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Verbetering Workshop 9

*The author of this computation has been verified*
R Software Module: /rwasp_autocorrelation.wasp (opens new window with default values)
Title produced by software: (Partial) Autocorrelation Function
Date of computation: Wed, 09 Dec 2009 12:28:23 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/09/t12603870444et77r6rs3uhvsf.htm/, Retrieved Wed, 09 Dec 2009 20:30:46 +0100
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Dec/09/t12603870444et77r6rs3uhvsf.htm/},
    year = {2009},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2009},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
ShwWs9 verbetering
 
Dataseries X:
» Textbox « » Textfile « » CSV «
423.4 404.1 500 472.6 496.1 562 434.8 538.2 577.6 518.1 625.2 561.2 523.3 536.1 607.3 637.3 606.9 652.9 617.2 670.4 729.9 677.2 710 844.3 748.2 653.9 742.6 854.2 808.4 1819 1936.5 1966.1 2083.1 1620.1 1527.6 1795 1685.1 1851.8 2164.4 1981.8 1726.5 2144.6 1758.2 1672.9 1837.3 1596.1 1446 1898.4 1964.1 1755.9 2255.3 1881.2 2117.9 1656.5 1544.1 2098.9 2133.3 1963.5 1801.2 2365.4 1936.5 1667.6 1983.5 2058.6 2448.3 1858.1 1625.4 2130.6 2515.7 2230.2 2086.9 2235 2100.2 2288.6 2490 2573.7 2543.8 2004.7 2390 2338.4 2724.5 2292.5 2386 2477.9 2337 2605.1 2560.8 2839.3 2407.2 2085.2 2735.6 2798.7 3053.2 2405 2471.9 2727.3 2790.7 2385.4 3206.6 2705.6 3518.4 1954.9 2584.3 2535.8 2685.9 2866 2236.6 2934.9 2668.6 2371.2 3165.9 2887.2 3112.2 2671.2 2432.6 2812.3 3095.7 2862.9 2607.3 2862.5
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.5310425.51880
20.5497945.71360
30.31333.25590.000755
40.2639642.74320.003563
50.2847982.95970.001893
60.1563771.62510.053526
70.1994382.07260.020294
80.1491131.54960.062078
90.1373911.42780.078116
10-0.036796-0.38240.351459
11-0.095297-0.99040.162108
12-0.28843-2.99740.001689
13-0.182828-1.90.03005
14-0.289845-3.01220.001616
15-0.158137-1.64340.051604
16-0.167499-1.74070.042292
17-0.106471-1.10650.13549
18-0.024721-0.25690.398871
19-0.144899-1.50580.067515
20-0.064273-0.66790.252797
21-0.133646-1.38890.083862
22-0.086261-0.89640.186003
23-0.098533-1.0240.154066
24-0.154306-1.60360.055862
25-0.068971-0.71680.237531
260.0113190.11760.453288
270.0028350.02950.488274
28-0.030544-0.31740.375768
29-0.10714-1.11340.133997
30-0.080028-0.83170.203714
31-0.092823-0.96460.168438
32-0.051547-0.53570.296637
33-0.039625-0.41180.340654
34-0.029239-0.30390.380909
350.0636610.66160.254823
360.0116710.12130.451842
370.0456950.47490.317918
380.0201750.20970.417163
390.040070.41640.338966
400.0649150.67460.250679
410.088560.92030.179722
420.0525340.54590.293113
430.1067681.10960.134826
440.0739580.76860.221907
450.0467240.48560.314128
460.0454570.47240.318796
47-0.02967-0.30830.379207
480.0569980.59230.277432
49-0.010502-0.10910.456649
500.0302080.31390.377091
51-0.025661-0.26670.395112
520.0172580.17930.429001
530.0235550.24480.403541
54-0.00738-0.07670.469502
550.0178230.18520.426701
56-0.033292-0.3460.365017
57-0.00336-0.03490.486105
580.023960.2490.401919
590.0455710.47360.318374
600.0620740.64510.260119


Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.5310425.51880
20.3729683.8769.1e-05
3-0.109739-1.14040.128312
4-0.032835-0.34120.366795
50.205832.13910.017342
6-0.101831-1.05830.146149
70.0133010.13820.445159
80.0923340.95960.16971
9-0.028797-0.29930.382657
10-0.305118-3.17090.000989
11-0.074423-0.77340.220481
12-0.222636-2.31370.011289
130.083110.86370.194833
14-0.085321-0.88670.18861
150.117831.22450.111709
160.0279320.29030.386082
170.0996271.03540.151409
180.1056031.09750.137441
19-0.080288-0.83440.202955
200.0098030.10190.459524
210.0448390.4660.321085
22-0.155921-1.62040.054033
23-0.113247-1.17690.120911
24-0.247515-2.57230.005731
250.0549450.5710.28459
260.1276861.32690.093662
27-0.000539-0.00560.497771
28-0.110987-1.15340.125644
290.0324550.33730.368281
300.1314771.36630.087334
31-0.059121-0.61440.270119
320.1456971.51410.066457
330.0409650.42570.335581
34-0.142582-1.48180.070658
350.0309090.32120.374332
36-0.142259-1.47840.071106
370.015220.15820.437309
380.0403960.41980.337729
390.0456870.47480.317946
40-0.035759-0.37160.355454
410.0538720.55990.288368
420.0036170.03760.485044
43-0.015572-0.16180.435873
440.0559150.58110.281198
45-0.060771-0.63160.264507
46-0.019071-0.19820.421635
47-0.056494-0.58710.279179
48-0.059009-0.61320.270503
490.0507180.52710.29961
500.05820.60480.273281
510.0421830.43840.330995
520.0167210.17380.431186
530.0341380.35480.361727
540.0218360.22690.410453
550.0187490.19480.422938
56-0.006576-0.06830.472823
57-0.105681-1.09830.137265
580.0441910.45930.323489
59-0.000754-0.00780.49688
600.0043910.04560.481843
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/09/t12603870444et77r6rs3uhvsf/1pcwg1260386901.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/09/t12603870444et77r6rs3uhvsf/1pcwg1260386901.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/09/t12603870444et77r6rs3uhvsf/2nx6y1260386901.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/09/t12603870444et77r6rs3uhvsf/2nx6y1260386901.ps (open in new window)


 
Parameters (Session):
par1 = 60 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
 
Parameters (R input):
par1 = 60 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
 
R code (references can be found in the software module):
if (par1 == 'Default') {
par1 = 10*log10(length(x))
} else {
par1 <- as.numeric(par1)
}
par2 <- as.numeric(par2)
par3 <- as.numeric(par3)
par4 <- as.numeric(par4)
par5 <- as.numeric(par5)
if (par6 == 'White Noise') par6 <- 'white' else par6 <- 'ma'
par7 <- as.numeric(par7)
if (par2 == 0) {
x <- log(x)
} else {
x <- (x ^ par2 - 1) / par2
}
if (par3 > 0) x <- diff(x,lag=1,difference=par3)
if (par4 > 0) x <- diff(x,lag=par5,difference=par4)
bitmap(file='pic1.png')
racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep=''))
dev.off()
bitmap(file='pic2.png')
rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF')
dev.off()
(myacf <- c(racf$acf))
(mypacf <- c(rpacf$acf))
lengthx <- length(x)
sqrtn <- sqrt(lengthx)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Autocorrelation Function',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Time lag k',header=TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','ACF(k)','click here for more information about the Autocorrelation Function'),header=TRUE)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,'P-value',header=TRUE)
a<-table.row.end(a)
for (i in 2:(par1+1)) {
a<-table.row.start(a)
a<-table.element(a,i-1,header=TRUE)
a<-table.element(a,round(myacf[i],6))
mytstat <- myacf[i]*sqrtn
a<-table.element(a,round(mytstat,4))
a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Partial Autocorrelation Function',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Time lag k',header=TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','PACF(k)','click here for more information about the Partial Autocorrelation Function'),header=TRUE)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,'P-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:par1) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,round(mypacf[i],6))
mytstat <- mypacf[i]*sqrtn
a<-table.element(a,round(mytstat,4))
a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
 





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