How can i compute multi-dimension mean matrix?












0















I want to compute dynamic conditional correlation(dcc) mean matrix at all time.
Rho is each day matrix.
but i want to make a mean matrix all day (not each day)



this is DCC-garch model.



library(rugarch)

library(rmgarch)

library(xts)

stockdata<-read.zoo("C:\Users\Taehee Cha\Desktop\\data\EUCall.csv",

                header=TRUE,

                sep=",",

                format = "%Y-%m-%d", 

                nrow=4433)

stock_xts<-as.xts(stockdata,dateFormat='POSIXct')



DKOSPI200 <-diff(log(stock_xts$KOSPI200))*100

DDAX30 <-diff(log(stock_xts$DAX30))*100

DSNP500 <-diff(log(stock_xts$SNP500))*100

DFTSE100  <-diff(log(stock_xts$FTSE100))*100

...

data2<-data.frame(DKOSPI200,DDAX30,DSNP500,DFTSE100,DEUROSTOXX50,DFTSEMIB,DNIKKEI225,DSNPTSX,DCAC40,DJSE40,

  DRTS,DIPC,DBOVESPA,DTadawul,DMERV,DSENSEX,DJSX,DSSE,DXU100,DSNPASX,

  DATHEX,DAEX,DOBX,DSNPNZ50,DTAIEX,DOMXC20,DKLCI,DBEL20,DOMXS30,DSMI,

  DIBEX,DSTI,DADX,DISEQ,DATX,DTA35,DEGX30,DPX50,DIPSA,DQE,

  DIGBC,DSET,DKarachi100,DSNPBVL,DPSI20,DWIG20,DOMXH25,DPSEI,DBUX,DHANGSENG)


Univarate GARCH specification



gjrGARCH.spec <-ugarchspec(variance.model=list(model="gjrGARCH",garchOrder=c(1,1)),mean.model=list(armaOrder=c(1,0)))


Multivarate GARCH specification



dcc.gjrGARCH.spec=dccspec(uspec=multispec(replicate(50,gjrGARCH.spec)),

                      dccOrder=c(1,1),

                      distribution="mvnorm")       

OUTCOME=dccfit(dcc.gjrGARCH.spec, data=data2)

Fit<-fitted(OUTCOME)

Sigma<-sigma(OUTCOME)

Rho<-rcor(OUTCOME)



dim(Rho)

>50 50 4432


I tried this, but I failed.



dccmean<-apply(Rho, c(1:4432), mean)


Error message:



Error in if (d2 == 0L) { : missing value where TRUE/FALSE needed


please help me....



> dput(head(data2))



structure(list(KOSPI200 = c(-0.64107155376405, -2.63428514277591, 

-3.02361881202007, -0.348129159251176, -0.8463497185911, 0.933495693605124

), DAX30 = c(-0.275158289647415, -2.31165589473132, 0.132288230298805, 

-0.916302412003311, -0.249954511741102, 1.5067409708557), SNP500 = c(0, 

-1.23978129442266, -2.37787312356224, 0.68553903957973, -1.45026278352685, 

-0.113477591886735), FTSE100 = c(-0.912643889420117, -1.27612498082748, 

-0.189622000351441, -0.0175554735125871, -1.40042215908522, 1.65029716900129

), EUROSTOXX50 = c(-0.693995680230763, -2.63873467285407, 0.0340923169277474, 

-1.10692637764913, -1.45688992650648, 0.90690109636018), FTSEMIB = c(-0.207307997135331, 

-2.20987523065315, 0.11929602916414, -0.591164923299203, -1.23334692081265, 

0.0674451667508791), NIKKEI225 = c(-0.172132904979705, -2.41825620786038, 

-0.541031623946608, 0.494946220458736, -2.43002026761108, 3.30050037277818

), SNPTSX = c(-0.228827286223954, -0.618158165738869, -1.163192751949, 

0.611234081963197, -1.07887096999253, 0.15982056970163), CAC40 = c(-0.996225333005363, 

-2.45923020374033, 0.558708030675348, -1.07932737047562, -1.18770661796788, 

0.946736097241008), JSE40 = c(-1.24801019778307, -0.660388491185593, 

-0.378077731086712, 1.12913917165454, -0.270357667812782, 0.925230160391344

), RTS = c(1.69252595201295, -2.47912051247043, -1.68278329224893, 

-0.628193976078784, -5.81832854668214, 2.33324601133127), IPC = c(0.410660821053099, 

-0.793221688816637, -1.46058560736098, -0.0995162816563422, 0.0582618177329408, 

-2.24304020680233), BOVESPA = c(-2.08399544316435, -0.0640501036023977, 

-1.00884481719454, 0, -2.45326225130107, 1.76775969319749), Tadawul = c(-0.0751612871260576, 

