
40 -0.3094290434 0.3565169930
41 0.0465200024 -0.3094290434
42 -0.1720089111 0.0465200024
43 0.3959781560 -0.1720089111
44 -0.1012860667 0.3959781560
45 -0.1479489014 -0.1012860667
46 -0.0377648940 -0.1479489014
47 -0.0844277287 -0.0377648940
48 -0.1479489014 -0.0844277287
49 -0.0377648940 -0.1479489014
50 0.5509858968 -0.0377648940
51 0.1709768413 0.5509858968
52 -0.0844277287 0.1709768413
53 -0.1992450361 -0.0844277287
54 -0.0377648940 -0.1992450361
55 0.5043230622 -0.0377648940
56 -0.0170011703 0.5043230622
57 -0.0844277287 -0.0170011703
58 -0.0844277287 -0.0844277287
59 0.1243140066 -0.0844277287
60 0.3493153214 0.1243140066
61 0.0296616644 0.3493153214
62 -0.0377648940 0.0296616644
63 0.3493153214 -0.0377648940
64 -0.0377648940 0.3493153214
65 -0.0377648940 -0.0377648940
66 0.1950368510 -0.0377648940
67 -0.0618249037 0.1950368510
68 -0.0844277287 -0.0618249037
69 0.0931828371 -0.0844277287
70 -0.0377648940 0.0931828371
71 -0.0844277287 -0.0377648940
72 0.0465200024 -0.0844277287
73 0.0691228274 0.0465200024
74 -0.0844277287 0.0691228274
75 0.3098541584 -0.0844277287
76 -0.0844277287 0.3098541584
77 -0.0170011703 -0.0844277287
78 0.2118951890 -0.0170011703
79 0.3565169930 0.2118951890
80 -0.0377648940 0.3565169930
81 0.0224599927 -0.0377648940
82 -0.0377648940 0.0224599927
83 -0.1992450361 -0.0377648940
84 -0.1479489014 -0.1992450361
85 -0.0618249037 -0.1479489014
86 0.0389737632 -0.0618249037
87 -0.4460061967 0.0389737632
88 0.1096966076 -0.4460061967
89 0.0630337729 0.1096966076
90 0.0461754349 0.0630337729
91 -0.5302910931 0.0461754349
92 0.0221154252 -0.5302910931
93 0.1096966076 0.0221154252
94 -0.5062310834 0.1096966076
95 0.0630337729 -0.5062310834
96 -0.5302910931 0.0630337729
97 0.1096966076 -0.5302910931
98 0.0856365979 0.1096966076
99 0.0630337729 0.0856365979
100 0.0389737632 0.0630337729
101 0.1096966076 0.0389737632
102 0.1096966076 0.1096966076
103 0.1096966076 0.1096966076
104 -0.3752833523 0.1096966076
105 0.1096966076 -0.3752833523
106 0.1096966076 0.1096966076
107 -0.3993433620 0.1096966076
108 0.1096966076 -0.3993433620
109 0.0856365979 0.1096966076
110 -0.4628645347 0.0856365979
111 -0.5062310834 -0.4628645347
112 0.2406443387 -0.5062310834
113 -0.3993433620 0.2406443387
114 0.0856365979 -0.3993433620
115 0.1096966076 0.0856365979
116 0.0389737632 0.1096966076
117 0.0856365979 0.0389737632
118 0.1096966076 0.0856365979
119 0.0630337729 0.1096966076
120 0.0856365979 0.0630337729
121 0.1096966076 0.0856365979
122 -0.3993433620 0.1096966076
123 0.1304603314 -0.3993433620
124 0.0630337729 0.1304603314
125 -0.5062310834 0.0630337729
126 0.0461754349 -0.5062310834
127 0.0630337729 0.0461754349
128 0.1096966076 0.0630337729
129 0.0630337729 0.1096966076
130 0.0856365979 0.0630337729
131 0.0389737632 0.0856365979
132 0.2165843290 0.0389737632
133 0.1096966076 0.2165843290
134 0.1096966076 0.1096966076
135 0.1096966076 0.1096966076
136 0.1064003217 0.1096966076
137 -0.5095273694 0.1064003217
138 -0.5062310834 -0.5095273694
139 0.1096966076 -0.5062310834
140 -0.0984463691 0.1096966076
141 -0.4219461870 -0.0984463691
142 0.0856365979 -0.4219461870
143 -0.0004873997 0.0856365979
144 0.0461754349 -0.0004873997
145 -0.5528939181 0.0461754349
146 -0.3752833523 -0.5528939181
147 -0.5062310834 -0.3752833523
148 0.0856365979 -0.5062310834
149 -0.0004873997 0.0856365979
150 0.0630337729 -0.0004873997
151 -0.0758435441 0.0630337729
152 -0.1393647168 -0.0758435441
153 0.2165843290 -0.1393647168
> 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/7uwfl1355473774.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/8cit21355473774.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/9wz2n1355473774.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/10bvoe1355473774.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1130v01355473774.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/124h6o1355473774.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/131zcn1355473774.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/1439y01355473774.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/15srrs1355473774.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/16w9s71355473774.tab")
+ }
>
> try(system("convert tmp/111pp1355473774.ps tmp/111pp1355473774.png",intern=TRUE))
character(0)
> try(system("convert tmp/24kgi1355473774.ps tmp/24kgi1355473774.png",intern=TRUE))
character(0)
> try(system("convert tmp/3q1r41355473774.ps tmp/3q1r41355473774.png",intern=TRUE))
character(0)
> try(system("convert tmp/4cp3x1355473774.ps tmp/4cp3x1355473774.png",intern=TRUE))
character(0)
> try(system("convert tmp/5svbr1355473774.ps tmp/5svbr1355473774.png",intern=TRUE))
character(0)
> try(system("convert tmp/6l8qd1355473774.ps tmp/6l8qd1355473774.png",intern=TRUE))
character(0)
> try(system("convert tmp/7uwfl1355473774.ps tmp/7uwfl1355473774.png",intern=TRUE))
character(0)
> try(system("convert tmp/8cit21355473774.ps tmp/8cit21355473774.png",intern=TRUE))
character(0)
> try(system("convert tmp/9wz2n1355473774.ps tmp/9wz2n1355473774.png",intern=TRUE))
character(0)
> try(system("convert tmp/10bvoe1355473774.ps tmp/10bvoe1355473774.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.243 1.005 9.337