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voedingsnijverheid partiële autocorrelatie

*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: Fri, 11 Dec 2009 01:42:42 -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/11/t12605211165hwlobh3g534fuq.htm/, Retrieved Fri, 11 Dec 2009 09:45:18 +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/11/t12605211165hwlobh3g534fuq.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:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
46 43 59 57 44 63 52 53 55 57 62 69 60 54 56 66 67 65 63 63 70 71 66 66 70 70 67 65 75 80 76 75 77 74 81 75 79 82 77 72 81 79 76 75 71 69 69 57 74 57 61 59 67 57 55 63 61 65 58 55 50
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.432461-3.34980.000701
2-0.038661-0.29950.382809
30.0142250.11020.456313
40.0393320.30470.380839
50.0393770.3050.380706
60.0537230.41610.339398
7-0.089776-0.69540.244745
80.0334650.25920.398178
90.1275880.98830.163489
10-0.08474-0.65640.257041
11-0.042286-0.32750.372199
120.0580270.44950.327354
13-0.011343-0.08790.465138
140.103410.8010.213143
15-0.045808-0.35480.361981
16-0.009965-0.07720.469365
17-0.046327-0.35880.360484
180.047140.36510.358144
190.0294850.22840.410058
200.0028760.02230.491151
21-0.017639-0.13660.445891
22-0.053743-0.41630.339341
230.0962930.74590.229326
24-0.051941-0.40230.344434
25-0.074003-0.57320.284318
260.0650380.50380.308129
270.0725380.56190.288145
28-0.087652-0.67890.249891
290.0496550.38460.350938
30-0.101322-0.78480.217818
310.0226920.17580.430534
320.0618870.47940.316706
33-0.060324-0.46730.320999
34-0.062319-0.48270.315526
350.1115360.8640.195528
36-0.11378-0.88130.190826
370.0590430.45730.324538
380.0065280.05060.479919
39-0.081972-0.6350.263937
400.0005470.00420.498317
410.1121360.86860.194263
42-0.175586-1.36010.089446
430.1700141.31690.096436
44-0.201471-1.56060.061941
450.0186370.14440.442848
460.0899040.69640.244436
47-0.001761-0.01360.49458
48-0.053198-0.41210.340879
49-0.040446-0.31330.377572
500.1099650.85180.19886
51-0.136825-1.05980.146733
520.0522440.40470.343575
53-0.00667-0.05170.479482
540.0087770.0680.473011
550.009760.07560.469993
56-0.018326-0.1420.443795
57-0.00588-0.04550.481913
58-0.025081-0.19430.423307
590.0054640.04230.483191
60NANANA


Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.432461-3.34980.000701
2-0.277602-2.15030.017786
3-0.169509-1.3130.097089
4-0.054611-0.4230.336899
50.0515510.39930.345541
60.1608491.24590.108817
70.0550720.42660.335604
80.0446620.3460.365295
90.1888781.4630.074338
100.089580.69390.245216
11-0.024184-0.18730.426017
12-0.003032-0.02350.49067
13-0.031944-0.24740.402707
140.0922450.71450.238835
150.0719720.55750.289633
160.077050.59680.276434
17-0.041598-0.32220.374205
18-0.068702-0.53220.298289
19-0.010076-0.0780.469025
200.0182780.14160.443943
210.0095760.07420.47056
22-0.08546-0.6620.255262
230.0041610.03220.487198
24-0.027452-0.21260.416165
25-0.126589-0.98060.165376
26-0.049933-0.38680.350145
270.0780960.60490.273754
28-0.025974-0.20120.420614
290.0415040.32150.374478
30-0.050948-0.39460.347254
31-0.054206-0.41990.338038
32-0.028029-0.21710.414429
33-0.062716-0.48580.314441
34-0.112015-0.86770.194517
350.0025690.01990.492095
36-0.081979-0.6350.263918
370.0178990.13860.445098
380.0778720.60320.274327
39-0.003974-0.03080.487771
40-0.063022-0.48820.313605
410.0511930.39650.346557
42-0.112251-0.86950.194022
430.1110530.86020.196549
44-0.109342-0.8470.200192
45-0.138379-1.07190.144034
46-0.042571-0.32970.371369
470.0155760.12060.452186
480.0915440.70910.240506
49-0.001915-0.01480.494108
500.1365751.05790.147169
51-0.041655-0.32270.374039
52-0.055936-0.43330.333182
530.0010170.00790.496871
540.0281860.21830.413957
55-0.029537-0.22880.409903
56-0.012579-0.09740.461351
570.0460370.35660.36132
580.0193360.14980.440722
59-0.032842-0.25440.400032
60NANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/11/t12605211165hwlobh3g534fuq/1wbet1260520960.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/11/t12605211165hwlobh3g534fuq/1wbet1260520960.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/11/t12605211165hwlobh3g534fuq/2p1tf1260520960.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/11/t12605211165hwlobh3g534fuq/2p1tf1260520960.ps (open in new window)


 
Parameters (Session):
par1 = 60 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
 
Parameters (R input):
par1 = 60 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; 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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