| Type: | Package |
| Title: | Data Sets for Discrete Probability Models |
| Version: | 0.1.0 |
| Author: | Christophe Chesneau [aut], Muhammad Imran [aut, cre], M.H Tahir [aut], Farrukh Jamal [aut] |
| Maintainer: | Muhammad Imran <imranshakoor84@yahoo.com> |
| Depends: | R (≥ 4.0) |
| Description: | A wide collection of univariate discrete data sets from various applied domains related to distribution theory. The functions allow quick, easy, and efficient access to 100 univariate discrete data sets. The data are related to different applied domains, including medical, reliability analysis, engineering, manufacturing, occupational safety, geological sciences, terrorism, psychology, agriculture, environmental sciences, road traffic accidents, demography, actuarial science, law, and justice. The documentation, along with associated references for further details and uses, is presented. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.3 |
| NeedsCompilation: | no |
| Packaged: | 2023-06-14 17:12:34 UTC; Amir Computers |
| Repository: | CRAN |
| Date/Publication: | 2023-06-15 07:40:02 UTC |
Data Sets for Discrete Probability Models
Description
A wide range of univariate discrete data sets from various applied domains related to distribution theory. The functions allow quick, easy, and efficient access to 100 univariate discrete data sets. The data are related to different applied domains as follows: medical, reliability analysis, engineering, manufacturing, occupational safety, geological sciences, terrorism, psychology, agriculture, environmental sciences, road traffic accidents, demography, actuarial science, law, and justice. The documentation, along with associated references for further details and uses, is presented.
Details
| Package: | DDPM |
| Type: | Package |
| Version: | 0.1.0 |
| Date: | 2023-06-14 |
| License: | GPL-2 |
Maintainers
Muhammad Imran <imranshakoor84@yahoo.com>
Author(s)
Christophe Chesneau <christophe.chesneau@unicaen.fr>, Muhammad Imran <imranshakoor84@yahoo.com>, M.H Tahir <mht@iub.edu.pk> and Farrukh Jamal <farrukh.jamal@iub.edu.pk>.
The data show the observed number of pap smear tests a female took in the last six years for females aged more than 18 years
Description
The function gives the observed number of pap smear tests a female took in the last six years for females aged more than 18 years.
Usage
data_pap
Arguments
data_pap |
A vector of (non-negative integer) count values. |
Details
The data show the observed number of pap smear tests a female took in the last six years for females aged more than 18 years. They were used by Arora and Chaganty (2021) and fitted by the zero-and-k-inflated Poisson distribution.
Value
data_pap gives the observed number of pap smear tests a female took in the last six years for females aged more than 18 years.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Arora, M., & Chaganty, N. R. (2021). EM estimation for zero-and k-inflated Poisson regression Model. Computation, 9(9), 94.
See Also
Examples
x<-data_pap
summary(x)
table (x)
The data show the frequency distribution of decayed, missing, and filled teeth of children aged 12 years old
Description
The function gives the frequency distribution of decayed, missing, and filled teeth of children aged 12 years old.
Usage
data_teeth
Arguments
data_teeth |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of decayed, missing, and filled teeth of children aged 12 years old. They were used by Moghimbeigi et al. (2008) and fitted by the zero-inflated negative binomial regression modeling for over-dispersed count data with extra zeros.
Value
data_teeth gives the frequency distribution of decayed, missing, and filled teeth of children aged 12 years old.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Moghimbeigi, A., Eshraghian, M. R., Mohammad, K., & Mcardle, B. (2008). Multilevel zero-inflated negative binomial regression modeling for over-dispersed count data with extra zeros. Journal of Applied Statistics, 35(10), 1193-1202.
See Also
Examples
x<-data_teeth
summary(x)
table (x)
The data show the number of absences of individuals
Description
The function gives the number of absences of individuals for studying absence proneness.
Usage
data_absen
Arguments
data_absen |
A vector of (non-negative integer) count values. |
Details
The data show the number of absences of individuals for studying absence proneness. They were used by Sichel (1951) and fitted by the negative binomial distribution.
Value
data_absen gives the number of absences of individuals.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Sichel, H. S. (1951). The estimation of the parameters of a negative binomial distribution with special reference to psychological data. Psychometrika, 16(1), 107-127.
See Also
Examples
x<-data_absen
summary(x)
table (x)
The data consist of the number of accident insurance claims
Description
The function gives the number of accident insurance claims based on 16760 policies.
Usage
data_claims
Arguments
data_claims |
A vector of (non-negative integer) count values. |
Details
The data consist of the number of accident insurance claims based on 16760 policies in Mazandaran Province. Recently, they were used by Alshkaki (2016) and fitted by the zero-and-one inflated Poisson distribution.
Value
data_claims gives the number of accident insurance claims based on 16760 policies.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Alshkaki, R. S. A. (2016). On the zero-one inflated Poisson distribution. International Journal of Statistical Distributions and Applications, 2(4), 42-8.
Momeni, F. (2011). The generalized power series distribution and their application. The Journal of Mathematics and Computer Science, 2(4), 691-697.
See Also
data_claims, data_claim1, data_claim2, data_claim3, data_claim6, data_claim7
Examples
x<-data_claims
summary(x)
table (x)
The data show the number of accidents of women working on Shells for 5 weeks
Description
The function gives the number of accidents of women working on Shells for 5 weeks.
Usage
data_wacci
Arguments
data_wacci |
A vector of (non-negative integer) count values. |
Details
The data show the number of accidents of women working on Shells for 5 weeks. They were used by Nekoukhou et al. (2013) and fitted by the discrete generalized exponential distribution of a second type.
Value
data_wacci gives the number of accidents of women working on Shells for 5 weeks.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Nekoukhou, V., Alamatsaz, M. H., & Bidram, H. (2013). Discrete generalized exponential distribution of a second type. Statistics, 47(4), 876-887.
See Also
Examples
x<-data_wacci
summary(x)
table (x)
The data show the number of accident proneness of individuals
Description
The function gives the number of accident proneness of individuals.
Usage
data_acci
Arguments
data_acci |
A vector of (non-negative integer) count values. |
Details
The data show the number of accident proneness of individuals. They were used by Sichel (1951) and fitted by the negative binomial distribution.
Value
data_acci gives the number of accident proneness of individuals.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Sichel, H. S. (1951). The estimation of the parameters of a negative binomial distribution with special reference to psychological data. Psychometrika, 16(1), 107-127.
See Also
Examples
x<-data_acci
summary(x)
table (x)
The data show the observed number of accidents in a 60-lb shrapnel shop
Description
The function gives the observed number of accidents in a 60-lb shrapnel shop.
Usage
data_accide
Arguments
data_accide |
A vector of (non-negative integer) count values. |
Details
The data show the observed number of accidents in a 60-lb shrapnel shop. They were used by Greenwood and Yule (1920) and underlined an inquiry into the nature of frequency distributions.
Value
data_accide gives the observed number of accidents in a 60-lb shrapnel shop.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Greenwood, M., & Yule, G. U. (1920). An inquiry into the nature of frequency distributions representative of multiple happenings with particular reference to the occurrence of multiple attacks of disease or of repeated accidents. Journal of the Royal Statistical Society, 83(2), 255-279.
See Also
Examples
x<-data_accide
summary(x)
table (x)
The data show the frequency distribution of accidents to Belfast Corporation Transport bus drivers
Description
The function gives the frequency distribution of accidents to Belfast Corporation Transport bus drivers.
Usage
data_belfast
Arguments
data_belfast |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of accidents to Belfast Corporation Transport bus drivers. They were used by Xekalaki (1984) and fitted by the bivariate generalized Waring distribution.
Value
data_belfast gives the frequency distribution of accidents to Belfast Corporation Transport bus drivers.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Xekalaki, E. (1984). The bivariate generalized Waring distribution and its application to accident theory. Journal of the Royal Statistical Society: Series A (General), 147(3), 488-498.
See Also
Examples
x<-data_belfast
summary(x)
table (x)
The data show the frequency distribution of accidents to Connecticut general driver
Description
The function gives the frequency distribution of accidents to Connecticut general drivers.
Usage
data_connecticut
Arguments
data_connecticut |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of accidents to Connecticut general drivers. They were used by Xekalaki (1984) and fitted by the bivariate generalized Waring distribution.
Value
data_connecticut gives the frequency distribution of accidents to Connecticut general drivers.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Xekalaki, E. (1984). The bivariate generalized Waring distribution and its application to accident theory. Journal of the Royal Statistical Society: Series A (General), 147(3), 488-498.
See Also
Examples
x<-data_connecticut
summary(x)
table (x)
Twenty-five leaves were selected at random from each of six similar apple trees
Description
The function gives the number of adult female European red mites on each leaf.
Usage
data_mites
Arguments
data_mites |
A vector of (non-negative integer) count values. |
Details
Twenty-five leaves were selected at random from each of six similar apple trees in an orchard, and the adult female European red mites on each leaf were counted. They were used by Ross and Preece (1985) and studied by the negative binomial distribution.
Value
data_mites gives the number of adult female European red mites on each leaf.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Ross, G. J. S., & Preece, D. A. (1985). The negative binomial distribution. Journal of the Royal Statistical Society: Series D (The Statistician), 34(3), 323-335.
See Also
Examples
x<-data_mites
summary(x)
table (x)
The data consist of the number of accidents of 647 female workers in an ammunition factory
Description
The function gives the number of observed count of accidents of 647 female workers in an ammunition factory.
Usage
data_ammunition
Arguments
data_ammunition |
A vector of (non-negative integer) count values. |
Details
The data consists of the number of accidents of 647 female workers in an ammunition factory. Recently, they were used by Zhang et al. (2016) and fitted by the zero-and-one inflated Poisson distribution.
Value
data_ammunition gives the number of observed count of accidents of 647 female workers in an ammunition factory.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Zhang, C., Tian, G. L., & Ng, K. W. (2016). Properties of the zero-and-one inflated Poisson distribution and likelihood-based inference methods. Statistics and its Interface, 9(1), 11-32.
