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I'm trying to run an R code for calculating the ssGSEA score between a gene expression dataset and a gene list.
The function I'm trying to run is:
GSVAtumor_UCS_Epi<-gsva(file, EM_gene_signature_tumor_KS_Epi_list, method=c("gsva", "ssgsea", "zscore", "plage"))
I'm facing the same error and two warning messages every time I run the code and there is not much information available for it over the internet for the same:
Error in relist(v, part) :
shape of 'skeleton' is not compatible with 'NROW(flesh)'
In addition: Warning messages:
1: In .filterFeatures(expr, method) :
6171 genes with constant expression values throuhgout the samples.
2: In .filterFeatures(expr, method) :
Since argument method!="ssgsea", genes with constant expression values are discarded.
Here EM_gene_signature_tumor_KS_Epi_list is
list(structure(list(...1 = c("KRT19", "AGR2", "RAB25", "CDH1",
"ERBB3", "FXYD3", "SLC44A4", "S100P", "SCNN1A", "GALNT3", "PRSS8",
"ELF3", "CEACAM6", "TMPRSS4", "CLDN7", "TACSTD2", "CLDN3", "EPCAM",
"SPINT1", "TSPAN1", "PLS1", "TMEM30B", "PRR15L", "KRT8", "ST14",
NA, "RBM47", "S100A14", "C1orf106", "NQO1", "TOX3", "PTK6", "TFF1",
"CLDN4", "GPRC5A", "TJP3", "KRT18", "MAP7", "CKMT1A", "ESRP1",
"MUC1", "SPINT2", "ESRP2", "CDS1", "PPAP2C", "CEACAM7", "TTC39A",
"OVOL2", "EHF", "AP1M2", "CEACAM5", "LAD1", "ARHGAP8", "TFF3",
"JUP", "CD24", "TMC5", "MLPH", "ELMO3", "ERBB2", "LLGL2", "DDR1",
"FA2H", "CBLC", "TMPRSS2", "LSR", "PERP", "POF1B", "MYO5C", "RAB11FIP1",
"MAPK13", "KRT7", "CEACAM1", "CXADR", "ATP2C2", "RNF128", "MPZL2",
"EPS8L1", "GALNT7", "CORO2A", "BCAS1", "TPD52", "ARHGAP32", "FUT2",
"OR7E14P", "GALE", "GRHL2", "BIK", "RAPGEFL1", "STYK1", "F11R",
"PKP3", "CYB561", "SH3YL1", "GDF15", "PSCA", "EZR", "TJP2", "FGFR3",
"FUT3", "BSPRY", "TOM1L1", "IRF6", "EPB41L4B", "OCLN", "LRRC1",
"C19orf21", "ABHD11", "EPS8L2", "MYO6", "TSPAN8", "MST1R", "SLC16A5",
"GPR56", "AZGP1", "TOB1", "SLC35A3", "TRPM4", "PHLDA2", "VAMP8",
"SLC22A18", "AKR1B10", "VAV3", "SPAG1", "ABCC3", "SYNGR2", "STAP2",
"C4orf19", "PPL", "PLLP", "DSG2", "HDHD3", "CD2AP", "MANSC1",
"DHCR24", "EPN3", "TUFT1", "GMDS", "EXPH5", "DSP", "SDC4", "IL20RA",
"FAM174B", "PTPRF", "SORD")), row.names = c(NA, -145L), class = c("tbl_df",
"tbl", "data.frame")))
And file is
new("ExpressionSet", experimentData = new("MIAME", name = "",
lab = "", contact = "", title = "", abstract = "", url = "",
pubMedIds = "", samples = list(), hybridizations = list(),
normControls = list(), preprocessing = list(), other = list(),
.__classVersion__ = new("Versions", .Data = list(c(1L, 0L,
0L), c(1L, 1L, 0L)))), assayData = <environment>, phenoData = new("AnnotatedDataFrame",
varMetadata = structure(list(labelDescription = "state"), row.names = "state", class = "data.frame"),
data = structure(list(state = c("TCGA-N6-A4V9-01A", "TCGA-QM-A5NM-01A",
"TCGA-N8-A4PM-01A", "TCGA-NG-A4VU-01A", "TCGA-NG-A4VW-01A",
"TCGA-N8-A4PN-01A", "TCGA-N5-A4RA-01A", "TCGA-N6-A4VD-01A",
"TCGA-N7-A4Y8-01A", "TCGA-N6-A4VE-01A", "TCGA-N5-A59F-01A",
"TCGA-N9-A4PZ-01A", "TCGA-N6-A4VF-01A", "TCGA-NF-A5CP-01A",
"TCGA-N6-A4VC-01A", "TCGA-N5-A4RF-01A", "TCGA-N8-A4PL-01A",
"TCGA-ND-A4WA-01A", "TCGA-N5-A4RU-01A", "TCGA-N9-A4Q3-01A",
"TCGA-NA-A4R1-01A", "TCGA-N7-A4Y5-01A", "TCGA-N5-A4RV-01A",
"TCGA-QN-A5NN-01A", "TCGA-N9-A4Q1-01A", "TCGA-N5-A59E-01A",
"TCGA-NA-A4QW-01A", "TCGA-N5-A4RM-01A", "TCGA-NF-A4X2-01A",
"TCGA-N5-A4RN-01A", "TCGA-N5-A4RS-01A", "TCGA-N8-A4PQ-01A",
"TCGA-N9-A4Q4-01A", "TCGA-NA-A4QY-01A", "TCGA-N5-A4RJ-01A",
"TCGA-N5-A4RD-01A", "TCGA-NA-A5I1-01A", "TCGA-NA-A4R0-01A",
"TCGA-NA-A4QV-01A", "TCGA-N7-A4Y0-01A", "TCGA-N5-A4R8-01A",
"TCGA-NF-A4WU-01A", "TCGA-N6-A4VG-01A", "TCGA-N8-A4PI-01A",
"TCGA-N8-A4PO-01A", "TCGA-N8-A4PP-01A", "TCGA-ND-A4WF-01A",
"TCGA-NA-A4QX-01A", "TCGA-N9-A4Q7-01A", "TCGA-N5-A4RO-01A",
"TCGA-N5-A4RT-01A", "TCGA-NF-A4WX-01A", "TCGA-N7-A59B-01A",
"TCGA-ND-A4W6-01A", "TCGA-N8-A56S-01A", "TCGA-ND-A4WC-01A"
)), row.names = c("TCGA-N6-A4V9-01A", "TCGA-QM-A5NM-01A",
"TCGA-N8-A4PM-01A", "TCGA-NG-A4VU-01A", "TCGA-NG-A4VW-01A",
"TCGA-N8-A4PN-01A", "TCGA-N5-A4RA-01A", "TCGA-N6-A4VD-01A",
"TCGA-N7-A4Y8-01A", "TCGA-N6-A4VE-01A", "TCGA-N5-A59F-01A",
"TCGA-N9-A4PZ-01A", "TCGA-N6-A4VF-01A", "TCGA-NF-A5CP-01A",
"TCGA-N6-A4VC-01A", "TCGA-N5-A4RF-01A", "TCGA-N8-A4PL-01A",
"TCGA-ND-A4WA-01A", "TCGA-N5-A4RU-01A", "TCGA-N9-A4Q3-01A",
"TCGA-NA-A4R1-01A", "TCGA-N7-A4Y5-01A", "TCGA-N5-A4RV-01A",
"TCGA-QN-A5NN-01A", "TCGA-N9-A4Q1-01A", "TCGA-N5-A59E-01A",
"TCGA-NA-A4QW-01A", "TCGA-N5-A4RM-01A", "TCGA-NF-A4X2-01A",
"TCGA-N5-A4RN-01A", "TCGA-N5-A4RS-01A", "TCGA-N8-A4PQ-01A",
"TCGA-N9-A4Q4-01A", "TCGA-NA-A4QY-01A", "TCGA-N5-A4RJ-01A",
"TCGA-N5-A4RD-01A", "TCGA-NA-A5I1-01A", "TCGA-NA-A4R0-01A",
"TCGA-NA-A4QV-01A", "TCGA-N7-A4Y0-01A", "TCGA-N5-A4R8-01A",
"TCGA-NF-A4WU-01A", "TCGA-N6-A4VG-01A", "TCGA-N8-A4PI-01A",
"TCGA-N8-A4PO-01A", "TCGA-N8-A4PP-01A", "TCGA-ND-A4WF-01A",
"TCGA-NA-A4QX-01A", "TCGA-N9-A4Q7-01A", "TCGA-N5-A4RO-01A",
"TCGA-N5-A4RT-01A", "TCGA-NF-A4WX-01A", "TCGA-N7-A59B-01A",
"TCGA-ND-A4W6-01A", "TCGA-N8-A56S-01A", "TCGA-ND-A4WC-01A"
), class = "data.frame"), dimLabels = c("sampleNames", "sampleColumns"
), .__classVersion__ = new("Versions", .Data = list(c(1L,
1L, 0L)))), featureData = new("AnnotatedDataFrame", varMetadata = structure(list(
labelDescription = character(0)), row.names = character(0), class = "data.frame"),
data = structure(list(), .Names = character(0), class = "data.frame", row.names = c("5_8S_rRNA",
"5S_rRNA", "7SK", "A1BG", "A1BG-AS1", "A1CF", "A2M", "A2M-AS1",
"A2ML1", "A2ML1-AS1", "A2ML1-AS2", "A2MP1", "A3GALT2", "A4GALT",
"A4GNT", "AA06", "AAAS", "AACS", "AACSP1", "AADAC", "AADACL2",
"AADACL2-AS1", "AADACL3", "AADACL4", "AADACP1", "AADAT",
"AAED1", "AAGAB", "AAK1", "AAMDC", "AAMP", "AANAT", "AAR2",
"AARD", "AARS", "AARS2", "AARSD1", "AARSP1", "AASDH", "AASDHPPT",
"AASS", "AATBC", "AATF", "AATK", "AATK-AS1", "AB015752.3",
"AB019438.66", "AB019440.50", "AB019441.29", "ABALON", "ABAT",
"ABBA01017803.1", "ABC12-47043100G14.2", "ABC12-47964100C23.1",
"ABC12-49244600F4.4", "ABC14-1080714F14.1", "ABC7-42391500H16.2",
"ABC7-42418200C9.1", "ABC7-43041300I9.1", "ABC7-481722F1.1",
"ABCA1", "ABCA10", "ABCA11P", "ABCA12", "ABCA13", "ABCA17P",
"ABCA2", "ABCA3", "ABCA4", "ABCA5", "ABCA6", "ABCA7", "ABCA8",
"ABCA9", "ABCA9-AS1", "ABCB1", "ABCB10", "ABCB10P1", "ABCB10P3",
"ABCB10P4", "ABCB11", "ABCB4", "ABCB5", "ABCB6", "ABCB7",
"ABCB8", "ABCB9", "ABCC1", "ABCC10", "ABCC11", "ABCC12",
"ABCC13", "ABCC2", "ABCC3", "ABCC4", "ABCC5", "ABCC5-AS1",
"ABCC6", "ABCC6P1", "ABCC6P2", "ABCC8", "ABCC9", "ABCD1",
"ABCD1P2", "ABCD1P3", "ABCD1P4", "ABCD1P5", "ABCD2", "ABCD3",