1.06988441714364, 0.467558379080923, 0.015629948658713, -0.479468927554549, 

0.115645625170213), MERV = c(-0.812834284218145, -3.48526114996641, 

-0.789176542098158, 0, -6.3347627637631, -2.25802417775345), 

    SENSEX = c(0.173466860804083, -0.357822108056638, -0.45387164272519, 

    1.1318914393021, 1.4301129114795, 2.24172363852979), JSX = c(0.464645515331874, 

    0.433624214120076, 0.382746462253802, 0.245017602482989, 

    0.417626097951018, 0.919117215344212), SSE = c(-0.9304947624722, 

    -0.512019911931727, -0.0399062163143782, 0.923519675548068, 

    -0.965701174169897, -0.149922817153048), XU100 = c(0.83997183599358, 

    -9.44220433432204, -1.19486088286003, -5.93037167728525, 

    -8.13019316543659, 4.39805147087551), SNPASX = c(-1.52481837941529, 

    -0.710076219124112, -0.355441763724151, 0.43889183264465, 

    0.632546988595983, 0.431277069387725), ATHEX = c(0.217251999604962, 

    -0.113093185749946, -4.08702446400842, -1.49759637062132, 

    -2.89449541976028, 2.84821759239637), AEX = c(-0.302724751872496, 

    -1.93350674725812, -0.334531789971226, -0.27844233175518, 

    -1.47467944242914, 0.59320521917563), OBX = c(-0.633345799178109, 

    -0.0872437271016935, -0.0686020794624653, -0.751459482064121, 

    -0.314782430663918, -0.138818800687446), SNPNZ50 = c(-0.589289243578772, 

    -0.212470628567818, -1.09525597597484, 1.56062961309287, 

    -0.594970797797778, 0.308439154148132), TAIEX = c(-0.634186259073921, 

    -0.0465155309724352, -1.06170058328026, -0.875355087660168, 

    -1.4930992055346, 1.85698166429091), OMXC20 = c(0.0415607672345608, 

    -0.892547743122307, 0.895744009882993, 0.00639222705407505, 

    -0.515886239547392, -0.744864387047794), KLCI = c(3.12710622798287, 

    1.55475618644338, 0.141888091655584, -0.2344629562125, -1.32130772048118, 

    -1.3324408754646), BEL20 = c(0.916239489510673, -0.838871120292239, 

    -0.349848101836958, 0.131119279926573, -0.220949965581685, 

    0.925072128183135), OMXS30 = c(-2.30694346428235, -3.59314374436401, 

    -0.132526843920378, -0.26558175544098, -0.700219536449254, 

    2.21356560948696), SMI = c(-0.923178651961543, -1.9888683347407, 

    0.190440901601185, -1.31581319814948, -1.24166996356809, 

    1.74938111396603), IBEX = c(-1.10228247337272, -3.30519689438287, 

    0.28541077977593, -0.719194686241131, -2.37890150982238, 

    -1.20979032592885), STI = c(-0.376825693193794, -1.56959227075815, 

    -0.209405887533087, 1.59189359203387, -0.998471722830718, 

    0.524545937488519), ADX = c(0, 0.234110707042312, -0.140988509294804, 

    -0.107833632972643, -0.00392341494084292, 0), ISEQ = c(-0.497210204416199, 

    -1.28827940816549, 1.05427460869301, 0.691923594974142, -1.2985333059083, 

    1.05885040639215), ATX = c(0.196176688098504, -0.397720264421864, 

    0.765032409498989, 0.634860192671294, -0.718109388684507, 

    0.288191756274081), TA35 = c(-0.467643907976623, -1.6933214269244, 

    0.638360404963567, 1.66873087937374, -1.03207784150818, 1.98476257832283

    ), EGX30 = c(-1.60808620054533, 0.486603179063838, 0.042060989053283, 

    -0.582335360174024, -1.05485347621963, 0.329916835288824), 

    PX50 = c(0, 0, -1.53717800478548, -0.567132576686191, -1.79645549752987, 

    0.100654261140143), IPSA = c(0.561439712306022, 0.849792667039129, 

    -0.327031029818503, -0.154168073702365, -0.737696755949457, 

    -0.476817106576277), QE = c(0.124155575561335, -0.299696872832733, 

    -0.087886339332055, 0.0366286953290995, 0.109805655043083, 

    0.0585137524009127), IGBC = c(0.151884596926966, -0.461362987666014, 

    1.12811837108895, 0.0931901841028981, 0.149517561508272, 

    0.312176099410877), SET = c(-0.475964725298805, 0, -0.126280143061397, 

    -0.243770852799319, -3.47030045650181, 0.752026997121558), 

    Karachi100 = c(-1.2595250361283, -1.09859841657665, -0.467295948347779, 

    -0.176282035250352, 1.06144685237481, 0.614077017080827), 

    SNPBVL = c(-0.0916272845215893, -0.488603970907153, -0.635609686024718, 

    0.72544080956467, -1.05989054433602, -0.290056131898186), 

    PSI20 = c(-0.498150623441695, -2.4995090145449, -0.916173928749231, 

    0.134620833204835, -1.96674118284665, 0.125363501017972), 

    WIG20 = c(-0.186154209522993, 2.29090171954054, -1.21234613135197, 

    -2.35741884661973, -1.39537944100319, 4.04050146384076), 

    OMXH25 = c(-0.734161177012371, -3.2781107280087, -0.274240630336653, 

    0.164265778301331, -2.88555610228327, 0.0196123710402674), 

    PSEI = c(1.16491407781849, -0.379401344663943, 0.604503127264966, 

    -0.551543468404248, -0.542372265371061, 0.668217806598204

    ), BUX = c(0.745432319945749, -0.407296683745706, 1.4847908668413, 

    -1.73656177766137, -1.70838647119353, 0.642365839539494), 

    HANGSENG = c(-1.58777648289217, 0, -2.40414908704771, 0.182801734695737, 

    -1.47377260414583, 1.05050572830176)), row.names = c("2001-07-05", 

"2001-07-06", "2001-07-09", "2001-07-10", "2001-07-11", "2001-07-12"

), class = "data.frame")


I think it will be more useful to solve my problem.



dim(Rho)

>50 50 4432



> dput(head(Rho))

c(1, 0.28274756492168, 0.366762864615763, 0.287187938929745, 

0.279313675150249, 0.25099353597608)



> str(Rho)

 num [1:50, 1:50, 1:4432] 1 0.283 0.367 0.287 0.279 ...

 - attr(*, "dimnames")=List of 3

  ..$ : chr [1:50] "KOSPI200" "DAX30" "SNP500" "FTSE100" ...

  ..$ : chr [1:50] "KOSPI200" "DAX30" "SNP500" "FTSE100" ...

  ..$ : chr [1:4432] "2001-07-05" "2001-07-06" "2001-07-09" "2001-07-10" ...


My questions



#, , "2001-07-05"



#       KOSPI200 DAX30 SNP500 FTSE100

#KOSPI200    1     5     9    13

#DAX30       2     6    10    14

#SNP500      3     7    11    15

#FTSE100     4     8    12    16



#, , "2001-07-06"



#       KOSPI200 DAX30 SNP500 FTSE100

#KOSPI200    1     5     9    13

#DAX30       2     6    10    14

#SNP500      3     7    11    15

#FTSE100     4     8    12    16



##----->I want to this ((ex)sum) 



#, , 



#       KOSPI200 DAX30 SNP500 FTSE100

#KOSPI200    2     10    18    26

#DAX30       4     12    20    28

#SNP500      6     14    22    30

#FTSE100     8     16    24    32





r multidimensional-array apply mean correlation







share|improve this question




















  • 1





    Please specify the used libraries and provide data2.

    – jay.sf
    Nov 20 '18 at 13:41






  • 1





    data2 is difference time data.

    – 차태희
    Nov 20 '18 at 15:12






  • 1





    Can you post the output of dput(data2), or if its too long dput(head(data2)) into the question?

    – jay.sf
    Nov 20 '18 at 15:14











  • I modified the edit.

    – 차태희
    Nov 20 '18 at 15:31






  • 1





    Better. But we cannot reproduce "C:\Users\Taehee Cha\Desktop\\data\EUCall.csv". You should provide stockdata too. Probably you should consider How to make a great reproducible example, thanks.

    – jay.sf
    Nov 20 '18 at 15:47
















0















I want to compute dynamic conditional correlation(dcc) mean matrix at all time.
Rho is each day matrix.
but i want to make a mean matrix all day (not each day)



this is DCC-garch model.



library(rugarch)

library(rmgarch)

library(xts)

stockdata<-read.zoo("C:\Users\Taehee Cha\Desktop\\data\EUCall.csv",

                header=TRUE,

                sep=",",

                format = "%Y-%m-%d", 

                nrow=4433)

stock_xts<-as.xts(stockdata,dateFormat='POSIXct')



DKOSPI200 <-diff(log(stock_xts$KOSPI200))*100

DDAX30 <-diff(log(stock_xts$DAX30))*100

DSNP500 <-diff(log(stock_xts$SNP500))*100

DFTSE100  <-diff(log(stock_xts$FTSE100))*100

...

data2<-data.frame(DKOSPI200,DDAX30,DSNP500,DFTSE100,DEUROSTOXX50,DFTSEMIB,DNIKKEI225,DSNPTSX,DCAC40,DJSE40,

  DRTS,DIPC,DBOVESPA,DTadawul,DMERV,DSENSEX,DJSX,DSSE,DXU100,DSNPASX,

  DATHEX,DAEX,DOBX,DSNPNZ50,DTAIEX,DOMXC20,DKLCI,DBEL20,DOMXS30,DSMI,

  DIBEX,DSTI,DADX,DISEQ,DATX,DTA35,DEGX30,DPX50,DIPSA,DQE,

  DIGBC,DSET,DKarachi100,DSNPBVL,DPSI20,DWIG20,DOMXH25,DPSEI,DBUX,DHANGSENG)


Univarate GARCH specification



gjrGARCH.spec <-ugarchspec(variance.model=list(model="gjrGARCH",garchOrder=c(1,1)),mean.model=list(armaOrder=c(1,0)))


Multivarate GARCH specification



dcc.gjrGARCH.spec=dccspec(uspec=multispec(replicate(50,gjrGARCH.spec)),

                      dccOrder=c(1,1),

                      distribution="mvnorm")       

OUTCOME=dccfit(dcc.gjrGARCH.spec, data=data2)

Fit<-fitted(OUTCOME)

Sigma<-sigma(OUTCOME)

Rho<-rcor(OUTCOME)



dim(Rho)

>50 50 4432


I tried this, but I failed.



dccmean<-apply(Rho, c(1:4432), mean)


Error message:



Error in if (d2 == 0L) { : missing value where TRUE/FALSE needed


please help me....



