Questions tagged [diagnostic]

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.

Filter by
Sorted by
Tagged with
0
votes
1answer
26 views

Is there a name for 'heteroskedasticity' but for shape, skewness or kurtosis of the distribution?

Heteroskedasticity means that the variance of the residuals changes with respect to input variables. Is there a name for an analogous concept where the shape of the distribution, i.e., skewness, ...
1
vote
0answers
13 views

Residual of ARIMA model

I've been using ARIMA recently so I'm a little bit unsettled in the chart interpretation. Currently I have 2 pictures of Ljung box and Residual in ARIMA (2,1,2). Can you help me comment on these 2 ...
1
vote
0answers
22 views

Result of a diagnostic test of a predictive model looking too good

I have created a predictive model that outputs a predictive density. I used 1000 rolling windows to estimate the model and predict one step ahead in each window. I collected the 1000 predictions and ...
2
votes
0answers
41 views

$H_0$ vs $H_1$ in diagnostic testing

Consider diagnostic testing of a fitted model, e.g. testing whether regression residuals are autocorrelated (a violation of an assumption) or not (no violation). I have a feeling that the null ...
0
votes
0answers
12 views

Demonstrating that proposed cutoff score is non optimal

I have a small data set with a test score and classification (positive or negative diagnosis). There is already clinical evidence that the cut point suggested by the test manufacturer produces too ...
0
votes
1answer
28 views

Standardized residuals check in a ARMA-GARCH model

If standardized residuals of an ARMA-GARCH model show some autocorrelation, while the squared standardized residuals look white noise, what can we infer about the specification of the model?
0
votes
0answers
12 views

How to Diagnose a GEE model?

I have been looking around the internet still can't find no definitive answer, After I fit my GEE model, what are the diagnosis that I can do to check the fit of my model? Another side question about ...
1
vote
2answers
57 views

Good convergence diagnostic; bad trace plot

I am fitting a multi-level state-space model and am running into a situation where the Gelman-Rubin diagnostic shows acceptable convergence (R-hat < 1.01), but when I look at the trace plots of the ...
7
votes
2answers
992 views

Why do I see a pattern in the residuals in this well specified model?

I made a model using simulated data. I then fitted an OLS on it. I know the assumptions of OLS are honored since this is simulated data. Regardless there is a pattern in the residuals, they don't seem ...
3
votes
1answer
58 views

Pearson Residuals in the Poisson GLM

I am quite new to GLMs and have just fit a Poisson Regression in R to model a positive response $y$. I now want to check how well it fits the data. In particular, I am unsure whether the variance ...
0
votes
0answers
37 views

Diagnostic checks and optimization of (ElasticNet) penalized logistic regression models (using glmnet and caret)

I was recently advised to use a penalized logistic regression model to better grasp what drivers influence my outcome (i.e. the eradication success/failure of an invasive plant species after a ...
0
votes
0answers
15 views

Heteroskedasticity Arima Residuals with Xregs using Breusch Pagan

I know there are several related questions, but my question is very specific. If I want to test the heteroskedasticity of the residuals of an Arima model with external regressors, what formula should ...
1
vote
0answers
14 views

How to correctly specify and diagnose one-inflated beta regression mixed-models (using GAMLSS)

I would like to find out what variables influence/explain an efficiency score for an invasive species control method. As the score is defined on (0,1] and as some observations were not independent, I ...
0
votes
0answers
10 views

Partial regression plots for post modeling processing

Reading Feature Engineering and Selection I came across partial regression plots. The authors put them in the section dedicated to post modeling processing (in the link), which confuses me a bit. ...
0
votes
1answer
80 views

Using the Hat Matrix to detect influential observations in logistic regression

I'm currently running residual diagnostics for a logistic regression model, aiming to identify possible influential record could influence the parameters estimate. I wonder about if it is possible to ...
1
vote
1answer
25 views

Which tests should be perfomed after quantile regressions have been estimated?

