R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
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> x <- array(list(-0.31664
+ ,-0.061163
+ ,-0.076389
+ ,-0.034761
+ ,0.147816
+ ,-0.00596
+ ,0.212195
+ ,0.047547
+ ,0.085082
+ ,0.010674
+ ,-0.112901
+ ,-0.085962
+ ,-0.050705
+ ,-0.009859
+ ,0.066562
+ ,0.012191
+ ,-0.329558
+ ,-0.090791
+ ,-0.053405
+ ,-0.169425
+ ,-0.138675
+ ,-0.074849
+ ,-0.07899
+ ,0.007822
+ ,0.056005
+ ,-0.085657
+ ,-0.009098
+ ,-0.109931
+ ,0.177024
+ ,0.085405
+ ,0.197013
+ ,0.093925
+ ,0.079313
+ ,0.053081
+ ,0.048745
+ ,0.000196
+ ,0.14716
+ ,0.074142
+ ,0.0295
+ ,0.03356
+ ,0.101896
+ ,0.035723
+ ,0.016995
+ ,-0.019762
+ ,0.060531
+ ,0.057364
+ ,0.054134
+ ,0.017771
+ ,-0.088591
+ ,-0.036974
+ ,0.06538
+ ,0.028514
+ ,0.14847
+ ,0.058796
+ ,0.111021
+ ,0.014759
+ ,-0.016125
+ ,-0.081976
+ ,-0.020827
+ ,-0.053882
+ ,0.022741
+ ,0.068778
+ ,-0.055005
+ ,-0.047449
+ ,0.167215
+ ,0.087551
+ ,0.060723
+ ,0.036856
+ ,0.03379
+ ,-0.00229
+ ,0.03667
+ ,0.0653
+ ,0.051959
+ ,0.022646
+ ,0.040935
+ ,0.031957
+ ,-0.013314
+ ,-0.001047
+ ,0.004656
+ ,0.028495
+ ,-0.006569
+ ,-0.013501
+ ,-0.03496
+ ,-0.018258
+ ,0.163285
+ ,-0.021474
+ ,-0.014469
+ ,-0.056791
+ ,-0.009121
+ ,-0.071762
+ ,0.061523
+ ,0.107723
+ ,-0.055783
+ ,-0.005059
+ ,0.059655
+ ,0.008533
+ ,0.127111
+ ,0.043583
+ ,0.188311
+ ,0.040589
+ ,0.105284
+ ,0.031332
+ ,-0.025969
+ ,-0.007497
+ ,-0.010702
+ ,-0.062651
+ ,0.010853
+ ,0.039555
+ ,0.045822
+ ,0.012598
+ ,0.093539
+ ,0.019763
+ ,0.002803
+ ,0.024236
+ ,-0.107607
+ ,-0.019789
+ ,-0.012413
+ ,0.002847
+ ,-0.090738
+ ,0.007068)
+ ,dim=c(2
+ ,60)
+ ,dimnames=list(c('Returns'
+ ,'S&P500')
+ ,1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Returns','S&P500'),1:60))
> 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. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects 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
Returns S&P500
1 -0.316640 -0.061163
2 -0.076389 -0.034761
3 0.147816 -0.005960
4 0.212195 0.047547
5 0.085082 0.010674
6 -0.112901 -0.085962
7 -0.050705 -0.009859
8 0.066562 0.012191
9 -0.329558 -0.090791
10 -0.053405 -0.169425
11 -0.138675 -0.074849
12 -0.078990 0.007822
13 0.056005 -0.085657
14 -0.009098 -0.109931
15 0.177024 0.085405
16 0.197013 0.093925
17 0.079313 0.053081
18 0.048745 0.000196
19 0.147160 0.074142
20 0.029500 0.033560
21 0.101896 0.035723
22 0.016995 -0.019762
23 0.060531 0.057364
24 0.054134 0.017771
25 -0.088591 -0.036974
26 0.065380 0.028514
27 0.148470 0.058796
28 0.111021 0.014759
29 -0.016125 -0.081976
30 -0.020827 -0.053882
31 0.022741 0.068778
32 -0.055005 -0.047449
33 0.167215 0.087551
34 0.060723 0.036856
35 0.033790 -0.002290
36 0.036670 0.065300
37 0.051959 0.022646
38 0.040935 0.031957
39 -0.013314 -0.001047
40 0.004656 0.028495
41 -0.006569 -0.013501
42 -0.034960 -0.018258
43 0.163285 -0.021474
44 -0.014469 -0.056791
45 -0.009121 -0.071762
46 0.061523 0.107723
47 -0.055783 -0.005059
48 0.059655 0.008533
49 0.127111 0.043583
50 0.188311 0.040589
51 0.105284 0.031332
52 -0.025969 -0.007497
53 -0.010702 -0.062651
54 0.010853 0.039555
55 0.045822 0.012598
56 0.093539 0.019763
57 0.002803 0.024236
58 -0.107607 -0.019789
59 -0.012413 0.002847
60 -0.090738 0.007068
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `S&P500`
0.0213 1.2091
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.263984 -0.048621 0.009451 0.048981 0.167953
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.02130 0.01058 2.012 0.0488 *
`S&P500` 1.20910 0.19394 6.234 5.58e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.08196 on 58 degrees of freedom
Multiple R-squared: 0.4012, Adjusted R-squared: 0.3909