> dput(head(data2))



structure(list(KOSPI200 = c(-0.64107155376405, -2.63428514277591, 

-3.02361881202007, -0.348129159251176, -0.8463497185911, 0.933495693605124

), DAX30 = c(-0.275158289647415, -2.31165589473132, 0.132288230298805, 

-0.916302412003311, -0.249954511741102, 1.5067409708557), SNP500 = c(0, 

-1.23978129442266, -2.37787312356224, 0.68553903957973, -1.45026278352685, 

-0.113477591886735), FTSE100 = c(-0.912643889420117, -1.27612498082748, 

-0.189622000351441, -0.0175554735125871, -1.40042215908522, 1.65029716900129

), EUROSTOXX50 = c(-0.693995680230763, -2.63873467285407, 0.0340923169277474, 

-1.10692637764913, -1.45688992650648, 0.90690109636018), FTSEMIB = c(-0.207307997135331, 

-2.20987523065315, 0.11929602916414, -0.591164923299203, -1.23334692081265, 

0.0674451667508791), NIKKEI225 = c(-0.172132904979705, -2.41825620786038, 

-0.541031623946608, 0.494946220458736, -2.43002026761108, 3.30050037277818

), SNPTSX = c(-0.228827286223954, -0.618158165738869, -1.163192751949, 

0.611234081963197, -1.07887096999253, 0.15982056970163), CAC40 = c(-0.996225333005363, 

-2.45923020374033, 0.558708030675348, -1.07932737047562, -1.18770661796788, 

0.946736097241008), JSE40 = c(-1.24801019778307, -0.660388491185593, 

-0.378077731086712, 1.12913917165454, -0.270357667812782, 0.925230160391344

), RTS = c(1.69252595201295, -2.47912051247043, -1.68278329224893, 

-0.628193976078784, -5.81832854668214, 2.33324601133127), IPC = c(0.410660821053099, 

-0.793221688816637, -1.46058560736098, -0.0995162816563422, 0.0582618177329408, 

-2.24304020680233), BOVESPA = c(-2.08399544316435, -0.0640501036023977, 

-1.00884481719454, 0, -2.45326225130107, 1.76775969319749), Tadawul = c(-0.0751612871260576, 

1.06988441714364, 0.467558379080923, 0.015629948658713, -0.479468927554549, 

0.115645625170213), MERV = c(-0.812834284218145, -3.48526114996641, 

-0.789176542098158, 0, -6.3347627637631, -2.25802417775345), 

    SENSEX = c(0.173466860804083, -0.357822108056638, -0.45387164272519, 

    1.1318914393021, 1.4301129114795, 2.24172363852979), JSX = c(0.464645515331874, 

    0.433624214120076, 0.382746462253802, 0.245017602482989, 

    0.417626097951018, 0.919117215344212), SSE = c(-0.9304947624722, 

    -0.512019911931727, -0.0399062163143782, 0.923519675548068, 

    -0.965701174169897, -0.149922817153048), XU100 = c(0.83997183599358, 

    -9.44220433432204, -1.19486088286003, -5.93037167728525, 

    -8.13019316543659, 4.39805147087551), SNPASX = c(-1.52481837941529, 

    -0.710076219124112, -0.355441763724151, 0.43889183264465, 

    0.632546988595983, 0.431277069387725), ATHEX = c(0.217251999604962, 

    -0.113093185749946, -4.08702446400842, -1.49759637062132, 

    -2.89449541976028, 2.84821759239637), AEX = c(-0.302724751872496, 

    -1.93350674725812, -0.334531789971226, -0.27844233175518, 

    -1.47467944242914, 0.59320521917563), OBX = c(-0.633345799178109, 

    -0.0872437271016935, -0.0686020794624653, -0.751459482064121, 

    -0.314782430663918, -0.138818800687446), SNPNZ50 = c(-0.589289243578772, 

    -0.212470628567818, -1.09525597597484, 1.56062961309287, 

    -0.594970797797778, 0.308439154148132), TAIEX = c(-0.634186259073921, 

    -0.0465155309724352, -1.06170058328026, -0.875355087660168, 

    -1.4930992055346, 1.85698166429091), OMXC20 = c(0.0415607672345608, 

    -0.892547743122307, 0.895744009882993, 0.00639222705407505, 

    -0.515886239547392, -0.744864387047794), KLCI = c(3.12710622798287, 

    1.55475618644338, 0.141888091655584, -0.2344629562125, -1.32130772048118, 

    -1.3324408754646), BEL20 = c(0.916239489510673, -0.838871120292239, 

    -0.349848101836958, 0.131119279926573, -0.220949965581685, 

    0.925072128183135), OMXS30 = c(-2.30694346428235, -3.59314374436401, 

    -0.132526843920378, -0.26558175544098, -0.700219536449254, 

    2.21356560948696), SMI = c(-0.923178651961543, -1.9888683347407, 

    0.190440901601185, -1.31581319814948, -1.24166996356809, 

    1.74938111396603), IBEX = c(-1.10228247337272, -3.30519689438287, 

    0.28541077977593, -0.719194686241131, -2.37890150982238, 

    -1.20979032592885), STI = c(-0.376825693193794, -1.56959227075815, 

    -0.209405887533087, 1.59189359203387, -0.998471722830718, 

    0.524545937488519), ADX = c(0, 0.234110707042312, -0.140988509294804, 

    -0.107833632972643, -0.00392341494084292, 0), ISEQ = c(-0.497210204416199, 

    -1.28827940816549, 1.05427460869301, 0.691923594974142, -1.2985333059083, 

    1.05885040639215), ATX = c(0.196176688098504, -0.397720264421864, 

    0.765032409498989, 0.634860192671294, -0.718109388684507, 

    0.288191756274081), TA35 = c(-0.467643907976623, -1.6933214269244, 

    0.638360404963567, 1.66873087937374, -1.03207784150818, 1.98476257832283

    ), EGX30 = c(-1.60808620054533, 0.486603179063838, 0.042060989053283, 

    -0.582335360174024, -1.05485347621963, 0.329916835288824), 

    PX50 = c(0, 0, -1.53717800478548, -0.567132576686191, -1.79645549752987, 

    0.100654261140143), IPSA = c(0.561439712306022, 0.849792667039129, 

    -0.327031029818503, -0.154168073702365, -0.737696755949457, 

    -0.476817106576277), QE = c(0.124155575561335, -0.299696872832733, 

    -0.087886339332055, 0.0366286953290995, 0.109805655043083, 

    0.0585137524009127), IGBC = c(0.151884596926966, -0.461362987666014, 

    1.12811837108895, 0.0931901841028981, 0.149517561508272, 

    0.312176099410877), SET = c(-0.475964725298805, 0, -0.126280143061397, 

    -0.243770852799319, -3.47030045650181, 0.752026997121558), 

    Karachi100 = c(-1.2595250361283, -1.09859841657665, -0.467295948347779, 

    -0.176282035250352, 1.06144685237481, 0.614077017080827), 

    SNPBVL = c(-0.0916272845215893, -0.488603970907153, -0.635609686024718, 

    0.72544080956467, -1.05989054433602, -0.290056131898186), 

    PSI20 = c(-0.498150623441695, -2.4995090145449, -0.916173928749231, 

    0.134620833204835, -1.96674118284665, 0.125363501017972), 

    WIG20 = c(-0.186154209522993, 2.29090171954054, -1.21234613135197, 

    -2.35741884661973, -1.39537944100319, 4.04050146384076), 

    OMXH25 = c(-0.734161177012371, -3.2781107280087, -0.274240630336653, 

    0.164265778301331, -2.88555610228327, 0.0196123710402674), 

    PSEI = c(1.16491407781849, -0.379401344663943, 0.604503127264966, 

    -0.551543468404248, -0.542372265371061, 0.668217806598204

    ), BUX = c(0.745432319945749, -0.407296683745706, 1.4847908668413, 

    -1.73656177766137, -1.70838647119353, 0.642365839539494), 

    HANGSENG = c(-1.58777648289217, 0, -2.40414908704771, 0.182801734695737, 

    -1.47377260414583, 1.05050572830176)), row.names = c("2001-07-05", 

"2001-07-06", "2001-07-09", "2001-07-10", "2001-07-11", "2001-07-12"

), class = "data.frame")


I think it will be more useful to solve my problem.



dim(Rho)

>50 50 4432



> dput(head(Rho))

c(1, 0.28274756492168, 0.366762864615763, 0.287187938929745, 

0.279313675150249, 0.25099353597608)



> str(Rho)

 num [1:50, 1:50, 1:4432] 1 0.283 0.367 0.287 0.279 ...