I´m performing a quantile regression. Initially I opted for a linear regression, but as I suspected that variations in X had different effects on the outcome variable across the distribution, I ...
0
votes
0answers
31 views

Difference between leverage plot, partial regression plot/added-variable plot, and component-plus-residual plot?

What is the difference between leverage plot, partial regression plot/added-variable plot, and component-plus-residual plot? In my intro stats course, I was only taught leverage plots, and not the ...
0
votes
0answers
20 views

Residual diagnostics for GLMM using Gamma distribution with identity link

I am using a two-level generalized linear mixed model (GLMM) with Gamma distribution and identity link. In terms of residual diagnostics, I understand that the following assumptions needs to be ...
0
votes
1answer
35 views

Interpretation of ACF and PACF plots

I have obtained these plots for my residuals, I used type = "pearson" as I am working with poisson distributed response data recorded yearly. Looking at ...
2
votes
1answer
119 views

DHARMa diagnostics: residuals vs. predicted for categorial predictor

I fitted different response variables (one after each other) to the same categorial predictor. Since it's a categorical predictor I get a boxplot for the residual vs. fitted with the DHARMa ...
5
votes
2answers
64 views

Using diagnostic plots in order to decide variables to set up multiple linear regression - R

I'm working with a baseball dataset using R. The dataset contains the baseball season records for teams between the years 1871 and 2016. One of the columns has the number of wins for the season and ...
1
vote
0answers
74 views

incongruent results of testZeroInflation (DHARMa) and check_zeroinflation (performance)

I have a count data set addressing the number of seeds being produced in dependence on the distance to a field margin (as categorical variable). From the histogram I guessed that zero-inflation might ...
0
votes
0answers
41 views

Checking Conway-Maxwell-Poisson model adequacy

I am trying to troubleshoot model adequacy problems for underdispersed count data (number of correct responses in a simple task; dispersion ratio is 0.3) that I modeled with Conway-Maxwell-Poisson. ...
0
votes
0answers
29 views

Is this Fitted vs Observed diagnostic plot strange?

I am running a linear regression model in R with generalized least squares gls() on my data to fix residuals with unequal variance. I seem to have achieved this; ...
0
votes
1answer
66 views

Diagnostics of a parametric Survival Regression in R

I am doing a Survival Analysis in R with the "survival" package and I don't know how to do any plots of the results for diagnostic purposes. Here is my model (I have given the variables self-...
0
votes
0answers
31 views

Heteroscedastic errors in logistic GLM - a problem? [duplicate]

I am fitting a logistic GLM (assumed binomial distribution) with a random intercept and slope: DV ~ 1 + IV + (1+IV|subject) The DV is the number of successes of ...
0
votes
0answers
11 views

What does the net-structure in a scale-location plot mean?

I have a multiple regression model (13 predictors, n = 500) and have trouble understanding where the net-like structure in the diagnostic scale-location plot comes from and how to interpret it, i.e. ...
0
votes
0answers
184 views

Valid alternative to Box Tidwell method for Linear Regression [duplicate]

I am building a Logistic Regression model (in sklearn) and want to verify that the assumption regarding the linearity between X and the logit function is correct. I am using Python so am looking for ...
0
votes
1answer
114 views

How to test linearity in ordinal logistic regression analysis?

One of the assumptions for performing ordinal regression is linearity. How to test this for this specific type of regression? Do you have to use logit etc.? And how does this work in SPSS?
0
votes
1answer
115 views

OLS on autoregressive models

Suppose I have a linear model with strongly correlated residuals. Suppose further that after adding one or more lags of the dependent variable, the residuals no longer appear to be autocorrelated ...
1
vote
0answers
320 views

Help understanding residual vs covariate plots in linear regression when covariate is transformed

I'm self-studying stats and in the books I've studying there usually aren't examples specifically dealing with residual plots for polynomial and spline fits. This has be thinking about residual vs ...
1
vote
0answers
31 views

Detecting Non-linearity in large data sets

I understand that the best way to test for non-linearity is to look at the residual plots. However, I have 20,000 or more points and any pattern in the residuals is not easy to spot. Are there ...
1
vote
1answer
39 views

lightgbm model diagnostics

I am currently building a house price predictor. My lightgbm errors look like the one the one below (illustrative). It shows that there is a pattern in my errors. Can someone explain how to resolve ...
1
vote
1answer
123 views

