Questions tagged [deviance]
Deviance is a measure of distance between two probability distributions. In the case of GLMs, (total) deviance is twice the difference in log-likelihood between the full model and the restricted model.
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Questions regarding the definition of the deviance in the context of GLMs
I've been self-studying GLMs and I have some questions regarding the deviance in the context of GLMs. In Generalized Additive Models An Introduction with R, the author defines the deviance of a model ...
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Comparing GLMs with different fitted distributions
I have a scenario where I need to compare some generalized liner models (with same link function, target variable, but not necessarily nested) with k fold cross validation, using a cost function to ...
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Rel. contribution of each term to deviance explained (in GAM)
I have seen this and similar questions all over the place, but no really satisfying answers: How can we quantify the contribution that each term in a GAM (using mgcv package) adds to the total ...
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How Do I Calculate the Scaled Deviance of a GLM with Gamma(Exponential) Distributed Dependent Variable?
I'm fitting a generalized linear model to a theoretically exponentially distributed dataset. The exponential distribution has PDF
$$
f(y;\lambda) = \lambda e^{-\lambda y}
$$
This question Deviance for ...
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Manual variable selection in GAM model based in deviance explained
I'm fitting a generalized additive model (GAM) to predict bottlenecks in a manufacturing process of a company. They have data of the bottlenecks that occurs in their process of making hot steel rolls. ...
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Logistic Regression Pattern in Deviance Variance Across Variables
I fitted a Logistic Regression model for a Customer Churn dataset with the following results
I tested this model with a validation set and calculated the ROC AUC score, which was approximately 0.85 – ...
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How can we justfify the assumption of equal scale/variance in the definition of R-squared from Deviances in GLMs?
If we take the R-squared to be the comparison of Deviances between models (the model of interest, the saturated model, and the constant model), we can write it as (see this answer CC BY-SA 4.0):
$$R_{...
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Prove that the Deviance and the Generalised Pearson Statistic are asymptotically equivalent
I am reading the paper Exponential Dispersion Models from Jørgesen and at page $137$ I have encountered a claim that I don't know how to prove.
The author claims that the Generalised Pearson Statistic,...
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Solution for Overdispersion in Poisson Regression
I have run poisson regression in SPSS (Generalized Linear Model), where BMI is my IV and length of stay (LOS) in hospital for certain disease is my DV (and it's a count variable).
When I run poisson, ...
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Should Kullback-Leibler as an R2 value be large or small for better goodness-of-fit
I am trying to use the Kullback-Leibler as an R2 value for goodnes-of-fit for GLM models.
The R package performance defines their function as:
...
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Is there a justification for the Bernoulli deviance in the R stats package?
Using the standard glm(...) function in R for Bernoulli regression, it appears that the residual deviance has the same value as the binomial deviance where each ...
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Hierarchical partitioning for GAM model?
I am fitting a gam model with multiple environmental factors as predictors (actual evapotranspiration, climate water deficit, wind speed, etc).
My goal is to understand how each one of them contribute ...
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Pearson chi squared test vs deviance test in GLM
From my understanding, both Pearson chi squared test and deviance test can be used to assess the goodness of fit for GLM, but they have different alternative hypotheses. For the Pearson chi-squared ...
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Is Pearson's chi-squared appropriate for models with low deviance explained?
I'm working on fitting a binomial GLM using LASSO in R (package glmnet). My response variable is a proportion which is generated using count data (successes and failures). The main purpose of my model ...
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Gam using mgcv is giving negative deviance explained
I run a null binomial generalized additive models (gam) using mgcv and it gives negative deviance explained!
As far as I know deviance explained is analogue of R^2 ...
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