"ABCD4", "ABCE1", "ABCF1", "ABCF2", "ABCF2P1", "ABCF2P2",
"ABCF3", "ABCG1", "ABCG2", "ABCG4", "ABCG5", "ABCG8", "ABHD1",
"ABHD10", "ABHD11", "ABHD11-AS1", "ABHD12", "ABHD12B", "ABHD13",
"ABHD14A", "ABHD14A-ACY1", "ABHD14B", "ABHD15", "ABHD15-AS1",
"ABHD16A", "ABHD16B", "ABHD17A", "ABHD17AP1", "ABHD17AP3",
"ABHD17AP4", "ABHD17AP6", "ABHD17AP9", "ABHD17B", "ABHD17C",
"ABHD2", "ABHD3", "ABHD4", "ABHD5", "ABHD6", "ABHD8", "ABI1",
"ABI2", "ABI3", "ABI3BP", "ABL1", "ABL2", "ABLIM1", "ABLIM2",
"ABLIM3", "ABO", "ABR", "ABRA", "ABRACL", "ABT1", "ABT1P1",
"ABTB1", "ABTB2", "AC000003.1", "AC000029.1", "AC000032.2",
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"AC005703.2", "AC005703.3", "AC005722.4", "AC005725.1", "AC005730.2",
"AC005740.3", "AC005740.4", "AC005740.5", "AC005740.6", "AC005741.2",
"AC005752.10", "AC005753.1", "AC005754.7", "AC005754.8",
"AC005757.6", "AC005757.7", "AC005758.1", "AC005772.2", "AC005775.2",
"AC005776.1", "AC005777.3", "AC005779.2", "AC005780.1", "AC005783.1",
"AC005785.2", "AC005785.5", "AC005786.3", "AC005786.5", "AC005786.6",
"AC005786.7", "AC005789.9", "AC005794.1", "AC005795.1", "AC005796.2",
"AC005808.3", "AC005822.1", "AC005828.1", "AC005838.2", "AC005863.1",
"AC005863.2", "AC005884.1", "AC005901.1", "AC005915.1", "AC005926.1",
"AC005932.1", "AC005943.6", "AC005944.2", "AC005954.3", "AC005954.4",
"AC006000.5", "AC006003.3", "AC006004.1", "AC006007.1", "AC006011.4",
"AC006014.7", "AC006014.8", "AC006019.1", "AC006019.3", "AC006019.4",
"AC006022.4", "AC006026.1", "AC006026.10", "AC006026.12",
"AC006026.13", "AC006026.9", "AC006028.11", "AC006033.22",
"AC006037.2", "AC006041.1", "AC006042.6", "AC006042.7", "AC006042.8",
"AC006050.2", "AC006070.12", "AC006076.1", "AC006077.3",
"AC006077.4", "AC006088.1", "AC006115.1", "AC006115.6", "AC006116.1",
"AC006116.12", "AC006116.13", "AC006116.14", "AC006116.15",
"AC006116.17", "AC006116.19", "AC006116.20", "AC006116.21",
"AC006116.22", "AC006116.24", "AC006116.27", "AC006126.3",
"AC006126.4", "AC006128.2", "AC006129.1", "AC006129.2", "AC006129.3",
"AC006130.1", "AC006130.3", "AC006130.4", "AC006133.3", "AC006133.7",
"AC006145.1", "AC006145.4", "AC006150.1", "AC006150.2", "AC006153.3",
"AC006156.2", "AC006159.4", "AC006159.5", "AC006160.5", "AC006195.2",
"AC006196.1", "AC006227.1", "AC006262.10", "AC006262.11",
"AC006262.4", "AC006262.5", "AC006272.1", "AC006272.2", "AC006273.4",
"AC006273.5", "AC006277.2", "AC006277.3", "AC006288.1", "AC006296.1",
"AC006296.2", "AC006322.1", "AC006326.3", "AC006326.5", "AC006328.1",
"AC006328.2", "AC006328.3", "AC006328.4", "AC006328.9", "AC006335.10",
"AC006335.11", "AC006335.2", "AC006335.6", "AC006355.3",
"AC006366.3", "AC006369.2", "AC006369.3", "AC006371.1", "AC006372.4",
"AC006372.5", "AC006372.6", "AC006373.1", "AC006377.1", "AC006378.2",
"AC006380.1", "AC006380.3", "AC006386.1", "AC006390.4", "AC006427.2",
"AC006445.1", "AC006445.6", "AC006445.7", "AC006449.2", "AC006452.1",
"AC006452.2", "AC006458.3", "AC006460.2", "AC006461.1", "AC006461.2",
"AC006466.5", "AC006478.1", "AC006480.1", "AC006482.1", "AC006483.1",
"AC006483.5", "AC006486.10", "AC006486.9", "AC006487.1",
"AC006499.1", "AC006499.2", "AC006499.3", "AC006499.4", "AC006499.5",
"AC006499.6", "AC006499.7", "AC006499.9", "AC006509.4", "AC006509.7",
"AC006534.1", "AC006534.2", "AC006534.3", "AC006538.1", "AC006538.4",
"AC006538.8", "AC006539.1", "AC006539.2", "AC006539.3", "AC006539.6",
"AC006547.13", "AC006547.14", "AC006547.15", "AC006547.8",
"AC006548.19", "AC006548.26", "AC006548.28", "AC006552.1",
"AC006557.1", "AC006840.1", "AC006925.1", "AC006926.1", "AC006942.4",
"AC006946.12", "AC006946.15", "AC006946.16", "AC006946.17",
"AC006947.1", "AC006960.5", "AC006960.7", "AC006978.6", "AC006987.4",
"AC006987.5", "AC006987.6", "AC006987.7", "AC006988.1", "AC006989.2",
"AC006994.1", "AC006994.2", "AC006994.3", "AC006995.8", "AC007000.10",
"AC007000.11", "AC007000.12", "AC007003.1", "AC007006.1",
"AC007009.1", "AC007009.2", "AC007016.3", "AC007036.4", "AC007036.5",
"AC007036.6", "AC007038.7", "AC007040.1", "AC007040.11",
"AC007040.2", "AC007040.3", "AC007040.6", "AC007040.8", "AC007041.1",
"AC007041.2", "AC007050.1", "AC007050.17", "AC007050.18",
"AC007056.1", "AC007064.22", "AC007064.24", "AC007064.25",
"AC007078.4", "AC007091.1", "AC007096.1", "AC007098.1", "AC007099.1",
"AC007099.2", "AC007106.1", "AC007115.3", "AC007126.1", "AC007128.1",
"AC007131.1", "AC007131.2", "AC007131.3", "AC007136.1", "AC007161.5",
"AC007163.1", "AC007163.10", "AC007163.11", "AC007163.2",
"AC007163.3", "AC007163.6", "AC007163.8", "AC007163.9", "AC007179.1",
"AC007179.2", "AC007182.6", "AC007191.4", "AC007192.6", "AC007193.10",
"AC007193.6", "AC007193.8", "AC007193.9", "AC007204.2", "AC007204.3",
"AC007216.1", "AC007224.1", "AC007228.11", "AC007228.5",
"AC007228.8", "AC007228.9", "AC007229.3", "AC007237.2", "AC007238.1",
"AC007241.1", "AC007242.1")), dimLabels = c("featureNames",
"featureColumns"), .__classVersion__ = new("Versions", .Data = list(
c(1L, 1L, 0L)))), annotation = character(0), protocolData = new("AnnotatedDataFrame",
varMetadata = structure(list(labelDescription = character(0)), row.names = character(0), class = "data.frame"),
data = structure(list(), .Names = character(0), class = "data.frame", row.names = c("TCGA-N6-A4V9-01A",
"TCGA-QM-A5NM-01A", "TCGA-N8-A4PM-01A", "TCGA-NG-A4VU-01A",
"TCGA-NG-A4VW-01A", "TCGA-N8-A4PN-01A", "TCGA-N5-A4RA-01A",
"TCGA-N6-A4VD-01A", "TCGA-N7-A4Y8-01A", "TCGA-N6-A4VE-01A",
"TCGA-N5-A59F-01A", "TCGA-N9-A4PZ-01A", "TCGA-N6-A4VF-01A",
"TCGA-NF-A5CP-01A", "TCGA-N6-A4VC-01A", "TCGA-N5-A4RF-01A",
"TCGA-N8-A4PL-01A", "TCGA-ND-A4WA-01A", "TCGA-N5-A4RU-01A",
"TCGA-N9-A4Q3-01A", "TCGA-NA-A4R1-01A", "TCGA-N7-A4Y5-01A",
"TCGA-N5-A4RV-01A", "TCGA-QN-A5NN-01A", "TCGA-N9-A4Q1-01A",
"TCGA-N5-A59E-01A", "TCGA-NA-A4QW-01A", "TCGA-N5-A4RM-01A",
"TCGA-NF-A4X2-01A", "TCGA-N5-A4RN-01A", "TCGA-N5-A4RS-01A",
"TCGA-N8-A4PQ-01A", "TCGA-N9-A4Q4-01A", "TCGA-NA-A4QY-01A",
"TCGA-N5-A4RJ-01A", "TCGA-N5-A4RD-01A", "TCGA-NA-A5I1-01A",
"TCGA-NA-A4R0-01A", "TCGA-NA-A4QV-01A", "TCGA-N7-A4Y0-01A",
"TCGA-N5-A4R8-01A", "TCGA-NF-A4WU-01A", "TCGA-N6-A4VG-01A",
"TCGA-N8-A4PI-01A", "TCGA-N8-A4PO-01A", "TCGA-N8-A4PP-01A",
"TCGA-ND-A4WF-01A", "TCGA-NA-A4QX-01A", "TCGA-N9-A4Q7-01A",
"TCGA-N5-A4RO-01A", "TCGA-N5-A4RT-01A", "TCGA-NF-A4WX-01A",
"TCGA-N7-A59B-01A", "TCGA-ND-A4W6-01A", "TCGA-N8-A56S-01A",
"TCGA-ND-A4WC-01A")), dimLabels = c("sampleNames", "sampleColumns"
), .__classVersion__ = new("Versions", .Data = list(c(1L,
1L, 0L)))), .__classVersion__ = new("Versions", .Data = list(
c(4L, 1L, 2L), c(2L, 54L, 0L), c(1L, 3L, 0L), c(1L, 0L, 0L
))))
All suggestions shall be helpful.
