| acf laaggeschoolden | *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: Tue, 21 Dec 2010 19:24:27 +0000 | | Cite this page as follows: | Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/21/t12929593498xde18836whbtss.htm/, Retrieved Tue, 21 Dec 2010 20:22:32 +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/2010/Dec/21/t12929593498xde18836whbtss.htm/},
year = {2010},
}
@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 = {2010},
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 « | 104708
101817
97898
95559
92822
90848
101141
105841
93647
90923
89130
90212
93196
91861
90593
89895
88819
87924
96906
101217
98709
98139
95529
98577
100772
100180
99200
96251
94514
93780
105192
107682
99687
99436
102049
102673
105813
105056
103916
103513
101893
102503
113149
116696
108500
107800
105941
108742
111680
111270
110698
108517
107127
107088
116321
125045
116779
122887
120162
123198
123610
122293
121289
119393
117494
116693
125062
127281
120195
119804
117113
119240
115823
116281
113816
114632
112987
111633
116721
114850
112797
105368
102524
101327
102612
98873
95993
93244
90403
88539
98106
96963
90781
89253
87794
89810
90864
89025
87621
87718
83433
84535
92223
91052
88456
88706
89137
94066
99258
100673
102269
100833
99314
101764
108242
108148
104761
103772
103737
111043
109906
109335
107247
105690
102755
102280
110590
109122
102803
101424
99138 | | Output produced by software: |
Autocorrelation Function | Time lag k | ACF(k) | T-STAT | P-value | 1 | 0.926975 | 10.6097 | 0 | 2 | 0.845859 | 9.6813 | 0 | 3 | 0.809024 | 9.2597 | 0 | 4 | 0.799938 | 9.1557 | 0 | 5 | 0.80404 | 9.2027 | 0 | 6 | 0.783399 | 8.9664 | 0 | 7 | 0.725139 | 8.2996 | 0 | 8 | 0.642607 | 7.355 | 0 | 9 | 0.575833 | 6.5907 | 0 | 10 | 0.531785 | 6.0866 | 0 | 11 | 0.536007 | 6.1349 | 0 | 12 | 0.528175 | 6.0452 | 0 | 13 | 0.423016 | 4.8416 | 2e-06 | 14 | 0.312672 | 3.5787 | 0.000242 | 15 | 0.24222 | 2.7723 | 0.003189 | 16 | 0.201967 | 2.3116 | 0.01118 | 17 | 0.177406 | 2.0305 | 0.022165 | 18 | 0.136389 | 1.561 | 0.060463 | 19 | 0.064247 | 0.7353 | 0.231725 | 20 | -0.022399 | -0.2564 | 0.399036 | 21 | -0.090426 | -1.035 | 0.151294 | 22 | -0.13426 | -1.5367 | 0.063392 | 23 | -0.126727 | -1.4505 | 0.07466 | 24 | -0.134541 | -1.5399 | 0.063 | 25 | -0.225413 | -2.58 | 0.005492 | 26 | -0.31535 | -3.6093 | 0.000218 | 27 | -0.354529 | -4.0578 | 4.2e-05 | 28 | -0.369107 | -4.2246 | 2.2e-05 | 29 | -0.366335 | -4.1929 | 2.5e-05 | 30 | -0.378389 | -4.3309 | 1.5e-05 | 31 | -0.412006 | -4.7156 | 3e-06 | 32 | -0.452968 | -5.1845 | 0 | 33 | -0.476675 | -5.4558 | 0 | 34 | -0.479 | -5.4824 | 0 | 35 | -0.435245 | -4.9816 | 1e-06 | 36 | -0.407469 | -4.6637 | 4e-06 | 37 | -0.454201 | -5.1986 | 0 | 38 | -0.499123 | -5.7127 | 0 | 39 | -0.504706 | -5.7766 | 0 | 40 | -0.488232 | -5.5881 | 0 | 41 | -0.454336 | -5.2001 | 0 | 42 | -0.438656 | -5.0207 | 1e-06 | 43 | -0.444233 | -5.0845 | 1e-06 | 44 | -0.459388 | -5.2579 | 0 | 45 | -0.462152 | -5.2896 | 0 | 46 | -0.443456 | -5.0756 | 1e-06 | 47 | -0.387787 | -4.4384 | 1e-05 | 48 | -0.343472 | -3.9312 | 6.8e-05 |
