Categorising Count Data into Ordinal Responses with Application to Ecological Communities

Abstract Count data sets may involve overdispersion from a set of species and underdispersion from another set which would require fitting different models (e.g. a negative binomial model for the overdispersed set and a binomial model for the underdispersed one). Additionally, many count data sets h...
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Autor*in:	Fernández, D. [verfasserIn] Pledger, S.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Cluster analysis Clustering measures EM algorithm Finite mixture model Ordinal data Stereotype model

Anmerkung:	© International Biometric Society 2015

Übergeordnetes Werk:	Enthalten in: Journal of agricultural, biological, and environmental statistics - New York, NY : Springer, 1996, 21(2015), 2 vom: 07. Dez., Seite 348-362
Übergeordnetes Werk:	volume:21 ; year:2015 ; number:2 ; day:07 ; month:12 ; pages:348-362

Links:	Volltext

DOI / URN:	10.1007/s13253-015-0240-3

Katalog-ID:	SPR031021379

Internformat


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100	1		\|a Fernández, D. \|e verfasserin \|0 (orcid)0000-0003-0012-2094 \|4 aut
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520			\|a Abstract Count data sets may involve overdispersion from a set of species and underdispersion from another set which would require fitting different models (e.g. a negative binomial model for the overdispersed set and a binomial model for the underdispersed one). Additionally, many count data sets have very high counts and very low counts. Categorising these counts into ordinal categories makes the actual counts less influential in the model fitting, giving broad categories which enable us to detect major broadly based patterns of turnover or nestedness shown by groups of species. In this paper, a strategy of categorising count data into ordinal data was carried out and also we implemented measures to compare different cluster structures. The application of this categorising strategy and a comparison of clustering results between count and categorised ordinal data in two ecological community data sets are shown. A major advantage of using our ordinal approach is that it allows for the inclusion of all different levels of dispersion in the data in one methodology, without treating the data differently. This reduction of the parameters on modelling different levels of dispersion does not substantially change the results in clustering structure. In the two data sets used in this paper, we observed ordinal clustering structure up to 93.1 % similar to those from the count data approaches. This has the important implication of supporting simpler, faster data collection using ordinal scales only. Supplementary materials accompanying this paper appear on-line.
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650		4	\|a Finite mixture model \|7 (dpeaa)DE-He213
650		4	\|a Ordinal data \|7 (dpeaa)DE-He213
650		4	\|a Stereotype model \|7 (dpeaa)DE-He213
700	1		\|a Pledger, S. \|4 aut
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publishDate	2015
allfields	10.1007/s13253-015-0240-3 doi (DE-627)SPR031021379 (SPR)s13253-015-0240-3-e DE-627 ger DE-627 rakwb eng Fernández, D. verfasserin (orcid)0000-0003-0012-2094 aut Categorising Count Data into Ordinal Responses with Application to Ecological Communities 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Biometric Society 2015 Abstract Count data sets may involve overdispersion from a set of species and underdispersion from another set which would require fitting different models (e.g. a negative binomial model for the overdispersed set and a binomial model for the underdispersed one). Additionally, many count data sets have very high counts and very low counts. Categorising these counts into ordinal categories makes the actual counts less influential in the model fitting, giving broad categories which enable us to detect major broadly based patterns of turnover or nestedness shown by groups of species. In this paper, a strategy of categorising count data into ordinal data was carried out and also we implemented measures to compare different cluster structures. The application of this categorising strategy and a comparison of clustering results between count and categorised ordinal data in two ecological community data sets are shown. A major advantage of using our ordinal approach is that it allows for the inclusion of all different levels of dispersion in the data in one methodology, without treating the data differently. This reduction of the parameters on modelling different levels of dispersion does not substantially change the results in clustering structure. In the two data sets used in this paper, we observed ordinal clustering structure up to 93.1 % similar to those from the count data approaches. This has the important implication of supporting simpler, faster data collection using ordinal scales only. Supplementary materials accompanying this paper appear on-line. Cluster analysis (dpeaa)DE-He213 Clustering measures (dpeaa)DE-He213 EM algorithm (dpeaa)DE-He213 Finite mixture model (dpeaa)DE-He213 Ordinal data (dpeaa)DE-He213 Stereotype model (dpeaa)DE-He213 Pledger, S. aut Enthalten in Journal of agricultural, biological, and environmental statistics New York, NY : Springer, 1996 21(2015), 2 vom: 07. Dez., Seite 348-362 (DE-627)327130652 (DE-600)2043957-X 1537-2693 nnns volume:21 year:2015 number:2 day:07 month:12 pages:348-362 https://dx.doi.org/10.1007/s13253-015-0240-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2938 GBV_ILN_2947 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 AR 21 2015 2 07 12 348-362
