Search or ask a question

How do you analyze logistic regression in R?

Categorical variable

Regression diagnostic

Multinomial logistic regression

Logistic regression

Regression analysis

Answers from top 7 papers

PDF

Open Access

More filters

Papers (7)	Insight
Journal Article•DOI Logistic Regression and Discriminant Analysis by Ordinary Least Squares Gus W. Haggstrom 01 Jul 1983-Journal of Business & Economic Statistics 57 Citations	This article shows that these estimates, their unbiased counterparts, and associated test statistics for variable selection can be calculated using ordinary least squares regression techniques, thereby providing a convenient method for fitting logistic regression models in the normal case.
Journal Article•DOI Measuring a binary response's range of influence in logistic regression Michael P. Fay 01 Feb 2002-The American Statistician 8 Citations	We show how statistics of this type may be helpful in checking the robustness of a logistic regression and study their relationship to earlier proposed diagnostics.
Open access•Journal Article•DOI Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression Jessica Pesantez-Narvaez, Montserrat Guillén, Manuela Alcañiz - Show less +2 more 20 Jun 2019 83 Citations	Our findings showed that logistic regression is a suitable model given its interpretability and good predictive capacity.
Journal Article•DOI Modeling categorical variables by logistic regression. Chao-Ying Joanne Peng, Barbara D. Manz, Juanita Keck - Show less +2 more 01 May 2001-American Journal of Health Behavior 37 Citations	Logistic regression is useful for health-related research in which outcomes of interest are often categorical.
Journal Article•DOI Relative risks and confidence intervals were easily computed indirectly from multivariable logistic regression. A. Russell Localio, David J. Margolis, Jesse A. Berlin - Show less +2 more 01 Sep 2007-Journal of Clinical Epidemiology 227 Citations	Especially when outcomes are common, relative risks and confidence intervals are easily computed indirectly from multivariable logistic regression.
Journal Article•DOI Analytical closed-form solution for binary logit regression by categorical predictors Stan Lipovetsky 02 Jan 2015-Journal of Applied Statistics 17 Citations	The explicit expressions for the characteristics of logit regression are convenient for the analysis and interpretation of the results of logistic modeling.
Journal Article•DOI Accounting for Nonignorable Verification Bias in Assessment of Diagnostic Tests Andrzej S. Kosinski, Huiman X. Barnhart - Show less +1 more 01 Mar 2003-Biometrics 81 Citations	The presented computational method can be utilized with any software that can repetitively use a logistic regression module.

My columns

Related Questions

How does the logistic regression work?5 answersLogistic regression is a statistical technique used for binary outcomes, where the dependent variable is categorical. It models the probability of an event occurring based on one or more independent variables. Unlike linear regression, logistic regression deals with dichotomous qualitative response variables. The model estimates the probability of the event of interest happening by transforming the linear regression equation into a logistic function, ensuring the output is between 0 and 1. This transformation allows for the prediction of the likelihood of a binary outcome, such as yes/no or 1/0, making logistic regression suitable for scenarios like predicting disease contraction, readmission rates, customer behavior, or loan defaults. Logistic regression coefficients are obtained through methods like Wald's test and likelihood ratio test for hypothesis testing.

What is Logistic Regression?4 answersLogistic regression is a commonly used classification algorithm in machine learning. It allows categorizing data into discrete classes by learning the relationship from a given set of labeled data. Logistic regression is applicable to multiple dependent variables and is often used with dichotomous dependent variables. It is a type of generalized linear model that is used when the outcome variable is not measured on a continuous scale. The goal of logistic regression is to create an equation that can estimate the probability of an event of interest based on independent variables. Logistic regression models are commonly used in biomedical research for modeling a dichotomous response variable as a function of explanatory variables.

What is logistic regression?4 answersLogistic regression is a commonly used classification algorithm in machine learning that allows categorizing data into discrete classes by learning the relationship from a given set of labeled data. It learns a linear relationship from the given dataset and introduces nonlinearity through an activation function to determine a hyperplane that separates the learning points into subclasses. The logistic function is the most used activation function in logistic regression for binary classification, as it transforms any real number into a probability between 0 and 1. Logistic regression is used to analyze the correlation between categorical response variables and predictor variables, which can be categorical or continuous. It aims to create an equation that estimates the probability of an event of interest based on independent variables. Logistic regression models are also called unconditional logistic regression models and can be extended to include conditional logistic regression analysis.

What are some common applications of logistic regression?5 answersLogistic regression is commonly used in various fields for risk assessment studies, such as credit risk assessment in financial institutions, estimation of risk factors for traffic accidents, identification of etiological factors for chronic diseases, and modeling a dichotomous response variable in biomedical research. It is also widely used in the financial service industry for binary classification tasks, such as building credit scorecards, estimating the probability of default or churn, and identifying the next best product in marketing. Logistic regression models are used when the outcome of interest is binary, and they produce odds ratios that have a useful interpretation. The applications of logistic regression can range from medical research to banking and marketing, making it a versatile and valuable tool in data analysis and machine learning.