 - attr(*, "dimnames")=List of 3

  ..$ : chr [1:50] "KOSPI200" "DAX30" "SNP500" "FTSE100" ...

  ..$ : chr [1:50] "KOSPI200" "DAX30" "SNP500" "FTSE100" ...

  ..$ : chr [1:4432] "2001-07-05" "2001-07-06" "2001-07-09" "2001-07-10" ...


My questions



#, , "2001-07-05"



#       KOSPI200 DAX30 SNP500 FTSE100

#KOSPI200    1     5     9    13

#DAX30       2     6    10    14

#SNP500      3     7    11    15

#FTSE100     4     8    12    16



#, , "2001-07-06"



#       KOSPI200 DAX30 SNP500 FTSE100

#KOSPI200    1     5     9    13

#DAX30       2     6    10    14

#SNP500      3     7    11    15

#FTSE100     4     8    12    16



##----->I want to this ((ex)sum) 



#, , 



#       KOSPI200 DAX30 SNP500 FTSE100

#KOSPI200    2     10    18    26

#DAX30       4     12    20    28

#SNP500      6     14    22    30

#FTSE100     8     16    24    32





r multidimensional-array apply mean correlation







share|improve this question




















  • 1





    Please specify the used libraries and provide data2.

    – jay.sf
    Nov 20 '18 at 13:41






  • 1





    data2 is difference time data.

    – 차태희
    Nov 20 '18 at 15:12






  • 1





    Can you post the output of dput(data2), or if its too long dput(head(data2)) into the question?

    – jay.sf
    Nov 20 '18 at 15:14











  • I modified the edit.

    – 차태희
    Nov 20 '18 at 15:31






  • 1





    Better. But we cannot reproduce "C:\Users\Taehee Cha\Desktop\\data\EUCall.csv". You should provide stockdata too. Probably you should consider How to make a great reproducible example, thanks.

    – jay.sf
    Nov 20 '18 at 15:47














0












0








0








I want to compute dynamic conditional correlation(dcc) mean matrix at all time.
Rho is each day matrix.
but i want to make a mean matrix all day (not each day)



this is DCC-garch model.



library(rugarch)

library(rmgarch)

library(xts)

stockdata<-read.zoo("C:\Users\Taehee Cha\Desktop\\data\EUCall.csv",

                header=TRUE,

                sep=",",

                format = "%Y-%m-%d", 

                nrow=4433)

stock_xts<-as.xts(stockdata,dateFormat='POSIXct')



DKOSPI200 <-diff(log(stock_xts$KOSPI200))*100

DDAX30 <-diff(log(stock_xts$DAX30))*100

DSNP500 <-diff(log(stock_xts$SNP500))*100

DFTSE100  <-diff(log(stock_xts$FTSE100))*100

...

data2<-data.frame(DKOSPI200,DDAX30,DSNP500,DFTSE100,DEUROSTOXX50,DFTSEMIB,DNIKKEI225,DSNPTSX,DCAC40,DJSE40,

  DRTS,DIPC,DBOVESPA,DTadawul,DMERV,DSENSEX,DJSX,DSSE,DXU100,DSNPASX,

  DATHEX,DAEX,DOBX,DSNPNZ50,DTAIEX,DOMXC20,DKLCI,DBEL20,DOMXS30,DSMI,

  DIBEX,DSTI,DADX,DISEQ,DATX,DTA35,DEGX30,DPX50,DIPSA,DQE,

  DIGBC,DSET,DKarachi100,DSNPBVL,DPSI20,DWIG20,DOMXH25,DPSEI,DBUX,DHANGSENG)


Univarate GARCH specification



gjrGARCH.spec <-ugarchspec(variance.model=list(model="gjrGARCH",garchOrder=c(1,1)),mean.model=list(armaOrder=c(1,0)))


Multivarate GARCH specification



dcc.gjrGARCH.spec=dccspec(uspec=multispec(replicate(50,gjrGARCH.spec)),

                      dccOrder=c(1,1),

                      distribution="mvnorm")       

OUTCOME=dccfit(dcc.gjrGARCH.spec, data=data2)

Fit<-fitted(OUTCOME)

Sigma<-sigma(OUTCOME)

Rho<-rcor(OUTCOME)



dim(Rho)

>50 50 4432


I tried this, but I failed.



dccmean<-apply(Rho, c(1:4432), mean)


Error message:



Error in if (d2 == 0L) { : missing value where TRUE/FALSE needed


please help me....



> dput(head(data2))



structure(list(KOSPI200 = c(-0.64107155376405, -2.63428514277591, 

-3.02361881202007, -0.348129159251176, -0.8463497185911, 0.933495693605124

), DAX30 = c(-0.275158289647415, -2.31165589473132, 0.132288230298805, 

-0.916302412003311, -0.249954511741102, 1.5067409708557), SNP500 = c(0, 

-1.23978129442266, -2.37787312356224, 0.68553903957973, -1.45026278352685, 

-0.113477591886735), FTSE100 = c(-0.912643889420117, -1.27612498082748, 

-0.189622000351441, -0.0175554735125871, -1.40042215908522, 1.65029716900129

), EUROSTOXX50 = c(-0.693995680230763, -2.63873467285407, 0.0340923169277474, 

-1.10692637764913, -1.45688992650648, 0.90690109636018), FTSEMIB = c(-0.207307997135331, 

-2.20987523065315, 0.11929602916414, -0.591164923299203, -1.23334692081265, 

0.0674451667508791), NIKKEI225 = c(-0.172132904979705, -2.41825620786038, 

-0.541031623946608, 0.494946220458736, -2.43002026761108, 3.30050037277818

), SNPTSX = c(-0.228827286223954, -0.618158165738869, -1.163192751949, 

0.611234081963197, -1.07887096999253, 0.15982056970163), CAC40 = c(-0.996225333005363, 

-2.45923020374033, 0.558708030675348, -1.07932737047562, -1.18770661796788, 

0.946736097241008), JSE40 = c(-1.24801019778307, -0.660388491185593, 

-0.378077731086712, 1.12913917165454, -0.270357667812782, 0.925230160391344

), RTS = c(1.69252595201295, -2.47912051247043, -1.68278329224893, 

-0.628193976078784, -5.81832854668214, 2.33324601133127), IPC = c(0.410660821053099, 

-0.793221688816637, -1.46058560736098, -0.0995162816563422, 0.0582618177329408, 

-2.24304020680233), BOVESPA = c(-2.08399544316435, -0.0640501036023977, 

-1.00884481719454, 0, -2.45326225130107, 1.76775969319749), Tadawul = c(-0.0751612871260576, 