Greenwood, M., & Yule, G. U. (1920). An inquiry into the nature of frequency distributions representative of multiple happenings with particular reference to the occurrence of multiple attacks of disease or repeated accidents. Journal of the Royal Statistical Society, 83(2), 255-279.
Bohning, D. (1998). Zero-inflated Poisson models and CA MAN: A tutorial collection of evidence. Biometrical Journal: Journal of Mathematical Methods in Biosciences, 40(7), 833-843.
See Also
Examples
x<-data_ammunition
summary(x)
table (x)
The data set consists of the number of antenatal care service visit
Description
The function gives the frequency distribution of the number of antenatal care service visits of 6450 women surveyed in EDHS 2016.
Usage
data_antenatal
Arguments
data_antenatal |
A vector of (non-negative integer) count values. |
Details
The data set consists of the number of antenatal care service visit of 6450 women surveyed in EDHS 2016. Recently, they were used by Bekalo and Kebede (2021) and fitted by the zero-inflated models for count data.
Value
data_antenatal gives the observed frequencies of the number of antenatal care service visits.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Bekalo, D. B., & Kebede, D. T. (2021). Zero-inflated models for count data: an application to the number of antenatal care service visits. Annals of Data Science, 8, 683-708.
See Also
Examples
x<-data_antenatal
summary(x)
table (x)
The data contain the frequency distribution of use of antenatal care services in 2011
Description
The function gives the frequency distribution of the use of antenatal care services in 2011 in Ethiopia.
Usage
data_anten
Arguments
data_anten |
A vector of (non-negative integer) count values. |
Details
The data contain the frequency distribution of the use of antenatal care services in 2011 in Ethiopia. They were used by Assefa and Tadesse (2017) and fitted by the zero-inflated negative binomial model.
Value
data_anten gives the frequency distribution of the use of antenatal care services in 2011 in Ethiopia.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Assefa, E., & Tadesse, M. (2017). Factors related to the use of antenatal care services in Ethiopia: application of the zero-inflated negative binomial model. Women & Health, 57(7), 804-821.
See Also
Examples
x<-data_anten
summary(x)
table (x)
The data show the frequency distributions of the number of roots
Description
The function gives the frequency distributions of the number of roots produced by 270 shoots of the apple cultivar Trajan.
Usage
data_root
Arguments
data_root |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distributions of the number of roots produced by 270 shoots of the apple cultivar Trajan. They were used by Rodrigues (2003) and fitted in the context of the Bayesian analysis of zero-inflated distributions.
Value
data_root gives the frequency distributions of the number of roots.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Rodrigues, J. (2003). Bayesian analysis of zero-inflated distributions. Communications in Statistics-Theory and Methods, 32(2), 281-289.
See Also
Examples
x<-data_root
summary(x)
table (x)
The data show the daily COVID-19 new cases of Argentina
Description
The function gives the daily number of COVID-19 new cases in Argentina.
Usage
data_argcovid
Arguments
data_argcovid |
A vector of (non-negative integer) count values. |
Details
The data show the daily COVID-19 new cases of Argentina of 80 days, that is recorded from 12 March to 30 May 2020. Recently, they were used by Ibrahim and Almetwally (2021) and fitted by the discrete marshall-Olkin Lomax distribution.
Value
data_argcovid gives the daily number of COVID-19 new cases in Argentina.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Ibrahim, G. M., & Almetwally, E. M. (2021). Discrete marshall-Olkin lomax distribution application of covid-19. Biomedical journal of Scientific & Technical Research, 32(5), 2021.
See Also
data_COVIDd, data_Algeriacovid, data_Bosniacovid
Examples
x<-data_argcovid
summary(x)
table (x)
The data represent the observed number of asynaptic in onion plants
Description
The function gives the observed number of asynaptic in onion plants.
Usage
data_as1
Arguments
data_as1 |
A vector of (non-negative integer) count values. |
Details
The data represent the observed number of asynaptic in onion plants. They were used by Jain (1959) and fitted by the negative binomial distribution.
Value
data_as1 gives the observed number of asynaptic in onion plants.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Jain, S. K. (1959). Fitting the negative binomial distribution to some data on asynaptic behavior of chromosomes. Genetica, 30(1), 108-122.
See Also
data_p806_7, data_p806_8, data_p806_9
Examples
x<-data_as1
summary(x)
table (x)
The data show the number of major Atlantic hurricanes
Description
The function gives the number of major Atlantic hurricanes.
Usage
data_hurricanes
Arguments
data_hurricanes |
A vector of (non-negative integer) count values. |
Details
The data show the number of major Atlantic hurricanes per year to have made landfall in the US from 1987 through 2012. Recently, they were used by Beckett et al. (2014) and fitted by the zero-inflated Poisson (ZIP) distribution.
Value
data_hurricanes gives the number of major Atlantic hurricanes per year to have made landfall in the US from 1987 through 2012.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Beckett, S., Jee, J., Ncube, T., Pompilus, S., Washington, Q., Singh, A., & Pal, N. (2014). Zero-inflated Poisson (ZIP) distribution: Parameter estimation and applications to model data from natural calamities. Involve, a Journal of Mathematics, 7(6), 751-767.
See Also
Examples
x<-data_hurricanes
summary(x)
table (x)
The data show the number of automobile insurance third party liability portfolios of Belgium 1958
Description
The function gives the number of automobile insurance third-party liability portfolios of Belgium in 1958.
Usage
data_claim3
Arguments
data_claim3 |
A vector of (non-negative integer) count values. |
Details
The data show the number of automobile insurance third party liability portfolios of Belgium 1958. They were used by Denuit (1997) and fitted by the zero-inflated Poisson (ZIP) distribution.
Value
data_claim3 gives the number of automobile insurance third-party liability portfolios in Belgium in 1958.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Denuit, M. (1997). A new distribution of Poisson-type for the number of claims. ASTIN Bulletin: The Journal of the IAA, 27(2), 229-242.
See Also
data_claims, data_claim1, data_claim2
Examples
x<-data_claim3
summary(x)
table (x)
The data show the number of automobile insurance third-party liability portfolios
Description
The function gives the number of automobile insurance third-party liability portfolios.
Usage
data_claim1
Arguments
data_claim1 |
A vector of (non-negative integer) count values. |
Details
The data show the number of automobile insurance third-party liability portfolios in Belgium 1975-76. Recently, they were used by Denuit (1997) and fitted by the zero-inflated Poisson (ZIP) distribution.
Value
data_claim1 gives the number of automobile insurance third-party liability portfolios.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Denuit, M. (1997). A new distribution of Poisson-type for the number of claims. ASTIN Bulletin: The Journal of the IAA, 27(2), 229-242.
See Also
Examples
x<-data_claim1
summary(x)
table (x)
The data show the number of automobile insurance third party liability portfolios in Great Britain 1968
Description
The function gives the number of automobile insurance third-party liability portfolios in Great Britain 1968.
Usage
data_claim4
Arguments
data_claim4 |
A vector of (non-negative integer) count values. |
Details
The data show the number of automobile insurance third party liability portfolios in Great Britain 1968. They were used by Denuit (1997) and fitted by the zero-inflated Poisson (ZIP) distribution.
Value
data_claim4 gives the number of automobile insurance third-party liability portfolios in Great Britain 1968.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Denuit, M. (1997). A new distribution of Poisson-type for the number of claims. ASTIN Bulletin: The Journal of the IAA, 27(2), 229-242.
See Also
data_claims, data_claim1, data_claim2, data_claim3
Examples
x<-data_claim4
summary(x)
table (x)
The data show the number of automobile insurance third party liability portfolios in Zaire 1974
Description
The function gives the number of automobile insurance third-party liability portfolios in Zaire 1974.
Usage
data_claim2
Arguments
data_claim2 |
A vector of (non-negative integer) count values. |
Details
The data show the number of automobile insurance third-party liability portfolios in Zaire 1974. They were used by Denuit (1997) and fitted by the zero-inflated Poisson (ZIP) distribution.
Value
data_claim2 gives the number of automobile insurance third-party liability portfolios in Zaire 1974.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Denuit, M. (1997). A new distribution of Poisson-type for the number of claims. ASTIN Bulletin: The Journal of the IAA, 27(2), 229-242.
See Also
Examples
x<-data_claim2
summary(x)
table (x)
The data show the number of automobile insurance third-party liability portfolios in Belgium 1993
Description
The function gives the number of automobile insurance third-party liability portfolios in Belgium 1993.
Usage
data_claim7
Arguments
data_claim7 |
A vector of (non-negative integer) count values. |
Details
The data show the number of automobile insurance third-party liability portfolios in Belgium 1993. They were used by Denuit (1997) and fitted by the zero-inflated Poisson (ZIP) distribution.
Value
data_claim7 gives the number of automobile insurance third-party liability portfolios in Belgium 1993.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Denuit, M. (1997). A new distribution of Poisson-type for the number of claims. ASTIN Bulletin: The Journal of the IAA, 27(2), 229-242.
See Also
data_claims, data_claim1, data_claim2, data_claim3, data_claim4, data_claim5, data_claim6
Examples
x<-data_claim7
summary(x)
table (x)
The data show the number of automobile insurance third-party liability portfolios in Belgium 1994
Description
The function gives the number of automobile insurance third-party liability portfolios in Belgium 1994.
Usage
data_claim8
Arguments
data_claim8 |
A vector of (non-negative integer) count values. |
Details
The data show the number of automobile insurance third-party liability portfolios in Belgium 1994. They were used by Denuit (1997) and fitted by the zero-inflated Poisson (ZIP) distribution.
Value
data_claim8 gives the number of automobile insurance third-party liability portfolios in Belgium 1994.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Denuit, M. (1997). A new distribution of Poisson-type for the number of claims. ASTIN Bulletin: The Journal of the IAA, 27(2), 229-242.
See Also
data_claims, data_claim1, data_claim2, data_claim3, data_claim6, data_claim7
Examples
x<-data_claim8
summary(x)
table (x)
The data show the number of automobile insurance third-party liability portfolios in Germany 1960
Description
The function gives the number of automobile insurance third-party liability portfolios in Germany 1960.
