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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> x <- array(list(4
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+ ,1)
+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('Q1'
+ ,'Q2'
+ ,'Q3'
+ ,'Q4'
+ ,'Q5'
+ ,'Q6'
+ ,'Q7'
+ ,'Gender')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('Q1','Q2','Q3','Q4','Q5','Q6','Q7','Gender'),1:162))
> 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'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Gender
1 4 7 7 6 1 5 7 1
2 5 5 6 4 1 4 5 1
3 4 6 6 6 2 5 5 1
4 3 4 5 4 2 4 5 2
5 6 5 6 2 2 4 5 1
6 5 6 7 5 1 6 7 1
7 5 7 7 1 1 5 7 2
8 1 6 7 6 1 3 5 1
9 4 6 7 3 1 4 3 1
10 5 6 6 4 1 4 6 1
11 6 5 4 3 1 2 7 2
12 7 5 6 2 1 5 6 2
13 5 4 6 4 1 3 5 1
14 6 6 7 3 1 5 3 1
15 5 6 6 5 1 6 7 1
16 4 5 6 3 2 4 5 2
17 7 3 4 3 1 3 7 1
18 7 7 7 6 1 6 7 2
19 6 3 7 1 1 7 7 2
20 6 5 6 1 2 2 6 1
21 2 3 3 1 1 6 5 2
22 7 5 7 5 1 4 7 1
23 5 2 5 2 1 1 4 1
24 4 6 7 3 1 4 7 2
25 7 3 6 3 1 3 7 1
26 1 6 5 5 1 6 7 1
27 1 6 5 5 1 6 7 1
28 7 5 6 1 1 3 3 1
29 4 5 5 2 1 6 7 2
30 5 7 6 3 1 4 5 1
31 5 6 6 5 1 7 7 2
32 5 5 5 3 1 4 5 1
33 4 5 4 5 4 4 5 1
34 5 4 5 3 3 3 4 2
35 5 4 4 2 1 4 5 2
36 4 6 6 4 2 6 6 2
37 7 5 6 3 1 3 7 1
38 7 5 7 3 1 2 5 2
39 7 7 7 6 1 7 7 2
40 4 5 7 6 1 7 7 1
41 7 5 7 3 1 5 6 1
42 7 6 5 3 1 5 6 1
43 4 5 6 4 2 6 7 1
44 3 6 6 4 1 7 7 2
45 2 7 3 2 1 6 6 2
46 6 5 6 2 4 3 6 1
47 4 5 5 3 1 4 4 2
48 5 5 4 2 3 4 7 2
49 4 6 6 5 2 4 5 1
50 7 2 6 5 3 2 6 2
51 4 4 6 6 2 4 5 1
52 6 4 5 3 1 3 3 1
53 7 6 6 3 2 5 7 1
54 1 3 5 2 1 3 6 1
55 5 6 7 5 1 5 6 2
56 4 6 6 2 1 5 5 1
57 5 5 6 4 1 4 5 1
58 5 6 7 4 1 5 7 1
59 5 1 4 1 1 5 7 2
60 5 5 3 6 2 6 7 2
61 5 7 4 2 1 4 6 1
62 5 4 4 3 3 4 6 1
63 6 5 5 4 1 7 7 1
64 3 6 4 3 1 6 7 2
65 4 4 6 4 4 5 5 1
66 6 6 7 3 1 6 7 2
67 6 6 6 6 1 6 6 2
68 3 5 6 4 1 5 5 2
69 5 5 6 5 1 4 6 1
70 2 3 6 3 1 2 5 1
71 7 5 7 4 1 7 5 1
72 7 6 6 3 1 5 6 2
73 4 5 6 3 1 5 6 2
74 6 6 6 6 1 6 6 1
75 6 6 7 6 1 6 7 1
76 3 4 5 2 2 4 6 1
77 3 4 4 2 2 4 5 1
78 6 6 7 6 1 6 7 2
79 7 7 7 5 1 6 7 2
80 3 4 6 1 1 5 6 2
81 1 5 7 2 1 7 7 2
82 5 6 6 5 1 3 6 1
83 5 6 5 3 1 6 6 5
84 5 7 3 1 5 7 1 5
85 3 6 4 2 6 6 2 6
86 7 5 6 1 7 7 2 6
87 6 6 4 1 4 7 1 5
88 4 5 2 4 2 5 1 7
89 4 7 4 3 3 3 1 6
90 5 6 4 2 5 6 1 1
91 3 2 2 1 5 6 1 3
92 7 5 4 1 6 5 2 5
93 6 7 3 1 3 6 1 1
94 6 7 6 1 3 6 1 6
95 4 7 4 2 5 6 1 4
96 5 7 4 1 5 7 1 5
97 6 6 3 1 3 6 2 5
98 5 5 3 2 4 6 1 6
99 6 6 3 1 1 6 2 5
100 6 6 4 3 7 7 2 5
101 4 5 2 1 4 6 1 4
102 5 7 2 1 7 5 2 6
103 6 5 3 2 4 5 1 6
104 5 6 3 1 5 6 1 4
105 5 5 4 1 5 6 2 5
106 4 5 4 2 6 6 1 5
107 4 5 2 2 4 5 2 2
108 6 5 5 1 4 6 1 7
109 5 7 5 1 4 4 1 5
110 6 6 4 1 6 6 1 5
111 5 7 4 1 4 7 1 2
112 6 6 3 1 3 7 1 3
113 5 5 4 1 3 5 1 5
114 4 5 4 2 5 5 1 5
115 6 7 5 1 5 7 1 5
116 4 6 4 1 3 3 2 6
117 5 5 5 2 4 7 1 6
118 5 7 3 2 5 5 1 4
119 6 4 2 1 5 7 2 6
120 3 3 1 2 3 5 1 3
121 5 7 5 2 3 3 1 6
122 4 5 5 2 5 6 1 4
123 5 6 3 2 3 5 1 3
124 5 4 4 4 4 4 1 4
125 7 7 4 1 7 7 1 6
126 5 7 5 2 6 6 2 4
127 7 5 5 1 5 7 1 6
128 5 7 3 1 2 2 1 5
129 4 3 4 1 4 5 2 5
130 6 6 6 1 6 6 2 5
131 4 5 4 3 6 6 1 3
132 4 5 5 2 5 6 2 5
133 4 6 4 1 4 2 2 4
134 4 5 4 1 4 6 2 5
135 6 6 5 1 5 7 2 5
136 6 6 4 2 3 4 1 1
137 5 7 3 5 5 7 1 4
138 3 5 3 1 5 7 2 7
139 6 7 6 1 5 6 2 4
140 5 6 5 2 6 6 1 6
141 4 6 2 1 4 2 1 7
142 5 7 4 2 3 7 1 6
143 2 7 3 1 3 7 1 5
144 5 5 3 1 4 5 2 6
145 7 7 5 1 5 5 1 5
146 4 5 3 1 5 6 1 5
147 4 6 3 2 3 5 2 6
148 7 7 4 1 6 6 2 5
149 6 6 5 1 5 6 2 3
150 5 5 5 2 4 6 2 6
151 5 6 5 1 5 5 1 7
152 5 7 5 1 4 6 1 4
153 7 6 3 1 7 7 1 5
154 6 7 4 1 5 7 2 4
155 6 7 4 1 3 6 2 5
156 5 6 3 2 5 6 2 2
157 2 6 4 2 2 6 1 7
158 4 4 4 4 4 7 1 5
159 6 7 3 1 3 6 1 4
160 5 6 2 1 4 5 1 2
161 5 4 4 1 5 5 2 4
162 5 5 4 1 4 5 1 4
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Q2 Q3 Q4 Q5 Q6
