Diagnostic measures (such as residuals or some summary statistics calculated from residuals) are used to evaluate some aspect of quality of model fit to data.

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19
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2answers
11k views

Interpreting residual diagnostic plots for glm models?

I am looking for guidelines on how to interpret residual plots of glm models. Especially poisson, negative binomial, binomial models. What can we expect from these plots when the models are ...
4
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0answers
3k views

Fitting a zero-inflated negative binomial regression with R

In this thread, I laid out a problem involving fitting a model that attempts to use minor league baseball statistics to predict success at the major league level (explained in full in the thread). ...
3
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1answer
14k views

How to interpret model diagnostics when doing linear regression in R?

I ran lm() on my data with models selected by individual lm's of each characteristic and then combined the top $R^2$ based on ...
10
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1answer
366 views

Why are diagnostics based on residuals?

In simple linear regression one often wants to verify if certain assumptions are met to be able to do inference (e.g. residuals are normally distributed). Is it reasonable to check the assumptions ...
7
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4answers
947 views

Diagonal lines in residuals vs fitted values plot for multiple regression

I am observing strange patterns in residuals for my data: [EDIT] Here are the partial regression plots for the two variables: [EDIT2] Added the PP Plot The distribution seems to be doing ...
21
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4answers
783 views

Why does my bootstrap interval have terrible coverage?

I wanted to do a class demonstration where I compare a t-interval to a bootstrap interval and calculate the coverage probability of both. I wanted the data to come from a skewed distribution so I ...
8
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1answer
616 views

What type of post-fit analysis of residuals do you use?

When carrying out OLS multiple linear regression, rather than plot the residuals against fitted values, I plot the (internal) Studentized residuals against fitted values (ditto for covariates). These ...
4
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1answer
2k views

Explanation of R diagnostic plot for logistic regression

I'm hoping someone can explain this bit of R code for me related to glm(). I don't understand the diagnostic plot that has been suggested. It seems a more ...
0
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2answers
106 views

If the model fits well, nothing can be done?

I am playing a data without any background information. First, I try multiple linear regression. The model fits well, since the $r^2$ is larger than 90%. I deleted several variables by AIC. The fits ...
18
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2answers
23k views

How to read Cook's distance plots?

Does anyone know how to work out whether points 7, 16 and 29 are influential points or not? I read somewhere that because Cook's distance is lower than 1, they are not. Am, I right?
7
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2answers
936 views

Possible extensions to the default diagnostic plots for lm (in R and in general)?

I started digging a bit into the plot.lm function, this function gives six plots for lm, they are: a plot of residuals against fitted values a Scale-Location plot of sqrt(| residuals |) against ...
14
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2answers
1k views

What diagnostic plots exists for quantile regression?

Following on my question for OLS, I wonder: what diagnostic plots exists for quantile regression? (and are there R implementation of them?) A quick google search already came up with the worm plot ...
7
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3answers
2k views

Residuals for logistic regression and Cook's distance

Are there any particular assumptions regarding the errors for logistic regression such as the constant variance of the error terms and the normality of the residuals? Also typically when you have ...
4
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0answers
412 views

Diagnostics for GEE in R

I have been checking out which diagnostics to use for a GEE analysis. It seem that influence measures are appropriate (Preisser, 1996). Does anyone know of a package that can be used in R to examine ...
3
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4answers
566 views

Communicating Regression Model Results

I am concerned about how unequipped most people are (both within and without academia) to properly employ standard model building methods such as linear regression and to interpret the results of ...
4
votes
1answer
455 views

What diagnostics for random effects logistic regression?

I'm wondering what checks/diagnostics you usually calculate and report if you do a random effects logistic regression. C-statistic/ROC curve Check multicollinearity? Check heteroscedasticity? ...
3
votes
2answers
3k views

Standardized residuals in R's lm output

I have a quick question: if I plot the diagnostic plots to an R regression, a couple of them have "Standardized Residuals" as their y-axis such as in this plot: My question is this: what are the ...
2
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2answers
382 views

Is residuals autocorrelation always a problem?

I read that OLS underestimates variance when residuals are autocorrelated. I see why autocorrelation would be a problem in time series analysis, in the sense that the coefficient are not efficient ...
2
votes
1answer
498 views

Functions for regression diagnostics on mer objects in R

I'm particularly interested in plotting residuals against fitted values, and residuals against predictors. Often times I need to make boxplots of the residuals conditional on predictors. I'd be ...
0
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2answers
71 views

White Test , testing for heteroscedasticity

i used a White Test for testing the homoscedasticity assumption of my linear regression I am working on. I have a problem whit the interpretation as I have a result from the test in which the p-value ...