spelling	10.1007/s13253-015-0240-3 doi (DE-627)SPR031021379 (SPR)s13253-015-0240-3-e DE-627 ger DE-627 rakwb eng Fernández, D. verfasserin (orcid)0000-0003-0012-2094 aut Categorising Count Data into Ordinal Responses with Application to Ecological Communities 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Biometric Society 2015 Abstract Count data sets may involve overdispersion from a set of species and underdispersion from another set which would require fitting different models (e.g. a negative binomial model for the overdispersed set and a binomial model for the underdispersed one). Additionally, many count data sets have very high counts and very low counts. Categorising these counts into ordinal categories makes the actual counts less influential in the model fitting, giving broad categories which enable us to detect major broadly based patterns of turnover or nestedness shown by groups of species. In this paper, a strategy of categorising count data into ordinal data was carried out and also we implemented measures to compare different cluster structures. The application of this categorising strategy and a comparison of clustering results between count and categorised ordinal data in two ecological community data sets are shown. A major advantage of using our ordinal approach is that it allows for the inclusion of all different levels of dispersion in the data in one methodology, without treating the data differently. This reduction of the parameters on modelling different levels of dispersion does not substantially change the results in clustering structure. In the two data sets used in this paper, we observed ordinal clustering structure up to 93.1 % similar to those from the count data approaches. This has the important implication of supporting simpler, faster data collection using ordinal scales only. Supplementary materials accompanying this paper appear on-line. Cluster analysis (dpeaa)DE-He213 Clustering measures (dpeaa)DE-He213 EM algorithm (dpeaa)DE-He213 Finite mixture model (dpeaa)DE-He213 Ordinal data (dpeaa)DE-He213 Stereotype model (dpeaa)DE-He213 Pledger, S. aut Enthalten in Journal of agricultural, biological, and environmental statistics New York, NY : Springer, 1996 21(2015), 2 vom: 07. Dez., Seite 348-362 (DE-627)327130652 (DE-600)2043957-X 1537-2693 nnns volume:21 year:2015 number:2 day:07 month:12 pages:348-362 https://dx.doi.org/10.1007/s13253-015-0240-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2938 GBV_ILN_2947 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 AR 21 2015 2 07 12 348-362
allfields_unstemmed	10.1007/s13253-015-0240-3 doi (DE-627)SPR031021379 (SPR)s13253-015-0240-3-e DE-627 ger DE-627 rakwb eng Fernández, D. verfasserin (orcid)0000-0003-0012-2094 aut Categorising Count Data into Ordinal Responses with Application to Ecological Communities 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Biometric Society 2015 Abstract Count data sets may involve overdispersion from a set of species and underdispersion from another set which would require fitting different models (e.g. a negative binomial model for the overdispersed set and a binomial model for the underdispersed one). Additionally, many count data sets have very high counts and very low counts. Categorising these counts into ordinal categories makes the actual counts less influential in the model fitting, giving broad categories which enable us to detect major broadly based patterns of turnover or nestedness shown by groups of species. In this paper, a strategy of categorising count data into ordinal data was carried out and also we implemented measures to compare different cluster structures. The application of this categorising strategy and a comparison of clustering results between count and categorised ordinal data in two ecological community data sets are shown. A major advantage of using our ordinal approach is that it allows for the inclusion of all different levels of dispersion in the data in one methodology, without treating the data differently. This reduction of the parameters on modelling different levels of dispersion does not substantially change the results in clustering structure. In the two data sets used in this paper, we observed ordinal clustering structure up to 93.1 % similar to those from the count data approaches. This has the important implication of supporting simpler, faster data collection using ordinal scales only. Supplementary materials accompanying this paper appear on-line. Cluster analysis (dpeaa)DE-He213 Clustering measures (dpeaa)DE-He213 EM algorithm (dpeaa)DE-He213 Finite mixture model (dpeaa)DE-He213 Ordinal data (dpeaa)DE-He213 Stereotype model (dpeaa)DE-He213 Pledger, S. aut Enthalten in Journal of agricultural, biological, and environmental statistics New York, NY : Springer, 1996 21(2015), 2 vom: 07. Dez., Seite 348-362 (DE-627)327130652 (DE-600)2043957-X 1537-2693 nnns volume:21 year:2015 number:2 day:07 month:12 pages:348-362 https://dx.doi.org/10.1007/s13253-015-0240-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2938 GBV_ILN_2947 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 AR 21 2015 2 07 12 348-362