2.51086 0.15315 0.32907 -0.14322 0.21060 -0.04964
Q7 Gender
0.02841 -0.02672
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.3022 -0.8466 0.1087 0.9574 2.9092
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.51086 0.83694 3.000 0.00315 **
Q2 0.15315 0.09815 1.560 0.12072
Q3 0.32907 0.10161 3.238 0.00147 **
Q4 -0.14322 0.09113 -1.572 0.11808
Q5 0.21060 0.09821 2.144 0.03357 *
Q6 -0.04964 0.08665 -0.573 0.56759
Q7 0.02841 0.09148 0.311 0.75653
Gender -0.02672 0.08998 -0.297 0.76689
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.369 on 154 degrees of freedom
Multiple R-squared: 0.1177, Adjusted R-squared: 0.07757
F-statistic: 2.934 on 7 and 154 DF, p-value: 0.00653
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.05331321 1.066264e-01 9.466868e-01
[2,] 0.15809861 3.161972e-01 8.419014e-01
[3,] 0.10383046 2.076609e-01 8.961695e-01
[4,] 0.07086601 1.417320e-01 9.291340e-01
[5,] 0.14052681 2.810536e-01 8.594732e-01
[6,] 0.09534558 1.906912e-01 9.046544e-01
[7,] 0.07548571 1.509714e-01 9.245143e-01
[8,] 0.46995027 9.399005e-01 5.300497e-01
[9,] 0.43541390 8.708278e-01 5.645861e-01
[10,] 0.48579244 9.715849e-01 5.142076e-01
[11,] 0.89453991 2.109202e-01 1.054601e-01
[12,] 0.88654398 2.269120e-01 1.134560e-01
[13,] 0.84703693 3.059261e-01 1.529631e-01
[14,] 0.86157972 2.768406e-01 1.384203e-01
[15,] 0.84344694 3.131061e-01 1.565531e-01
[16,] 0.96022112 7.955775e-02 3.977888e-02
[17,] 0.98470295 3.059409e-02 1.529705e-02
[18,] 0.98870175 2.259650e-02 1.129825e-02
[19,] 0.98366382 3.267236e-02 1.633618e-02
[20,] 0.97734007 4.531986e-02 2.265993e-02
[21,] 0.97547121 4.905757e-02 2.452879e-02
[22,] 0.96789339 6.421322e-02 3.210661e-02
[23,] 0.96339141 7.321718e-02 3.660859e-02
[24,] 0.95056379 9.887242e-02 4.943621e-02
[25,] 0.94148983 1.170203e-01 5.851017e-02
[26,] 0.92752603 1.449479e-01 7.247397e-02
[27,] 0.92855809 1.428838e-01 7.144191e-02
[28,] 0.92500088 1.499982e-01 7.499912e-02
[29,] 0.96195339 7.609322e-02 3.804661e-02
[30,] 0.95372762 9.254476e-02 4.627238e-02
[31,] 0.95650679 8.698642e-02 4.349321e-02
[32,] 0.97979852 4.040295e-02 2.020148e-02
[33,] 0.97575434 4.849133e-02 2.424566e-02
[34,] 0.97885131 4.229738e-02 2.114869e-02
[35,] 0.98217984 3.564033e-02 1.782016e-02
[36,] 0.97672124 4.655752e-02 2.327876e-02
[37,] 0.96887478 6.225045e-02 3.112522e-02
[38,] 0.95941681 8.116638e-02 4.058319e-02
[39,] 0.95164824 9.670353e-02 4.835176e-02
[40,] 0.96742955 6.514089e-02 3.257045e-02
[41,] 0.95839564 8.320872e-02 4.160436e-02
[42,] 0.96939403 6.121194e-02 3.060597e-02
[43,] 0.97586169 4.827661e-02 2.413831e-02
[44,] 0.99797324 4.053511e-03 2.026755e-03
[45,] 0.99705318 5.893648e-03 2.946824e-03
[46,] 0.99644842 7.103167e-03 3.551583e-03
[47,] 0.99505944 9.881121e-03 4.940560e-03
[48,] 0.99325575 1.348850e-02 6.744250e-03
[49,] 0.99415748 1.168504e-02 5.842518e-03
[50,] 0.99566868 8.662641e-03 4.331321e-03
[51,] 0.99437862 1.124277e-02 5.621385e-03
[52,] 0.99314660 1.370680e-02 6.853401e-03
[53,] 0.99480876 1.038249e-02 5.191243e-03
[54,] 0.99476869 1.046262e-02 5.231309e-03
[55,] 0.99361264 1.277472e-02 6.387360e-03
[56,] 0.99176278 1.647444e-02 8.237222e-03
[57,] 0.99206240 1.587520e-02 7.937602e-03
[58,] 0.99326578 1.346844e-02 6.734218e-03
[59,] 0.99092104 1.815792e-02 9.078961e-03
[60,] 0.99651301 6.973978e-03 3.486989e-03
[61,] 0.99801073 3.978533e-03 1.989267e-03
[62,] 0.99888566 2.228678e-03 1.114339e-03
[63,] 0.99855420 2.891600e-03 1.445800e-03
[64,] 0.99862550 2.748995e-03 1.374497e-03
[65,] 0.99845219 3.095624e-03 1.547812e-03
[66,] 0.99849040 3.019193e-03 1.509597e-03
[67,] 0.99816526 3.669490e-03 1.834745e-03
[68,] 0.99807286 3.854273e-03 1.927137e-03
[69,] 0.99920668 1.586650e-03 7.933248e-04