Usage
data_claim6
Arguments
data_claim6 |
A vector of (non-negative integer) count values. |
Details
The data show the number of automobile insurance third-party liability portfolios in Germany 1960. They were used by Denuit (1997) and fitted by the zero-inflated Poisson (ZIP) distribution.
Value
data_claim6 gives the number of automobile insurance third-party liability portfolios in Germany 1960.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Denuit, M. (1997). A new distribution of Poisson-type for the number of claims. ASTIN Bulletin: The Journal of the IAA, 27(2), 229-242.
See Also
data_claims, data_claim1, data_claim2, data_claim3, data_claim4, data_claim5
Examples
x<-data_claim6
summary(x)
table (x)
The data show the number of automobile insurance third-party liability portfolios in Switzerland 1961
Description
The function gives the number of automobile insurance third-party liability portfolios in Switzerland 1961.
Usage
data_claim5
Arguments
data_claim5 |
A vector of (non-negative integer) count values. |
Details
The data show the number of automobile insurance third-party liability portfolios in Switzerland 1961. They were used by Denuit (1997) and fitted by the zero-inflated Poisson (ZIP) distribution.
Value
data_claim5 gives the number of automobile insurance third-party liability portfolios in Switzerland 1961.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Denuit, M. (1997). A new distribution of Poisson-type for the number of claims. ASTIN Bulletin: The Journal of the IAA, 27(2), 229-242.
See Also
data_claims, data_claim1, data_claim2, data_claim3, data_claim4
Examples
x<-data_claim5
summary(x)
table (x)
The data show the observed number of births of female children
Description
The function gives the observed number of births of female children.
Usage
data_bfemale
Arguments
data_bfemale |
A vector of (non-negative integer) count values. |
Details
The data show the observed number of births of female children studied with mothers of parity 2. They were used by Rahman et al. (2021) and fitted by the one inflated binomial distribution.
Value
data_bfemale gives the observed number of births of female children.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Rahman, T., Hazarika, P. J., & Barman, M. P. (2021). One inflated binomial distribution and its real-life applications. Indian Journal of Science and Technology, 14(22), 1839-1854.
See Also
Examples
x<-data_bfemale
summary(x)
table (x)
The data show the observed number of births male children
Description
The function gives the observed number of births male children.
Usage
data_bmale
Arguments
data_bmale |
A vector of (non-negative integer) count values. |
Details
The data show the observed number of births male children studied with mothers of parity 2. They were used by Rahman et al. (2021) and fitted by the one inflated binomial distribution.
Value
data_bmale gives the observed number of births male children.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Rahman, T., Hazarika, P. J., & Barman, M. P. (2021). One inflated binomial distribution and its real-life applications. Indian Journal of Science and Technology, 14(22), 1839-1854.
See Also
Examples
x<-data_bmale
summary(x)
table (x)
The data show the number of lightning fatalities in Louisiana caused by boats
Description
The function gives the number of lightning fatalities in Louisiana caused by boats.
Usage
data_bfatality
Arguments
data_bfatality |
A vector of (non-negative integer) count values. |
Details
The data show the number of lightning fatalities in Louisiana caused by boats per year from 1995 through 2012. Recently, they were used by Beckett et al. (2014) and fitted by the zero-inflated Poisson (ZIP) distribution.
Value
data_bfatality gives the number of lightning fatalities in Louisiana caused by boats.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Beckett, S., Jee, J., Ncube, T., Pompilus, S., Washington, Q., Singh, A., & Pal, N. (2014). Zero-inflated Poisson (ZIP) distribution: Parameter estimation and applications to model data from natural calamities. Involve, a Journal of Mathematics, 7(6), 751-767.
See Also
Examples
x<-data_bfatality
summary(x)
table (x)
The data show the observed number of COVID-19 daily new deaths in Luxembourg in 2020
Description
The function gives the observed number of COVID-19 daily new deaths in Luxembourg in 2020.
Usage
data_COVIDd
Arguments
data_COVIDd |
A vector of (non-negative integer) count values. |
Details
The data show the observed number of COVID-19 daily new deaths in Luxembourg in 2020. Recently, they were used by Junnumtuam et al. (2022) and fitted by the zero and one inflated cosine geometric models.
Value
data_COVIDd gives the observed number of COVID-19 daily new deaths in Luxembourg in 2020.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Junnumtuam, S., Niwitpong, S. A., & Niwitpong, S. (2022). A zero-and-one inflated cosine geometric distribution and its application. Mathematics, 10(21), 4012.
See Also
data_argcovid, data_Algeriacovid, data_Bosniacovid
Examples
x<-data_COVIDd
summary(x)
table (x)
The data show the observed number of cancer houses
Description
The function gives the observed number of cancer houses.
Usage
data_can
Arguments
data_can |
A vector of (non-negative integer) count values. |
Details
The data show the observed number of cancer houses. They were used by Greenwood and Yule (1920) and underlined an inquiry into the nature of frequency distributions.
Value
data_can gives the observed number of cancer houses.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Greenwood, M., & Yule, G. U. (1920). An inquiry into the nature of frequency distributions representative of multiple happenings with particular reference to the occurrence of multiple attacks of disease or of repeated accidents. Journal of the Royal Statistical Society, 83(2), 255-279.
See Also
Examples
x<-data_can
summary(x)
table (x)
The data show the frequency distribution of the number of carious teeth among the four deciduous molars
Description
The function gives the frequency distribution of the number of carious teeth among the four deciduous molars.
Usage
data_carious
Arguments
data_carious |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of the number of carious teeth among the four deciduous molars. Recently, They were used by Morshedy et al. (2020) and fitted by the discrete Burr-Hatke distribution.
Value
data_carious gives the frequency distribution of the number of carious teeth among the four deciduous molars.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
El-Morshedy, M., Eliwa, M. S., & Altun, E. (2020). Discrete Burr-Hatke distribution with properties, estimation methods, and regression model. IEEE Access, 8, 74359-74370.
See Also
Examples
x<-data_carious
summary(x)
table (x)
The data show the traffic accidents in Changhua city
Description
The function gives the frequency distribution of the traffic accidents in Changhua City.
Usage
data_tacci
Arguments
data_tacci |
A vector of (non-negative integer) count values. |
Details
The data show the traffic accidents that were collected in Changhua city (mainly rural) locates in the central part of Taiwan from 2011-2013 by the Taiwan National Police Agency (NPA). Recently, they were used by Lukusa and Phoa (2020) and fitted by the zero-inflated Poisson model.
Value
data_tacci gives the frequency distribution of the traffic accidents in Changhua city.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Lukusa, M. T., & Phoa, F. K. H. (2020). A Horvitz-type estimation on incomplete traffic accident data analyzed via a zero-inflated Poisson model. Accident Analysis & Prevention, 134, 105235.
See Also
Examples
x<-data_tacci
summary(x)
table (x)
The data show the frequency distribution of child deaths in the Bundelkhand region of Uttar Pradesh
Description
The function gives the frequency distribution of child deaths in the Bundelkhand region of Uttar Pradesh.
Usage
data_bregion
Arguments
data_bregion |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of child deaths in the Bundelkhand region of Uttar Pradesh. They were used by Singh et al. (2012) and fitted by a probabilistic study of variation in the number of child deaths.
Value
data_bregion gives the frequency distribution of child deaths in the Bundelkhand region of Uttar Pradesh.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Singh, K. K., Singh, B. P., & Singh, N. (2012). A probabilistic study of variation in number of child deaths. Journal of Rajasthan Statistical Association, 1(1), 54-67.
See Also
data_hregion, data_cregion, data_eregion
Examples
x<-data_bregion
summary(x)
table (x)
The data show the frequency distribution of child deaths in the Central region of Uttar Pradesh
Description
The function gives the frequency distribution of child deaths in the Central region of Uttar Pradesh.
Usage
data_cregion
Arguments
data_cregion |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of child deaths in the Central region of Uttar Pradesh. They were used by Singh et al. (2012) and fitted by a probabilistic study of variation in the number of child deaths.
Value
data_cregion gives the frequency distribution of child deaths in the Central region of Uttar Pradesh.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Singh, K. K., Singh, B. P., & Singh, N. (2012). A probabilistic study of variation in number of child deaths. Journal of Rajasthan Statistical Association, 1(1), 54-67.
See Also
Examples
x<-data_cregion
summary(x)
table (x)
The data show the frequency distribution of child deaths in the Eastern region of Uttar Pradesh
Description
The function gives the frequency distribution of child deaths in the Eastern region of Uttar Pradesh.
Usage
data_eregion
Arguments
data_eregion |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of child deaths in the Eastern region of Uttar Pradesh. They were used by Singh et al. (2012) and fitted by a probabilistic study of variation in the number of child deaths.
Value
data_eregion gives the frequency distribution of child deaths in the Eastern region of Uttar Pradesh.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Singh, K. K., Singh, B. P., & Singh, N. (2012). A probabilistic study of variation in number of child deaths. Journal of Rajasthan Statistical Association, 1(1), 54-67.
See Also
Examples
x<-data_eregion
summary(x)
table (x)
The data show the frequency distribution of child deaths in the Hill region of Uttar Pradesh
Description
The function gives the frequency distribution of child deaths in the Hill region of Uttar Pradesh.
Usage
data_hregion
Arguments
data_hregion |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of child deaths in the Hill region of Uttar Pradesh. They were used by Singh et al. (2012) and fitted by a probabilistic study of variation in the number of child deaths.
Value
data_hregion gives the frequency distribution of child deaths in the Hill region of Uttar Pradesh.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Singh, K. K., Singh, B. P., & Singh, N. (2012). A probabilistic study of variation in number of child deaths. Journal of Rajasthan Statistical Association, 1(1), 54-67.
See Also
data_argcovid, data_inj2, data_inj3
Examples
x<-data_hregion
summary(x)
table (x)
The data show the frequency distribution of child deaths in Uttar Pradesh
Description
The function gives the frequency distribution of child deaths in Uttar Pradesh.