allfieldsGer	10.1007/s13253-015-0240-3 doi (DE-627)SPR031021379 (SPR)s13253-015-0240-3-e DE-627 ger DE-627 rakwb eng Fernández, D. verfasserin (orcid)0000-0003-0012-2094 aut Categorising Count Data into Ordinal Responses with Application to Ecological Communities 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Biometric Society 2015 Abstract Count data sets may involve overdispersion from a set of species and underdispersion from another set which would require fitting different models (e.g. a negative binomial model for the overdispersed set and a binomial model for the underdispersed one). Additionally, many count data sets have very high counts and very low counts. Categorising these counts into ordinal categories makes the actual counts less influential in the model fitting, giving broad categories which enable us to detect major broadly based patterns of turnover or nestedness shown by groups of species. In this paper, a strategy of categorising count data into ordinal data was carried out and also we implemented measures to compare different cluster structures. The application of this categorising strategy and a comparison of clustering results between count and categorised ordinal data in two ecological community data sets are shown. A major advantage of using our ordinal approach is that it allows for the inclusion of all different levels of dispersion in the data in one methodology, without treating the data differently. This reduction of the parameters on modelling different levels of dispersion does not substantially change the results in clustering structure. In the two data sets used in this paper, we observed ordinal clustering structure up to 93.1 % similar to those from the count data approaches. This has the important implication of supporting simpler, faster data collection using ordinal scales only. Supplementary materials accompanying this paper appear on-line. Cluster analysis (dpeaa)DE-He213 Clustering measures (dpeaa)DE-He213 EM algorithm (dpeaa)DE-He213 Finite mixture model (dpeaa)DE-He213 Ordinal data (dpeaa)DE-He213 Stereotype model (dpeaa)DE-He213 Pledger, S. aut Enthalten in Journal of agricultural, biological, and environmental statistics New York, NY : Springer, 1996 21(2015), 2 vom: 07. Dez., Seite 348-362 (DE-627)327130652 (DE-600)2043957-X 1537-2693 nnns volume:21 year:2015 number:2 day:07 month:12 pages:348-362 https://dx.doi.org/10.1007/s13253-015-0240-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2938 GBV_ILN_2947 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 AR 21 2015 2 07 12 348-362
allfieldsSound	10.1007/s13253-015-0240-3 doi (DE-627)SPR031021379 (SPR)s13253-015-0240-3-e DE-627 ger DE-627 rakwb eng Fernández, D. verfasserin (orcid)0000-0003-0012-2094 aut Categorising Count Data into Ordinal Responses with Application to Ecological Communities 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Biometric Society 2015 Abstract Count data sets may involve overdispersion from a set of species and underdispersion from another set which would require fitting different models (e.g. a negative binomial model for the overdispersed set and a binomial model for the underdispersed one). Additionally, many count data sets have very high counts and very low counts. Categorising these counts into ordinal categories makes the actual counts less influential in the model fitting, giving broad categories which enable us to detect major broadly based patterns of turnover or nestedness shown by groups of species. In this paper, a strategy of categorising count data into ordinal data was carried out and also we implemented measures to compare different cluster structures. The application of this categorising strategy and a comparison of clustering results between count and categorised ordinal data in two ecological community data sets are shown. A major advantage of using our ordinal approach is that it allows for the inclusion of all different levels of dispersion in the data in one methodology, without treating the data differently. This reduction of the parameters on modelling different levels of dispersion does not substantially change the results in clustering structure. In the two data sets used in this paper, we observed ordinal clustering structure up to 93.1 % similar to those from the count data approaches. This has the important implication of supporting simpler, faster data collection using ordinal scales only. Supplementary materials accompanying this paper appear on-line. Cluster analysis (dpeaa)DE-He213 Clustering measures (dpeaa)DE-He213 EM algorithm (dpeaa)DE-He213 Finite mixture model (dpeaa)DE-He213 Ordinal data (dpeaa)DE-He213 Stereotype model (dpeaa)DE-He213 Pledger, S. aut Enthalten in Journal of agricultural, biological, and environmental statistics New York, NY : Springer, 1996 21(2015), 2 vom: 07. Dez., Seite 348-362 (DE-627)327130652 (DE-600)2043957-X 1537-2693 nnns volume:21 year:2015 number:2 day:07 month:12 pages:348-362 https://dx.doi.org/10.1007/s13253-015-0240-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2938 GBV_ILN_2947 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 AR 21 2015 2 07 12 348-362