[70,] 0.99930184 1.396311e-03 6.981555e-04
[71,] 0.99998908 2.183076e-05 1.091538e-05
[72,] 0.99998220 3.560132e-05 1.780066e-05
[73,] 0.99997066 5.868265e-05 2.934132e-05
[74,] 0.99996101 7.797340e-05 3.898670e-05
[75,] 0.99998565 2.870553e-05 1.435276e-05
[76,] 0.99998467 3.066984e-05 1.533492e-05
[77,] 0.99998241 3.518173e-05 1.759086e-05
[78,] 0.99997574 4.851981e-05 2.425991e-05
[79,] 0.99996404 7.191194e-05 3.595597e-05
[80,] 0.99994663 1.067344e-04 5.336719e-05
[81,] 0.99993779 1.244118e-04 6.220588e-05
[82,] 0.99996103 7.794270e-05 3.897135e-05
[83,] 0.99995901 8.198832e-05 4.099416e-05
[84,] 0.99993598 1.280319e-04 6.401597e-05
[85,] 0.99994080 1.183975e-04 5.919874e-05
[86,] 0.99991526 1.694722e-04 8.473612e-05
[87,] 0.99992102 1.579608e-04 7.898042e-05
[88,] 0.99988414 2.317207e-04 1.158604e-04
[89,] 0.99994450 1.109989e-04 5.549943e-05
[90,] 0.99992013 1.597331e-04 7.986656e-05
[91,] 0.99988092 2.381633e-04 1.190816e-04
[92,] 0.99981954 3.609110e-04 1.804555e-04
[93,] 0.99988841 2.231756e-04 1.115878e-04
[94,] 0.99982279 3.544270e-04 1.772135e-04
[95,] 0.99970994 5.801192e-04 2.900596e-04
[96,] 0.99972122 5.575672e-04 2.787836e-04
[97,] 0.99956313 8.737366e-04 4.368683e-04
[98,] 0.99954551 9.089713e-04 4.544856e-04
[99,] 0.99933069 1.338625e-03 6.693125e-04
[100,] 0.99895993 2.080132e-03 1.040066e-03
[101,] 0.99873227 2.535469e-03 1.267734e-03
[102,] 0.99867404 2.651921e-03 1.325960e-03
[103,] 0.99812849 3.743012e-03 1.871506e-03
[104,] 0.99775476 4.490485e-03 2.245243e-03
[105,] 0.99654783 6.904333e-03 3.452166e-03
[106,] 0.99526749 9.465026e-03 4.732513e-03
[107,] 0.99333688 1.332624e-02 6.663119e-03
[108,] 0.99063188 1.873624e-02 9.368119e-03
[109,] 0.99484525 1.030949e-02 5.154746e-03
[110,] 0.99222333 1.555334e-02 7.776672e-03
[111,] 0.98853543 2.292914e-02 1.146457e-02
[112,] 0.98954785 2.090430e-02 1.045215e-02
[113,] 0.98521663 2.956675e-02 1.478337e-02
[114,] 0.98437483 3.125033e-02 1.562517e-02
[115,] 0.97978201 4.043598e-02 2.021799e-02
[116,] 0.97948396 4.103209e-02 2.051604e-02
[117,] 0.99118997 1.762006e-02 8.810030e-03
[118,] 0.98775342 2.449316e-02 1.224658e-02
[119,] 0.98310303 3.379394e-02 1.689697e-02
[120,] 0.97486237 5.027526e-02 2.513763e-02
[121,] 0.97920116 4.159768e-02 2.079884e-02
[122,] 0.97804961 4.390078e-02 2.195039e-02
[123,] 0.98733621 2.532759e-02 1.266379e-02
[124,] 0.98158497 3.683006e-02 1.841503e-02
[125,] 0.97289843 5.420313e-02 2.710157e-02
[126,] 0.96107301 7.785399e-02 3.892699e-02
[127,] 0.94415746 1.116851e-01 5.584254e-02
[128,] 0.94694645 1.061071e-01 5.305355e-02
[129,] 0.93212720 1.357456e-01 6.787280e-02
[130,] 0.92562432 1.487514e-01 7.437568e-02
[131,] 0.90637650 1.872470e-01 9.362350e-02
[132,] 0.89559978 2.088004e-01 1.044002e-01
[133,] 0.96357528 7.284945e-02 3.642472e-02
[134,] 0.94511369 1.097726e-01 5.488631e-02
[135,] 0.93592248 1.281550e-01 6.407752e-02
[136,] 0.94162525 1.167495e-01 5.837475e-02
[137,] 0.89515625 2.096875e-01 1.048438e-01
[138,] 0.84334219 3.133156e-01 1.566578e-01
[139,] 0.74275947 5.144811e-01 2.572405e-01
[140,] 0.63634239 7.273152e-01 3.636576e-01
[141,] 0.58048127 8.390375e-01 4.195187e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1f5nh1355655179.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2jdt11355655179.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/331641355655179.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4g3ia1355655179.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5vpoi1355655179.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 = 162
Frequency = 1
1 2 3 4 5 6
-1.161636946 0.194475610 -0.833200375 -1.507190482 0.697432343 -0.102072206
7 8 9 10 11 12
-0.851012773 -4.050936544 -1.374133847 0.012914958 1.580014557 1.955982674