Usage
data_uttar
Arguments
data_uttar |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of child deaths in Uttar Pradesh. They were used by Singh et al. (2012) and fitted by a probabilistic study of variation in the number of child deaths.
Value
data_uttar gives the frequency distribution of child deaths in Uttar Pradesh.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Singh, K. K., Singh, B. P., & Singh, N. (2012). A probabilistic study of variation in number of child deaths. Journal of Rajasthan Statistical Association, 1(1), 54-67.
See Also
Examples
x<-data_uttar
summary(x)
table (x)
The data show the frequency distribution of child deaths in the Western region of Uttar Pradesh
Description
The function gives the frequency distribution of child deaths in the Western region of Uttar Pradesh.
Usage
data_wregion
Arguments
data_wregion |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of child deaths in the Western region of Uttar Pradesh. They were used by Singh et al. (2012) and fitted by a probabilistic study of variation in the number of child deaths.
Value
data_wregion gives the frequency distribution of child deaths in the Western region of Uttar Pradesh.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Singh, K. K., Singh, B. P., & Singh, N. (2012). A probabilistic study of variation in number of child deaths. Journal of Rajasthan Statistical Association, 1(1), 54-67.
See Also
Examples
x<-data_wregion
summary(x)
table (x)
The data show the frequency distribution of child deaths in rural females of Uttar Pradesh
Description
The function gives the frequency distribution of child deaths in rural females of Uttar Pradesh.
Usage
data_rfemale
Arguments
data_rfemale |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of child deaths in a rural female of Uttar Pradesh. They were used by Singh et al. (2012) and fitted by a probabilistic study of variation in the number of child deaths.
Value
data_rfemale gives the frequency distribution of child deaths in a rural female of Uttar Pradesh.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Singh, K. K., Singh, B. P., & Singh, N. (2012). A probabilistic study of variation in number of child deaths. Journal of Rajasthan Statistical Association, 1(1), 54-67.
See Also
Examples
x<-data_rfemale
summary(x)
table (x)
The data show the frequency distribution of child deaths in the age group 30-39 in Uttar Pradesh
Description
The function gives the frequency distribution of child deaths in the age group 30-39 of Uttar Pradesh.
Usage
data_age_30
Arguments
data_age_30 |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of child deaths in the age group 30-39 of Uttar Pradesh. They were used by Singh et al. (2012) and fitted by a probabilistic study of variation in the number of child deaths.
Value
data_age_30 gives the frequency distribution of child deaths in the age group 30-39 of Uttar Pradesh.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Singh, K. K., Singh, B. P., & Singh, N. (2012). A probabilistic study of variation in number of child deaths. Journal of Rajasthan Statistical Association, 1(1), 54-67.
See Also
data_age_40, data_cregion, data_eregion
Examples
x<-data_age_30
summary(x)
table (x)
The data show the frequency distribution of child deaths in the age group 40-49 of Uttar Pradesh
Description
The function gives the frequency distribution of child deaths in the age group 40-49 of Uttar Pradesh.
Usage
data_age_40
Arguments
data_age_40 |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of child deaths in the age group 40-49 of Uttar Pradesh. They were used by Singh et al. (2012) and fitted by a probabilistic study of variation in the number of child deaths.
Value
data_age_40 gives the frequency distribution of child deaths in the age group 40-49 of Uttar Pradesh.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Singh, K. K., Singh, B. P., & Singh, N. (2012). A probabilistic study of variation in number of child deaths. Journal of Rajasthan Statistical Association, 1(1), 54-67.
See Also
Examples
x<-data_age_40
summary(x)
table (x)
The data show the frequency distribution of child deaths in urban females of Uttar Pradesh
Description
The function gives the frequency distribution of child deaths in urban females of Uttar Pradesh.
Usage
data_ufemale
Arguments
data_ufemale |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of child deaths in urban females of Uttar Pradesh. They were used by Singh et al. (2012) and fitted by a probabilistic study of variation in the number of child deaths.
Value
data_ufemale gives the frequency distribution of child deaths in urban females of Uttar Pradesh.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Singh, K. K., Singh, B. P., & Singh, N. (2012). A probabilistic study of variation in number of child deaths. Journal of Rajasthan Statistical Association, 1(1), 54-67.
See Also
Examples
x<-data_ufemale
summary(x)
table (x)
The data show the observed number of children per woman
Description
The function gives the observed number of children per woman.
Usage
data_child
Arguments
data_child |
A vector of (non-negative integer) count values. |
Details
The data show the observed number of children per woman. They were used by Melkersson and Rooth (2000) and fitted by the inflated count data models.
Value
data_child gives the observed number of children per woman.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Melkersson, M., & Rooth, D. O. (2000). Modeling female fertility using inflated count data models. Journal of Population Economics, 13, 189-203.
See Also
Examples
x<-data_child
summary(x)
table (x)
The data show the frequency distribution of claims of the third liability vehicle insurance in a Chinese insurance company
Description
The function gives the frequency distribution of claims of the third liability vehicle insurance in a Chinese insurance company.
Usage
data_vinsurance
Arguments
data_vinsurance |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of claims of the third liability vehicle insurance in a Chinese insurance company. They were used by Wang (2011) and fitted by the one mixed negative binomial distribution.
Value
data_vinsurance gives the frequency distribution of claims of the third liability vehicle insurance in a Chinese insurance company.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Wang, Z. (2011). One mixed negative binomial distribution with the application. Journal of Statistical Planning and Inference, 141(3), 1153-1160.
See Also
data_claims, data_claim1, data_claim2
Examples
x<-data_vinsurance
summary(x)
table (x)
The data show the frequency distribution of chromatid aberrations in human leukocyte
Description
The function gives the frequency distribution of chromatid aberrations in human leukocytes.
Usage
data_chromatid
Arguments
data_chromatid |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of chromatid aberrations in human leukocytes. They were used by Para and Jan (2016) and fitted by the discrete version of the log-logistic distribution.
Value
data_chromatid gives the frequency distribution of chromatid aberrations in human leukocytes.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Para, B. A., & Jan, T. R. (2016). Discrete version of log-logistic distribution and its applications in genetics. International Journal of Mathematics and Mathematical Sciences, 14(4), 407-422.
Examples
x<-data_chromatid
summary(x)
table (x)
The data show the number of chromosome pairing at I metaphase in three plants of Secale vavilovii
Description
The function gives the number of chromosome pairing count data at I metaphase in three plants of Secale vavilovii.
Usage
data_p806_8
Arguments
data_p806_8 |
A vector of (non-negative integer) count values. |
Details
The data show the number of chromosome pairing at I metaphase in three plants of Secale vavilovii. They were used by Jain (1959) and fitted by the negative binomial distribution.
Value
data_p806_8 gives the observed number of chromosome pairing count data at I metaphase in three plants of Secale vavilovii.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Jain, S. K. (1959). Fitting the negative binomial distribution to some data on asynaptic behaviour of chromosomes. Genetica, 30(1), 108-122.
See Also
Examples
x<-data_p806_8
summary(x)
table (x)
The number of chromosome pairing at I metaphase in three plants of Secale vavilovii
Description
The function gives the number of chromosome pairing count data at I metaphase in three plants of Secale vavilovii.
Usage
data_p806_7
Arguments
data_p806_7 |
A vector of (non-negative integer) count values. |
Details
The data show the number of chromosome pairing count data at I metaphase in three plants of Secale vavilovii. They were used by Jain (1959) and fitted by the negative binomial distribution.
Value
data_p806_7 gives the number of chromosome pairing count data at I metaphase in three plants of Secale vavilovii.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Jain, S. K. (1959). Fitting the negative binomial distribution to some data on asynaptic behavior of chromosomes. Genetica, 30(1), 108-122.
See Also
Examples
x<-data_p806_7
summary(x)
table (x)
The data represent the number of chromosome pairing at I metaphase in three plants of Secale vavilovii
Description
The function gives the number of chromosome pairing count data at I metaphase in three plants of Secale vavilovii.
Usage
data_p806_9
Arguments
data_p806_9 |
A vector of (non-negative integer) count values. |
Details
The data represent the number of chromosome pairing at I metaphase in three plants of Secale vavilovii. They were used by Jain (1959) and fitted by the negative binomial distribution.
Value
data_p806_9 provides the observed number of chromosome pairing count data at I metaphase in three plants of Secale vavilovii.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Jain, S. K. (1959). Fitting the negative binomial distribution to some data on asynaptic behaviour of chromosomes. Genetica, 30(1), 108-122.
See Also
Examples
x<-data_p806_9
summary(x)
table (x)
The data show the number of claims per accident
Description
The function gives the number of claims per accident.
Usage
data_aclaim
Arguments
data_aclaim |
A vector of (non-negative integer) count values. |
Details
The data show the number of claims per accident. They were used by Willmot (1987) and fitted by the Poisson-inverse Gaussian distribution.
Value
data_aclaim gives the number of claims per accident.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Willmot, G. E. (1987). The Poisson-inverse Gaussian distribution is an alternative to the negative binomial. Scandinavian Actuarial Journal, 1987(3-4), 113-127.
See Also
data_claims, data_claim1, data_claim2, data_claim3
Examples
x<-data_aclaim
summary(x)
table (x)
The data show the daily newly reported COVID-19 cases
Description
The function gives the daily newly reported COVID-19 cases.
Usage
data_Algeriacovid
Arguments
data_Algeriacovid |
A vector of (non-negative integer) count values. |
Details
The data show the daily newly reported COVID-19 cases from Algeria in East Africa, recorded between 13 June 2022 to 3 October 2022. They were used by Shibu et al. (2023) and fitted by the zero-truncated Katz distribution.
Value
data_Algeriacovid gives the daily newly reported COVID-19 cases.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Shibu, D. S., Chesneau, C., Monisha, M., Maya, R., & Irshad, M. R. (2023). A novel zero-truncated Katz distribution by the Lagrange expansion of the second kind with associated inferences. Analytics, 2(2), 463-484.