language	English
source	Enthalten in Journal of agricultural, biological, and environmental statistics 21(2015), 2 vom: 07. Dez., Seite 348-362 volume:21 year:2015 number:2 day:07 month:12 pages:348-362
sourceStr	Enthalten in Journal of agricultural, biological, and environmental statistics 21(2015), 2 vom: 07. Dez., Seite 348-362 volume:21 year:2015 number:2 day:07 month:12 pages:348-362
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Cluster analysis Clustering measures EM algorithm Finite mixture model Ordinal data Stereotype model
isfreeaccess_bool	false
container_title	Journal of agricultural, biological, and environmental statistics
authorswithroles_txt_mv	Fernández, D. @@aut@@ Pledger, S. @@aut@@
publishDateDaySort_date	2015-12-07T00:00:00Z
hierarchy_top_id	327130652
id	SPR031021379
language_de	englisch
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author	Fernández, D.
spellingShingle	Fernández, D. misc Cluster analysis misc Clustering measures misc EM algorithm misc Finite mixture model misc Ordinal data misc Stereotype model Categorising Count Data into Ordinal Responses with Application to Ecological Communities
authorStr	Fernández, D.
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topic_title	Categorising Count Data into Ordinal Responses with Application to Ecological Communities Cluster analysis (dpeaa)DE-He213 Clustering measures (dpeaa)DE-He213 EM algorithm (dpeaa)DE-He213 Finite mixture model (dpeaa)DE-He213 Ordinal data (dpeaa)DE-He213 Stereotype model (dpeaa)DE-He213
topic	misc Cluster analysis misc Clustering measures misc EM algorithm misc Finite mixture model misc Ordinal data misc Stereotype model
topic_unstemmed	misc Cluster analysis misc Clustering measures misc EM algorithm misc Finite mixture model misc Ordinal data misc Stereotype model
topic_browse	misc Cluster analysis misc Clustering measures misc EM algorithm misc Finite mixture model misc Ordinal data misc Stereotype model
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title	Categorising Count Data into Ordinal Responses with Application to Ecological Communities
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title_full	Categorising Count Data into Ordinal Responses with Application to Ecological Communities
author_sort	Fernández, D.
journal	Journal of agricultural, biological, and environmental statistics
journalStr	Journal of agricultural, biological, and environmental statistics
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author_browse	Fernández, D. Pledger, S.
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author-letter	Fernández, D.
doi_str_mv	10.1007/s13253-015-0240-3
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title_sort	categorising count data into ordinal responses with application to ecological communities
title_auth	Categorising Count Data into Ordinal Responses with Application to Ecological Communities
abstract	Abstract Count data sets may involve overdispersion from a set of species and underdispersion from another set which would require fitting different models (e.g. a negative binomial model for the overdispersed set and a binomial model for the underdispersed one). Additionally, many count data sets have very high counts and very low counts. Categorising these counts into ordinal categories makes the actual counts less influential in the model fitting, giving broad categories which enable us to detect major broadly based patterns of turnover or nestedness shown by groups of species. In this paper, a strategy of categorising count data into ordinal data was carried out and also we implemented measures to compare different cluster structures. The application of this categorising strategy and a comparison of clustering results between count and categorised ordinal data in two ecological community data sets are shown. A major advantage of using our ordinal approach is that it allows for the inclusion of all different levels of dispersion in the data in one methodology, without treating the data differently. This reduction of the parameters on modelling different levels of dispersion does not substantially change the results in clustering structure. In the two data sets used in this paper, we observed ordinal clustering structure up to 93.1 % similar to those from the count data approaches. This has the important implication of supporting simpler, faster data collection using ordinal scales only. Supplementary materials accompanying this paper appear on-line. © International Biometric Society 2015