13 14 15 16 17 18
0.297987131 0.675502612 0.226994908 -1.132625279 2.909224301 1.914722190
19 20 21 22 23 24
0.860852070 0.426527054 -1.815690591 1.951802856 0.576050558 -1.461061850
25 26 27 28 29 30
2.251090072 -3.443937977 -3.443937977 1.772005387 -0.693726421 -0.255040054
31 32 33 34 35 36
0.303354045 0.380323023 -0.635982054 0.117762163 0.746041095 -1.071693310
37 38 39 40 41 42
1.944794110 1.649638553 1.964358650 -0.756068065 1.743412584 2.248398832
43 44 45 46 47 48
-0.973680676 -1.839865656 -2.313475485 0.198175485 -0.564541630 0.114860046
49 50 51 52 53 54
-1.026056535 2.274968614 -0.576540872 1.540659885 1.680315183 -3.534649844
55 56 57 58 59 60
-0.096573319 -1.195475314 0.194475610 -0.294928367 1.055076458 1.326682747
61 62 63 64 65 66
0.231461805 0.412917720 1.615626762 -1.374587587 -1.234551543 0.638211069
67 68 69 70 71 72
1.425349956 -1.729165254 0.309282641 -2.741721048 2.014317873 1.946054394
73 74 75 76 77 78
-0.900797625 1.398627279 1.041147494 -1.848765231 -1.491285446 1.067870172
79 80 81 82 83 84
1.771502490 -2.034089045 -4.302224191 0.106498200 0.404926000 -0.027242803
85 86 87 88 89 90
-2.321872552 0.868954094 1.007441928 0.623763406 -0.820485947 -0.216469404
91 92 93 94 95 96
-1.035517596 1.611696591 1.237437757 0.383849800 -1.289449354 -0.356309918
97 98 99 100 101 102
1.469063778 0.609962942 1.890271507 0.633657066 -0.257634998 -0.220346329
103 104 105 106 107 108
1.560326483 0.049546041 -0.128063085 -1.167034578 -0.245909781 0.835331689
109 110 111 112 113 114
-0.623682546 0.536597739 -0.225874085 1.493667552 0.271920855 -1.006067173
115 116 117 118 119 120
0.314622967 -0.982190038 0.001465173 -0.010018698 1.759578262 -0.344807492
121 122 123 124 125 126
-0.292772763 -1.312220506 0.537614335 0.567764938 1.249205030 -0.857533003
127 128 129 130 131 132
1.647641607 0.356386493 -0.660799717 -0.149949160 -1.077260232 -1.313910498
133 134 135 136 137 138
-1.295875716 -0.917459220 0.439358279 1.105465406 0.518913323 -1.695914156
139 140 141 142 143 144
-0.119215954 -0.622526997 -0.529160785 0.234840189 -2.606035074 0.388694112
145 146 147 148 149 150
1.215350049 -0.770583300 -0.410630304 1.355037088 0.336276465 -0.076583957
151 152 153 154 155 156
-0.578056616 -0.551132305 1.704697449 0.588554735 0.986848682 0.110907718
157 158 159 160 161 162
-2.424321747 -0.256603007 1.317605789 0.486135207 -0.051274240 0.034594313
> postscript(file="/var/wessaorg/rcomp/tmp/64m9n1355655179.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 = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.161636946 NA
1 0.194475610 -1.161636946
2 -0.833200375 0.194475610
3 -1.507190482 -0.833200375
4 0.697432343 -1.507190482
5 -0.102072206 0.697432343
6 -0.851012773 -0.102072206
7 -4.050936544 -0.851012773
8 -1.374133847 -4.050936544
9 0.012914958 -1.374133847
10 1.580014557 0.012914958
11 1.955982674 1.580014557
12 0.297987131 1.955982674
13 0.675502612 0.297987131
14 0.226994908 0.675502612
15 -1.132625279 0.226994908
16 2.909224301 -1.132625279
17 1.914722190 2.909224301
18 0.860852070 1.914722190
19 0.426527054 0.860852070
20 -1.815690591 0.426527054
21 1.951802856 -1.815690591
22 0.576050558 1.951802856
23 -1.461061850 0.576050558
24 2.251090072 -1.461061850
25 -3.443937977 2.251090072
26 -3.443937977 -3.443937977
27 1.772005387 -3.443937977
28 -0.693726421 1.772005387
29 -0.255040054 -0.693726421
30 0.303354045 -0.255040054
31 0.380323023 0.303354045
32 -0.635982054 0.380323023
33 0.117762163 -0.635982054
34 0.746041095 0.117762163
35 -1.071693310 0.746041095
36 1.944794110 -1.071693310
37 1.649638553 1.944794110
38 1.964358650 1.649638553
39 -0.756068065 1.964358650
40 1.743412584 -0.756068065
41 2.248398832 1.743412584