Partial Autocorrelation Function | Time lag k | PACF(k) | T-STAT | P-value | 1 | 0.926975 | 10.6097 | 0 | 2 | -0.095396 | -1.0919 | 0.13845 | 3 | 0.276571 | 3.1655 | 0.000963 | 4 | 0.147298 | 1.6859 | 0.047098 | 5 | 0.175605 | 2.0099 | 0.023248 | 6 | -0.087052 | -0.9964 | 0.160456 | 7 | -0.181594 | -2.0784 | 0.019811 | 8 | -0.267132 | -3.0575 | 0.001353 | 9 | -0.10655 | -1.2195 | 0.11242 | 10 | -0.114698 | -1.3128 | 0.095776 | 11 | 0.32713 | 3.7442 | 0.000135 | 12 | 0.001665 | 0.0191 | 0.492414 | 13 | -0.538178 | -6.1597 | 0 | 14 | -0.027624 | -0.3162 | 0.37619 | 15 | -0.006197 | -0.0709 | 0.47178 | 16 | -0.045314 | -0.5186 | 0.302442 | 17 | 0.003404 | 0.039 | 0.484493 | 18 | -0.011866 | -0.1358 | 0.44609 | 19 | 0.00346 | 0.0396 | 0.484234 | 20 | 0.000993 | 0.0114 | 0.495474 | 21 | 0.024061 | 0.2754 | 0.391723 | 22 | -0.01003 | -0.1148 | 0.454389 | 23 | 0.094553 | 1.0822 | 0.140575 | 24 | -0.065005 | -0.744 | 0.229099 | 25 | -0.232364 | -2.6595 | 0.004401 | 26 | -0.013567 | -0.1553 | 0.438418 | 27 | 0.162447 | 1.8593 | 0.032615 | 28 | -0.117922 | -1.3497 | 0.089724 | 29 | 0.053321 | 0.6103 | 0.271365 | 30 | -0.020018 | -0.2291 | 0.409568 | 31 | 0.125072 | 1.4315 | 0.077332 | 32 | 0.010137 | 0.116 | 0.453906 | 33 | 0.031907 | 0.3652 | 0.357778 | 34 | -0.07188 | -0.8227 | 0.206086 | 35 | 0.012979 | 0.1486 | 0.441068 | 36 | -0.063213 | -0.7235 | 0.235331 | 37 | -0.095857 | -1.0971 | 0.137298 | 38 | -0.084381 | -0.9658 | 0.167967 | 39 | -0.088243 | -1.01 | 0.157181 | 40 | -0.083386 | -0.9544 | 0.17082 | 41 | 0.048882 | 0.5595 | 0.288396 | 42 | -0.056375 | -0.6452 | 0.259948 | 43 | 0.043228 | 0.4948 | 0.310795 | 44 | -0.064711 | -0.7407 | 0.230115 | 45 | -0.00618 | -0.0707 | 0.47186 | 46 | 0.002794 | 0.032 | 0.487271 | 47 | -0.057156 | -0.6542 | 0.25707 | 48 | 0.070084 | 0.8021 | 0.211961 |
| | Charts produced by software: | | http://www.freestatistics.org/blog/date/2010/Dec/21/t12929593498xde18836whbtss/19jqm1292959465.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/21/t12929593498xde18836whbtss/19jqm1292959465.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/21/t12929593498xde18836whbtss/29jqm1292959465.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/21/t12929593498xde18836whbtss/29jqm1292959465.ps (open in new window) |
| | Parameters (Session): | par1 = multiplicative ; par2 = 12 ; | | Parameters (R input): | par1 = 48 ; par2 = 1 ; par3 = 0 ; 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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