abstractGer	Abstract Count data sets may involve overdispersion from a set of species and underdispersion from another set which would require fitting different models (e.g. a negative binomial model for the overdispersed set and a binomial model for the underdispersed one). Additionally, many count data sets have very high counts and very low counts. Categorising these counts into ordinal categories makes the actual counts less influential in the model fitting, giving broad categories which enable us to detect major broadly based patterns of turnover or nestedness shown by groups of species. In this paper, a strategy of categorising count data into ordinal data was carried out and also we implemented measures to compare different cluster structures. The application of this categorising strategy and a comparison of clustering results between count and categorised ordinal data in two ecological community data sets are shown. A major advantage of using our ordinal approach is that it allows for the inclusion of all different levels of dispersion in the data in one methodology, without treating the data differently. This reduction of the parameters on modelling different levels of dispersion does not substantially change the results in clustering structure. In the two data sets used in this paper, we observed ordinal clustering structure up to 93.1 % similar to those from the count data approaches. This has the important implication of supporting simpler, faster data collection using ordinal scales only. Supplementary materials accompanying this paper appear on-line. © International Biometric Society 2015
abstract_unstemmed	Abstract Count data sets may involve overdispersion from a set of species and underdispersion from another set which would require fitting different models (e.g. a negative binomial model for the overdispersed set and a binomial model for the underdispersed one). Additionally, many count data sets have very high counts and very low counts. Categorising these counts into ordinal categories makes the actual counts less influential in the model fitting, giving broad categories which enable us to detect major broadly based patterns of turnover or nestedness shown by groups of species. In this paper, a strategy of categorising count data into ordinal data was carried out and also we implemented measures to compare different cluster structures. The application of this categorising strategy and a comparison of clustering results between count and categorised ordinal data in two ecological community data sets are shown. A major advantage of using our ordinal approach is that it allows for the inclusion of all different levels of dispersion in the data in one methodology, without treating the data differently. This reduction of the parameters on modelling different levels of dispersion does not substantially change the results in clustering structure. In the two data sets used in this paper, we observed ordinal clustering structure up to 93.1 % similar to those from the count data approaches. This has the important implication of supporting simpler, faster data collection using ordinal scales only. Supplementary materials accompanying this paper appear on-line. © International Biometric Society 2015
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title_short	Categorising Count Data into Ordinal Responses with Application to Ecological Communities
url	https://dx.doi.org/10.1007/s13253-015-0240-3
remote_bool	true
author2	Pledger, S.
author2Str	Pledger, S.
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doi_str	10.1007/s13253-015-0240-3
up_date	2024-07-03T21:31:56.991Z
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Additionally, many count data sets have very high counts and very low counts. Categorising these counts into ordinal categories makes the actual counts less influential in the model fitting, giving broad categories which enable us to detect major broadly based patterns of turnover or nestedness shown by groups of species. In this paper, a strategy of categorising count data into ordinal data was carried out and also we implemented measures to compare different cluster structures. The application of this categorising strategy and a comparison of clustering results between count and categorised ordinal data in two ecological community data sets are shown. A major advantage of using our ordinal approach is that it allows for the inclusion of all different levels of dispersion in the data in one methodology, without treating the data differently. This reduction of the parameters on modelling different levels of dispersion does not substantially change the results in clustering structure. In the two data sets used in this paper, we observed ordinal clustering structure up to 93.1 % similar to those from the count data approaches. This has the important implication of supporting simpler, faster data collection using ordinal scales only. 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Categorising Count Data into Ordinal Responses with Application to Ecological Communities

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