The deviance tag has no wiki summary.
2
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1answer
57 views
Goodness of fit in a GLM with scaled deviance
On this page, I am interested in the section “goodness of fit”, which is near the bottom of the page and contains the table of deviance functions.
The author states that the scaled deviance, i.e. ...
1
vote
1answer
32 views
Manually evaluating the DIC: very big number of effective parameters?
My problem is the following: I'm evaluating the fit to a function $f(\mathbf{x},\theta)$ via MCMC (because I have some priors on the parameters), and I'm trying to evaluate the DIC, given by:
...
3
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0answers
118 views
R-squared in linear model verses deviance in generalized linear model?
Here's my context for this question: From what I can tell, we cannot run an ordinary least squares regression in R when using weighted data and the survey package. ...
1
vote
0answers
52 views
Symmetric measure of variable contribution to a regression
Take a linear regression setup with two "blocks" of predictors. The typical measures for the contribution of each block over the other are asymmetric (say, likelihood change, RSS change etc.). Is ...
2
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2answers
372 views
0
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0answers
88 views
Difference between R-sq and proportional reduction in deviance
For experiment I have a 3 treatment manipulation and 4 response variables of interest. 3 of the 4 are count data, and the 4th is presence absence data. Sample size for the first 3 is 15, but for the ...
3
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0answers
68 views
Negative number of parameters in hierarchical bayesian model
I'm using Deviance information criterion to assess the fitness in my Bayesian hierarchical model.
The functional form of this criterion is as follows:
$$DIC=p_{D}+\bar{D}$$
where ...
1
vote
0answers
253 views
Confidence Interval of estimator for the exponential distribution
We have an exponential distribution $$f(x)=\frac{e^\frac{-x}{\theta}}{\theta}$$
We are told that $n=3$ and that the data are given as
$x_1=1, x_2=2.5, x_3=5.5$
a.) Determine an ...
0
votes
1answer
196 views
Good model vs. AIC
Suppose I run a bidirectional stepwise in R with the model:
step(glm(y ~ a + b + c + d, poisson))
And the result may be:
...
7
votes
1answer
1k views
Can a -2 Log likelihood be calculated with only one model?
I am using the glmfit function in MATLAB. The function only returns the deviance and not the log likelihood. I understand that the deviance is basically twice the ...
9
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0answers
1k views
Is the percent of total deviance explained a useful model summary? [duplicate]
My question is regarding the interpretation of the percent of deviance explained (and other $R^2$ anaologs or pseudo $R^2$ values for GLMs.
Is this a meaningful summary statistic for models other ...
2
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0answers
138 views
“Brute force” expected deviance for logistic regression?
A commonly used goodness of fit statistic for logistic regression is the deviance. This is also known as the likelihood ratio chi-square statistic. It is defined as:
$$D=\sum_{i=1}^{N}d_i^2$$
...
3
votes
1answer
365 views
Poisson deviance - and what about zero observed values?
According to Crawley *), the Poisson deviance is computed as
$2 \sum{O \cdot \log ({O \over E})}$
But what if observed value $O$ is zero?
*) Statistical Computing - An Introduction to Data Analysis ...
6
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3answers
1k views
How to assess goodness of fit of a particular nonlinear model? [closed]
I have a nonlinear model $y=\Phi(f(x,a)) + \varepsilon$, where $\Phi$ is the cdf of the standard normal distribution and f is nonlinear (see below). I want to test the goodness of fit of this model ...
7
votes
1answer
1k views
How to calculate the hat matrix for logistic regression in R?
I want to calculate the hat matrix directly in R for a logit model. According to Long (1997) the hat matrix for logit models is defined as:
$$H = VX(X'VX)^{-1} X'V$$
X is the vector of ...
8
votes
3answers
5k views
What is Deviance? (specifically in CART/rpart)
What is "Deviance," how is it calculated, and what are its uses in different fields in statistics?
In particular, I'm personally interested in its uses in CART (and its implementation in rpart in R). ...
