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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationThu, 14 Nov 2013 07:19:34 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Nov/14/t13844315856o0gbrqbf69uj6n.htm/, Retrieved Mon, 29 Apr 2024 10:04:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=225298, Retrieved Mon, 29 Apr 2024 10:04:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact61

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [dlkslyqvg] [2013-11-14 12:19:34] [e931f330ae8eb739e69629b6955c783c] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1045620
1050293
1030185
1015746
999514
990204
962206
947314
958768

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225298&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225298&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225298&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
110456201045620000
210502931048002.836823552167.29206323675107.772380156780.436071746164103
310301851035294.05406359-7637.4678286673443.3404661682234-1.38937563330039
410157461018528.1763615-13200.151310773581.870610043749-0.798985982497967
59995141000821.66790261-15960.5635876739101.06127084468-0.393534169145448
6990204988869.218075307-13489.169877125592.01849738000130.348521583534876
7962206965241.122280607-19751.766923909299.2775390925243-0.880525794093094
8947314946807.325111461-18937.301338202699.25932553987420.114486258310363
9958768951392.613029892-4399.08759593366102.9902865532042.04361409609551

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 1045620 & 1045620 & 0 & 0 & 0 \tabularnewline
2 & 1050293 & 1048002.83682355 & 2167.29206323675 & 107.77238015678 & 0.436071746164103 \tabularnewline
3 & 1030185 & 1035294.05406359 & -7637.46782866734 & 43.3404661682234 & -1.38937563330039 \tabularnewline
4 & 1015746 & 1018528.1763615 & -13200.1513107735 & 81.870610043749 & -0.798985982497967 \tabularnewline
5 & 999514 & 1000821.66790261 & -15960.5635876739 & 101.06127084468 & -0.393534169145448 \tabularnewline
6 & 990204 & 988869.218075307 & -13489.1698771255 & 92.0184973800013 & 0.348521583534876 \tabularnewline
7 & 962206 & 965241.122280607 & -19751.7669239092 & 99.2775390925243 & -0.880525794093094 \tabularnewline
8 & 947314 & 946807.325111461 & -18937.3013382026 & 99.2593255398742 & 0.114486258310363 \tabularnewline
9 & 958768 & 951392.613029892 & -4399.08759593366 & 102.990286553204 & 2.04361409609551 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225298&T=1

[TABLE]
[ROW][C]Structural Time Series Model[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Level[/C][C]Slope[/C][C]Seasonal[/C][C]Stand. Residuals[/C][/ROW]
[ROW][C]1[/C][C]1045620[/C][C]1045620[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]1050293[/C][C]1048002.83682355[/C][C]2167.29206323675[/C][C]107.77238015678[/C][C]0.436071746164103[/C][/ROW]
[ROW][C]3[/C][C]1030185[/C][C]1035294.05406359[/C][C]-7637.46782866734[/C][C]43.3404661682234[/C][C]-1.38937563330039[/C][/ROW]
[ROW][C]4[/C][C]1015746[/C][C]1018528.1763615[/C][C]-13200.1513107735[/C][C]81.870610043749[/C][C]-0.798985982497967[/C][/ROW]
[ROW][C]5[/C][C]999514[/C][C]1000821.66790261[/C][C]-15960.5635876739[/C][C]101.06127084468[/C][C]-0.393534169145448[/C][/ROW]
[ROW][C]6[/C][C]990204[/C][C]988869.218075307[/C][C]-13489.1698771255[/C][C]92.0184973800013[/C][C]0.348521583534876[/C][/ROW]
[ROW][C]7[/C][C]962206[/C][C]965241.122280607[/C][C]-19751.7669239092[/C][C]99.2775390925243[/C][C]-0.880525794093094[/C][/ROW]
[ROW][C]8[/C][C]947314[/C][C]946807.325111461[/C][C]-18937.3013382026[/C][C]99.2593255398742[/C][C]0.114486258310363[/C][/ROW]
[ROW][C]9[/C][C]958768[/C][C]951392.613029892[/C][C]-4399.08759593366[/C][C]102.990286553204[/C][C]2.04361409609551[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225298&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225298&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
110456201045620000
210502931048002.836823552167.29206323675107.772380156780.436071746164103
310301851035294.05406359-7637.4678286673443.3404661682234-1.38937563330039
410157461018528.1763615-13200.151310773581.870610043749-0.798985982497967
59995141000821.66790261-15960.5635876739101.06127084468-0.393534169145448
6990204988869.218075307-13489.169877125592.01849738000130.348521583534876
7962206965241.122280607-19751.766923909299.2775390925243-0.880525794093094
8947314946807.325111461-18937.301338202699.25932553987420.114486258310363
9958768951392.613029892-4399.08759593366102.9902865532042.04361409609551


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
nx <- length(x)
x <- ts(x,frequency=par1)
m <- StructTS(x,type='BSM')
m$coef
m$fitted
m$resid
mylevel <- as.numeric(m$fitted[,'level'])
myslope <- as.numeric(m$fitted[,'slope'])
myseas <- as.numeric(m$fitted[,'sea'])
myresid <- as.numeric(m$resid)
myfit <- mylevel+myseas
mylagmax <- nx/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(mylevel,na.action=na.pass,lag.max = mylagmax,main='Level')
acf(myseas,na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(myresid,na.action=na.pass,lag.max = mylagmax,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(mylevel,main='Level')
spectrum(myseas,main='Seasonal')
spectrum(myresid,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(mylevel,main='Level')
cpgram(myseas,main='Seasonal')
cpgram(myresid,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test1.png')
plot(as.numeric(m$resid),main='Standardized Residuals',ylab='Residuals',xlab='time',type='b')
grid()
dev.off()
bitmap(file='test5.png')
op <- par(mfrow = c(2,2))
hist(m$resid,main='Residual Histogram')
plot(density(m$resid),main='Residual Kernel Density')
qqnorm(m$resid,main='Residual Normal QQ Plot')
qqline(m$resid)
plot(m$resid^2, myfit^2,main='Sq.Resid vs. Sq.Fit',xlab='Squared residuals',ylab='Squared Fit')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Structural Time Series Model',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observed',header=TRUE)
a<-table.element(a,'Level',header=TRUE)
a<-table.element(a,'Slope',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Stand. Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:nx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,mylevel[i])
a<-table.element(a,myslope[i])
a<-table.element(a,myseas[i])
a<-table.element(a,myresid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')