F-statistic: 38.87 on 1 and 58 DF, p-value: 5.58e-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,] 0.8342438 3.315125e-01 1.657562e-01
[2,] 0.9540945 9.181097e-02 4.590548e-02
[3,] 0.9403533 1.192934e-01 5.964668e-02
[4,] 0.8989990 2.020019e-01 1.010010e-01
[5,] 0.9596909 8.061811e-02 4.030906e-02
[6,] 0.9999464 1.072857e-04 5.364285e-05
[7,] 0.9999217 1.565080e-04 7.825398e-05
[8,] 0.9999552 8.965436e-05 4.482718e-05
[9,] 0.9999903 1.943000e-05 9.715001e-06
[10,] 0.9999929 1.426141e-05 7.130704e-06
[11,] 0.9999875 2.500939e-05 1.250470e-05
[12,] 0.9999822 3.566675e-05 1.783337e-05
[13,] 0.9999590 8.200582e-05 4.100291e-05
[14,] 0.9999131 1.738307e-04 8.691536e-05
[15,] 0.9998477 3.045149e-04 1.522574e-04
[16,] 0.9997177 5.645220e-04 2.822610e-04
[17,] 0.9995150 9.700049e-04 4.850024e-04
[18,] 0.9990662 1.867542e-03 9.337709e-04
[19,] 0.9983803 3.239357e-03 1.619678e-03
[20,] 0.9970673 5.865377e-03 2.932689e-03
[21,] 0.9967487 6.502587e-03 3.251294e-03
[22,] 0.9943551 1.128989e-02 5.644947e-03
[23,] 0.9933577 1.328450e-02 6.642251e-03
[24,] 0.9929469 1.410616e-02 7.053080e-03
[25,] 0.9902335 1.953292e-02 9.766460e-03
[26,] 0.9841374 3.172526e-02 1.586263e-02
[27,] 0.9829648 3.407041e-02 1.703520e-02
[28,] 0.9743703 5.125943e-02 2.562972e-02
[29,] 0.9705284 5.894326e-02 2.947163e-02
[30,] 0.9552013 8.959741e-02 4.479870e-02
[31,] 0.9339379 1.321243e-01 6.606215e-02
[32,] 0.9162039 1.675921e-01 8.379606e-02
[33,] 0.8815902 2.368195e-01 1.184098e-01
[34,] 0.8375347 3.249306e-01 1.624653e-01
[35,] 0.7918050 4.163899e-01 2.081950e-01
[36,] 0.7470206 5.059588e-01 2.529794e-01
[37,] 0.6781834 6.436333e-01 3.218166e-01
[38,] 0.6196417 7.607166e-01 3.803583e-01
[39,] 0.8366666 3.266668e-01 1.633334e-01
[40,] 0.7813811 4.372377e-01 2.186189e-01
[41,] 0.7449020 5.101960e-01 2.550980e-01
[42,] 0.7897752 4.204497e-01 2.102248e-01
[43,] 0.7551003 4.897994e-01 2.448997e-01
[44,] 0.6844318 6.311364e-01 3.155682e-01
[45,] 0.6195747 7.608506e-01 3.804253e-01
[46,] 0.7883821 4.232359e-01 2.116179e-01
[47,] 0.8049258 3.901484e-01 1.950742e-01
[48,] 0.7057326 5.885348e-01 2.942674e-01
[49,] 0.7581661 4.836678e-01 2.418339e-01
[50,] 0.7385984 5.228032e-01 2.614016e-01
[51,] 0.6315342 7.369316e-01 3.684658e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1g4f31411236389.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2iwt91411236389.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3b8l31411236389.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4r5zt1411236389.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5r7ss1411236389.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 = 60
Frequency = 1
1 2 3 4 5 6
-0.263984016 -0.055655615 0.133726161 0.133409969 0.050880030 -0.030260601
7 8 9 10 11 12
-0.060080566 0.030525828 -0.241078868 0.130150323 -0.069471305 -0.109743624
13 14 15 16 17 18
0.138276624 0.102523262 0.052464947 0.062152434 -0.006163178 0.027211956
19 20 21 22 23 24
0.036219014 -0.032373381 0.037407340 0.019593128 -0.030123744 0.011351063
25 26 27 28 29 30
-0.065181881 0.009607726 0.056083828 0.071879865 0.061695935 0.023025543
31 32 33 34 35 36
-0.081714385 -0.018930583 0.040061223 -0.005135567 0.015262773 -0.063580143
37 38 39 40 41 42
0.003281712 -0.019000197 -0.033344136 -0.051093301 -0.011541033 -0.034180355
43 44 45 46 47 48
0.167953104 0.032900809 0.056350211 -0.090020697 -0.070962236 0.028041708
49 50 51 52 53 54
0.053118832 0.117938871 0.046104489 -0.038200455 0.043753121 -0.058268922
55 56 57 58 59 60
0.009293726 0.048347540 -0.047796754 -0.104976226 -0.037151362 -0.120579964
> postscript(file="/var/wessaorg/rcomp/tmp/63ntd1411236389.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.263984016 NA
1 -0.055655615 -0.263984016
2 0.133726161 -0.055655615
3 0.133409969 0.133726161