I have a panel dataset C_sf1 of 922 cities and 155 variables that looks like this:
structure(list(FID = c("AT001C1", "AT002C1"), E_pc_2000 = c(10.2,
10.2), E_pc_2001 = c(10.7, 10.7), E_pc_2002 = c(10.8, 10.8),
E_pc_2003 = c(11.5, 11.5), E_pc_2004 = c(11.4, 11.4), E_pc_2005 = c(11.5,
11.5), geometry = structure(list(structure(list(list(structure(c(16.4197,
16.4176, 16.4156, 16.4065, 16.4038, 16.4018, 16.4015, 16.4011,
16.398, 16.3951, 16.3941, 16.394, 16.3946, 16.3948, 16.3944,
16.3921, 16.3917, 16.3914, 16.3907, 16.3889, 16.3887, 16.3894,
16.3894, 16.3889, 16.3885, 16.3866, 16.3858, 16.3834, 16.3826,
16.3802, 16.3797, 16.3797, 16.3799, 16.3815, 16.3826, 16.3838,
16.3833, 16.3846, 16.3838, 16.3853, 16.3855, 16.3801, 16.3793,
16.3784, 16.378, 16.3773, 16.3763, 16.3755, 16.3751, 16.3752,
16.3783, 16.3782, 16.3775, 16.3763, 16.3723, 16.3694, 16.3691,
16.369, 16.3692, 16.3696, 16.3692, 16.3685, 16.3657, 16.3631,
16.3619, 16.3603, 16.3596, 16.3587, 16.3585, 16.3575, 16.3553,
16.3545, 16.3533, 16.3529, 16.3495, 16.3475, 16.3465, 16.3454,
16.3446, 16.3445, 16.3444, 16.3446, 16.3458, 16.348, 16.346,
16.3445, 16.3437, 16.3408, 16.3348, 16.3306, 16.329, 16.3236,
16.3212, 16.3193, 16.3179, 16.316, 16.3136, 16.3099, 16.3074,
16.3056, 16.3043, 16.3031, 16.3015, 16.2998, 16.2962, 16.2918,
16.2888, 16.2881, 16.2866, 16.2865, 16.2869, 16.2884, 16.2898,
16.2909, 16.2909, 16.291, 16.2902, 16.2856, 16.282, 16.2785,
16.2718, 16.2688, 16.2657, 16.2644, 16.2639, 16.2638, 16.2645,
16.2652, 16.268, 16.269, 16.2695, 16.2694, 16.2689, 16.2664,
16.2655, 16.2654, 16.2654, 16.265, 16.264, 16.2635, 16.2595,
16.2582, 16.2578, 16.2569, 16.2564, 16.2518, 16.2488, 16.2474,
16.2454, 16.2439, 16.2427, 16.2413, 16.2387, 16.2374, 16.2373,
16.2376, 16.2387, 16.2397, 16.2402, 16.2398, 16.2367, 16.2335,
16.2301, 16.2264, 16.2242, 16.2221, 16.2198, 16.2166, 16.2149,
16.2137, 16.2104, 16.2085, 16.2067, 16.2057, 16.2049, 16.2046,
16.2051, 16.2051, 16.2047, 16.2009, 16.1996, 16.1978, 16.1961,
16.1959, 16.196, 16.1957, 16.1951, 16.1951, 16.1958, 16.1969,
16.1982, 16.1984, 16.1978, 16.1967, 16.1955, 16.1938, 16.1901,
16.1848, 16.184, 16.184, 16.1839, 16.1841, 16.1844, 16.1847,
16.1854, 16.1856, 16.1869, 16.1886, 16.1932, 16.1951, 16.1965,
16.1975, 16.1978, 16.1965, 16.1961, 16.1962, 16.1968, 16.1971,
16.1977, 16.1994, 16.2008, 16.2035, 16.2057, 16.2067, 16.2077,
16.2085, 16.2073, 16.2065, 16.2063, 16.2054, 16.2042, 16.2027,
16.2037, 16.2086, 16.2088, 16.207, 16.2055, 16.2048, 16.2048,
16.2047, 16.2045, 16.204, 16.2035, 16.2016, 16.1997, 16.1986,
16.1975, 16.1961, 16.1947, 16.193, 16.1913, 16.1906, 16.19,
16.1894, 16.1885, 16.1875, 16.1867, 16.1858, 16.1851, 16.1844,
16.1842, 16.1845, 16.1848, 16.1856, 16.1864, 16.1888, 16.1911,
16.1936, 16.1946, 16.1956, 16.1961, 16.1967, 16.1969, 16.1971,
16.197, 16.1969, 16.1971, 16.198, 16.1988, 16.1992, 16.1996,
16.2006, 16.2016, 16.2032, 16.2048, 16.2105, 16.2114, 16.2181,
16.2238, 16.2239, 16.2226, 16.2197, 16.2193, 16.2179, 16.2154,
16.2137, 16.211, 16.2104, 16.2104, 16.2106, 16.2113, 16.2125,
16.2134, 16.216, 16.2184, 16.2194, 16.2199, 16.2205, 16.2208,
16.2215, 16.2241, 16.2314, 16.2348, 16.2369, 16.2376, 16.2364,
16.2385, 16.2383, 16.2312, 16.2265, 16.2186, 16.2169, 16.2154,
16.2151, 16.216, 16.2193, 16.2209, 16.2235, 16.2245, 16.2291,
16.2307, 16.2328, 16.2348, 16.2377, 16.2418, 16.2456, 16.2484,
16.2496, 16.25, 16.2512, 16.2522, 16.2527, 16.2534, 16.2559,
16.2569, 16.2594, 16.2599, 16.2668, 16.2717, 16.2729, 16.2818,
16.2834, 16.2846, 16.2915, 16.2975, 16.2985, 16.2985, 16.2986,
16.2987, 16.299, 16.2991, 16.2992, 16.2992, 16.2989, 16.3003,
16.3008, 16.3061, 16.3073, 16.3103, 16.3121, 16.3125, 16.3127,
16.314, 16.3146, 16.3178, 16.3183, 16.3187, 16.3197, 16.3206,
16.3222, 16.325, 16.3266, 16.3319, 16.3324, 16.3348, 16.3362,
16.3387, 16.3425, 16.3429, 16.3443, 16.3453, 16.3463, 16.349,
16.351, 16.3524, 16.3527, 16.3546, 16.3548, 16.3564, 16.3585,
16.3587, 16.3605, 16.3649, 16.3688, 16.3731, 16.3814, 16.3816,
16.3818, 16.3848, 16.3857, 16.3873, 16.3936, 16.3945, 16.3971,
16.398, 16.3994, 16.403, 16.405, 16.4081, 16.4088, 16.4099,
16.4109, 16.4115, 16.4119, 16.4119, 16.4121, 16.4123, 16.4139,
16.4186, 16.4192, 16.4208, 16.421, 16.4215, 16.4224, 16.4232,
16.424, 16.4251, 16.4268, 16.4272, 16.4296, 16.4333, 16.4351,
16.436, 16.4366, 16.4366, 16.4362, 16.4359, 16.4352, 16.4351,
16.4348, 16.4346, 16.4341, 16.4337, 16.4332, 16.4325, 16.4321,
16.4335, 16.4347, 16.437, 16.4437, 16.4457, 16.4492, 16.4517,
16.4536, 16.4551, 16.4645, 16.4658, 16.467, 16.4681, 16.4715,
16.4724, 16.4735, 16.4751, 16.4762, 16.477, 16.4774, 16.4776,
16.4795, 16.4811, 16.4845, 16.4884, 16.4926, 16.4943, 16.4976,
16.4992, 16.5036, 16.5087, 16.5105, 16.5114, 16.5118, 16.5142,
16.5196, 16.5284, 16.5309, 16.5331, 16.5378, 16.5401, 16.5404,
16.5426, 16.5473, 16.5506, 16.5529, 16.5623, 16.5684, 16.575,
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16.578, 16.5782, 16.578, 16.578, 16.5772, 16.5767, 16.5757,
16.5747, 16.5742, 16.5743, 16.5743, 16.5744, 16.5742, 16.5737,
16.5733, 16.5725, 16.5714, 16.5706, 16.5695, 16.5689, 16.5674,
16.5666, 16.5655, 16.5647, 16.5643, 16.5636, 16.563, 16.5628,
16.5623, 16.5616, 16.5609, 16.5605, 16.5602, 16.5597, 16.559,
16.5581, 16.5575, 16.5567, 16.5561, 16.5553, 16.5548, 16.5544,
16.5543, 16.5545, 16.5549, 16.5551, 16.5553, 16.5552, 16.5548,
16.5542, 16.5535, 16.5529, 16.5524, 16.552, 16.5514, 16.5507,
16.5501, 16.5499, 16.5497, 16.5495, 16.5485, 16.5482, 16.5478,
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16.54, 16.54, 16.5401, 16.5401, 16.5401, 16.5399, 16.5396,
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16.545, 16.5456, 16.5461, 16.5467, 16.5469, 16.5465, 16.5467,
16.548, 16.55, 16.5518, 16.5521, 16.552, 16.5502, 16.5488,
16.5484, 16.5482, 16.5479, 16.5474, 16.5463, 16.5445, 16.5423,
16.5417, 16.5414, 16.5424, 16.5419, 16.5416, 16.542, 16.5421,
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16.5192, 16.5128, 16.514, 16.5087, 16.5074, 16.5079, 16.5083,
16.5088, 16.5095, 16.5098, 16.5116, 16.5129, 16.5137, 16.5145,
16.5142, 16.5125, 16.5128, 16.5094, 16.5041, 16.5021, 16.4983,
16.4951, 16.494, 16.4898, 16.484, 16.4813, 16.4809, 16.4809,
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16.4836, 16.4829, 16.4813, 16.4809, 16.4816, 16.4772, 16.4748,