What is a logistic regression?5 answersLogistic regression is a statistical method used to analyze the relationship between categorical response variables and predictor variables, which can be categorical or continuous. It is commonly used in machine learning for classification tasks, where it categorizes data into discrete classes based on a given set of labeled data. Logistic regression models the relationship between the predictor variables and the log-odds of the outcome variable using a logistic function, which transforms any real number into a probability between 0 and 1. It can be applied to binary outcomes or extended to handle multiple dependent variables. The goal of logistic regression is to estimate the probability of an event of interest based on the independent variables. Hypothesis testing methods, such as the Wald's test and likelihood ratio test, can be used to assess the significance of the regression coefficients.

How do you improve the accuracy of a logistic regression model in R?18 answers

See what other people are reading

What is the relationship between level of frailty of elders and activities of daily living?

The level of frailty in older adults has a significant impact on their activities of daily living (ADL). Frailty, characterized by a decline in biological reserves and vulnerability to adverse outcomes, is associated with decreased exercise capacity, reduced muscle strength, and adverse health outcomes. Engaging in diverse daily activities, rather than a few, can help prevent frailty in older adults. Frailty negatively correlates with ADL, indicating a strong association between frailty and limitations in daily activities among the elderly in nursing homes. Self-reported frailty assessments can serve as a screening tool for identifying potential issues with ADL, especially in older women using walking aids. Understanding and managing frailty in older adults is crucial for maintaining cognitive function, life satisfaction, and overall health.What are the variables indicator in this research?

The research papers provide insights into various variables and indicators studied in different contexts. In the research on a waterworks system in the Brantas river area, variables such as technical aspects (physical infrastructure, service, operation, environment damage, and maintenance), social aspects (economy and socio-culture conditions), and management aspects (water management institutions, human resources, and monetary aspect) were evaluated as indicators influencing the performance index model of the waterworks system. Additionally, other studies focused on variables like learning strategies, motivation, psychological well-being, violence, sociodemographic factors, personality traits, and learning styles as predictors of academic achievement in university students. Furthermore, factors like educational background, educational modality, and academic performance were examined as predictors of academic success in a Chilean university.What is the definition of volume in science?

Volume in science is a crucial concept that varies in its significance across different fields. In the realm of thermodynamics, volume plays a central role in predicting thermodynamic quantities through correlation methods. On the other hand, in the context of optical systems, the quality of images is assessed based on computations involving the volume bounded by the modulation transfer function (MTF) surface in a three-dimensional space. Furthermore, the relationship between figure and volume is explored in learning models, highlighting the evolution of these relationships among subjects after learning, with some individuals memorizing and extrapolating from models to understand volume concepts. In the measurement of volume for determining density, historical perspectives reveal that units of volume were once more significant in trade measurements, transitioning to mass-based definitions over time.What are the weather-related factors that influence traffic accident severity in urban arterial roads?

Weather-related factors influencing traffic accident severity on urban arterial roads include time, road characteristics, temperature, and humidity. Studies have shown that time and temperature have significant effects on accident severity, while humidity and stop signs near the accident site do not significantly impact severity. Additionally, research highlights that sunlight intensity and rainy days can increase the probability of accidents in urban traffic, emphasizing the role of weather conditions in accident occurrence. Furthermore, air pollution and weather conditions like hot and humid weather have been linked to elevated injury severity scores for road traffic injuries, indicating a broader impact of environmental factors on accident severity in urban areas.How age affect in self-efficacy and academic achievement?

Age plays a significant role in self-efficacy and academic achievement among students. Research indicates that as athletes' age increases, their academic success rate also tends to increase, highlighting a positive correlation between age and academic achievement. Additionally, in families with multiple children, the age gap between siblings can impact the academic self-efficacy of the eldest child, with a negative correlation observed. Parental involvement acts as a mediator in this relationship, influencing the eldest child's self-efficacy based on the age gap with their siblings. Moreover, studies on adolescents show that younger students tend to exhibit higher levels of academic self-efficacy and academic performance compared to older adolescents, emphasizing age as a significant factor in academic success.How does travel to treatment impact oncology service access in Australia?

Travel to treatment significantly impacts oncology service access in Australia, particularly for individuals residing in regional and rural areas. Studies have shown that geographic remoteness and long travel distances can lead to disparities in service utilization and outcomes. Patients from rural areas often face challenges accessing inpatient palliative care, radiotherapy, and specialist services, while also experiencing higher rates of emergency department visits and ICU admissions. However, efforts to improve access, such as the establishment of comprehensive cancer care centers, tele-oncology services, and investment in infrastructure like PET scanners, have shown promising results in reducing the need for extensive travel for cancer diagnosis and treatment. Despite these advancements, ongoing initiatives are needed to enhance travel assistance, accommodation availability, and telehealth utilization to further bridge the gap in oncology service access across different geographic regions in Australia.What is the difference between training testing and validation set?