1.06988441714364, 0.467558379080923, 0.015629948658713, -0.479468927554549, 

0.115645625170213), MERV = c(-0.812834284218145, -3.48526114996641, 

-0.789176542098158, 0, -6.3347627637631, -2.25802417775345), 

    SENSEX = c(0.173466860804083, -0.357822108056638, -0.45387164272519, 

    1.1318914393021, 1.4301129114795, 2.24172363852979), JSX = c(0.464645515331874, 

    0.433624214120076, 0.382746462253802, 0.245017602482989, 

    0.417626097951018, 0.919117215344212), SSE = c(-0.9304947624722, 

    -0.512019911931727, -0.0399062163143782, 0.923519675548068, 

    -0.965701174169897, -0.149922817153048), XU100 = c(0.83997183599358, 

    -9.44220433432204, -1.19486088286003, -5.93037167728525, 

    -8.13019316543659, 4.39805147087551), SNPASX = c(-1.52481837941529, 

    -0.710076219124112, -0.355441763724151, 0.43889183264465, 

    0.632546988595983, 0.431277069387725), ATHEX = c(0.217251999604962, 

    -0.113093185749946, -4.08702446400842, -1.49759637062132, 

    -2.89449541976028, 2.84821759239637), AEX = c(-0.302724751872496, 

    -1.93350674725812, -0.334531789971226, -0.27844233175518, 

    -1.47467944242914, 0.59320521917563), OBX = c(-0.633345799178109, 

    -0.0872437271016935, -0.0686020794624653, -0.751459482064121, 

    -0.314782430663918, -0.138818800687446), SNPNZ50 = c(-0.589289243578772, 

    -0.212470628567818, -1.09525597597484, 1.56062961309287, 

    -0.594970797797778, 0.308439154148132), TAIEX = c(-0.634186259073921, 

    -0.0465155309724352, -1.06170058328026, -0.875355087660168, 

    -1.4930992055346, 1.85698166429091), OMXC20 = c(0.0415607672345608, 

    -0.892547743122307, 0.895744009882993, 0.00639222705407505, 

    -0.515886239547392, -0.744864387047794), KLCI = c(3.12710622798287, 

    1.55475618644338, 0.141888091655584, -0.2344629562125, -1.32130772048118, 

    -1.3324408754646), BEL20 = c(0.916239489510673, -0.838871120292239, 

    -0.349848101836958, 0.131119279926573, -0.220949965581685, 

    0.925072128183135), OMXS30 = c(-2.30694346428235, -3.59314374436401, 

    -0.132526843920378, -0.26558175544098, -0.700219536449254, 

    2.21356560948696), SMI = c(-0.923178651961543, -1.9888683347407, 

    0.190440901601185, -1.31581319814948, -1.24166996356809, 

    1.74938111396603), IBEX = c(-1.10228247337272, -3.30519689438287, 

    0.28541077977593, -0.719194686241131, -2.37890150982238, 

    -1.20979032592885), STI = c(-0.376825693193794, -1.56959227075815, 

    -0.209405887533087, 1.59189359203387, -0.998471722830718, 

    0.524545937488519), ADX = c(0, 0.234110707042312, -0.140988509294804, 

    -0.107833632972643, -0.00392341494084292, 0), ISEQ = c(-0.497210204416199, 

    -1.28827940816549, 1.05427460869301, 0.691923594974142, -1.2985333059083, 

    1.05885040639215), ATX = c(0.196176688098504, -0.397720264421864, 

    0.765032409498989, 0.634860192671294, -0.718109388684507, 

    0.288191756274081), TA35 = c(-0.467643907976623, -1.6933214269244, 

    0.638360404963567, 1.66873087937374, -1.03207784150818, 1.98476257832283

    ), EGX30 = c(-1.60808620054533, 0.486603179063838, 0.042060989053283, 

    -0.582335360174024, -1.05485347621963, 0.329916835288824), 

    PX50 = c(0, 0, -1.53717800478548, -0.567132576686191, -1.79645549752987, 

    0.100654261140143), IPSA = c(0.561439712306022, 0.849792667039129, 

    -0.327031029818503, -0.154168073702365, -0.737696755949457, 

    -0.476817106576277), QE = c(0.124155575561335, -0.299696872832733, 

    -0.087886339332055, 0.0366286953290995, 0.109805655043083, 

    0.0585137524009127), IGBC = c(0.151884596926966, -0.461362987666014, 

    1.12811837108895, 0.0931901841028981, 0.149517561508272, 

    0.312176099410877), SET = c(-0.475964725298805, 0, -0.126280143061397, 

    -0.243770852799319, -3.47030045650181, 0.752026997121558), 

    Karachi100 = c(-1.2595250361283, -1.09859841657665, -0.467295948347779, 

    -0.176282035250352, 1.06144685237481, 0.614077017080827), 

    SNPBVL = c(-0.0916272845215893, -0.488603970907153, -0.635609686024718, 

    0.72544080956467, -1.05989054433602, -0.290056131898186), 

    PSI20 = c(-0.498150623441695, -2.4995090145449, -0.916173928749231, 

    0.134620833204835, -1.96674118284665, 0.125363501017972), 

    WIG20 = c(-0.186154209522993, 2.29090171954054, -1.21234613135197, 

    -2.35741884661973, -1.39537944100319, 4.04050146384076), 

    OMXH25 = c(-0.734161177012371, -3.2781107280087, -0.274240630336653, 

    0.164265778301331, -2.88555610228327, 0.0196123710402674), 

    PSEI = c(1.16491407781849, -0.379401344663943, 0.604503127264966, 

    -0.551543468404248, -0.542372265371061, 0.668217806598204

    ), BUX = c(0.745432319945749, -0.407296683745706, 1.4847908668413, 

    -1.73656177766137, -1.70838647119353, 0.642365839539494), 

    HANGSENG = c(-1.58777648289217, 0, -2.40414908704771, 0.182801734695737, 

    -1.47377260414583, 1.05050572830176)), row.names = c("2001-07-05", 

"2001-07-06", "2001-07-09", "2001-07-10", "2001-07-11", "2001-07-12"

), class = "data.frame")


I think it will be more useful to solve my problem.



dim(Rho)

>50 50 4432



> dput(head(Rho))

c(1, 0.28274756492168, 0.366762864615763, 0.287187938929745, 

0.279313675150249, 0.25099353597608)



> str(Rho)

 num [1:50, 1:50, 1:4432] 1 0.283 0.367 0.287 0.279 ...

 - attr(*, "dimnames")=List of 3

  ..$ : chr [1:50] "KOSPI200" "DAX30" "SNP500" "FTSE100" ...

  ..$ : chr [1:50] "KOSPI200" "DAX30" "SNP500" "FTSE100" ...

  ..$ : chr [1:4432] "2001-07-05" "2001-07-06" "2001-07-09" "2001-07-10" ...