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47.1309, 47.1332, 47.1338, 47.1339, 47.1336, 47.1349), .Dim = c(266L,
2L)))), class = c("XY", "MULTIPOLYGON", "sfg"))), class = c("sfc_MULTIPOLYGON",
"sfc"), precision = 0, bbox = structure(c(xmin = 15.3512,
ymin = 47.012, xmax = 16.5782, ymax = 48.3222), class = "bbox"), crs = structure(list(
input = "WGS 84", wkt = "GEOGCRS[\"WGS 84\",\n DATUM[\"World Geodetic System 1984\",\n ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n LENGTHUNIT[\"metre\",1]]],\n PRIMEM[\"Greenwich\",0,\n ANGLEUNIT[\"degree\",0.0174532925199433]],\n CS[ellipsoidal,2],\n AXIS[\"latitude\",north,\n ORDER[1],\n ANGLEUNIT[\"degree\",0.0174532925199433]],\n AXIS[\"longitude\",east,\n ORDER[2],\n ANGLEUNIT[\"degree\",0.0174532925199433]],\n ID[\"EPSG\",4326]]"), class = "crs"), n_empty = 0L)), row.names = 1:2, class = c("sf",
"data.frame"), sf_column = "geometry", agr = structure(c(FID = NA_integer_,
E_pc_2000 = NA_integer_, E_pc_2001 = NA_integer_, E_pc_2002 = NA_integer_,
E_pc_2003 = NA_integer_, E_pc_2004 = NA_integer_, E_pc_2005 = NA_integer_
), .Label = c("constant", "aggregate", "identity"), class = "factor"))
My task is to calculate the spatial weights matrix and the spatial lags associated with the future dependent variable of my model: log(E).
I built the spatial weights matrix for the wide dataframe.
library(spdep)
coord <- st_coordinates(st_centroid(C_sf1))
nb2.knn1 <- knn2nb(knearneigh(coord,k=1,longlat=T))
all.linkedT <- max(unlist(nbdists(nb2.knn1, coord,longlat=T)))
all.linkedT
nb3 <- dnearneigh(coord, 0, all.linkedT,row.names=C_sf$FDI,longlat=TRUE)
n.nb3 <- nbdists(nb3, coord,longlat=TRUE);n.nb3
n.nb3.gl <- lapply(n.nb3, function(x) 1/x); n.nb3.gl
nb3.listw.gl <- nb2listw(nb3, glist=n.nb3.gl,zero.policy=F)
My problem is that I want to shift to a long format (17k+ observations), keeping the spatial weight matrix. The long format would allow me to have a variable called E (and not E_2001, E_2002, etc.) from which I can calculate the logarithm.
I tried to use the same code for the dataframe C_sf_long (obtained with reshape), but R returns me a spatial weight matrix of 0 distances. The reason is that in the panel dataset, the same coordinates are repeated for each time unit, so distance and neighbour functions only recognize a city as the neighbour of itself.
The code for the spatial lags is the following:
lag.E<- lag.listw(nb2listw(nb3),C_sf1$E)
but, as you can see, C_sf1 does not have the variable E, because it is still in the wide format.
So, I want C_sf1 to be in long format, in order to compute the spatial lags, but still I want to keep the spatial weight matrix computed with C_sf1 in wide format.
I cant run my MCa with a ind.sup, I dont know why. Here's my code :
library(FactoMineR)
data_ACM <- data[80000:81000,]
res.AMC <- MCA(data_ACM, ncp = 6, graph = F, ind.sup = 700)
Error in eigen(crossprod(t(X), t(X)), symmetric = TRUE) :
infinite or missing values in 'x'
dpu(data[1:10,]) returns this :
"SOHDZET", "SOHDZF", "SOHDZFT", "SOKFXD", "SOKFXF", "SOM31BL4B1D",
"SONFZD", "SONFZF", "SOVJZC", "SOVJZE", "SOVJZET", "SOVJZF",
"SOVJZFT", "SOVJZJ", "SOZAAZ", "SP01", "SP21", "SP308", "SP41",
"SP4DWC", "SP61", "SP81", "SR11", "SR1F0H", "SR1G0H", "SR1H06",
"SR1J0H", "SR1J22", "SR1J32", "SR1L22", "SR1L32", "SR1M22",
"SR270E", "SR31", "SR4DWC", "SR51", "SR81", "SR8J32", "SR8L32",
"SREU0H", "SRH1ELC", "SRH1ELCD", "SS11", "SS31", "SS51",
"SS55", "SS71", "SS91", "ST12", "ST72", "ST785E", "STA",
"STADRX01AG", "STADRX01SG", "STAFNX01AG", "STAFNX01SG", "STAGZX01SG",
"STAHLX01AG", "STAHLX01SG", "STAHUX01AG", "STAHUX01SG", "STAV",
"STB", "STBV", "STC", "STCDCA", "STCV", "SU31", "SU35", "SU55",
"SU55A", "SV10", "SV10EM", "SV1V12", "SV31", "SVA", "SVAV",
"SW11", "SW31", "SW51", "SW52X400", "SW52X700", "SW53K2",
"SW53K200", "SW53K9", "SW53K900", "SW5441", "SW5449", "SW58K2",
"SW58K200", "SW58K3", "SW58K7", "SW58K9", "SW58K900", "SW5952",
"SW5953", "SW5957", "SW5959", "SW59G200", "SW59G300", "SW59G700",
"SW59G900", "SW6142", "SW6149", "SW61P2", "SW61P200", "SW61P9",
"SW61P900", "SW61S200", "SW61S900", "SW6542", "SW6549", "SW65P2",
"SW65P200", "SW65P9", "SW65P900", "SW65S200", "SW65S900",
"SW6942", "SW71", "SW7140", "SW7141", "SW7144", "SW7146",
"SW7362", "SW743200", "SW743900", "SW7462", "SW7469", "SW793200",
"SW793700", "SW793900", "SW7962", "SW7964", "SW7967", "SW7969",
"SW8042", "SW8142", "SX11", "SX31", "SX71", "SXBHW6", "SXBHY6",
"SXHMP6", "SXHMZ6", "SXHNZ6", "SXHNZT", "SY11", "SY31", "SY33",
"SY34", "SY53", "SY54", "SY71", "SYN1E", "SZ58K3", "SZ58K7",
"SZ5953", "SZ5957", "SZ59G300", "SZ59G700", "SZ7144", "SZ7146",
"SZ793700", "SZ7964", "SZ7967", "SZ799400", "T11", "T12",
"T1X000", "T1X004", "T1X300", "T1X305", "T1X805", "T1XE05",
"T1XF05", "T27WWT270", "T27WWT271", "T27ZRT270", "T27ZRT271",
"T2DG1", "T2DH1", "T2DH2", "T2DJ1", "T2DJ2", "T2W300", "T2W305",
"T2W800", "T2W805", "T2WE05", "T2WF05", "T31DD03", "T31EE01",
"T32AA", "T32AA01", "T32AA02", "T32AA03", "T32AA04", "T32BB",
"T32BB01", "T32BB02", "T32BB05", "T32BB06", "T32CC", "T32CC01",
"T32CC02", "T32CC03", "T32CC04", "T32DD", "T32EE", "T3408",
"T3419", "T3435", "T3448", "T3467", "T3DB1", "T3DE1", "T3DE2",
"T3LA1", "T3LA2", "T3X000", "T3X100", "T3X300", "T3X305",
"T3X800", "T3X805", "T3XE05", "T3XF05", "T4BBD", "T4BBG",
"T4BCD", "T4BCE", "T4BCF", "T4DCA", "T4DCD", "T4DCT", "T4DCX",
"T4DCY", "T4X000", "T4X100", "T4X305", "T4X405", "T4X800",
"T4X805", "T4XE05", "T4XF05", "T4XG05", "T5408", "T5419",
"T5435", "T5448", "T5808", "T5819", "T5835", "T5848", "T5X000",
"T5X200", "T5X205", "T5X300", "T5X305", "T5X405", "T5X800",
"T5X805", "T5XF05", "T5XG05", "T6EB1", "T6EB4", "T6EB6",
"T6EB7", "T6ED5", "T6EE2", "T6MB1", "T6MB6", "T6MB7", "T6MC1",
"T6MD5", "T72", "T7JA1", "T7W200", "T7W205", "T7W300", "T7W405",
"T7WG05", "T824833", "T82W433", "T82W434", "T8344", "T8358A",
"T8378A", "T8380", "T8390A", "T8394", "T83T4", "T844433",
"T844434", "T844533", "T844544", "T845034", "T845043", "T845044",
"T8458", "T847433", "T847443", "T847833", "T8478A", "T8480",
"T8488", "T849033", "T849043", "T8490A", "T8494", "T8494B43",
"T8494B44", "T84T4", "T84T443", "T84T444", "T84T543", "T84T544",
"T84T833", "T84T843", "T855", "T857", "T85T4", "T85T8", "T9408",
"T9419", "T9435", "T9448", "T9508", "T9519", "T9535", "T9548",
