generalized linear model with binomial errors

USAGE SUMMARY

The phrase "generalized linear model with binomial errors" is correct and usable in written English.
It can be used in statistical or data analysis contexts, particularly when discussing models that predict binary outcomes. Example: "In our analysis, we employed a generalized linear model with binomial errors to assess the impact of various factors on the likelihood of success."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

1 human-written examples

To investigate relationship between genetic, clinical and anthropological variables and probability of a good response to MTX treatment, a generalized linear model with binomial errors was used.

BMC Musculoskeletal Disorders

Human-verified similar examples from authoritative sources

Similar Expressions

59 human-written examples

In all three cases we used a generalised linear model with binomial errors.

BMC Ecology

Data on earthworm mortality and change in body mass during the experiment were analyzed by a generalized linear model with binomial error distribution and linear model, respectively in both cases with the type of microplastic as predictor variable.

Nature

We analysed differences in knotweed regeneration success using a generalized linear model with binomial error that included the main effects of taxon (3 levels), clone nested within taxon (50 levels), activated carbon (2 levels) and the interactions.

Evolutionary Applications

For probability of reproduction, defined as the fraction of individuals in the lineage type that survived and reproduced, we compared effects of salinity and lineage type using a generalized linear model with binomial error distribution and a logit fit.

Evolutionary Applications

Therefore, we also used a generalized linear model with binomial error to fit an analogous model [ 75]: (3) As described above for the linear regressions, we performed the joint scaling test of the typical quantitative genetic series starting with the additive effect only and added dominance and epistasis terms up to the full second order polynomial in Eq. 3.

BMC Evolutionary Biology

We tested for a difference across populations in P1 and P2 at each time-point using generalized linear models, with binomial errors and logit link functions.

BMC Evolutionary Biology

Infection prevalence at both oocyst and sporozoite stages and gametocyte sex ratios were analysed using generalized linear models with binomial error structures.

Evolutionary Applications

This is a generalized linear model with binomial distribution family and identity link function [ 32].

BMC Public Health

Prevalence was analyzed by generalized linear models with binomial distributed error, logit link, and randomly choosing 1 observation per bird.

Emerging Infectious Diseases

The AR was estimated using a linear model with binomial errors and accounting for correlation using a generalized estimating equation (Zeger et al. 1988).

Environmental Health Perspectives

Expert writing Tips

Best practice

When reporting results, clearly state the link function (e.g. logit, probit) used in conjunction with the "generalized linear model with binomial errors" to ensure reproducibility.

Common error

Avoid assuming that a "generalized linear model with binomial errors" is always appropriate. Check for overdispersion, which can indicate that the binomial assumption is violated and a quasi-binomial or negative binomial model might be more suitable.

Linguistic Context

The phrase "generalized linear model with binomial errors" functions as a technical term within the field of statistics. It is used to describe a specific type of statistical model suitable for analyzing data where the response variable is binary or represents proportions, as suggested by Ludwig's examples.

Expression frequency: Common

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Academia

0%

Ludwig's WRAP-UP

The phrase "generalized linear model with binomial errors" is a precise statistical term used to describe a model suitable for binary or proportion data. As Ludwig AI confirms, its use is grammatically correct and highly relevant in scientific contexts. When using this model, it's crucial to specify the link function and check for potential overdispersion. Alternatives like "glm with binomial error structure" offer conciseness, but maintain the core meaning. This type of model is prevalent in the science domain and less common in other areas.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

glm with binomial error structure

This alternative is a more concise way of expressing the same concept, using the abbreviation GLM for generalized linear model.

binomial glm

This alternative inverts the order of the terms but retains the core meaning.

generalized linear model for binomial data

This alternative emphasizes the type of data being modeled.

logistic regression

Logistic regression is a specific type of generalized linear model used for binomial data, making it a closely related, though less general, term.

binomial response glm

This alternative focuses on the response variable being binomial within the GLM framework.

glm with binary response

Using 'binary' instead of 'binomial', implies there are only two outcomes.

generalized linear model with logit link

Specifies the link function commonly used with binomial data in a GLM.

model with binomial distribution

Focuses on the distribution assumption of the model.

regression model for proportions

Highlights the application of the model to proportional data, which are often modeled using a binomial GLM.

statistical model for binary outcomes

Broader term that encompasses various models used for binary outcomes, including binomial GLMs.

FAQs

When is it appropriate to use a "generalized linear model with binomial errors"?

A "generalized linear model with binomial errors" is appropriate when modeling a binary outcome or proportion data where the errors follow a binomial distribution. The outcome should represent the number of successes in a fixed number of trials.

What is the difference between a "generalized linear model with binomial errors" and logistic regression?

Logistic regression is a specific type of "generalized linear model" that is commonly used when the response variable has a binomial distribution. It uses a logit link function to model the relationship between the predictors and the probability of success.

What link functions can be used with a "generalized linear model with binomial errors"?

Common link functions for a "generalized linear model with binomial errors" include the logit link, probit link, and complementary log-log link. The choice of link function depends on the specific application and the desired interpretation of the model coefficients.

What are some alternatives to using "generalized linear model with binomial errors"?

Alternatives include using a "beta regression" for proportion data, or a "quasi-binomial GLM" if overdispersion is present. For binary data, you might also consider non-parametric methods if the assumptions of the binomial model are not met.