My questions



#, , "2001-07-05"



#       KOSPI200 DAX30 SNP500 FTSE100

#KOSPI200    1     5     9    13

#DAX30       2     6    10    14

#SNP500      3     7    11    15

#FTSE100     4     8    12    16



#, , "2001-07-06"



#       KOSPI200 DAX30 SNP500 FTSE100

#KOSPI200    1     5     9    13

#DAX30       2     6    10    14

#SNP500      3     7    11    15

#FTSE100     4     8    12    16



##----->I want to this ((ex)sum) 



#, , 



#       KOSPI200 DAX30 SNP500 FTSE100

#KOSPI200    2     10    18    26

#DAX30       4     12    20    28

#SNP500      6     14    22    30

#FTSE100     8     16    24    32





r multidimensional-array apply mean correlation







share|improve this question
















I want to compute dynamic conditional correlation(dcc) mean matrix at all time.
Rho is each day matrix.
but i want to make a mean matrix all day (not each day)



this is DCC-garch model.



library(rugarch)

library(rmgarch)

library(xts)

stockdata<-read.zoo("C:\Users\Taehee Cha\Desktop\\data\EUCall.csv",

                header=TRUE,

                sep=",",

                format = "%Y-%m-%d", 

                nrow=4433)

stock_xts<-as.xts(stockdata,dateFormat='POSIXct')



DKOSPI200 <-diff(log(stock_xts$KOSPI200))*100

DDAX30 <-diff(log(stock_xts$DAX30))*100

DSNP500 <-diff(log(stock_xts$SNP500))*100

DFTSE100  <-diff(log(stock_xts$FTSE100))*100

...

data2<-data.frame(DKOSPI200,DDAX30,DSNP500,DFTSE100,DEUROSTOXX50,DFTSEMIB,DNIKKEI225,DSNPTSX,DCAC40,DJSE40,

  DRTS,DIPC,DBOVESPA,DTadawul,DMERV,DSENSEX,DJSX,DSSE,DXU100,DSNPASX,

  DATHEX,DAEX,DOBX,DSNPNZ50,DTAIEX,DOMXC20,DKLCI,DBEL20,DOMXS30,DSMI,

  DIBEX,DSTI,DADX,DISEQ,DATX,DTA35,DEGX30,DPX50,DIPSA,DQE,

  DIGBC,DSET,DKarachi100,DSNPBVL,DPSI20,DWIG20,DOMXH25,DPSEI,DBUX,DHANGSENG)


Univarate GARCH specification



gjrGARCH.spec <-ugarchspec(variance.model=list(model="gjrGARCH",garchOrder=c(1,1)),mean.model=list(armaOrder=c(1,0)))


Multivarate GARCH specification



dcc.gjrGARCH.spec=dccspec(uspec=multispec(replicate(50,gjrGARCH.spec)),

                      dccOrder=c(1,1),

                      distribution="mvnorm")       

OUTCOME=dccfit(dcc.gjrGARCH.spec, data=data2)

Fit<-fitted(OUTCOME)

Sigma<-sigma(OUTCOME)

Rho<-rcor(OUTCOME)



dim(Rho)

>50 50 4432


I tried this, but I failed.



dccmean<-apply(Rho, c(1:4432), mean)


Error message:



Error in if (d2 == 0L) { : missing value where TRUE/FALSE needed


please help me....



> dput(head(data2))



structure(list(KOSPI200 = c(-0.64107155376405, -2.63428514277591, 

-3.02361881202007, -0.348129159251176, -0.8463497185911, 0.933495693605124

), DAX30 = c(-0.275158289647415, -2.31165589473132, 0.132288230298805, 

-0.916302412003311, -0.249954511741102, 1.5067409708557), SNP500 = c(0, 

-1.23978129442266, -2.37787312356224, 0.68553903957973, -1.45026278352685, 

-0.113477591886735), FTSE100 = c(-0.912643889420117, -1.27612498082748, 

-0.189622000351441, -0.0175554735125871, -1.40042215908522, 1.65029716900129

), EUROSTOXX50 = c(-0.693995680230763, -2.63873467285407, 0.0340923169277474, 

-1.10692637764913, -1.45688992650648, 0.90690109636018), FTSEMIB = c(-0.207307997135331, 

-2.20987523065315, 0.11929602916414, -0.591164923299203, -1.23334692081265, 

0.0674451667508791), NIKKEI225 = c(-0.172132904979705, -2.41825620786038, 

-0.541031623946608, 0.494946220458736, -2.43002026761108, 3.30050037277818

), SNPTSX = c(-0.228827286223954, -0.618158165738869, -1.163192751949, 

0.611234081963197, -1.07887096999253, 0.15982056970163), CAC40 = c(-0.996225333005363, 

-2.45923020374033, 0.558708030675348, -1.07932737047562, -1.18770661796788, 

0.946736097241008), JSE40 = c(-1.24801019778307, -0.660388491185593, 

-0.378077731086712, 1.12913917165454, -0.270357667812782, 0.925230160391344

), RTS = c(1.69252595201295, -2.47912051247043, -1.68278329224893, 

-0.628193976078784, -5.81832854668214, 2.33324601133127), IPC = c(0.410660821053099, 

-0.793221688816637, -1.46058560736098, -0.0995162816563422, 0.0582618177329408, 

-2.24304020680233), BOVESPA = c(-2.08399544316435, -0.0640501036023977, 

-1.00884481719454, 0, -2.45326225130107, 1.76775969319749), Tadawul = c(-0.0751612871260576, 