"T9808", "T9819", "T9835", "T9848", "T9PG2", "TA", "TA12",
"TA12F2", "TA12J5", "TA12L2", "TA12L5", "TA13008", "TA13035",
"TA13068", "TA1308", "TA1335", "TA1368", "TA14", "TA16008",
"TA16035", "TA16068", "TA1608", "TA1635", "TA1668", "TA2",
"TA22", "TA23H", "TA23MB", "TA23MQG", "TA2422", "TA2422A",
"TA28MB", "TA32408", "TA32419", "TA32468", "TA40", "TA40AKS",
"TA40AMS", "TA43035", "TA43068", "TA4308", "TA4319", "TA4335",
"TA4368", "TA4408", "TA4419", "TA4435", "TA4468", "TA60AMS",
"TA60BLMB", "TA72L2", "TA72L5", "TA8408", "TA8419", "TA8435",
"TA8468", "TA9408", "TA9419", "TA9435", "TA9468", "TA94E08",
"TA94E19", "TA94E35", "TA94E68", "TAA408", "TAA419", "TAA435",
"TAA468", "TAA8408", "TAA9408", "TAA94E08", "TAAA408", "TAAP11",
"TAAW10", "TABCAS", "TABCDS", "TAC408", "TAC419", "TAC468",
"TAD308", "TAD3G08", "TAF408", "TAF419", "TAF435", "TAF468",
"TAJ3G008", "TAJ3G08", "TAK1367", "TAK4367", "TAKD467", "TAKU467",
"TAKV467", "TAM3019", "TAM3035", "TAM3068", "TAM319", "TAM335",
"TAM368", "TAM6019", "TAM6035", "TAM6068", "TAM619", "TAM635",
"TAM668", "TAN308", "TAN419", "TAN435", "TAN468", "TAN619",
"TAN635", "TAN668", "TANT30", "TAS335", "TAS3G08", "TATB",
"TATF", "TATHA", "TATHB", "TATJA", "TATJB", "TAU8470", "TAUA470",
"TAW4035", "TAW4068", "TAW6035", "TAW6068", "TAX100", "TAX300",
"TAZ100", "TAZ300", "TB", "TB2", "TB2412", "TB367", "TBAABS",
"TBABBS", "TBAP12", "TBAV10", "TBBCBS", "TBC1NPLY", "TBC1NRLY",
"TBCAAS", "TBCADS", "TBCAES", "TBCBAS", "TBCBDS", "TBCBES",
"TBDCAS", "TBDCDS", "TBDCES", "TBE1OVM", "TBE1OVN", "TBE2PZN",
"TBE2UZN", "TBE4TWN", "TBE4TYN", "TBE4TYNC", "TBE5IWN", "TBE5IWNC",
"TBE5TWM", "TBE5TWN", "TBE5TWNC", "TBE5TYN", "TBGMSLV", "TBGMTLVI",
"TBGNSLV", "TBGNSLVC", "TBGNSLY", "TBGNTLVI", "TBIF5P11M51AZ1",
"TBIF5P21M52CZ1", "TBNT30", "TBUR20", "TBX200", "TBX205",
"TBX300", "TBX305", "TBX405", "TBXE05", "TBXG05", "TBZ200",
"TBZ205", "TBZ300", "TBZ305", "TBZ405", "TBZ800", "TBZE05",
"TBZG05", "TC2", "TCAP11", "TCAP12", "TCAV10", "TCTG05",
"TCY405", "TCYG05", "TD2", "TD367", "TD4HSJ", "TD4HTH", "TDABKS",
"TDABTS", "TDABUS", "TDAP11", "TDAV10", "TDAW10", "TDAW10M",
"TDAW10U", "TDFBAS", "TDGCKS", "TDGCTS", "TDGCUS", "TDHCAS",
"TDSY61", "TDUHZJ", "TDX8ZA", "TDXFVJ", "TE2", "TE51", "TEABKL",
"TEABTL", "TEAP12", "TEBBAL", "TECCKL", "TECCTL", "TEDCAL",
"TENT30", "TESY61", "TF08II51", "TF08J551", "TF08M351", "TF35II51",
"TF35J551", "TF35M351", "TF408", "TF419", "TF435", "TF448",
"TF48II51", "TF48J551", "TF48M351", "TFABAL", "TFABKL", "TFABSL",
"TFABTL", "TFABUL", "TFAP12", "TFAY10", "TFBCAL", "TFBCKL",
"TFBCSL", "TFBCTL", "TFBCUL", "TG08GCA1", "TG08GCS1", "TG08ICA1",
"TG08ICS1", "TG08M351", "TG08M3A1", "TG2", "TG35GCA1", "TG35GCS1",
"TG35ICA1", "TG35ICS1", "TG35M351", "TG35M3A1", "TG408",
"TG419", "TG435", "TG448", "TG48GCA1", "TG48GCS1", "TG48ICA1",
"TG48ICS1", "TG48M351", "TG48M3A1", "TGABAL", "TGABKL", "TGABSL",
"TGABTE", "TGABTL", "TGBN", "TGCCAL", "TGCCKL", "TGCCSL",
"TGCCTE", "TGCCTL", "TGFCAL", "TGFCTL", "TGFCUL", "TH308",
"TH348", "TJAC9S", "TJAN91", "TJAN9S", "TJBBAL", "TJBBTE",
"TJBBTL", "TJBBUL", "TJBCAL", "TJBCTE", "TJBCTL", "TJBCUL",
"TK308", "TK348", "TKACAL", "TKACTE", "TKACTL", "TKACUL",
"TLB310", "TLEF5D14A63ZZ1", "TLEF5D24M62ZZ1", "TLEF5D31M62ZZ1",
"TLEF5D51M66ZZ1", "TLEF5D61D7", "TLEF5P31M64ZZ1", "TLEF5P41D71ZZ1",
"TLEF5P44D71ZZ1", "TM4BUL", "TM4DBC", "TP260", "TPAD", "TPADJ",
"TPAE", "TPAEJ", "TQF8D62M53AZ1", "TQF8D82A51AZ1", "TR160",
"TR160T", "TR467", "TR567", "TR7969", "TR9145", "TR9245",
"TS08ICS1", "TS08MCS1", "TS31", "TS48ICS1", "TS6142", "TS6149",
"TS61E2", "TS61P2", "TS61P200", "TS61P9", "TS61P900", "TS61S200",
"TS61S900", "TS65E2", "TS65P2", "TS65P200", "TS65P9", "TS65P900",
"TS65S200", "TS65S900", "TS7202", "TS7206", "TS7432", "TS743200",
"TS7439", "TS743900", "TS7462", "TS7469", "TS7932", "TS793200",
"TS793900", "TS7962", "TS7969", "TS90C5", "TS90K5", "TS9145",
"TS91J500", "TS91K5", "TS91K500", "TS9245", "TS92K5", "TS92K500",
"TS94K2", "TS94K5", "TS97C2", "TS97C5", "TT132", "TT408",
"TT419", "TT435", "TT448", "TT508", "TT519", "TT535", "TT548",
"TT808", "TT819", "TT835", "TT848", "TTFN44", "TU308", "TU319",
"TU348", "TU408", "TU419", "TU435", "TU448", "TU467", "TU508",
"TU519", "TU535", "TU548", "TU567", "TU808", "TU819", "TU835",
"TU848", "TVUF", "TVUR20", "TVUR20U", "TW308", "TW319", "TW348",
"TW3T08", "TW3T19", "TW3T35", "TW3T48", "TWUR20", "TX31",
"TX71", "TY260", "TY260T", "U11", "U11T", "U2", "U2MAC",
"U2NAC", "U6UA", "U6UB", "U6UC", "U6UE", "U6UF", "U6UG",
"U6UJ", "U6UJT", "U6UK", "U6UR", "U6UR9", "U6UT", "U6UT9",
"U6UU", "U6UU9", "U6UW", "U6UWT", "U857", "U858", "U859533",
"U85T", "U9C1G6", "U9C1GH", "U9C1K6", "U9C1KH", "U9C2G6",
"U9C2K6", "U9CYG6", "U9CYK6", "U9VCK5", "UA5FV81P", "UA5FWC",
"UA5FWC1", "UA5FXH1P", "UA6FYC", "UA71", "UA9HR8", "UA9HR82PS",
"UA9HR8P", "UA9HR8PS", "UA9HZC", "UA9HZC1", "UA9HZH1P", "UA9HZHP",
"UARFJHP", "UARHB8P", "UARHE8", "UARHHA", "UARHJH1P", "UARHJHP",
"UARHRJ", "UB11", "UB31", "UB51", "UB53", "UB71", "UB91",
"UC11", "UC31", "UC71", "UC91", "UD11", "UD1Y11", "UD51",
"UD5FS0", "UD5FV81P", "UD5FWC", "UD5FXH1P", "UD5FXHP", "UD6FYC",
"UD71", "UD91", "UD9HR8", "UD9HR82PS", "UD9HR8P", "UD9HR8PS",
"UD9HZC", "UD9HZC1", "UD9HZC1CU1", "UD9HZH1P", "UD9HZH1PCU1",
"UD9HZHP", "UDC1G6", "UDC1K6", "UDC2G6", "UDC2K6", "UDC3G6",
"UDC3K6", "UDCAG5", "UDCAK5", "UDCCG5", "UDCCK5", "UDCEG5",
"UDCEK5", "UDCGG5", "UDCGK5", "UDCJG5", "UDCJK5", "UDCMG5",
"UDCMK5", "UDCNG5", "UDCNK5", "UDCSG6", "UDCSK6", "UDCUG5",
"UDCUG6", "UDCUK5", "UDCUK6", "UDCVG5", "UDCVK5", "UDCYG6",
"UDCYK6", "UDRFJHP", "UDRHB8P", "UDRHE8", "UDRHHA", "UDRHJH1P",
"UDRHJHP", "UDRHRJ", "UE11", "UE31", "UE51", "UE9HR8", "UE9HR8P",
"UE9HZC1", "UE9HZH1P", "UF11", "UF1Y32", "UF31", "UF51",
"UF8U52", "UF8UL2", "UF91", "UG31", "UG51", "UH11", "UH31",
"UH51", "UK11", "UK31", "UK51", "UL91", "UM11", "UM51", "UM71",
"UM91", "UMC7D2", "UN10", "UN13", "UN1A22", "UN71", "UN8B42",
"UN8D32", "UN8F42", "UN91", "UP11", "UR11", "UR31", "UR51",
"UR91", "URMD21", "US31", "US91", "USD135", "USD145", "USD1F5",
"USD1K5", "USD1W5", "USDBL5", "USDUK5", "USDUW5", "UT31",
"UT71", "UU11", "UU51", "UU6M", "UU71", "UU91", "UV51", "UV71",
"UV91", "UW71", "UW91", "UX11", "UX31", "UX51", "UX71", "UX91",
"UXC1", "UY11", "UY31", "UY51", "UY71", "UY91", "UZ10BC",