42 -0.973680676 2.248398832
43 -1.839865656 -0.973680676
44 -2.313475485 -1.839865656
45 0.198175485 -2.313475485
46 -0.564541630 0.198175485
47 0.114860046 -0.564541630
48 -1.026056535 0.114860046
49 2.274968614 -1.026056535
50 -0.576540872 2.274968614
51 1.540659885 -0.576540872
52 1.680315183 1.540659885
53 -3.534649844 1.680315183
54 -0.096573319 -3.534649844
55 -1.195475314 -0.096573319
56 0.194475610 -1.195475314
57 -0.294928367 0.194475610
58 1.055076458 -0.294928367
59 1.326682747 1.055076458
60 0.231461805 1.326682747
61 0.412917720 0.231461805
62 1.615626762 0.412917720
63 -1.374587587 1.615626762
64 -1.234551543 -1.374587587
65 0.638211069 -1.234551543
66 1.425349956 0.638211069
67 -1.729165254 1.425349956
68 0.309282641 -1.729165254
69 -2.741721048 0.309282641
70 2.014317873 -2.741721048
71 1.946054394 2.014317873
72 -0.900797625 1.946054394
73 1.398627279 -0.900797625
74 1.041147494 1.398627279
75 -1.848765231 1.041147494
76 -1.491285446 -1.848765231
77 1.067870172 -1.491285446
78 1.771502490 1.067870172
79 -2.034089045 1.771502490
80 -4.302224191 -2.034089045
81 0.106498200 -4.302224191
82 0.404926000 0.106498200
83 -0.027242803 0.404926000
84 -2.321872552 -0.027242803
85 0.868954094 -2.321872552
86 1.007441928 0.868954094
87 0.623763406 1.007441928
88 -0.820485947 0.623763406
89 -0.216469404 -0.820485947
90 -1.035517596 -0.216469404
91 1.611696591 -1.035517596
92 1.237437757 1.611696591
93 0.383849800 1.237437757
94 -1.289449354 0.383849800
95 -0.356309918 -1.289449354
96 1.469063778 -0.356309918
97 0.609962942 1.469063778
98 1.890271507 0.609962942
99 0.633657066 1.890271507
100 -0.257634998 0.633657066
101 -0.220346329 -0.257634998
102 1.560326483 -0.220346329
103 0.049546041 1.560326483
104 -0.128063085 0.049546041
105 -1.167034578 -0.128063085
106 -0.245909781 -1.167034578
107 0.835331689 -0.245909781
108 -0.623682546 0.835331689
109 0.536597739 -0.623682546
110 -0.225874085 0.536597739
111 1.493667552 -0.225874085
112 0.271920855 1.493667552
113 -1.006067173 0.271920855
114 0.314622967 -1.006067173
115 -0.982190038 0.314622967
116 0.001465173 -0.982190038
117 -0.010018698 0.001465173
118 1.759578262 -0.010018698
119 -0.344807492 1.759578262
120 -0.292772763 -0.344807492
121 -1.312220506 -0.292772763
122 0.537614335 -1.312220506
123 0.567764938 0.537614335
124 1.249205030 0.567764938
125 -0.857533003 1.249205030
126 1.647641607 -0.857533003
127 0.356386493 1.647641607
128 -0.660799717 0.356386493
129 -0.149949160 -0.660799717
130 -1.077260232 -0.149949160
131 -1.313910498 -1.077260232
132 -1.295875716 -1.313910498
133 -0.917459220 -1.295875716
134 0.439358279 -0.917459220
135 1.105465406 0.439358279
136 0.518913323 1.105465406
137 -1.695914156 0.518913323
138 -0.119215954 -1.695914156
139 -0.622526997 -0.119215954
140 -0.529160785 -0.622526997
141 0.234840189 -0.529160785
142 -2.606035074 0.234840189
143 0.388694112 -2.606035074
144 1.215350049 0.388694112
145 -0.770583300 1.215350049
146 -0.410630304 -0.770583300
147 1.355037088 -0.410630304
148 0.336276465 1.355037088
149 -0.076583957 0.336276465
150 -0.578056616 -0.076583957
151 -0.551132305 -0.578056616
152 1.704697449 -0.551132305
153 0.588554735 1.704697449
154 0.986848682 0.588554735
155 0.110907718 0.986848682
156 -2.424321747 0.110907718
157 -0.256603007 -2.424321747
158 1.317605789 -0.256603007
159 0.486135207 1.317605789
160 -0.051274240 0.486135207
161 0.034594313 -0.051274240
162 NA 0.034594313
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.194475610 -1.161636946
[2,] -0.833200375 0.194475610
[3,] -1.507190482 -0.833200375
[4,] 0.697432343 -1.507190482
[5,] -0.102072206 0.697432343
[6,] -0.851012773 -0.102072206
[7,] -4.050936544 -0.851012773
