Seeking Alternative Solutions: Copula Family Analysis with Drought SPI Monthly Data

Hello everyone, I'm utilizing the monthly data from the Drought SPI for conducting copula family analysis. However, I'm encountering an error indicating that FitCopula is not available. Any alternative solutions would be greatly appreciated. Here are the formulas I'm employing.

Load necessary libraries

library(copula)

Assuming you have a matrix of bivariate data stored in data_matrix

library(readxl)
DS <- read_excel("D:/Lab_Works/MODELs/Drought Indices/DS.xlsx")
str(DS)
data_matrix <- matrix(DS$SPI, nrow = 12, byrow = TRUE)
data_matrix <- t(data_matrix)
data_df <- as.data.frame(data_matrix)

data_df <- as.data.frame(data_matrix)

Fit Clayton Copula

clayton_fit <- fitCopula(data_matrix, family = "clayton")

Fit Gumbel Copula

gumbel_fit <- fitCopula(data_matrix, family = "gumbel")

Fit Frank Copula

frank_fit <- fitCopula(data_matrix, family = "frank")

Evaluate Goodness of Fit

we will use AIC, BIC, or likelihood ratio tests

For example, you can print AIC and BIC values for each copula

print("AIC Values:")
print(c(clayton_fit@aic, gumbel_fit@aic, frank_fit@aic))
print("BIC Values:")
print(c(clayton_fit@bic, gumbel_fit@bic, frank_fit@bic))

Select the Best Copula

Choose the copula with the lowest AIC or BIC value

best_copula <- which.min(c(clayton_fit@aic, gumbel_fit@aic, frank_fit@aic))

Generate Graphs

Visualize copula fits using scatter plots or density plots

For example, you can plot the density of the fitted copulas

plot(clayton_fit, main = "Clayton Copula Density")
plot(gumbel_fit, main = "Gumbel Copula Density")
plot(frank_fit, main = "Frank Copula Density")

Can you give us the exact error message and some sample data?

A handy way to supply some sample data is the dput() function. In the case of a large dataset something like dput(head(mydata, 100)) should supply the data we need. Just do dput(mydata) where mydata is your data. Copy the output and paste it here between
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Error: unable to find an inherited method for function ‘fitCopula’ for signature ‘copula = "numeric"’
This is the data:

Month Year SPI
Jan 2009 -0.26397191
Feb 2009 -0.20605094
Mar 2009 -0.14396105
Apr 2009 -0.38207919
May 2009 -0.20945352
Jun 2009 -0.22154146
Jul 2009 1.07240342
Aug 2009 0.82845697
Sep 2009 0.53299504
Oct 2009 0.57630733
Nov 2009 0.54686849
Dec 2009 0.44089301
Jan 2010 0.49924778
Feb 2010 0.46245311
Mar 2010 0.58845629
Apr 2010 -0.42756782
May 2010 -0.3354378
Jun 2010 0.24019233
Jul 2010 -0.53468604
Aug 2010 0.02464352
Sep 2010 1.19248029
Oct 2010 1.18622649
Nov 2010 1.08222157
Dec 2010 1.01070086
Jan 2011 1.04593662
Feb 2011 0.98516175
Mar 2011 0.97253969
Apr 2011 1.56621549
May 2011 1.80979095
Jun 2011 1.26488243
Jul 2011 2.44755015
Aug 2011 1.90828386
Sep 2011 1.48563096
Oct 2011 1.50713619
Nov 2011 1.48969659
Dec 2011 1.5412774
Jan 2012 1.41314368
Feb 2012 1.38429025
Mar 2012 0.90821136
Apr 2012 -0.78079438
May 2012 -1.14119098
Jun 2012 -0.91914637
Jul 2012 -0.26551031
Aug 2012 0.4989908
Sep 2012 0.7894907
Oct 2012 0.87754847
Nov 2012 0.95467669
Dec 2012 1.00707821
Jan 2013 0.86776929
Feb 2013 1.0270336
Mar 2013 1.07720464
Apr 2013 1.16089096
May 2013 1.00601563
Jun 2013 -0.13069081
Jul 2013 0.84050509
Aug 2013 0.32899463
Sep 2013 0.40272548
Oct 2013 0.26611284
Nov 2013 0.22018955
Dec 2013 0.27754779
Jan 2014 0.32785041
Feb 2014 0.21469241
Mar 2014 0.35137363
Apr 2014 -0.60096189
May 2014 -0.57792846
Jun 2014 -0.52089858
Jul 2014 -1.0120304
Aug 2014 -1.24481862
Sep 2014 -1.23054915
Oct 2014 -1.23456503
Nov 2014 -1.31963218
Dec 2014 -1.28784594
Jan 2015 -1.07595523
Feb 2015 -0.89909527
Mar 2015 -1.52196379
Apr 2015 -0.60678511
May 2015 -0.86410207
Jun 2015 -0.9240581
Jul 2015 -1.25653457
Aug 2015 -1.91799041
Sep 2015 -1.95647788
Oct 2015 -1.92238268
Nov 2015 -1.68098791
Dec 2015 -1.59441186
Jan 2016 -1.64515628
Feb 2016 -1.28115574
Mar 2016 -1.35564857
Apr 2016 -0.68951787
May 2016 0.42117676
Jun 2016 0.93428164
Jul 2016 0.33817343
Aug 2016 -0.72007715
Sep 2016 -0.92702067
Oct 2016 -0.87691226
Nov 2016 -1.14843421
Dec 2016 -1.04264272
Jan 2017 -0.98153831
Feb 2017 -1.1854838
Mar 2017 -1.20819781
Apr 2017 -1.38278054
May 2017 -1.32712183
Jun 2017 -1.57520419
Jul 2017 -0.01391321
Aug 2017 0.34481497
Sep 2017 -0.11282996
Oct 2017 -0.17862514
Nov 2017 -0.22161587
Dec 2017 -0.1244979
Jan 2018 -0.0601134
Feb 2018 0.13419147
Mar 2018 0.3288504
Apr 2018 0.14851389
May 2018 0.51951671
Jun 2018 1.25098205
Jul 2018 0.02142659
Aug 2018 -0.09157937
Sep 2018 -0.24831489
Oct 2018 -0.02273472
Nov 2018 0.01718164
Dec 2018 -0.07604798
Jan 2019 -0.28469371
Feb 2019 -0.69785683
Mar 2019 -0.61516808
Apr 2019 -0.07234226
May 2019 -0.3265182
Jun 2019 -0.51174116
Jul 2019 -0.80886262
Aug 2019 -1.13653834
Sep 2019 -1.17425838
Oct 2019 -1.10453219
Nov 2019 -1.02107121
Dec 2019 -1.05682338
Jan 2020 -0.74094602
Feb 2020 -0.41013219
Mar 2020 -0.36358868
Apr 2020 -0.28600225
May 2020 0.02866663
Jun 2020 0.41581319
Jul 2020 -0.19881899
Aug 2020 0.99352199
Sep 2020 0.96753075
Oct 2020 0.76564234
Nov 2020 0.76353863
Dec 2020 0.7768291
Jan 2021 0.93481746
Feb 2021 0.84079145
Mar 2021 1.0737883
Apr 2021 1.49054387
May 2021 0.96421028
Jun 2021 0.84262474
Jul 2021 -0.34530442
Aug 2021 -0.66293992
Sep 2021 -0.53971326
Oct 2021 -0.6572469
Nov 2021 -0.59963363
Dec 2021 -0.89107728
Jan 2022 -0.98846179
Feb 2022 -1.34387442
Mar 2022 -1.15083322
Apr 2022 -0.42024513
May 2022 -0.86410207
Jun 2022 -1.22176093
Jul 2022 -0.73747055
Aug 2022 0.59836805
Sep 2022 0.87704287
Oct 2022 1.03006989
Nov 2022 1.09263212
Dec 2022 0.98165859
Jan 2023 1.16163358
Feb 2023 1.22316397
Mar 2023 1.22199859
Apr 2023 1.55655887
May 2023 1.20350489
Jun 2023 1.25098205
Jul 2023 0.66579773
Aug 2023 0.37773578
Sep 2023 0.35729374
Oct 2023 0.21539771
Nov 2023 0.36105961
Dec 2023 0.63836084

It might be useful for you to run your code one line at a time, then copy and paste both the line the triggers the first error together with the first error itself.