See Also
data_argcovid, data_Bosniacovid
Examples
x<-data_Algeriacovid
summary(x)
table (x)
The data show the daily reported COVID-19 death cases
Description
The function gives the daily reported COVID-19 death cases.
Usage
data_Bosniacovid
Arguments
data_Bosniacovid |
A vector of (non-negative integer) count values. |
Details
The data show the daily reported COVID-19 death cases from Bosnia and Herzegovina in Europe, recorded between 2 August 2020 to 28 June 2021. They were used by Shibu et al. (2023) and fitted by the zero truncated Katz distribution.
Value
data_Bosniacovid gives the daily reported COVID-19 death cases.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Shibu, D. S., Chesneau, C., Monisha, M., Maya, R., & Irshad, M. R. (2023). A novel zero truncated Katz distribution by the Lagrange expansion of the second kind with associated inferences. Analytics, 2(2), 463-484.
See Also
data_argcovid, data_Algeriacovid
Examples
x<-data_Bosniacovid
summary(x)
table (x)
The data set is from crime sociology consisting of a sample of 4301 people with deviating behavior
Description
The function gives a sample of 4301 people with deviating behavior.
Usage
data_crime
Arguments
data_crime |
A vector of (non-negative integer) count values. |
Details
The data set is from crime sociology consisting of a sample of 4301 people with deviating behavior. Recently, it was used by Zhang et al. (2016) and fitted by the zero-and-one inflated Poisson distribution.
Value
data_crime gives a sample of 4301 people with deviating behavior.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Zhang, C., Tian, G. L., & Ng, K. W. (2016). Properties of the zero-and-one inflated Poisson distribution and likelihood-based inference methods. Statistics and its Interface, 9(1), 11-32.
Examples
x<-data_crime
summary(x)
table (x)
The data show the frequency distribution of cysts of kidneys using steroids
Description
The function gives the frequency distribution of cysts of kidneys using steroids.
Usage
data_cysts
Arguments
data_cysts |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of cysts of kidneys using steroids. Recently, they were used by Morshedy et al. (2020) and fitted by the discrete Burr-Hatke distribution.
Value
data_cysts gives the frequency distribution of cysts of kidneys using steroids.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
El-Morshedy, M., Eliwa, M. S., & Altun, E. (2020). Discrete Burr-Hatke distribution with properties, estimation methods, and regression model. IEEE Access, 8, 74359-74370.
Para, B. A., & Jan, T. R. (2016). On discrete three-parameter Burr type XII and discrete Lomax distributions and their applications to model count data from medical science. Biometrics and Biostatistics International Journal, 4(2), 1-15.
See Also
Examples
x<-data_cysts
summary(x)
table (x)
A data set of size n = 280 concerning the number of deaths from horse-kicks
Description
The function gives the number of deaths from horse-kicks.
Usage
data_deaths
Arguments
data_deaths |
A vector of (non-negative integer) count values. |
Details
A data set of size n = 280 concerns the number of deaths from horse-kicks. It was used by Preece et al. (1988) and fitted by the generalized linear model.
Value
data_deaths gives the number of deaths from horse-kicks.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Preece, D. A., Ross, G. J. S., & Kirby, S. P. J. (1988). Bortkewitsch's horse-kicks and the generalized linear model. Journal of the Royal Statistical Society: Series D (The Statistician), 37(3), 313-318.
See Also
Examples
x<-data_deaths
summary(x)
table (x)
The data show the number of death notices for women who are 80 years of age or older
Description
The function gives the number of death notices for women who are 80 years of age or older.
Usage
data_death
Arguments
data_death |
A vector of (non-negative integer) count values. |
Details
The data show the number of death notices for women who are 80 years of age or older, appearing in the London Times on each day for three consecutive years. Recently, they were used by Zhang et al. (2016) and fitted by the zero-and-one inflated Poisson distribution.
Value
data_death gives the number of death notices for women who are 80 years of age or older.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Zhang, C., Tian, G. L., & Ng, K. W. (2016). Properties of the zero-and-one inflated Poisson distribution and likelihood-based inference methods. Statistics and its Interface, 9(1), 11-32.
Gupta, P. L., Gupta, R. C., & Tripathi, R. C. (1996). Analysis of zero-adjusted count data. Computational Statistics & Data Analysis, 23(2), 207-218.
Hasselblad, V. (1969). Estimation of finite mixtures of distributions from the exponential family. Journal of the American Statistical Association, 64(328), 1459-1471.
Schilling, W. (1947). A frequency distribution is represented as the sum of two Poisson distributions. Journal of the American Statistical Association, 42(239), 407-424.
Examples
x<-data_death
summary(x)
table (x)
The data set represents a panel data from Swedish Level of Living Surveys
Description
The function gives the number of dentists visiting data from Swedish Level of Living Surveys.
Usage
data_dentist
Arguments
data_dentist |
A vector of (non-negative integer) count values. |
Details
The data set represents a panel data from Swedish Level of Living Surveys in 1974 and 1991. To examine the long-term impact of frequent dental checkups during adolescents and childhood. Recently, it was used by Zhang (2016) and fitted by the zero-and-one inflated Poisson distribution.
Value
data_dentist gives the number of dentists visiting data from Swedish Level of Living Surveys.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Zhang, C., Tian, G. L., & Ng, K. W. (2016). Properties of the zero-and-one inflated Poisson distribution and likelihood-based inference methods. Statistics and its Interface, 9(1), 11-32.
Erikson, R., & Åberg, R. (Eds.) (1987). Welfare in transition: A survey of living conditions in Sweden, 1968-1981. Oxford University Press.
See Also
Examples
x<-data_dentist
summary(x)
table (x)
The data show the number of lightning fatalities in Louisiana caused by a tree
Description
The function gives the number of lightning fatalities in Louisiana caused by a tree.
Usage
data_tfatality
Arguments
data_tfatality |
A vector of (non-negative integer) count values. |
Details
The data show the number of lightning fatalities in Louisiana caused by a tree per year from 1995 through 2012. Recently, they were used by Beckett et al. (2014) and fitted by the zero-inflated Poisson (ZIP) distribution.
Value
data_tfatality gives the number of lightning fatalities in Louisiana caused by a tree.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Beckett, S., Jee, J., Ncube, T., Pompilus, S., Washington, Q., Singh, A., & Pal, N. (2014). Zero-inflated Poisson (ZIP) distribution: Parameter estimation and applications to model data from natural calamities. Involve, a Journal of Mathematics, 7(6), 751-767.
See Also
Examples
x<-data_tfatality
summary(x)
table (x)
The data show the number of lightning fatalities in Louisiana caused by out in the open
Description
The function gives the number of lightning fatalities in Louisiana caused out in the open.
Usage
data_ofatality
Arguments
data_ofatality |
A vector of (non-negative integer) count values. |
Details
The data show the number of lightning fatalities in Louisiana caused out in the open per year from 1995 through 2012. They were used by Beckett et al. (2014) and fitted by the zero-inflated Poisson (ZIP) distribution.
Value
data_ofatality gives the number of lightning fatalities in Louisiana caused by out in the open.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Beckett, S., Jee, J., Ncube, T., Pompilus, S., Washington, Q., Singh, A., & Pal, N. (2014). Zero-inflated Poisson (ZIP) distribution: Parameter estimation and applications to model data from natural calamities. Involve, a Journal of Mathematics, 7(6), 751-767.
See Also
Examples
x<-data_ofatality
summary(x)
table (x)
The data show the number of lightning fatalities in Louisiana caused by golf courses
Description
The function gives the number of lightning fatalities in Louisiana caused by golf courses.
Usage
data_gfatality
Arguments
data_gfatality |
A vector of (non-negative integer) count values. |
Details
The data show the number of lightning fatalities in Louisiana caused by golf courses per year from 1995 through 2012. Recently, they were used by Beckett et al. (2014) and fitted by the zero-inflated Poisson (ZIP) distribution.
Value
data_gfatality gives the number of lightning fatalities in Louisiana caused by golf courses.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Beckett, S., Jee, J., Ncube, T., Pompilus, S., Washington, Q., Singh, A., & Pal, N. (2014). Zero-inflated Poisson (ZIP) distribution: Parameter estimation and applications to model data from natural calamities. Involve, a Journal of Mathematics, 7(6), 751-767.
See Also
Examples
x<-data_gfatality
summary(x)
table (x)
The data show the frequency distribution of female childbirth in Bihar
Description
The function gives the frequency distribution of female childbirth in Bihar.
Usage
data_bihar
Arguments
data_bihar |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of female childbirth in Bihar. Recently, they were used by Kumar (2020) and fitted by a probability model for the number of female childbirths.
Value
data_bihar gives the frequency distribution of female childbirth in Bihar.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Kumar, A. (2020). A probability model for the number of female childbirths. Journal of Statistics Applications & Probability. 9(3), 525-534.
See Also
Examples
x<-data_bihar
summary(x)
table (x)
The data show the frequency distribution of female childbirth in Orissa
Description
The function gives the frequency distribution of female childbirth in Orissa.
Usage
data_orissa
Arguments
data_orissa |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of female childbirth in Orissa. Recently, they were used by Kumar (2020) and fitted by a probability model for the number of female childbirths.
Value
data_orissa gives the frequency distribution of female childbirth in Orissa.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Kumar, A. (2020). A probability model for the number of female childbirths. Journal of Statistics Applications & Probability, 9 (3), 525-534.
See Also
Examples
x<-data_orissa
summary(x)
table (x)
The data show the frequency distribution of female childbirth in Rajasthan
Description
The function gives the frequency distribution of female childbirth in Rajasthan.
Usage
data_rajasthan
Arguments
data_rajasthan |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of female childbirth in Rajasthan. Recently, they were used by Kumar (2020) and fitted by a probability model for the number of female childbirths.
Value
data_rajasthan gives the frequency distribution of female childbirth in Rajasthan.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Kumar, A. (2020). A probability model for the number of female childbirths. Journal of Statistics Applications & Probability. 9(3), 525-534.
See Also
Examples
x<-data_rajasthan
summary(x)
table (x)
The data show the frequency distribution of female childbirth in West Bengal
Description
The function gives the frequency distribution of female childbirth in West Bengal.