"UZ68BC", "UZA2BC", "UZA8BC", "UZBABD", "UZJ100LGNAEKW",
"V1DKS", "V1DVS", "V1JKS", "V1JVS", "V23CGRHE", "V23WGNXE",
"V23WGNXES", "V24WGNXF", "V24WGNXFS", "V24WNDF", "V24WNDFS",
"V24WNHF", "V24WNHFS", "V25WGNX", "V25WGRX", "V26WGNX", "V26WGNXS",
"V2DAC", "V2JAC", "V2W200", "V2W300", "V36MM01", "V36NN01",
"V36PP01", "V36RR01", "V36SS01", "V36TT01", "V37CC03", "V37DD03",
"V37EE01", "V37FF01", "V37GG01", "V3X208", "V3X308", "V3XG08",
"V43WGRXE", "V44WGNXF", "V44WGNXFS", "V44WNHF", "V45WGRX",
"V46WGNX", "V46WGRX", "V46WNH", "V46WNHS", "V4X208", "V4X300",
"V4X308", "V4X408", "V4XE08", "V4XG08", "V4Y208", "V4Y408",
"V4YG08", "V521L22HCR167", "V521SLLDA5865", "V521SLLDAR165",
"V5W208", "V5W408", "V5WG08", "V6419", "V6435", "V64WMN",
"V7419", "V7435", "V9X300", "VA", "VA51", "VA71", "VA91",
"VABHSH", "VABHVB", "VABHXH", "VAJ", "VAJI", "VAJIA", "VAL",
"VAM", "VAN", "VANA", "VB11", "VB31", "VB71", "VB84035",
"VB84035M", "VB84069", "VB84069M", "VB8435", "VB8469", "VB86035",
"VB86035M", "VB86069", "VB86069M", "VB8635", "VB8669", "VB94035",
"VB94069", "VB9435", "VB9469", "VB96035", "VB96069", "VB9635",
"VB9669", "VBBHSH", "VBBHVB", "VBBHXH", "VBW4035", "VBW4069",
"VBW435", "VBW469", "VBW6035", "VBW6069", "VBW635", "VBW669",
"VBY4035", "VBY4069", "VBY6035", "VBY6069", "VBZ4035", "VBZ4069",
"VBZ6035", "VBZ6069", "VBZ6E035", "VBZ6E069", "VC11", "VC31",
"VC419", "VC435", "VC51", "VC619", "VC635", "VC91", "VCA419",
"VCA435", "VCA619", "VCA635", "VCDZ", "VCM36V", "VD0419",
"VD0619", "VD11", "VD21SDDAAN735", "VD21SEEAAN735", "VD31",
"VD419", "VD435", "VD51", "VD619", "VD635", "VD71", "VD91",
"VDGCS", "VDGDS", "VDL", "VDM", "VDNS", "VDPA", "VDPAF",
"XWYWMX", "XWYWTP", "XWYWTX", "XWYWXT", "XWZLUR", "XX11",
"XX31", "XX51", "XY11", "XY31", "XY51", "Y3AA", "Y3AB", "Y3AC",
"Y3ACA", "Y3AE", "Y3AF", "Y3AG", "Y3AH", "Y3AHA", "Y3AL",
"Y3AP", "Y3AR", "Y3AS", "Y3AT", "Y3AW", "Y3AX", "Y3AY", "Y3AZ",
"Y3CN", "Y4CW", "Y4CZ", "Y4GB", "Y4GG", "Y4GM", "Y4GN", "Y4GR",
"Y4GU", "Y4GV", "Y4GW", "Y4GX", "Y4GY", "Y4GZ", "Y4MZ", "Y4NW",
"Y4NX", "Y4NY", "Y4NZ", "Y4RM", "Y4RN", "Y4TD", "Y4TN", "Y4TR",
"Y4TS", "Y4TT", "Y4TU", "Y4TV", "Y4TX", "Y4WC", "Y4WG", "Y4WH",
"Y4WK", "Y51AA01", "Y51BB0", "Y51BB01", "Y51CC01", "Y51DD01",
"Y51FF0", "Y51HEE0", "Y51HEE01", "Y910", "YA01", "YA1MFA",
"YA1MFB", "YA1MRA", "YA2MFA", "YA2MFB", "YA2MRA", "YA3MFA",
"YA3MFB", "YA3MRA", "YA41", "YA61", "YA81", "YA9S", "YAAMFA",
"YAAMFAAX", "YAAMFB", "YAAMFBBX", "YAAMPA", "YAAMRA", "YABMFA",
"YABMFAAX", "YABMFB", "YABMFBBX", "YABMPA", "YABMRA", "YASMFA",
"YASMFB", "YASMPA", "YASMRA", "YATMFA", "YATMFB", "YATMPA",
"YATMRA", "YAUMPA", "YAUMRA", "YB01", "YB1MFA", "YB1MFB",
"YB1MFC", "YB1MRB", "YB2MFA", "YB2MFB", "YB2MFC", "YB2MRB",
"YB2R", "YB3MFA", "YB3MFB", "YB3MFC", "YB3MRB", "YB4R", "YB61",
"YB81", "YBAMAA", "YBAMAAAX", "YBAMAB", "YBAMABAX", "YBAMAC",
"YBAMACAX", "YBAMADAX", "YBAMCB", "YBAMCBAX", "YBAMCC", "YBAMCCAX",
"YBAMDBAX", "YBAMDCAX", "YBAMFA", "YBAMFAAX", "YBAMFB", "YBAMFBAX",
"YBAMFBBX", "YBAMFC", "YBAMFCBX", "YBAMFCCX", "YBAMGBAX",
"YBAMHBAX", "YBAMHCAX", "YBAMRB", "YBB5D11M6", "YBB5P11M6",
"YBB5P21M5", "YBB5P31M5", "YBB5P51A4", "YBBMAA", "YBBMAB",
"YBBMABAX", "YBBMAC", "YBBMACAX", "YBBMADAX", "YBBMCB", "YBBMCBAX",
"YBBMCC", "YBBMCCAX", "YBBMDBAX", "YBBMDC", "YBBMDCAX", "YBBMFA",
"YBBMFAAX", "YBBMFB", "YBBMFBAX", "YBBMFBBX", "YBBMFC", "YBBMFCBX",
"YBBMFCCX", "YBBMGBAX", "YBBMHBAX", "YBBMHCAX", "YBBMPB",
"YBBMRB", "YBC5D31M6", "YBCP11M6", "YBCP41M6", "YBDMAB",
"YBDMAC", "YBDMADAX", "YBDMDC", "YBDMDCAX", "YBDMFA", "YBDMFB",
"YBDMFBAX", "YBDMFBBX", "YBDMFC", "YBDMFCBX", "YBDMFCCX",
"YBDMHCAX", "YBDMPB", "YBPMFB", "YBPMFC", "YBPMPB", "YBSMFA",
"YBSMFB", "YBSMFC", "YBSMRB", "YBTMFA", "YBTMFB", "YBTMFC",
"YBTMPB", "YBTMRB", "YBUMFA", "YBUMFB", "YBUMFC", "YBUMPB",
"YBUMRB", "YC1MFA", "YC1MFB", "YC1MFC", "YC21", "YC2MFA",
"YC2MFB", "YC2MFC", "YC3MFB", "YC3MFC", "YC41", "YC81", "YCAMAB",
"YCAMABAX", "YCAMAC", "YCAMACAX", "YCAMACAXL", "YCAMAD",
"YCAMADAX", "YCAMCB", "YCAMCBAX", "YCAMCC", "YCAMCCAX", "YCAMCCAXL",
"YCAMDB", "YCAMDBAX", "YCAMDC", "YCAMDCAX", "YCAMFB", "YCAMFBAX",
"YCAMFBBX", "YCAMFC", "YCAMFCBX", "YCAMFCCX", "YCAMGCAX",
"YCAMHC", "YCAMHCAX", "YCBMAB", "YCBMABAX", "YCBMAC", "YCBMACAX",
"YCBMACAXL", "YCBMAD", "YCBMADAX", "YCBMCB", "YCBMCBAX",
"YCBMCC", "YCBMCCAX", "YCBMCCAXL", "YCBMDB", "YCBMDBAX",
"YCBMDC", "YCBMDCAX", "YCBMFB", "YCBMFBAX", "YCBMFBBX", "YCBMFC",
"YCBMFCBX", "YCBMFCCX", "YCBMGC", "YCBMGCAX", "YCBMHC", "YCBMHCAX",
"YCDMAB", "YCDMABAX", "YCDMAC", "YCDMACAX", "YCDMACAXL",
"YCDMAD", "YCDMADAX", "YCDMCB", "YCDMCC", "YCDMCCAX", "YCDMCCAXL",
"YCDMDB", "YCDMDC", "YCDMDCAX", "YCDMFB", "YCDMFBAX", "YCDMFBBX",
"YCDMFC", "YCDMFCBX", "YCDMFCCX", "YCPMFB", "YCPMFC", "YCSMFB",
"YCSMFC", "YCTMFA", "YCTMFB", "YCTMFC", "YCUMFB", "YCUMFC",
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Thank you for your kind help
After merging two unequal time series I want to fill the blanks with a custom function. Lets say my Series1 is daily data and Series2 is monthly data. So now Series1 has for example 30 data points for one month and Series2 only one. If I make a left join Series 2 has 29 NAs which I don't like. Ideally I would like a fill function so that Series 2 takes always the previous value to fill these 29 days.
So for example if the 31. of January has a value of 10 and the 28th of February a value of 15, February 1-27. should have a value of 10 as well. Of course in the beginning this doesn't work (since the first row is probably also a NA), so the first row should take the value of the first row containing a value at all.
At the moment I have this, but still, all NAs are present:
Test<-merge.xts(Series1, Series2, join="left", fill=function(x) x[index(x)-1,])
Series1:
structure(c(1.51762156049755, 1.52103159497526, 1.51401262063846,
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"zoo"), index = structure(c(978307200, 978393600, 978480000,
978566400, 978652800, 978912000, 978998400, 979084800, 979171200,
979257600, 979516800, 979603200, 979689600, 979776000, 979862400,
980121600, 980208000, 980294400, 980380800, 980467200, 980726400,
980812800, 980899200, 980985600, 981072000, 981331200, 981417600,
981504000, 981590400, 981676800, 981936000, 982022400, 982108800,
982195200, 982281600, 982540800, 982627200, 982713600, 982800000,