4 0.050880030 0.133409969
5 -0.030260601 0.050880030
6 -0.060080566 -0.030260601
7 0.030525828 -0.060080566
8 -0.241078868 0.030525828
9 0.130150323 -0.241078868
10 -0.069471305 0.130150323
11 -0.109743624 -0.069471305
12 0.138276624 -0.109743624
13 0.102523262 0.138276624
14 0.052464947 0.102523262
15 0.062152434 0.052464947
16 -0.006163178 0.062152434
17 0.027211956 -0.006163178
18 0.036219014 0.027211956
19 -0.032373381 0.036219014
20 0.037407340 -0.032373381
21 0.019593128 0.037407340
22 -0.030123744 0.019593128
23 0.011351063 -0.030123744
24 -0.065181881 0.011351063
25 0.009607726 -0.065181881
26 0.056083828 0.009607726
27 0.071879865 0.056083828
28 0.061695935 0.071879865
29 0.023025543 0.061695935
30 -0.081714385 0.023025543
31 -0.018930583 -0.081714385
32 0.040061223 -0.018930583
33 -0.005135567 0.040061223
34 0.015262773 -0.005135567
35 -0.063580143 0.015262773
36 0.003281712 -0.063580143
37 -0.019000197 0.003281712
38 -0.033344136 -0.019000197
39 -0.051093301 -0.033344136
40 -0.011541033 -0.051093301
41 -0.034180355 -0.011541033
42 0.167953104 -0.034180355
43 0.032900809 0.167953104
44 0.056350211 0.032900809
45 -0.090020697 0.056350211
46 -0.070962236 -0.090020697
47 0.028041708 -0.070962236
48 0.053118832 0.028041708
49 0.117938871 0.053118832
50 0.046104489 0.117938871
51 -0.038200455 0.046104489
52 0.043753121 -0.038200455
53 -0.058268922 0.043753121
54 0.009293726 -0.058268922
55 0.048347540 0.009293726
56 -0.047796754 0.048347540
57 -0.104976226 -0.047796754
58 -0.037151362 -0.104976226
59 -0.120579964 -0.037151362
60 NA -0.120579964
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.055655615 -0.263984016
[2,] 0.133726161 -0.055655615
[3,] 0.133409969 0.133726161
[4,] 0.050880030 0.133409969
[5,] -0.030260601 0.050880030
[6,] -0.060080566 -0.030260601
[7,] 0.030525828 -0.060080566
[8,] -0.241078868 0.030525828
[9,] 0.130150323 -0.241078868
[10,] -0.069471305 0.130150323
[11,] -0.109743624 -0.069471305
[12,] 0.138276624 -0.109743624
[13,] 0.102523262 0.138276624
[14,] 0.052464947 0.102523262
[15,] 0.062152434 0.052464947
[16,] -0.006163178 0.062152434
[17,] 0.027211956 -0.006163178
[18,] 0.036219014 0.027211956
[19,] -0.032373381 0.036219014
[20,] 0.037407340 -0.032373381
[21,] 0.019593128 0.037407340
[22,] -0.030123744 0.019593128
[23,] 0.011351063 -0.030123744
[24,] -0.065181881 0.011351063
[25,] 0.009607726 -0.065181881
[26,] 0.056083828 0.009607726
[27,] 0.071879865 0.056083828
[28,] 0.061695935 0.071879865
[29,] 0.023025543 0.061695935
[30,] -0.081714385 0.023025543
[31,] -0.018930583 -0.081714385
[32,] 0.040061223 -0.018930583
[33,] -0.005135567 0.040061223
[34,] 0.015262773 -0.005135567
[35,] -0.063580143 0.015262773
[36,] 0.003281712 -0.063580143
[37,] -0.019000197 0.003281712
[38,] -0.033344136 -0.019000197
[39,] -0.051093301 -0.033344136
[40,] -0.011541033 -0.051093301
[41,] -0.034180355 -0.011541033
[42,] 0.167953104 -0.034180355
[43,] 0.032900809 0.167953104
[44,] 0.056350211 0.032900809
[45,] -0.090020697 0.056350211
[46,] -0.070962236 -0.090020697
[47,] 0.028041708 -0.070962236
[48,] 0.053118832 0.028041708
[49,] 0.117938871 0.053118832
[50,] 0.046104489 0.117938871
[51,] -0.038200455 0.046104489
[52,] 0.043753121 -0.038200455
[53,] -0.058268922 0.043753121
[54,] 0.009293726 -0.058268922
[55,] 0.048347540 0.009293726
[56,] -0.047796754 0.048347540
[57,] -0.104976226 -0.047796754
[58,] -0.037151362 -0.104976226
[59,] -0.120579964 -0.037151362
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.055655615 -0.263984016
2 0.133726161 -0.055655615
3 0.133409969 0.133726161
4 0.050880030 0.133409969
5 -0.030260601 0.050880030
6 -0.060080566 -0.030260601
7 0.030525828 -0.060080566
8 -0.241078868 0.030525828
9 0.130150323 -0.241078868