Usage
data_bengal
Arguments
data_bengal |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of female childbirth in West Bengal. Recently, they were used by Kumar (2020) and fitted by a probability model for the number of female childbirths.
Value
data_bengal gives the frequency distribution of female childbirth in West Bengal.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Kumar, A. (2020). A probability model for the number of female childbirths. Journal of Statistics Applications & Probability. 9 (3), 525-534.
See Also
Examples
x<-data_bengal
summary(x)
table (x)
The data represent the number of movements made by a fetal lamb
Description
The function gives the number of movements made by a fetal lamb.
Usage
data_fetalm
Arguments
data_fetalm |
A vector of (non-negative integer) count values. |
Details
The data correspond to a certain order of counts in a study of fetal lambs' breathing and movement patterns to look at potential changes in the amount and pattern of fetal activity throughout the last two-thirds of gestation. Recently, they were used by Zhang et al. (2016) and fitted by the zero-and-one inflated Poisson distribution.
Value
data_fetalm gives many movements made by a fetus during the last two-thirds of gestation.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Zhang, C., Tian, G. L., & Ng, K. W. (2016). Properties of the zero-and-one inflated Poisson distribution and likelihood-based inference methods. Statistics and its Interface, 9(1), 11-32.
Leroux, B. G., & Puterman, M. L. (1992). Maximum penalized likelihood estimation for independent and Markov-dependent mixture models. Biometrics, 545-558.
Examples
x<-data_fetalm
summary(x)
table (x)
The data show the observed number of high explosive shell manufacture accidents
Description
The function gives the observed number of high explosive shell manufacture accidents.
Usage
data_accid
Arguments
data_accid |
A vector of (non-negative integer) count values. |
Details
The data show the observed number of high explosive shell manufacture accidents. They were used by Greenwood and Yule (1920) and underlined an inquiry into the nature of frequency distributions.
Value
data_accid gives the observed number of High explosive shell manufacture accidents.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Greenwood, M., & Yule, G. U. (1920). An inquiry into the nature of frequency distributions representative of multiple happenings with particular reference to the occurrence of multiple attacks of disease or of repeated accidents. Journal of the Royal Statistical Society, 83(2), 255-279.
See Also
Examples
x<-data_accid
summary(x)
table (x)
A data set of size n = 200 concerning the number of deaths due to horse-kicks
Description
The function gives the number of deaths due to horse kicks excluding crops G, I, VI, and XI.
Usage
data_edeath
Arguments
data_edeath |
A vector of (non-negative integer) count values. |
Details
A data set of size n = 200 concerning the number of deaths due to horse-kicks excluding crops G, I, VI, and XI. It was used by Preece et al. (1988) and studied by the Bortkewitsch's horse-kicks and the generalized linear model.
Value
data_edeath gives the number of deaths from horse-kicks excluding crops G, I, VI, and XI.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Preece, D. A., Ross, G. J. S., & Kirby, S. P. J. (1988). Bortkewitsch's horse-kicks and the generalized linear model. Journal of the Royal Statistical Society: Series D (The Statistician), 37(3), 313-318.
See Also
Examples
x<-data_edeath
summary(x)
table (x)
The data show the frequency distribution of length of hospital stay
Description
The function gives the frequency distribution of the length of hospital stay.
Usage
data_stays
Arguments
data_stays |
A vector of (non-negative integer) count values. |
Details
The data set consists of the number of hospital stays by United States residents aged 66 and over. Recently, it was used by Aryuyuen et al. (2014) and fitted by the zero-inflated negative binomial-generalized exponential distribution.
Value
data_stays gives the observed frequencies of the number of hospital stays by United States residents aged 66 and over.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Aryuyuen, S., Bodhisuwan, W., & Supapakorn, T. (2014). Zero-inflated negative binomial-generalized exponential distribution and its applications. Songklanakarin Journal of Science and Technology, 36(4), 483-91.
Flynn, M., & Francis, L. A. (2009). More flexible GLMs zero-inflated models and hybrid models. Casualty Actuarial Society, 2009, 148-224.
Examples
x<-data_stays
summary(x)
table (x)
The data show the observed number of Iranian household sizes
Description
The function gives the observed number of Iranian household sizes.
Usage
data_household
Arguments
data_household |
A vector of (non-negative integer) count values. |
Details
A data set that comes from a pseudo panel constructed from information from the 2010-2011 Household Expenditure and Income Survey, which includes details on household size but excludes the head of the family. Therefore, given these data, 0 indicates that there is just one resident of the house. They were used by Mersad et al. (2015) and fitted by the zero-inflated models.
Value
data_household gives the observed number of Iranian household size.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Mersad, M., Ganjali, M., & Rivaz, F. (2015). Some extensions of zero-inflated models and Bayesian tests for them. Journal of Statistical Computation and Simulation, 85(18), 3792-3810.
Examples
x<-data_household
summary(x)
table (x)
The data show the observed number of industrial accidents
Description
The function gives the observed number of industrial accidents.
Usage
data_indusacci
Arguments
data_indusacci |
A vector of (non-negative integer) count values. |
Details
The data show the observed number of industrial accidents. They were used by Greenwood and Yule (1920) and underlined an inquiry into the nature of frequency distributions.
Value
data_indusacci gives the observed number of industrial accidents.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Greenwood, M., & Yule, G. U. (1920). An inquiry into the nature of frequency distributions representative of multiple happenings with particular reference to the occurrence of multiple attacks of disease or of repeated accidents. Journal of the Royal Statistical Society, 83(2), 255-279.
See Also
Examples
x<-data_indusacci
summary(x)
table (x)
The data show the observed number of females in 100 queues of length 10 in a London underground station
Description
The function gives the observed number of females in 100 queues.
Usage
data_queue
Arguments
data_queue |
A vector of (non-negative integer) count values. |
Details
The data show the observed number of females in 100 queues of length 10 in a London underground station. They were used by Conigliani et al. (2000) and fitted by the zero-inflated models.
Value
data_queue gives the observed number of females in 100 queues.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Conigliani, C., Castro, J. I., & O'Hagan, A. (2000). Bayesian assessment of goodness of fit against nonparametric alternatives. Canadian Journal of Statistics, 28(2), 327-342.
See Also
Examples
x<-data_queue
summary(x)
table (x)
The data show the frequancy distribution of lost shoes at a Museum gate
Description
The function gives the frequancy distribution of lost shoes at a Museum gate.
Usage
data_lost
Arguments
data_lost |
A vector of (non-negative integer) count values. |
Details
The data show the frequancy distribution of lost shoes at a Museum gate. They were used by Chandra and Ghosh (2013) and fitted by the generalized Poisson distribution.
Value
data_lost gives the frequancy distribution of lost shoes at a Museum gate.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Chandra, N. K., Roy, D., & Ghosh, T. (2013). A generalized Poisson distribution. Communications in Statistics-Theory and Methods, 42(15), 2786-2797.
Examples
x<-data_lost
summary(x)
table (x)
The data show the observed number of machinists accidents six months study
Description
The function gives the observed number of machinist accidents in six months of study.
Usage
data_machinist
Arguments
data_machinist |
A vector of (non-negative integer) count values. |
Details
The data show the observed number of machinists accidents six months study. They were used by Greenwood and Yule (1920) and underlined an inquiry into the nature of frequency distributions.
Value
data_machinist gives the observed number of Machinists accidents in six monthly studies.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Greenwood, M., & Yule, G. U. (1920). An inquiry into the nature of frequency distributions representative of multiple happenings with particular reference to the occurrence of multiple attacks of disease or of repeated accidents. Journal of the Royal Statistical Society, 83(2), 255-279.
See Also
Examples
x<-data_machinist
summary(x)
table (x)
The data show the number of major US wildfires
Description
The function gives the number of major US wildfires per year from 1997 through 2012.
Usage
data_wildfire
Arguments
data_wildfire |
A vector of (non-negative integer) count values. |
Details
The data show the number of major US wildfires per year from 1997 through 2012. Recently, they were used by Beckett et al. (2014) and fitted by the zero-inflated Poisson (ZIP) distribution.
Value
data_wildfire gives the number of major US wildfires per year from 1997 through 2012.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Beckett, S., Jee, J., Ncube, T., Pompilus, S., Washington, Q., Singh, A., & Pal, N. (2014). Zero-inflated Poisson (ZIP) distribution: Parameter estimation and applications to model data from natural calamities. Involve, a Journal of Mathematics, 7(6), 751-767.
See Also
Examples
x<-data_wildfire
summary(x)
table (x)
The data set consists of the number of major derogatory reports in the credit history
Description
The function gives the number of major derogatory reports in the credit history of individual credit card applicants.
Usage
data_derogatory
Arguments
data_derogatory |
A vector of (non-negative integer) count values. |
Details
The data set consists of the number of major derogatory reports in the credit history of individual credit card applicants. Recently, it was used by Saengthong et al. (2015) and fitted by the zero-inflated negative binomial-Crack distribution.
Value
data_derogatory gives the number of major derogatory reports in the credit history of individual credit card applicants.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Saengthong, P., Bodhisuwan, W., & Thongteeraparp, A. (2015). The zero-inflated negative binomial-Crack distribution: some properties and parameter estimation. Songklanakarin Journal of Science and Technology, 37(6), 701-711.
Examples
x<-data_derogatory
summary(x)
table (x)
The data show the number of major US earthquakes
Description
The function gives the number of major US earthquakes per year from 1950 through 2012.
Usage
data_earthq
Arguments
data_earthq |
A vector of (non-negative integer) count values. |
Details
The data show the number of major US earthquakes per year from 1950 through 2012. Recently, they were used by Beckett et al. (2014) and fitted by the zero-inflated Poisson (ZIP) distribution.
Value
data_earthq gives the observed frequencies for the number of major US earthquakes per year from 1950 through 2012.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Beckett, S., Jee, J., Ncube, T., Pompilus, S., Washington, Q., Singh, A., & Pal, N. (2014). Zero-inflated Poisson (ZIP) distribution: Parameter estimation and applications to model data from natural calamities. Involve, a Journal of Mathematics, 7(6), 751-767.