982886400, 983145600, 983232000, 983318400, 983404800, 983491200,
983750400, 983836800, 983923200, 984009600, 984096000, 984355200,
984441600, 984528000, 984614400, 984700800, 984960000, 985046400,
985132800, 985219200, 985305600, 985564800, 985651200, 985737600,
985824000, 985910400, 986169600, 986256000, 986342400, 986428800,
986515200, 986774400, 986860800, 986947200, 987033600, 987120000,
987379200, 987465600, 987552000, 987638400, 987724800, 987984000,
988070400, 988156800, 988243200, 988329600, 988588800, 988675200,
988761600, 988848000, 988934400, 989193600, 989280000, 989366400,
989452800, 989539200, 989798400, 989884800, 989971200, 990057600,
990144000, 990403200, 990489600, 990576000, 990662400, 990748800,
991008000, 991094400, 991180800, 991267200, 991353600, 991612800,
991699200, 991785600, 991872000, 991958400, 992217600, 992304000,
992390400, 992476800, 992563200, 992822400, 992908800, 992995200,
993081600, 993168000, 993427200, 993513600, 993600000, 993686400,
993772800, 994032000, 994118400, 994204800, 994291200, 994377600,
994636800, 994723200, 994809600, 994896000, 994982400, 995241600,
995328000, 995414400, 995500800, 995587200, 995846400, 995932800,
996019200, 996105600, 996192000, 996451200, 996537600, 996624000,
996710400, 996796800, 997056000, 997142400, 997228800, 997315200,
997401600, 997660800, 997747200, 997833600, 997920000, 998006400,
998265600, 998352000, 998438400, 998524800, 998611200, 998870400,
998956800, 999043200, 999129600, 999216000, 999475200, 999561600,
999648000, 999734400, 999820800, 1000080000, 1000166400, 1000252800,
1000339200, 1000425600, 1000684800, 1000771200, 1000857600, 1000944000,
1001030400, 1001289600, 1001376000, 1001462400, 1001548800, 1001635200,
1001894400, 1001980800, 1002067200, 1002153600, 1002240000, 1002499200,
1002585600, 1002672000, 1002758400, 1002844800, 1003104000, 1003190400,
1003276800, 1003363200, 1003449600, 1003708800, 1003795200, 1003881600,
1003968000, 1004054400, 1004313600, 1004400000, 1004486400, 1004572800,
1004659200, 1004918400, 1005004800, 1005091200, 1005177600, 1005264000,
1005523200, 1005609600, 1005696000, 1005782400, 1005868800, 1006128000,
1006214400, 1006300800, 1006387200, 1006473600, 1006732800, 1006819200,
1006905600, 1006992000, 1007078400, 1007337600, 1007424000, 1007510400,
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1008288000, 1008547200, 1008633600, 1008720000, 1008806400, 1008892800,
1009152000, 1009238400, 1009324800, 1009411200, 1009497600, 1009756800,
1009843200, 1009929600, 1010016000, 1010102400, 1010361600, 1010448000,
1010534400, 1010620800, 1010707200, 1010966400, 1011052800, 1011139200,
1011225600, 1011312000, 1011571200, 1011657600, 1011744000, 1011830400,
1011916800, 1012176000, 1012262400, 1012348800, 1012435200, 1012521600,
1012780800, 1012867200, 1012953600, 1013040000, 1013126400, 1013385600,
1013472000, 1013558400, 1013644800, 1013731200, 1013990400, 1014076800,
1014163200, 1014249600, 1014336000, 1014595200, 1014681600, 1014768000,
1014854400, 1014940800, 1015200000, 1015286400, 1015372800, 1015459200,
1015545600, 1015804800, 1015891200, 1015977600, 1016064000, 1016150400,
1016409600, 1016496000, 1016582400, 1016668800, 1016755200, 1017014400,
1017100800, 1017187200, 1017273600, 1017360000, 1017619200, 1017705600,
1017792000, 1017878400, 1017964800, 1018224000, 1018310400, 1018396800,
1018483200, 1018569600, 1018828800, 1018915200, 1019001600, 1019088000,
1019174400, 1019433600, 1019520000, 1019606400, 1019692800, 1019779200,
1020038400, 1020124800, 1020211200, 1020297600, 1020384000, 1020643200,
1020729600, 1020816000, 1020902400, 1020988800, 1021248000, 1021334400,
1021420800, 1021507200, 1021593600, 1021852800, 1021939200, 1022025600,
1022112000, 1022198400, 1022457600, 1022544000, 1022630400, 1022716800,
1022803200, 1023062400, 1023148800, 1023235200, 1023321600, 1023408000,
1023667200, 1023753600, 1023840000, 1023926400, 1024012800, 1024272000,
1024358400, 1024444800, 1024531200, 1024617600, 1024876800, 1024963200,
1025049600, 1025136000, 1025222400, 1025481600, 1025568000, 1025654400,
1025740800, 1025827200, 1026086400, 1026172800, 1026259200, 1026345600,
1026432000, 1026691200, 1026777600, 1026864000, 1026950400, 1027036800,
1027296000, 1027382400, 1027468800, 1027555200, 1027641600, 1027900800,
1027987200, 1028073600, 1028160000, 1028246400, 1028505600, 1028592000,
1028678400, 1028764800, 1028851200, 1029110400, 1029196800, 1029283200,
1029369600, 1029456000, 1029715200, 1029801600, 1029888000, 1029974400,
1030060800, 1030320000, 1030406400, 1030492800, 1030579200, 1030665600,
1030924800, 1031011200, 1031097600, 1031184000, 1031270400, 1031529600,
1031616000, 1031702400, 1031788800, 1031875200, 1032134400, 1032220800,
1032307200, 1032393600, 1032480000, 1032739200, 1032825600, 1032912000,
1032998400, 1033084800, 1033344000, 1033430400, 1033516800, 1033603200,
1033689600, 1033948800, 1034035200, 1034121600, 1034208000, 1034294400,
1034553600, 1034640000, 1034726400, 1034812800, 1034899200, 1035158400,
1035244800, 1035331200, 1035417600, 1035504000, 1035763200, 1035849600,
1035936000, 1036022400, 1036108800, 1036368000, 1036454400, 1036540800,
1036627200, 1036713600, 1036972800, 1037059200, 1037145600, 1037232000,
1037318400, 1037577600, 1037664000, 1037750400, 1037836800, 1037923200,
1038182400, 1038268800, 1038355200, 1038441600, 1038528000, 1038787200,
1038873600, 1038960000, 1039046400, 1039132800, 1039392000, 1039478400,
1039564800, 1039651200, 1039737600, 1039996800, 1040083200, 1040169600,
1040256000, 1040342400, 1040601600, 1040688000, 1040774400, 1040860800,
1040947200, 1041206400, 1041292800), tzone = "UTC", tclass = "Date"), .Dim = c(522L,
1L), .Dimnames = list(NULL, "Series1"))
Series2:
structure(c(100, 100.32, 100.57, 100.82, 100.98, 101.01, 101.16,
101.3, 101.75, 102.07, 102.12, 102.3, 102.44, 102.59, 102.62,
102.74, 102.84, 103.09, 103.25, 103.31, 103.35, 103.48, 103.6,
103.72, 103.84, 103.96, 104.1, 104.35, 104.52, 104.69, 104.82,
104.96, 104.9, 105.03, 105.08, 105.27, 105.46, 105.55, 105.78,
105.94, 106.11, 106.36, 106.52, 106.6, 106.7, 106.92, 107.1,
107.27, 107.39, 107.41, 107.54, 107.72, 107.96, 108.13, 108.3,
108.43, 108.56, 108.68, 108.77), .indexCLASS = "Date", tclass = "Date", .indexTZ = "UTC", tzone = "UTC", class = c("xts",