1.06988441714364, 0.467558379080923, 0.015629948658713, -0.479468927554549, 

0.115645625170213), MERV = c(-0.812834284218145, -3.48526114996641, 

-0.789176542098158, 0, -6.3347627637631, -2.25802417775345), 

    SENSEX = c(0.173466860804083, -0.357822108056638, -0.45387164272519, 

    1.1318914393021, 1.4301129114795, 2.24172363852979), JSX = c(0.464645515331874, 

    0.433624214120076, 0.382746462253802, 0.245017602482989, 

    0.417626097951018, 0.919117215344212), SSE = c(-0.9304947624722, 

    -0.512019911931727, -0.0399062163143782, 0.923519675548068, 

    -0.965701174169897, -0.149922817153048), XU100 = c(0.83997183599358, 

    -9.44220433432204, -1.19486088286003, -5.93037167728525, 

    -8.13019316543659, 4.39805147087551), SNPASX = c(-1.52481837941529, 

    -0.710076219124112, -0.355441763724151, 0.43889183264465, 

    0.632546988595983, 0.431277069387725), ATHEX = c(0.217251999604962, 

    -0.113093185749946, -4.08702446400842, -1.49759637062132, 

    -2.89449541976028, 2.84821759239637), AEX = c(-0.302724751872496, 

    -1.93350674725812, -0.334531789971226, -0.27844233175518, 

    -1.47467944242914, 0.59320521917563), OBX = c(-0.633345799178109, 

    -0.0872437271016935, -0.0686020794624653, -0.751459482064121, 

    -0.314782430663918, -0.138818800687446), SNPNZ50 = c(-0.589289243578772, 

    -0.212470628567818, -1.09525597597484, 1.56062961309287, 

    -0.594970797797778, 0.308439154148132), TAIEX = c(-0.634186259073921, 

    -0.0465155309724352, -1.06170058328026, -0.875355087660168, 

    -1.4930992055346, 1.85698166429091), OMXC20 = c(0.0415607672345608, 

    -0.892547743122307, 0.895744009882993, 0.00639222705407505, 

    -0.515886239547392, -0.744864387047794), KLCI = c(3.12710622798287, 

    1.55475618644338, 0.141888091655584, -0.2344629562125, -1.32130772048118, 

    -1.3324408754646), BEL20 = c(0.916239489510673, -0.838871120292239, 

    -0.349848101836958, 0.131119279926573, -0.220949965581685, 

    0.925072128183135), OMXS30 = c(-2.30694346428235, -3.59314374436401, 

    -0.132526843920378, -0.26558175544098, -0.700219536449254, 

    2.21356560948696), SMI = c(-0.923178651961543, -1.9888683347407, 

    0.190440901601185, -1.31581319814948, -1.24166996356809, 

    1.74938111396603), IBEX = c(-1.10228247337272, -3.30519689438287, 

    0.28541077977593, -0.719194686241131, -2.37890150982238, 

    -1.20979032592885), STI = c(-0.376825693193794, -1.56959227075815, 

    -0.209405887533087, 1.59189359203387, -0.998471722830718, 

    0.524545937488519), ADX = c(0, 0.234110707042312, -0.140988509294804, 

    -0.107833632972643, -0.00392341494084292, 0), ISEQ = c(-0.497210204416199, 

    -1.28827940816549, 1.05427460869301, 0.691923594974142, -1.2985333059083, 

    1.05885040639215), ATX = c(0.196176688098504, -0.397720264421864, 

    0.765032409498989, 0.634860192671294, -0.718109388684507, 

    0.288191756274081), TA35 = c(-0.467643907976623, -1.6933214269244, 

    0.638360404963567, 1.66873087937374, -1.03207784150818, 1.98476257832283

    ), EGX30 = c(-1.60808620054533, 0.486603179063838, 0.042060989053283, 

    -0.582335360174024, -1.05485347621963, 0.329916835288824), 

    PX50 = c(0, 0, -1.53717800478548, -0.567132576686191, -1.79645549752987, 

    0.100654261140143), IPSA = c(0.561439712306022, 0.849792667039129, 

    -0.327031029818503, -0.154168073702365, -0.737696755949457, 

    -0.476817106576277), QE = c(0.124155575561335, -0.299696872832733, 

    -0.087886339332055, 0.0366286953290995, 0.109805655043083, 

    0.0585137524009127), IGBC = c(0.151884596926966, -0.461362987666014, 

    1.12811837108895, 0.0931901841028981, 0.149517561508272, 

    0.312176099410877), SET = c(-0.475964725298805, 0, -0.126280143061397, 

    -0.243770852799319, -3.47030045650181, 0.752026997121558), 

    Karachi100 = c(-1.2595250361283, -1.09859841657665, -0.467295948347779, 

    -0.176282035250352, 1.06144685237481, 0.614077017080827), 

    SNPBVL = c(-0.0916272845215893, -0.488603970907153, -0.635609686024718, 

    0.72544080956467, -1.05989054433602, -0.290056131898186), 

    PSI20 = c(-0.498150623441695, -2.4995090145449, -0.916173928749231, 

    0.134620833204835, -1.96674118284665, 0.125363501017972), 

    WIG20 = c(-0.186154209522993, 2.29090171954054, -1.21234613135197, 

    -2.35741884661973, -1.39537944100319, 4.04050146384076), 

    OMXH25 = c(-0.734161177012371, -3.2781107280087, -0.274240630336653, 

    0.164265778301331, -2.88555610228327, 0.0196123710402674), 

    PSEI = c(1.16491407781849, -0.379401344663943, 0.604503127264966, 

    -0.551543468404248, -0.542372265371061, 0.668217806598204

    ), BUX = c(0.745432319945749, -0.407296683745706, 1.4847908668413, 

    -1.73656177766137, -1.70838647119353, 0.642365839539494), 

    HANGSENG = c(-1.58777648289217, 0, -2.40414908704771, 0.182801734695737, 

    -1.47377260414583, 1.05050572830176)), row.names = c("2001-07-05", 

"2001-07-06", "2001-07-09", "2001-07-10", "2001-07-11", "2001-07-12"

), class = "data.frame")


I think it will be more useful to solve my problem.



dim(Rho)

>50 50 4432



> dput(head(Rho))

c(1, 0.28274756492168, 0.366762864615763, 0.287187938929745, 

0.279313675150249, 0.25099353597608)



> str(Rho)

 num [1:50, 1:50, 1:4432] 1 0.283 0.367 0.287 0.279 ...

 - attr(*, "dimnames")=List of 3

  ..$ : chr [1:50] "KOSPI200" "DAX30" "SNP500" "FTSE100" ...

  ..$ : chr [1:50] "KOSPI200" "DAX30" "SNP500" "FTSE100" ...

  ..$ : chr [1:4432] "2001-07-05" "2001-07-06" "2001-07-09" "2001-07-10" ...


My questions



#, , "2001-07-05"



#       KOSPI200 DAX30 SNP500 FTSE100

#KOSPI200    1     5     9    13

#DAX30       2     6    10    14

#SNP500      3     7    11    15

#FTSE100     4     8    12    16



#, , "2001-07-06"



#       KOSPI200 DAX30 SNP500 FTSE100

#KOSPI200    1     5     9    13

#DAX30       2     6    10    14

#SNP500      3     7    11    15

#FTSE100     4     8    12    16



##----->I want to this ((ex)sum) 



#, , 



#       KOSPI200 DAX30 SNP500 FTSE100

#KOSPI200    2     10    18    26

#DAX30       4     12    20    28

#SNP500      6     14    22    30

#FTSE100     8     16    24    32





r multidimensional-array apply mean correlation




r multidimensional-array apply mean correlation






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 21 '18 at 7:59







차태희

















asked Nov 20 '18 at 13:24









차태희차태희

62




62








  • 1





    Please specify the used libraries and provide data2.

    – jay.sf
    Nov 20 '18 at 13:41






  • 1





    data2 is difference time data.

    – 차태희
    Nov 20 '18 at 15:12






  • 1





    Can you post the output of dput(data2), or if its too long dput(head(data2)) into the question?

    – jay.sf
    Nov 20 '18 at 15:14











  • I modified the edit.

    – 차태희
    Nov 20 '18 at 15:31






  • 1





    Better. But we cannot reproduce "C:\Users\Taehee Cha\Desktop\\data\EUCall.csv". You should provide stockdata too. Probably you should consider How to make a great reproducible example, thanks.

    – jay.sf
    Nov 20 '18 at 15:47














  • 1





    Please specify the used libraries and provide data2.

    – jay.sf
    Nov 20 '18 at 13:41






  • 1





    data2 is difference time data.

    – 차태희
    Nov 20 '18 at 15:12






  • 1





    Can you post the output of dput(data2), or if its too long dput(head(data2)) into the question?

    – jay.sf
    Nov 20 '18 at 15:14











  • I modified the edit.

    – 차태희
    Nov 20 '18 at 15:31






  • 1





    Better. But we cannot reproduce "C:\Users\Taehee Cha\Desktop\\data\EUCall.csv". You should provide stockdata too. Probably you should consider How to make a great reproducible example, thanks.

    – jay.sf
    Nov 20 '18 at 15:47








1




1





Please specify the used libraries and provide data2.

– jay.sf
Nov 20 '18 at 13:41





Please specify the used libraries and provide data2.

– jay.sf
Nov 20 '18 at 13:41




1




1





data2 is difference time data.

– 차태희
Nov 20 '18 at 15:12





data2 is difference time data.

– 차태희
Nov 20 '18 at 15:12




1




1





Can you post the output of dput(data2), or if its too long dput(head(data2)) into the question?

– jay.sf
Nov 20 '18 at 15:14





Can you post the output of dput(data2), or if its too long dput(head(data2)) into the question?

– jay.sf
Nov 20 '18 at 15:14













I modified the edit.

– 차태희
Nov 20 '18 at 15:31





I modified the edit.

– 차태희
Nov 20 '18 at 15:31




1




1





Better. But we cannot reproduce "C:\Users\Taehee Cha\Desktop\\data\EUCall.csv". You should provide stockdata too. Probably you should consider How to make a great reproducible example, thanks.

– jay.sf
Nov 20 '18 at 15:47





Better. But we cannot reproduce "C:\Users\Taehee Cha\Desktop\\data\EUCall.csv". You should provide stockdata too. Probably you should consider How to make a great reproducible example, thanks.