See Also
Examples
x<-data_earthq
summary(x)
table (x)
The data show the frequency distribution of male sibship
Description
The function gives the frequency distribution of male sibship.
Usage
data_sibship
Arguments
data_sibship |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of male sibship. They were used by Sweeney et al. (2014) and fitted by the zero & N inflated binomial distribution.
Value
data_sibship gives the frequency distribution of male sibship.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Sweeney, J., Haslett, J., & Parnell, A. C. (2014). The zero & N inflated binomial distribution with applications. arXiv preprint arXiv:1407.0064.
Examples
x<-data_sibship
summary(x)
table (x)
The data set consists of the number of migrants from a household in the semi-urban type of village
Description
The function gives the observed frequencies for the number of migrants from a household in the semi-urban type of village.
Usage
data_migrants
Arguments
data_migrants |
A vector of (non-negative integer) count values. |
Details
The data set consists of the number of migrants from a household in the semi-urban type of village. It was used by Pandey et al. (2015) and fitted by the inflated probability model on rural out-migration.
Value
data_migrants gives the observed frequencies for the number of migrants from a household in the semi-urban type of village.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Pandey, A., Pandey, H., & Shukla, V. K. (2015). An inflated probability model on rural out migration. Journal of Computer and Mathematical Sciences, 6(12), 702-711.
See Also
Examples
x<-data_migrants
summary(x)
table (x)
The data set consists of the number of migrants from a household in a growth centre type of village
Description
The function gives the observed frequencies for the number of migrants from a household in a growth centre type of village.
Usage
data_migrant
Arguments
data_migrant |
A vector of (non-negative integer) count values. |
Details
The data set consists of the number of migrants from a household in a growth centre type of village. It was used by Pandey et al. (2015) and fitted by the inflated probability model on rural outmigration.
Value
data_migrant gives the observed frequencies for the number of migrants from a household in a growth centre type of village.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Pandey, A., Pandey, H., & Shukla, V. K. (2015). An inflated probability model on rural out migration. Journal of Computer and Mathematical Sciences, 6(12), 702-711.
See Also
Examples
x<-data_migrant
summary(x)
table (x)
The data contain the frequency distribution of the number of actions taken in response to a decision by the Court
Description
The function gives the frequency distribution of the number of actions taken in response to a decision by the Court from 1979-1988.
Usage
data_action
Arguments
data_action |
A vector of (non-negative integer) count values. |
Details
The data contain the frequency distribution of the number of actions taken in response to a decision by the Court from 1979-1988. They were used by Zorn (1998) and fitted by the zero-inflated and hurdle models.
Value
data_action gives the frequency distribution of the number of actions taken in response to a decision by the Court from 1979-1988.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Zorn, C. J. (1998). An analytic and empirical examination of zero-inflated and hurdle Poisson specifications. Sociological Methods & Research, 26(3), 368-400.
Examples
x<-data_action
summary(x)
table (x)
The data set consists of the total number of migrants in Bangladesh
Description
The function gives the total number of migrants in household cohort excluding international migrants from the rural areas of Comilla district of Bangladesh.
Usage
data_migran
Arguments
data_migran |
A vector of (non-negative integer) count values. |
Details
The data set consists of the number of households according to the total number of migrants in the household cohort excluding international migrants from the rural areas of Comilla district of Bangladesh. It was used by Pandey and Tiwari (2011) and fitted by the inflated probability model on rural out-migration.
Value
data_migran gives the observed frequencies for the number of migrants from a household in a growth center type of village.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Pandey, H. & Tiwari, R. (2011), An inflated probability model for the rural out-migration, Recent Research in Science and Technology 2011, 3(7): 100-103
See Also
Examples
x<-data_migran
summary(x)
table (x)
A data set of size n = 262 concerning the number of times that the word may appear per block
Description
The function gives the number of times that the word may appear per block.
Usage
data_block
Arguments
data_block |
A vector of (non-negative integer) count values. |
Details
A data set of size n = 262 concerns the number of times that the word may appear per block in papers by James Madison. It was used by Conigliani et al. (2000) and underlined the Bayesian assessment of goodness of fit against nonparametric alternatives.
Value
data_block gives the number of times that the word may appear per block.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Conigliani, C., Castro, J. I., & O'Hagan, A. (2000). Bayesian assessment of goodness of fit against nonparametric alternatives. Canadian Journal of Statistics, 28(2), 327-342.
Examples
x<-data_block
summary(x)
table (x)
The data show the observed number of occupational injuries among post cleaners
Description
The function gives the observed number of occupational injuries among post-cleaners.
Usage
data_inj2
Arguments
data_inj2 |
A vector of (non-negative integer) count values. |
Details
The data evaluate the effectiveness of a consultative manual handling workplace risk assessment team (WRATS) in reducing the risk of occupational injury among cleaners within a 600-bed hospital. They were used by Carrivick et al. (2003) and fitted by the zero-inflated Poisson modeling to evaluate occupational safety interventions.
Value
data_inj2 gives the observed number of occupational injuries among post-cleaners.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Carrivick, P. J., Lee, A. H., & Yau, K. K. (2003). Zero-inflated Poisson modeling to evaluate occupational safety interventions. Safety Science, 41(1), 53-63.
See Also
Examples
x<-data_inj2
summary(x)
table (x)
The data show the frequency distributions for orderly post workplace risk assessment team
Description
The function gives the frequency distributions for orderly post-WRATS (workplace risk assessment team).
Usage
data_inj4
Arguments
data_inj4 |
A vector of (non-negative integer) count values. |
Details
The data evaluate the effectiveness of a consultative manual handling workplace risk assessment team (WRATS) in reducing the risk of occupational injury among cleaners within a 600-bed hospital. They were used by Carrivick et al. (2003) and fitted by the zero-inflated Poisson modeling to evaluate occupational safety interventions.
Value
data_inj4 gives the frequency distributions for orderly post-WRATS.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Carrivick, P. J., Lee, A. H., & Yau, K. K. (2003). Zero-inflated Poisson modeling to evaluate occupational safety interventions. Safety Science, 41(1), 53-63.
See Also
data_inj1, data_inj2, data_inj3
Examples
x<-data_inj4
summary(x)
table (x)
The data show the frequency distributions for the orderly pre workplace risk assessment team
Description
The function gives the frequency distributions for orderly pre-WRATS (workplace risk assessment team).
Usage
data_inj3
Arguments
data_inj3 |
A vector of (non-negative integer) count values. |
Details
The data evaluate the effectiveness of a consultative manual handling workplace risk assessment team (WRATS) in reducing the risk of occupational injury among cleaners within a 600-bed hospital. They were used by Carrivick et al. (2003) and fitted by the zero-inflated Poisson modeling to evaluate occupational safety interventions.
Value
data_inj3 gives the frequency distributions for orderly pre-WRATS.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Carrivick, P. J., Lee, A. H., & Yau, K. K. (2003). Zero-inflated Poisson modeling to evaluate occupational safety interventions. Safety Science, 41(1), 53-63.
See Also
Examples
x<-data_inj3
summary(x)
table (x)
The data represent the observed number of onion plants asynaptic
Description
The function gives the observed number of onion plants asynaptic.
Usage
data_as2
Arguments
data_as2 |
A vector of (non-negative integer) count values. |
Details
The data represent the observed number of onion plants asynaptic. They were used by Jain (1959) and fitted by the negative binomial distribution.
Value
data_as2 gives the observed number of onion plants asynaptic.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Jain, S. K. (1959). Fitting the negative binomial distribution to some data on asynaptic behavior of chromosomes. Genetica, 30(1), 108-122.
See Also
data_p806_7, data_as7, data_p806_9, data_as1
Examples
x<-data_as2
summary(x)
table (x)
The data show the observed number of onion plants asynaptic
Description
The function gives the observed number of onion plants asynaptic.
Usage
data_as7
Arguments
data_as7 |
A vector of (non-negative integer) count values. |
Details
The data show the observed number of onion plants asynaptic. They were used by Jain (1959) and fitted by the negative binomial distribution.
Value
data_as7 gives the observed number of onion plants asynaptic.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Jain, S. K. (1959). Fitting the negative binomial distribution to some data on asynaptic behavior of chromosomes. Genetica, 30(1), 108-122.
See Also
data_as1, data_p806_8, data_p806_9, data_as1, data_as2
Examples
x<-data_as7
summary(x)
table (x)
The data contain the frequency distribution of patent citation fall in a category of typical count data
Description
The function gives the frequency distribution of patent citations that fall in a category of typical count data.
Usage
data_citation
Arguments
data_citation |
A vector of (non-negative integer) count values. |
Details
The data contain the frequency distribution of patent citations that fall in a category of typical count data. They were used by Lee et al. (2007) and fitted by the zero-inflated models.
Value
data_citation gives the frequency distribution of patent citations falling in a category of typical count data.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Lee, Y. G., Lee, J. D., Song, Y. I., & Lee, S. J. (2007). An in-depth empirical analysis of patent citation counts using zero-inflated count data model: The case of KIST. Scientometrics, 70(1), 27-39.
See Also
Examples
x<-data_citation
summary(x)
table (x)
The data show the tumor count frequencies from 158 NF2 patients
Description
The function gives tumor count frequencies from 158 NF2 patients.
Usage
data_tumor
Arguments
data_tumor |
A vector of (non-negative integer) count values. |
Details
The data show the tumor count frequencies from 158 NF2 patients. They were used by Joe and Zhu (2005) and fitted by the generalized Poisson distribution.
Value
data_tumor gives tumor count frequencies from 158 NF2 patients.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Joe, H., & Zhu, R. (2005). Generalized Poisson distribution: the property of mixture of Poisson and comparison with negative binomial distribution. Biometrical Journal: Journal of Mathematical Methods in Biosciences, 47(2), 219-229.
See Also
Examples
x<-data_tumor
summary(x)
table (x)
The data represent the number of stillbirths of New Zealand white rabbits
Description
The function gives the frequency of stillbirths in 402 litters of New Zealand white rabbits.