10 -0.069471305 0.130150323
11 -0.109743624 -0.069471305
12 0.138276624 -0.109743624
13 0.102523262 0.138276624
14 0.052464947 0.102523262
15 0.062152434 0.052464947
16 -0.006163178 0.062152434
17 0.027211956 -0.006163178
18 0.036219014 0.027211956
19 -0.032373381 0.036219014
20 0.037407340 -0.032373381
21 0.019593128 0.037407340
22 -0.030123744 0.019593128
23 0.011351063 -0.030123744
24 -0.065181881 0.011351063
25 0.009607726 -0.065181881
26 0.056083828 0.009607726
27 0.071879865 0.056083828
28 0.061695935 0.071879865
29 0.023025543 0.061695935
30 -0.081714385 0.023025543
31 -0.018930583 -0.081714385
32 0.040061223 -0.018930583
33 -0.005135567 0.040061223
34 0.015262773 -0.005135567
35 -0.063580143 0.015262773
36 0.003281712 -0.063580143
37 -0.019000197 0.003281712
38 -0.033344136 -0.019000197
39 -0.051093301 -0.033344136
40 -0.011541033 -0.051093301
41 -0.034180355 -0.011541033
42 0.167953104 -0.034180355
43 0.032900809 0.167953104
44 0.056350211 0.032900809
45 -0.090020697 0.056350211
46 -0.070962236 -0.090020697
47 0.028041708 -0.070962236
48 0.053118832 0.028041708
49 0.117938871 0.053118832
50 0.046104489 0.117938871
51 -0.038200455 0.046104489
52 0.043753121 -0.038200455
53 -0.058268922 0.043753121
54 0.009293726 -0.058268922
55 0.048347540 0.009293726
56 -0.047796754 0.048347540
57 -0.104976226 -0.047796754
58 -0.037151362 -0.104976226
59 -0.120579964 -0.037151362
> 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/7s97a1411236389.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/83a8m1411236389.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/913tu1411236389.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/1030cu1411236389.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$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/11kfxa1411236389.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$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12oh0r1411236389.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, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13ohmy1411236389.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,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14ufna1411236389.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,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15nb831411236389.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,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ 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,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ 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,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ 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/16ckqe1411236389.tab")
+ }
>
> try(system("convert tmp/1g4f31411236389.ps tmp/1g4f31411236389.png",intern=TRUE))
character(0)
> try(system("convert tmp/2iwt91411236389.ps tmp/2iwt91411236389.png",intern=TRUE))
character(0)
> try(system("convert tmp/3b8l31411236389.ps tmp/3b8l31411236389.png",intern=TRUE))
character(0)
> try(system("convert tmp/4r5zt1411236389.ps tmp/4r5zt1411236389.png",intern=TRUE))
character(0)
> try(system("convert tmp/5r7ss1411236389.ps tmp/5r7ss1411236389.png",intern=TRUE))
character(0)
> try(system("convert tmp/63ntd1411236389.ps tmp/63ntd1411236389.png",intern=TRUE))
character(0)
> try(system("convert tmp/7s97a1411236389.ps tmp/7s97a1411236389.png",intern=TRUE))
character(0)
> try(system("convert tmp/83a8m1411236389.ps tmp/83a8m1411236389.png",intern=TRUE))
character(0)
> try(system("convert tmp/913tu1411236389.ps tmp/913tu1411236389.png",intern=TRUE))
character(0)
> try(system("convert tmp/1030cu1411236389.ps tmp/1030cu1411236389.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.079 0.628 4.750