– jay.sf
Nov 20 '18 at 15:47












1 Answer
1






active

oldest

votes


















1














The error of your code is in the MARGIN of your apply call. Do not use c(1:4412), use the actual margins which are the first and second dimension (hence c(1,2))



In the last example of your question:



day1 <- matrix(1:16, 4, 4, dimnames = list(letters[1:4], LETTERS[1:4]))

day2 <- matrix(1:16, 4, 4, dimnames = list(letters[1:4], LETTERS[1:4]))



Rho <- abind::abind(day1=day1,day2=day2, along = 3)



apply(Rho, c(1,2), sum)

  A  B  C  D

a 2 10 18 26

b 4 12 20 28

c 6 14 22 30

d 8 16 24 32


gives the wanted output.






share|improve this answer























    Your Answer






    StackExchange.ifUsing("editor", function () {
    StackExchange.using("externalEditor", function () {
    StackExchange.using("snippets", function () {
    StackExchange.snippets.init();
    });
    });
    }, "code-snippets");

    StackExchange.ready(function() {
    var channelOptions = {
    tags: "".split(" "),
    id: "1"
    };
    initTagRenderer("".split(" "), "".split(" "), channelOptions);

    StackExchange.using("externalEditor", function() {
    // Have to fire editor after snippets, if snippets enabled
    if (StackExchange.settings.snippets.snippetsEnabled) {
    StackExchange.using("snippets", function() {
    createEditor();
    });
    }
    else {
    createEditor();
    }
    });

    function createEditor() {
    StackExchange.prepareEditor({
    heartbeatType: 'answer',
    autoActivateHeartbeat: false,
    convertImagesToLinks: true,
    noModals: true,
    showLowRepImageUploadWarning: true,
    reputationToPostImages: 10,
    bindNavPrevention: true,
    postfix: "",
    imageUploader: {
    brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
    contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
    allowUrls: true
    },
    onDemand: true,
    discardSelector: ".discard-answer"
    ,immediatelyShowMarkdownHelp:true
    });


    }
    });














    draft saved

    draft discarded


















    StackExchange.ready(
    function () {
    StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53393987%2fhow-can-i-compute-multi-dimension-mean-matrix%23new-answer', 'question_page');
    }
    );

    Post as a guest















    Required, but never shown

























    1 Answer
    1






    active

    oldest

    votes








    1 Answer
    1






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    1














    The error of your code is in the MARGIN of your apply call. Do not use c(1:4412), use the actual margins which are the first and second dimension (hence c(1,2))



    In the last example of your question:



    day1 <- matrix(1:16, 4, 4, dimnames = list(letters[1:4], LETTERS[1:4]))
    
day2 <- matrix(1:16, 4, 4, dimnames = list(letters[1:4], LETTERS[1:4]))
    

    
Rho <- abind::abind(day1=day1,day2=day2, along = 3)
    

    
apply(Rho, c(1,2), sum)
    
  A  B  C  D
    
a 2 10 18 26
    
b 4 12 20 28
    
c 6 14 22 30
    
d 8 16 24 32


    gives the wanted output.






    share|improve this answer




























      1














      The error of your code is in the MARGIN of your apply call. Do not use c(1:4412), use the actual margins which are the first and second dimension (hence c(1,2))



      In the last example of your question:



      day1 <- matrix(1:16, 4, 4, dimnames = list(letters[1:4], LETTERS[1:4]))
      
day2 <- matrix(1:16, 4, 4, dimnames = list(letters[1:4], LETTERS[1:4]))
      

      
Rho <- abind::abind(day1=day1,day2=day2, along = 3)
      

      
apply(Rho, c(1,2), sum)
      
  A  B  C  D
      
a 2 10 18 26
      
b 4 12 20 28
      
c 6 14 22 30
      
d 8 16 24 32


      gives the wanted output.






      share|improve this answer


























        1












        1








        1







        The error of your code is in the MARGIN of your apply call. Do not use c(1:4412), use the actual margins which are the first and second dimension (hence c(1,2))



        In the last example of your question:



        day1 <- matrix(1:16, 4, 4, dimnames = list(letters[1:4], LETTERS[1:4]))
        
day2 <- matrix(1:16, 4, 4, dimnames = list(letters[1:4], LETTERS[1:4]))
        

        
Rho <- abind::abind(day1=day1,day2=day2, along = 3)
        

        
apply(Rho, c(1,2), sum)
        
  A  B  C  D
        
a 2 10 18 26
        
b 4 12 20 28
        
c 6 14 22 30
        
d 8 16 24 32


        gives the wanted output.






        share|improve this answer













        The error of your code is in the MARGIN of your apply call. Do not use c(1:4412), use the actual margins which are the first and second dimension (hence c(1,2))



        In the last example of your question:



        day1 <- matrix(1:16, 4, 4, dimnames = list(letters[1:4], LETTERS[1:4]))
        
day2 <- matrix(1:16, 4, 4, dimnames = list(letters[1:4], LETTERS[1:4]))
        

        
Rho <- abind::abind(day1=day1,day2=day2, along = 3)
        

        
apply(Rho, c(1,2), sum)
        
  A  B  C  D
        
a 2 10 18 26
        
b 4 12 20 28
        
c 6 14 22 30
        
d 8 16 24 32


        gives the wanted output.







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered Nov 21 '18 at 18:13









        BastienBastien

        1,428721




        1,428721
































            draft saved

            draft discarded




















































            Thanks for contributing an answer to Stack Overflow!


            • Please be sure to answer the question. Provide details and share your research!

            But avoid …



            • Asking for help, clarification, or responding to other answers.

            • Making statements based on opinion; back them up with references or personal experience.


            To learn more, see our tips on writing great answers.




            draft saved


            draft discarded














            StackExchange.ready(
            function () {
            StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53393987%2fhow-can-i-compute-multi-dimension-mean-matrix%23new-answer', 'question_page');
            }
            );

            Post as a guest















            Required, but never shown





















































            Required, but never shown














            Required, but never shown












            Required, but never shown







            Required, but never shown

































            Required, but never shown














            Required, but never shown












            Required, but never shown







            Required, but never shown







            -

            Popular posts from this blog

            Guess what letter conforming each word

            Image
            6 $begingroup$ This is an entry in the Fortnightly Topic Challenge #41: Short and Sweet. 1 letter only could be mean anything . With an additional letter in front , could be used as medicine component and something you definitely learn or at least heard once. Add another letter in front , you would possibly hear that many times . Add an additional letter in front , you would found an independent country . With an additional letter, in the end , you would found something related to apple . Add another letter in the end , you would found something bad . Hint: Each letter is unique . word knowledge letters chemistry share | improve this question ...
            Read more

            Port of Spain

            Image
            For ports in the country of Spain, see List of ports in Spain. City in City of Port of Spain, Trinidad and Tobago Port of Spain City City of Port of Spain Top to bottom (left to right): Skyline of Port of Spain from Gulf of Paria; National Academy for the Performing Arts, Independence Square, Eric Williams Plaza; MovieTowne, Port of Port of Spain Motto(s):  "We Gather Strength As We Go Along" Port of Spain Coordinates: 10°40′N 61°31′W  /  10.667°N 61.517°W  / 10.667; -61.517 Coordinates: 10°40′N 61°31′W  /  10.667°N 61.517°W  / 10.667; -61.517 Country   Trinidad and Tobago Jurisdiction City of Port of Spain Settled 1560 Incorporated (town) 1853 Incorporated (city) 1914 Government  • Mayor Joel Martinez  • Governing body City Corporation Area [1]  • City 12 km 2 (5 sq mi) Elevation [2] 66 m (217 ft) Population (2011) [1]  • City 37,074  • Rank 3rd  • Density 3,090/km 2 (8,000/sq mi)...
            Read more

            Run scheduled task as local user group (not BUILTIN)

            Image
            0 I am trying to run a scheduled task with User Group (not a single user). This is working fine when I in the field "when running a task, use the following user account:" insert BUILTIN User Group (for example: Administrators or Users). But when I try it with the created Local User Group (example: TestGroup) it is not working. The Users in the TestGroup are the same as in the Administrators group. What could be the problem? Are there some restrictions to the Created User Group compared to BUILTINUsers? scheduled-tasks taskscheduler windowstaskschedule share | improve this question asked Nov 15 '18 at 15:36 Far...
            Read more