Interpretation of GAM diagnostics

I am hoping to get some advice on my below gam diagnostics and whether my model needs further refining or is adequate as is? I have conducted a randomised controlled trial to test if tagging impacts ...
2
votes
0answers
59 views

Check assumptions of linear regression before having the final model

There are usually four assumptions associated with a linear regression model: (1) linear relationship, (2) normal residuals, (3) homoscedastic residuals, and (4) i.i.d residuals. I think that it is ...
0
votes
1answer
28 views

Autocorrelation in residuals, what to do next? [duplicate]

I run an OLS, after check residuals, they are normally distributed, mean is zero with some randomness. But after the autocorrelation plot shows significant correlations to 15+ lags. What should I do ...
0
votes
0answers
64 views

Mixed-effects log-linear regression for counts -

I've read other answers but couldn't find exactly what I was looking. I have generated the following contingency table from my data: ...
0
votes
0answers
14 views

Diagnosing model performance in the serving time?

I was reading a very insightful paper from booking.com published at KDD 2019 150 successful Machine Learning models: 6 lessons learned at Booking.com. The paper shares practical lessons from deploying ...
1
vote
1answer
49 views

Variability in the fitted values

For a dataset, I have fitted a model. The fitted or predicted values have less variability than the observed values. What does it imply?
0
votes
0answers
11 views

How to graphically diagnose conditional zero-inflation in count response regression?

Is there any ubiquitous (or not so much) graphical method in count response models (e.g. Poisson GLM) to diagnose conditional zero-inflation? I'm aware of statistical tests that can be used for that, ...
0
votes
0answers
19 views

Will usual tests state that data is overdispersed if response mean in poisson regression can vary greatly?

Assume I have 1000 responses from some count data, where each response follows the Poisson distribution with a mean (and variance) falling somewhere in the large range of 1 - 100. There is one ...
0
votes
0answers
49 views

P-values for sensitivity/specificity when only improvement is possible

Imagine two diagnostic tests in a single population at risk of the disease, one of which is always "more strict" than the other, meaning that if test 1 is positive, test 2 will be positive ...
1
vote
0answers
36 views

How to find out about weird patterns in gam.check of a bam

I run a bam with timeseries data and a response variable defined as cbind(positive, negative), family = quasibinomial (because of overdispersion), a number of interactions, and some random effects. ...
0
votes
1answer
127 views

MASS::glmmPQL diagnostic

I am fitting models with MASS::glmmPQL of the form ...
0
votes
0answers
31 views

(multiple) Infinite/right censored independent values in diagnostics

We are facing a problem with infinite/right censored independent values. Case-control study where decay time (mono-exponential function) and half-value time seems to be of great diagnostic accuracy. ...
1
vote
0answers
60 views

ARIMA Model Suitability Testing

I'm attempting to forecast 24-month hydroelectric generation at various river systems in the United States. Because river flows -- which is the primary driver behind hydro generation -- are mean-...
1
vote
1answer
1k views

Interpretation of Ljung-Box tests for GARCH models from the 'rugarch' package in R

I have used the 'rugarch' R package to fit a GARCH model, as: ...
1
vote
1answer
63 views

Diagnostic Meta-Regression with mada in R

I am trying to figure out how to perform a meta-analysis of diagnostic test accuracy studies and I have a doubt that is driving me crazy. I am using the package mada written in R and following the ...
0
votes
0answers
54 views

What pitfalls should we avoid with Heidelberger-Welch convergence

I'm working through validating a Bayesian mixture model for multi-species occupancy with a collaborator. Initially, we relied on coda::heidel.diag to alert us to ...
1
vote
2answers
122 views

Ridge regression and autocorrelation

Im currently working with a strongley balanced data set. I have 15 countries over the period 1990-2017. My dependent variable is CO2 emissions. My independent variables are as follows, GDP per capita, ...

1
2 3 4 5
7