Usage
data_sbirths
Arguments
data_sbirths |
A vector of (non-negative integer) count values. |
Details
The data set consists of frequency of stillbirths in 402 litters of New Zealand white rabbits. Recently, it was used by Alshkaki (2016) and fitted by the zero-and-one inflated Poisson distribution.
Value
data_sbirths gives the frequency of stillbirths in 402 litters of New Zealand white rabbits.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Alshkaki, R. S. A. (2016). On the zero-one inflated Poisson distribution. International Journal of Statistical Distributions and Applications, 2(4), 42-8.
Morgan, B. T., Palmer, K. J., & Ridout, M. S. (2007). Negative score test statistic. The American Statistician, 61(4), 285-288.
Examples
x<-data_sbirths
summary(x)
table (x)
The data show the number of suicides per day during lockdown
Description
The function gives the number of suicides per day during lockdown.
Usage
data_suicides
Arguments
data_suicides |
A vector of (non-negative integer) count values. |
Details
The data show the number of suicides per day during lockdown. Recently, they were used by Rahman et al. (2022) and fitted by the three-inflated Poisson distribution.
Value
data_suicides gives the number of suicides per day during the lockdown.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Rahman, T., Hazarika, P. J., Ali, M. M., & Barman, M. P. (2022). Three-inflated Poisson distribution and its application in suicide cases of India during Covid-19 pandemic. Annals of Data Science, 9(5), 1103-1127.
See Also
Examples
x<-data_suicides
summary(x)
table (x)
The data show the frequency distributions of systemic adverse events
Description
The function gives the frequency distributions of systemic adverse events.
Usage
data_systemic
Arguments
data_systemic |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distributions of systemic adverse events after each of the four injections for the 1005 study participants, which results in 4020 observations. They were used by Rose et al. (2006) and fitted by the zero-inflated and hurdle models for modeling vaccine adverse event count data.
Value
data_systemic gives the frequency distributions of systemic adverse events.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Rose, C. E., Martin, S. W., Wannemuehler, K. A., & Plikaytis, B. D. (2006). On the use of zero-inflated and hurdle models for modeling vaccine adverse event count data. Journal of Biopharmaceutical Statistics, 16(4), 463-481.
Examples
x<-data_systemic
summary(x)
table (x)
The data show the observed number of incidents of international terrorism
Description
The function gives the observed number of incidents of international terrorism per month in the USA between 1968 and 1974.
Usage
data_terror
Arguments
data_terror |
A vector of (non-negative integer) count values. |
Details
The data show the observed number of incidents of international terrorism per month in the USA between 1968 and 1974. They were used by Mersad et al. (2015) and fitted by the zero-inflated models.
Value
data_terror gives the observed number of incidents of international terrorism per month in the USA between 1968 and 1974.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Mersad, M., Ganjali, M., & Rivaz, F. (2015). Some extensions of zero-inflated models and Bayesian tests for them. Journal of Statistical Computation and Simulation, 85(18), 3792-3810.
Conigliani, C., Castro, J. I., & O'Hagan, A. (2000). Bayesian assessment of goodness of fit against nonparametric alternatives. Canadian Journal of Statistics, 28(2), 327-342.
Examples
x<-data_terror
summary(x)
table (x)
The data show the word length of a Turkish poem
Description
The function gives the frequency distribution of the word length of a Turkish poem.
Usage
data_poem
Arguments
data_poem |
A vector of (non-negative integer) count values. |
Details
The data show the frequency distribution of the word length of a Turkish poem. Recently, they were used by Cueva et al. (2021) and fitted by the Waring distribution.
Value
data_poem gives the frequency distribution of the word length of a Turkish poem.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Cueva-Lopez, V., Olmo-Jimenez, M. J., & Rodriguez-Avi, J. (2021). An over and under dispersed Biparametric extension of the Waring Distribution. Mathematics, 9(2), 170.
See Also
Examples
x<-data_poem
summary(x)
table (x)
The number of ticks was counted on each of 82 sheep
Description
The function gives the number of tick counts on each of the 82 sheep.
Usage
data_ticks
Arguments
data_ticks |
A vector of (non-negative integer) count values. |
Details
The data show the number of ticks counted on each of the 82 sheep. They were used by Ross and Preece (1985) and fitted by the negative binomial distribution.
Value
data_ticks gives the number of ticks count on each of the 82 sheep.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Ross, G. J. S., & Preece, D. A. (1985). The negative binomial distribution. Journal of the Royal Statistical Society: Series D (The Statistician), 34(3), 323-335.
Examples
x<-data_ticks
summary(x)
table (x)
The data show the number of tornado occurrences in Lafayette
Description
The function gives the number of tornado occurrences in Lafayette.
Usage
data_tornado
Arguments
data_tornado |
A vector of (non-negative integer) count values. |
Details
The data show the number of tornado occurrences in Lafayette Parish, Louisiana, US per year from 1950 through 2012. Recently, they were used by Beckett et al. (2014) and fitted by the zero-inflated Poisson (ZIP) distribution.
Value
data_tornado gives the number of tornado occurrences in Lafayette.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Beckett, S., Jee, J., Ncube, T., Pompilus, S., Washington, Q., Singh, A., & Pal, N. (2014). Zero-inflated Poisson (ZIP) distribution: Parameter estimation and applications to model data from natural calamities. Involve, a Journal of Mathematics, 7(6), 751-767.
Examples
x<-data_tornado
summary(x)
table (x)
The data consist of observed frequencies for the heavy vehicle traffic accident
Description
The function gives the observed frequencies for the heavy vehicle traffic accident.
Usage
data_accident
Arguments
data_accident |
A vector of (non-negative integer) count values. |
Details
The data consist of the observed frequencies for the heavy vehicle traffic accident in India. Recently, they were used by Alshkaki (2016) and fitted by the zero-and-one inflated Poisson distribution.
Value
data_accident gives the observed frequencies for the heavy vehicle traffic accident.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Alshkaki, R. S. A. (2016). On the zero-one inflated Poisson distribution. International Journal of Statistical Distributions and Applications, 2(4), 42-8.
Sharma, A. K., & Landge, V. S. (2013). Zero inflated negative binomial for modeling heavy vehicle crash rate on Indian rural highway. International Journal of Advances in Engineering & Technology, 5(2), 292.
See Also
Examples
x<-data_accident
summary(x)
table (x)
The data contain claim frequency for automobile portfolios of a Turkish insurance company
Description
The function gives the claim frequency for automobile portfolios of a Turkish insurance company occurred between 2012 and 2014.
Usage
data_auto
Arguments
data_auto |
A vector of (non-negative integer) count values. |
Details
The data contain claim frequency for the automobile portfolios of a Turkish insurance company that occurred between 2012 and 2014. They were used by Sarul and Sahin (2015) and fitted by the zero-inflated and hurdle models in general insurance.
Value
data_auto gives the claim frequency for automobile portfolios of a Turkish insurance company occurred between 2012 and 2014.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Sarul, L. S., & Sahin, S. (2015). An application of claim frequency data using zero-inflated and hurdle models in general insurance. Journal of Business Economics and Finance, 4(4).
See Also
data_claims, data_claim1, data_claim2, data_claim3
Examples
x<-data_auto
summary(x)
table (x)
The data show the daily COVID-19 new cases of Uganda of 37 days
Description
The function gives the daily COVID-19 new cases in Uganda 37 days.
Usage
data_ugacovid
Arguments
data_ugacovid |
A vector of (non-negative integer) count values. |
Details
The data show the daily COVID-19 new cases of Uganda of 37 days, that is recorded from 17 August to 22 September 2020. Recently, they were used by Ibrahim and Almetwally (2021) and fitted by the discrete Marshall-Olkin Lomax distribution.
Value
data_ugacovid gives the daily COVID-19 new cases in Uganda of 37 days.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Ibrahim, G. M., & Almetwally, E. M. (2021). Discrete marshall-Olkin Lomax distribution application of covid-19. Biomedical journal of Scientific & Technical Research, 32(5), 2021.
See Also
data_argcovid, data_Algeriacovid, data_Bosniacovid
Examples
x<-data_ugacovid
summary(x)
The data show the frequency distribution of the number of units of consumers goods
Description
The function gives the number of units of consumers goods purchased by households over 26 weeks.
Usage
data_units
Arguments
data_units |
A vector of (non-negative integer) count values. |
Details
The data show the number of units of consumer goods purchased by households over 26 weeks. Recently, they were used by Aryuyuen et al. (2014) and fitted by the zero-inflated negative binomial-generalized exponential distribution.
Value
data_units gives the number of units of consumers goods purchased by households over 26 weeks.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Aryuyuen, S., Bodhisuwan, W., & Supapakorn, T. (2014). Zero-inflated negative binomial-generalized exponential distribution and its applications. Songklanakarin Journal of Science and Technology, 36(4), 483-91.
Lindsey, J. K. (1995). Modeling frequency and count data (Vol. 15). Oxford University Press.
Examples
x<-data_units
summary(x)
table (x)
The data show the observed number of occupational injuries among cleaners
Description
The function gives the observed number of occupational injuries among cleaners.
Usage
data_inj1
Arguments
data_inj1 |
A vector of (non-negative integer) count values. |
Details
The data evaluate the effectiveness of a consultative manual handling workplace risk assessment team in reducing the risk of occupational injury among cleaners within a 600-bed hospital. They were used by Carrivick et al. (2003) and fitted by the zero-inflated Poisson modeling to evaluate occupational safety interventions.
Value
data_inj1 gives the observed number of occupational injuries among cleaners.
Author(s)
Muhammad Imran
R implementation and documentation: Muhammad Imran imranshakoor84@yahoo.com.
References
Carrivick, P. J., Lee, A. H., & Yau, K. K. (2003). Zero-inflated Poisson modeling to evaluate occupational safety interventions. Safety Science, 41(1), 53-63.
See Also
data_inj2, data_inj3, data_inj4
Examples
x<-data_inj1
summary(x)
table (x)