In machine learning and data science, the concepts of training, testing, and validation sets represent distinct stages of model development, each serving a unique purpose in the lifecycle of a model to ensure its accuracy, robustness, and generalizability. The training set is used to teach or train the model, allowing it to learn the underlying patterns in the data. This learning process involves adjusting the model's parameters to minimize errors in predictions based on the input data it is exposed to. The training phase is crucial as it sets the foundation for the model's ability to make predictions. The testing set, on the other hand, is utilized after the model has been trained. It is a separate dataset not seen by the model during training. The primary purpose of the testing set is to evaluate the model's performance, specifically its ability to generalize what it has learned to new, unseen data. This helps in assessing the model's prediction accuracy and identifying potential overfitting, where the model performs well on the training data but poorly on new data. The validation set serves as a middle ground between the training and testing phases. It is used to fine-tune model parameters and select the best version of the model during the training process. Unlike the testing set, which is used to assess the final model's performance, the validation set is used iteratively to make adjustments and improvements to the model before it is finalized. This process involves using the validation set to evaluate the model's performance under various configurations, thereby guiding the selection of the best performing model that is neither overfit nor underfit. Each of these datasets plays a critical role in the development of a robust and effective machine learning model. By separating data into these three distinct sets, researchers and practitioners can ensure that their models are not only accurate on known data but also perform well on new, unseen data, thereby increasing the reliability and utility of their predictive models.What are the potential long-term effects of sugar consumption on the accommodation and pupil size of the eyes?

Long-term sugar consumption can impact eye health in various ways. Excessive sugar intake has been linked to an increased risk of myopia in children aged 11-14, although no correlation was found with the severity of myopia. Additionally, high sugar levels, especially in patients with diabetes, can lead to oxidative stress in the cornea, lens, and retina, potentially causing conditions like cornea edema, cataracts, and retinal issues that contribute to vision impairment. Furthermore, a study on young adults showed that glucose intake affected accommodation lag, with significant changes observed in the ability to focus on objects after consuming glucose. Moreover, long-term sugar consumption altered serotonin receptor responses in rats, affecting both pre and postsynaptic responses, which could impact mood and stress adaptation.The level of food consumption score among pregnant women?

The level of food consumption score among pregnant women varies in different studies. In Ethiopia, a study conducted in 2018 at Shegaw Motta Hospital found that 1.9% had poor food consumption scores, 16.6% were borderline, and 81.5% had acceptable scores. Another study in rural Amhara, Ethiopia, using the Global Diet Quality score (GDQS), showed that the mean GDQS was 18.2, with positive associations with maternal BMI and mid-upper arm circumference (MUAC). Additionally, a study in Ethiopia identified three major dietary patterns among pregnant women: "fruits and animal-source foods," "cereals, tubers, and sweet foods," and "legumes and vegetables," with various factors influencing these patterns. In Spain, a study reported that pregnant women did not meet recommendations for healthy food consumption, with medium adherence to the Mediterranean diet throughout pregnancy and post-partum period.What are the beneficial nutritional assessments for older people with dementia living in community?

Nutritional assessments like the Mini-Nutritional Assessment (MNA) and the Modified Mini-Nutritional Assessment Short Form (MNA-SF) are beneficial for older people with dementia living in the community. These assessments help identify malnutrition and the risk of malnutrition. Additionally, tools like the Edinburgh Feeding Evaluation in Dementia (EdFED) Scale can assess feeding difficulties in dementia patients, guiding potential clinical interventions. Furthermore, the Dietary Function Assessment Scale (DFAS) is a valuable tool for evaluating dietary functioning in community-dwelling persons with dementia, focusing on self-eating ability and dietary executive function. Implementing these assessments can aid in early detection of nutritional issues, allowing for timely interventions to improve the overall well-being of older individuals with dementia in community settings.What are the most significant barriers to accessing HBV vaccines in different regions or populations?

Accessing HBV vaccines faces various barriers across different regions and populations. Barriers include limited knowledge about HBV, misconceptions, and challenges in healthcare staffing capacity among healthcare providers. Sociodemographic factors like lower household wealth index, inadequate antenatal visits, home deliveries, and distance to health facilities contribute to incomplete immunization in West African countries. System issues, caregiver education, fear of side effects, and poor communication by healthcare workers are common barriers globally. In high-risk populations, separate appointments for vaccination, fear of side effects, lack of healthcare provider recommendations, and absence of monitoring for completing the 3-dose vaccination hinder HBV vaccine uptake. Addressing these barriers through education, improved healthcare access, and tailored vaccination strategies is crucial for achieving the WHO 2030 hepatitis elimination target.