scholarly journals Multinomial Logistic Regression Model to Identify Factors Associated with Food Insecurity in Rural Households in Nepal

2020 ◽  
Vol 4 ◽  
pp. 17-32
Author(s):  
Santosh Kumar Shah
Keyword(s):  
Logistic Regression ◽  
Food Insecurity ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Wealth Index ◽  
Multinomial Logistic Regression Model ◽  
Factors Associated ◽  
Independent Variables ◽  
Index Size

Background: Food is basics of our lives and many people experiences food insecurity at some time because of food deprivation and lack of access to food due to different resource constraints. It is a global challenge and threatens the rural people in developing countries like Nepal. Objective: The objective of the study is to identify the factors associated with food insecurity in rural area of Nepal. Materials and Methods: The analysis is based on rural household data extracted from the data of Nepal Demographic and Health Survey 2016. The dependent variable food insecurity status was measured in four levels namely food secure, mildly food insecure, moderately food insecure and severely food insecure household using Household Food Insecurity Access Scale. Independent variables were categorical and quantitative variables. In order to identify the factors associated with food insecurity, ordinal logit model was fitted initially. Due to violation of test of parallel lines by overall as well as some of the independent variables, multinomial logistic regression model was finally adopted by examining the model adequacy test. Results: The fitted multinomial logistic regression satisfied the diagnostic test including tests of goodness of fit, multicolinearity diagnostic criteria and minimum criteria of utilization of the model with about 29% predictive power. The variables ecological region, wealth index, size of agriculture land, any member(s) having saving account in any financial institution, any member(s) had gone to foreign employment in last 5 years other than India, family size, number of members completed secondary education and household member rearing cattle(s) were found to be significant. The poorest households (HHs) had 3.14 (CI: 1.88-5.26) times, poorer HHs 2.51 (CI: 1.55-4.07) times and moderate HHs 1.42 times higher chances of being severely food insecure relative to rich HHs. Conclusion: The study revealed that food insecurity of the rural HHs increases with decrease in the wealth index, size of land and number of members of the HHs with completed secondary education. The food insecurity of the households decreases with increase in the access to bank service.

scholarly journals Comparison Study of Logistic Regression Model for Albanian Texts

2016 ◽  
Vol 12 (9) ◽  
pp. 6572-6575
Author(s):  
Denisa Salillari ◽  
Luela Prifti
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Classification Problem ◽  
Comparison Study ◽  
Multinomial Logistic Regression Model ◽  
Independent Variables ◽  
Predicted Probability ◽  
The Right

Considering authorship attribution as a classification problem we attempt to estimate the probability to find the right author for each text under study. In this paper using R we first improve the simple model for six Albanian texts, (I) increasing number of texts and number of independent variables and then compare the results taken with them of the multinomial logistic regression (II). The model was applied on a set of one hundred texts of ten different authors. For all the authors under study the average correct predicted probability is 0.918. Analyzing data from different Albanian texts, results that about 40% of their letters consist of vowels. As conclusion comparing results taken with them of (II) multinomial logistic regression model for Albanian texts has more advantages than logistic regression model.

scholarly journals A multinomial logistic regression model for text in Albanian language

2016 ◽  
Vol 12 (7) ◽  
pp. 6407-6411
Author(s):  
Denisa Salillari ◽  
Luela Prifti
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Multinomial Logistic Regression Model ◽  
Independent Variables ◽  
The Third ◽  
Predicted Probability ◽  
Authorship Identification

In this paper we present a multinomial logistic regression model for authorship identification in the Albanian language texts. In the model fitted the dependent variable is categorical which takes different values from 1 to 10 for each of the author and the independent variables are number of words, number of letters, number of vowels, number of consonants, number of punctuations and number of sentences for each text. The model was applied with success in the set of ten authors, each of them being represented by a set of one hundred texts they authored. As results first, second and the third authors have the higher correct predicted percentage and the highest overall correct predicted probability taken was 0.738. As conclusion adding in the model number of consonants, number of punctuations and number of sentences as independent variables the overall correct predicted percentage is increased.

scholarly journals Burden and predictors ofStaphylococcus aureusandS. pseudintermediusinfections among dogs presented at an academic veterinary hospital in South Africa (2007–2012)

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3198 ◽  
Author(s):  
Daniel N. Qekwana ◽  
James Wabwire Oguttu ◽  
Fortune Sithole ◽  
Agricola Odoi
Keyword(s):  
South Africa ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Risk Of Infection ◽  
Multinomial Logistic Regression Model ◽  
Tract Infections ◽  
Veterinary Hospital ◽  
Isolated Species

BackgroundStaphylococci are commensals of the mucosal surface and skin of humans and animals, but have been implicated in infections such as otitis externa, pyoderma, urinary tract infections and post-surgical complications. Laboratory records provide useful information to help investigate these infections. Therefore, the objective of this study was to investigate the burdens of these infections and use multinomial regression to examine the associations between variousStaphylococcusinfections and demographic and temporal factors among dogs admitted to an academic veterinary hospital in South Africa.MethodsRecords of 1,497 clinical canine samples submitted to the bacteriology laboratory at a veterinary academic hospital between 2007 and 2012 were included in this study. Proportions of staphylococcal positive samples were calculated, and a multinomial logistic regression model was used to identify predictors of staphylococcal infections.ResultsTwenty-seven percent of the samples tested positive forStaphylococcusspp. The species ofStaphylococcusidentified wereS. pseudintermedius(19.0%),S. aureus(3.8%),S. epidermidis(0.7%) andS. felis(0.1%). The remaining 2.87% consisted of unspeciatedStaphylococcus. Distribution of the species by age of dog showed thatS. pseudintermediuswas the most common (25.6%) in dogs aged 2–4 years whileS. aureuswas most frequent (6.3%) in dogs aged 5–6 years.S. pseudintermedius(34.1%) andS. aureus(35.1%) were the most frequently isolated species from skin samples. The results of the multivariable multinomial logistic regression model identified specimen, year and age of the dog as significant predictors of the risk of infection withStaphylococcus. There was a significant temporal increase (RRR = 1.17; 95% CI [1.06–1.29]) in the likelihood of a dog testing positive forS. pseudintermediuscompared to testing negative. Dogs ≤ 8 years of age were significantly more likely to test positive forS. aureusthan those >8 years of age. Similarly, dogs between 2–8 years of age were significantly more likely to test positive forS. pseudintermediusthan those >8 years of age. In addition, dogs 2–4 years of age (RRR = 1.83; 1.09–3.06) were significantly more likely to test positive forS. pseudintermediuscompared to those <2 years of age. The risk of infection withS. pseudintermediusorS. aureuswas significantly higher in ear canal and skin specimens compared to other specimens.ConclusionsThe findings suggest thatS. pseudintermediusandS. aureuswere the most commonly isolated species from dogs presented at the study hospital. Age of the dog and the location of infection were significant predictors of infection with bothStaphylococcusspecies investigated. Significant increasing temporal trend was observed forS. pseudintermediusbut notS. aureus. This information is useful for guiding clinical decisions as well as future research.

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