"zoo"), index = structure(c(1010102400, 1010707200, 1011312000,
1011916800, 1012435200, 1012521600, 1013126400, 1013731200, 1014336000,
1014854400, 1014940800, 1015545600, 1016150400, 1016755200, 1017360000,
1017964800, 1018569600, 1019174400, 1019779200, 1020124800, 1020384000,
1020988800, 1021593600, 1022198400, 1022803200, 1023408000, 1024012800,
1024617600, 1025222400, 1025827200, 1026432000, 1027036800, 1027641600,
1028073600, 1028246400, 1028851200, 1029456000, 1030060800, 1030665600,
1031270400, 1031875200, 1032480000, 1033084800, 1033344000, 1033689600,
1034294400, 1034899200, 1035504000, 1036022400, 1036108800, 1036713600,
1037318400, 1037923200, 1038528000, 1039132800, 1039737600, 1040342400,
1040947200, 1041292800), tzone = "UTC", tclass = "Date"), .Dim = c(59L,
1L), .Dimnames = list(NULL, "Series2"))
na.locf() will do the job:
Test<-merge.xts(Series1, Series2, join="left", fill=na.locf())
This function "Last Observation Carried Forward" fills the NA with the last knowne value.
Hope this helps people landing here, years after you asked the question.
How can I calculate the rolling geometric mean of the following xts time-series called RET :
RET <- structure(c(0.235313703701719, 0.0842795890067098, -0.233550157364016,
0.193002483647028, -0.0689226509620672, 0.179534446046147, -0.127248883358903,
-0.0743470123083659, -0.135639827536771, -0.164152686819086,
-0.0204543751350705, 0.0664461087883466, 0.0272380829324071,
0.227128887694961, -0.178562010800951, 0.395443526993695, 0.314881406042804,
0.181818850951155, -0.206752910000026, 0.0263153570715485, -0.175020412517093,
-0.01581249554214, -0.15172107661987, 0.107045745638528, 0.0906084021475712,
0.132978183241953, -0.00909419932203037, 0.0140911196519935,
0.213807819676506, -0.18193702641405, -0.227678899382835, 0.301040730720012,
0.0601578951831836, 0.100516179349791, -0.404945499516844, -0.062500075,
-0.203765311964576, -0.07692312083335, -0.0836064900993851, 0.221099166706208,
-0.0815457861285995, 0.316192911249967, -0.0486118279242403,
-0.173325434709271, 0.59325308527751, -0.00727384538023071, -0.0506951075961164,
0.546153054783499, 0.251190702854411, 0.0541671297506965, -0.231331680522771,
0.343474297824453, -0.0996647581834428, -0.16666622673471, -0.0108547028404369,
0.181845988421045, -0.126238743703789, 0.115831564907218, -0.086580986998594,
-0.100940420552943, -0.206575364601145, 0.131537865642544, -0.165064660739094,
-0.0281081354328821, -0.121059125713363, 0.123960117666879, -0.0262060896817153,
0.233629847113372, -0.0283857077499662, 0.0414875891677511, -0.139489434074546,
-0.291593674635723, -0.0394733826899257, -0.154468409162332,
-0.0228881657209516, 0.0583326253535777, -0.304457954370351,
-0.26778991307437, -0.33333268430204, 0.0600079321234409, 0.438760787944855,
-0.223958720648733, 0.7370589189836, 0.090349969129766, -0.267827471745488,
0.00221064777031615, -0.704447751043575, -0.131282309489721,
0, 0, -0.378290758732488, -0.0216356021418299, 0.116571563338307,
-0.105867472732954, -0.172970309117826, 0.0636995840642282, 0.0364789030575694,
0.0952821024300478, 0.230263394963683, -0.0485614318546264, -0.115799574888004,
-0.0120447695973589, 0.760532897132213, -0.289305826263387, 0.0411514151084162,
0.0673184054668599, -0.233772989321894, -0.0892615248786987,
-0.159921094234639, -0.0449699589101911, 0.59114150445647, -0.00487865744023711,
0.0392264443174142, 0.083080680760974, -0.604569467038781, -0.239333037134679,
-0.225872722618789, -0.474746305159428, -0.490736714052568, 0.228916653763629,
0.0308321929055187, 0.0943914873080436, -0.0712560264787054,
0.0798695334307741, -0.135961317412806, 0.0412890533446434, -0.0991550904983131,
0.300616722621631, -0.0418878938161028, -0.380127803498024, -0.0611938814657424,
0.01369237597809, -0.251729062508919, -0.0775118446872033, -0.314275408058115,
0.0876429361863786, 0.062043712399028, 0.134824568530155, 0.0158774971284649,
0.716661259261616, 0.0709424520436908, -0.227571455891104, -0.176479546620475
), index = structure(c(299401200, 307263600, 315388800, 323251200,
331110000, 339058800, 347011200, 354787200, 362646000, 370594800,
378547200, 386323200, 394182000, 402130800, 410083200, 417859200,
425718000, 433666800, 441532800, 449395200, 457254000, 465116400,
473241600, 480844800, 488703600, 496825200, 504777600, 512553600,
520412400, 528361200, 536313600, 544089600, 551948400, 559897200,
567849600, 575712000, 583570800, 591519600, 599385600, 607248000,
615106800, 622969200, 630835200, 638697600, 646556400, 654418800,
662544000, 670060800, 678006000, 686127600, 694080000, 701942400,
709801200, 717750000, 725702400, 733478400, 741337200, 749286000,
757238400, 765014400, 772873200, 780822000, 788688000, 796550400,
804409200, 812271600, 820137600, 8.28e+08, 835858800, 843980400,
851932800, 859708800, 867567600, 875516400, 883468800, 891244800,
899103600, 907052400, 915004800, 922780800, 930639600, 938588400,
946540800, 954403200, 962262000, 970124400, 977990400, 985852800,
993711600, 1001574000, 1009699200, 1017216000, 1025161200, 1033282800,
1041235200, 1049011200, 1056870000, 1064818800, 1072771200, 1080633600,
1088492400, 1096441200, 1104393600, 1112169600, 1120028400, 1127977200,
1135843200, 1143705600, 1151564400, 1159426800, 1167292800, 1175151600,
1183014000, 1190876400, 1199001600, 1206860400, 1214722800, 1222671600,
1230624000, 1238396400, 1246258800, 1254207600, 1262160000, 1269932400,
1277794800, 1285743600, 1293696000, 1301468400, 1309330800, 1317279600,
1325145600, 1333004400, 1340866800, 1348729200, 1356854400, 1364367600,
1372316400, 1380438000, 1388390400, 1396162800, 1404025200, 1411974000,
1419926400), tclass = c("POSIXlt", "POSIXt")), .indexCLASS = c("POSIXlt",
"POSIXt"), .indexTZ = "", tclass = c("POSIXt", "POSIXct"), tzone = "", class = c("xts",
"zoo"), .Dim = c(143L, 1L), .Dimnames = list(NULL, "-1,-1"))
I have tried using rollapply from library(zoo) & also mean.geometric from library("PerformanceAnalytics) but it is incorrect:
rollapply(RET,1:nrow(RET), mean.geometric)
Transferred from comments.
RET is an xts object and rollapply.xts in the xts package does not support vector widths.
Try this to cause it to use rollapply.zoo in the zoo package:
rollapplyr(as.zoo(RET), 1:nrow(RET), mean.geometric)