[8,] -1.374133847 -4.050936544
[9,] 0.012914958 -1.374133847
[10,] 1.580014557 0.012914958
[11,] 1.955982674 1.580014557
[12,] 0.297987131 1.955982674
[13,] 0.675502612 0.297987131
[14,] 0.226994908 0.675502612
[15,] -1.132625279 0.226994908
[16,] 2.909224301 -1.132625279
[17,] 1.914722190 2.909224301
[18,] 0.860852070 1.914722190
[19,] 0.426527054 0.860852070
[20,] -1.815690591 0.426527054
[21,] 1.951802856 -1.815690591
[22,] 0.576050558 1.951802856
[23,] -1.461061850 0.576050558
[24,] 2.251090072 -1.461061850
[25,] -3.443937977 2.251090072
[26,] -3.443937977 -3.443937977
[27,] 1.772005387 -3.443937977
[28,] -0.693726421 1.772005387
[29,] -0.255040054 -0.693726421
[30,] 0.303354045 -0.255040054
[31,] 0.380323023 0.303354045
[32,] -0.635982054 0.380323023
[33,] 0.117762163 -0.635982054
[34,] 0.746041095 0.117762163
[35,] -1.071693310 0.746041095
[36,] 1.944794110 -1.071693310
[37,] 1.649638553 1.944794110
[38,] 1.964358650 1.649638553
[39,] -0.756068065 1.964358650
[40,] 1.743412584 -0.756068065
[41,] 2.248398832 1.743412584
[42,] -0.973680676 2.248398832
[43,] -1.839865656 -0.973680676
[44,] -2.313475485 -1.839865656
[45,] 0.198175485 -2.313475485
[46,] -0.564541630 0.198175485
[47,] 0.114860046 -0.564541630
[48,] -1.026056535 0.114860046
[49,] 2.274968614 -1.026056535
[50,] -0.576540872 2.274968614
[51,] 1.540659885 -0.576540872
[52,] 1.680315183 1.540659885
[53,] -3.534649844 1.680315183
[54,] -0.096573319 -3.534649844
[55,] -1.195475314 -0.096573319
[56,] 0.194475610 -1.195475314
[57,] -0.294928367 0.194475610
[58,] 1.055076458 -0.294928367
[59,] 1.326682747 1.055076458
[60,] 0.231461805 1.326682747
[61,] 0.412917720 0.231461805
[62,] 1.615626762 0.412917720
[63,] -1.374587587 1.615626762
[64,] -1.234551543 -1.374587587
[65,] 0.638211069 -1.234551543
[66,] 1.425349956 0.638211069
[67,] -1.729165254 1.425349956
[68,] 0.309282641 -1.729165254
[69,] -2.741721048 0.309282641
[70,] 2.014317873 -2.741721048
[71,] 1.946054394 2.014317873
[72,] -0.900797625 1.946054394
[73,] 1.398627279 -0.900797625
[74,] 1.041147494 1.398627279
[75,] -1.848765231 1.041147494
[76,] -1.491285446 -1.848765231
[77,] 1.067870172 -1.491285446
[78,] 1.771502490 1.067870172
[79,] -2.034089045 1.771502490
[80,] -4.302224191 -2.034089045
[81,] 0.106498200 -4.302224191
[82,] 0.404926000 0.106498200
[83,] -0.027242803 0.404926000
[84,] -2.321872552 -0.027242803
[85,] 0.868954094 -2.321872552
[86,] 1.007441928 0.868954094
[87,] 0.623763406 1.007441928
[88,] -0.820485947 0.623763406
[89,] -0.216469404 -0.820485947
[90,] -1.035517596 -0.216469404
[91,] 1.611696591 -1.035517596
[92,] 1.237437757 1.611696591
[93,] 0.383849800 1.237437757
[94,] -1.289449354 0.383849800
[95,] -0.356309918 -1.289449354
[96,] 1.469063778 -0.356309918
[97,] 0.609962942 1.469063778
[98,] 1.890271507 0.609962942
[99,] 0.633657066 1.890271507
[100,] -0.257634998 0.633657066
[101,] -0.220346329 -0.257634998
[102,] 1.560326483 -0.220346329
[103,] 0.049546041 1.560326483
[104,] -0.128063085 0.049546041
[105,] -1.167034578 -0.128063085
[106,] -0.245909781 -1.167034578
[107,] 0.835331689 -0.245909781
[108,] -0.623682546 0.835331689
[109,] 0.536597739 -0.623682546
[110,] -0.225874085 0.536597739
[111,] 1.493667552 -0.225874085
[112,] 0.271920855 1.493667552
[113,] -1.006067173 0.271920855
[114,] 0.314622967 -1.006067173
[115,] -0.982190038 0.314622967
[116,] 0.001465173 -0.982190038
[117,] -0.010018698 0.001465173
[118,] 1.759578262 -0.010018698
[119,] -0.344807492 1.759578262
[120,] -0.292772763 -0.344807492
[121,] -1.312220506 -0.292772763
[122,] 0.537614335 -1.312220506
[123,] 0.567764938 0.537614335
[124,] 1.249205030 0.567764938
[125,] -0.857533003 1.249205030
[126,] 1.647641607 -0.857533003
[127,] 0.356386493 1.647641607
[128,] -0.660799717 0.356386493
[129,] -0.149949160 -0.660799717
[130,] -1.077260232 -0.149949160
[131,] -1.313910498 -1.077260232
[132,] -1.295875716 -1.313910498
[133,] -0.917459220 -1.295875716
[134,] 0.439358279 -0.917459220
[135,] 1.105465406 0.439358279
[136,] 0.518913323 1.105465406
[137,] -1.695914156 0.518913323
[138,] -0.119215954 -1.695914156
[139,] -0.622526997 -0.119215954
[140,] -0.529160785 -0.622526997
[141,] 0.234840189 -0.529160785
[142,] -2.606035074 0.234840189
[143,] 0.388694112 -2.606035074
[144,] 1.215350049 0.388694112
[145,] -0.770583300 1.215350049
[146,] -0.410630304 -0.770583300
[147,] 1.355037088 -0.410630304
[148,] 0.336276465 1.355037088
[149,] -0.076583957 0.336276465
[150,] -0.578056616 -0.076583957
[151,] -0.551132305 -0.578056616
[152,] 1.704697449 -0.551132305
[153,] 0.588554735 1.704697449
[154,] 0.986848682 0.588554735
[155,] 0.110907718 0.986848682
[156,] -2.424321747 0.110907718
[157,] -0.256603007 -2.424321747
[158,] 1.317605789 -0.256603007
[159,] 0.486135207 1.317605789
[160,] -0.051274240 0.486135207
[161,] 0.034594313 -0.051274240
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.194475610 -1.161636946
2 -0.833200375 0.194475610
3 -1.507190482 -0.833200375
4 0.697432343 -1.507190482
5 -0.102072206 0.697432343
6 -0.851012773 -0.102072206
7 -4.050936544 -0.851012773
8 -1.374133847 -4.050936544
9 0.012914958 -1.374133847
10 1.580014557 0.012914958
11 1.955982674 1.580014557
12 0.297987131 1.955982674
13 0.675502612 0.297987131
14 0.226994908 0.675502612
15 -1.132625279 0.226994908
16 2.909224301 -1.132625279
17 1.914722190 2.909224301
18 0.860852070 1.914722190
19 0.426527054 0.860852070
20 -1.815690591 0.426527054
21 1.951802856 -1.815690591
22 0.576050558 1.951802856
23 -1.461061850 0.576050558
24 2.251090072 -1.461061850
25 -3.443937977 2.251090072
26 -3.443937977 -3.443937977
27 1.772005387 -3.443937977
28 -0.693726421 1.772005387
29 -0.255040054 -0.693726421
30 0.303354045 -0.255040054
31 0.380323023 0.303354045
32 -0.635982054 0.380323023
33 0.117762163 -0.635982054
34 0.746041095 0.117762163
35 -1.071693310 0.746041095
36 1.944794110 -1.071693310
37 1.649638553 1.944794110
38 1.964358650 1.649638553
39 -0.756068065 1.964358650
40 1.743412584 -0.756068065
41 2.248398832 1.743412584
42 -0.973680676 2.248398832
43 -1.839865656 -0.973680676
44 -2.313475485 -1.839865656
45 0.198175485 -2.313475485
46 -0.564541630 0.198175485
47 0.114860046 -0.564541630
48 -1.026056535 0.114860046
49 2.274968614 -1.026056535
50 -0.576540872 2.274968614
51 1.540659885 -0.576540872
52 1.680315183 1.540659885
53 -3.534649844 1.680315183
54 -0.096573319 -3.534649844
55 -1.195475314 -0.096573319
56 0.194475610 -1.195475314
57 -0.294928367 0.194475610
58 1.055076458 -0.294928367
59 1.326682747 1.055076458
60 0.231461805 1.326682747
61 0.412917720 0.231461805
62 1.615626762 0.412917720
63 -1.374587587 1.615626762
64 -1.234551543 -1.374587587
65 0.638211069 -1.234551543
66 1.425349956 0.638211069
67 -1.729165254 1.425349956
68 0.309282641 -1.729165254
69 -2.741721048 0.309282641
70 2.014317873 -2.741721048
71 1.946054394 2.014317873
72 -0.900797625 1.946054394
73 1.398627279 -0.900797625
74 1.041147494 1.398627279
75 -1.848765231 1.041147494
76 -1.491285446 -1.848765231
77 1.067870172 -1.491285446
78 1.771502490 1.067870172
79 -2.034089045 1.771502490
80 -4.302224191 -2.034089045
81 0.106498200 -4.302224191
82 0.404926000 0.106498200
83 -0.027242803 0.404926000
84 -2.321872552 -0.027242803
85 0.868954094 -2.321872552
86 1.007441928 0.868954094
87 0.623763406 1.007441928
88 -0.820485947 0.623763406
89 -0.216469404 -0.820485947
90 -1.035517596 -0.216469404
91 1.611696591 -1.035517596
92 1.237437757 1.611696591
93 0.383849800 1.237437757
94 -1.289449354 0.383849800
95 -0.356309918 -1.289449354
96 1.469063778 -0.356309918
97 0.609962942 1.469063778
98 1.890271507 0.609962942
99 0.633657066 1.890271507
100 -0.257634998 0.633657066
101 -0.220346329 -0.257634998
102 1.560326483 -0.220346329
103 0.049546041 1.560326483
104 -0.128063085 0.049546041
105 -1.167034578 -0.128063085
106 -0.245909781 -1.167034578
107 0.835331689 -0.245909781
108 -0.623682546 0.835331689
109 0.536597739 -0.623682546
110 -0.225874085 0.536597739
111 1.493667552 -0.225874085
112 0.271920855 1.493667552
113 -1.006067173 0.271920855
114 0.314622967 -1.006067173
115 -0.982190038 0.314622967
116 0.001465173 -0.982190038
117 -0.010018698 0.001465173
118 1.759578262 -0.010018698
119 -0.344807492 1.759578262
120 -0.292772763 -0.344807492
121 -1.312220506 -0.292772763
122 0.537614335 -1.312220506
123 0.567764938 0.537614335
124 1.249205030 0.567764938
125 -0.857533003 1.249205030
126 1.647641607 -0.857533003
127 0.356386493 1.647641607
128 -0.660799717 0.356386493
129 -0.149949160 -0.660799717
130 -1.077260232 -0.149949160
131 -1.313910498 -1.077260232
132 -1.295875716 -1.313910498
133 -0.917459220 -1.295875716
134 0.439358279 -0.917459220
135 1.105465406 0.439358279
136 0.518913323 1.105465406
137 -1.695914156 0.518913323
138 -0.119215954 -1.695914156
139 -0.622526997 -0.119215954
140 -0.529160785 -0.622526997
141 0.234840189 -0.529160785
142 -2.606035074 0.234840189
143 0.388694112 -2.606035074
144 1.215350049 0.388694112
145 -0.770583300 1.215350049
146 -0.410630304 -0.770583300
147 1.355037088 -0.410630304
148 0.336276465 1.355037088
149 -0.076583957 0.336276465
150 -0.578056616 -0.076583957
151 -0.551132305 -0.578056616
152 1.704697449 -0.551132305
153 0.588554735 1.704697449
154 0.986848682 0.588554735
155 0.110907718 0.986848682
156 -2.424321747 0.110907718
157 -0.256603007 -2.424321747
158 1.317605789 -0.256603007
159 0.486135207 1.317605789
160 -0.051274240 0.486135207
161 0.034594313 -0.051274240
> 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/79re31355655179.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8e5ts1355655179.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9gseu1355655179.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/1022481355655179.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11acd41355655180.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/12mo9n1355655180.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/13i2411355655180.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/14p0yr1355655180.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/15ruaw1355655180.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/1639vw1355655180.tab")
+ }
>
> try(system("convert tmp/1f5nh1355655179.ps tmp/1f5nh1355655179.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jdt11355655179.ps tmp/2jdt11355655179.png",intern=TRUE))
character(0)
> try(system("convert tmp/331641355655179.ps tmp/331641355655179.png",intern=TRUE))
character(0)
> try(system("convert tmp/4g3ia1355655179.ps tmp/4g3ia1355655179.png",intern=TRUE))
character(0)
> try(system("convert tmp/5vpoi1355655179.ps tmp/5vpoi1355655179.png",intern=TRUE))
character(0)
> try(system("convert tmp/64m9n1355655179.ps tmp/64m9n1355655179.png",intern=TRUE))
character(0)
> try(system("convert tmp/79re31355655179.ps tmp/79re31355655179.png",intern=TRUE))
character(0)
> try(system("convert tmp/8e5ts1355655179.ps tmp/8e5ts1355655179.png",intern=TRUE))
character(0)
> try(system("convert tmp/9gseu1355655179.ps tmp/9gseu1355655179.png",intern=TRUE))
character(0)
> try(system("convert tmp/1022481355655179.ps tmp/1022481355655179.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.825 0.986 9.817