All Questions
Tagged with model-selection regression
338
questions
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The function step.lmRob() is not working [closed]
I have a linear model, which i analyzed (in R) through: lmrob_object<-lmrob(diff_mg ~ age + bmi + energy + fiber + ca + phos + iron + potas + supp + uni, data = data), where:
diff_mg is the DV (...
- 151
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39
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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 ...
- 21
3
votes
1
answer
193
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Reduce the model sequentially
I was given an ANOVA table and asked to reduce the model sequentially.
I searched the online resources say: When reducing the model sequentially, you typically start by assessing the significance of ...
- 671
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41
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Confusion about Mallows' Cp
I am trying to use Mallows' $C_p$ to select linear regression models. I have been reading the excellent text by Cosma Shalizi at https://www.stat.cmu.edu/~cshalizi/TALR/TALR.pdf
(page 323 to 327).
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26
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How to fit a dataset like this, and what's the recommended evaluate metrics for it
the dataset seems like non-linear,
is there any recommended way to fit the datatset? since it's a non-linear regression problem, what's the correct way to evaluate the model's prediction? is the MSE ...
- 1
7
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2
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143
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How can I get a best model? An exploratory LMM
I'd like to inquire about the linear mixed model and its application to my dataset. The dataset comprises a dependent variable (DV) denoted as V, alongside three ...
- 71
3
votes
2
answers
144
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Variable selection in logistic regression [duplicate]
So I'm trying to make a multivariate logistic regression model in R studio. I'm not sure how to go about this. What seemed to make sense to me was to model every predictor against the response ...
- 127
2
votes
1
answer
107
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R-squared vs adjusted R-squared in Hierarchical multiple regression
In hierarchical multiple regression (not to be confused with hierarchical linear models that account for variance components), you add model terms by block. The fit of the new model is measured by the ...
- 21
1
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1
answer
201
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Which regression model would you choose?
Which regression model would you choose to model the following flood damage data? The variables are x1=water height, x2=dike height and x3=flood damage. The following plot shows how the flood damages ...
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4
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33
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Can cross-validation be involved in model-building rather than validation?
I have a general idea in mind that would go like this:
randomly split the data into training/testing
build a model on the training data by choosing from among candidate predictors
evaluate it on the ...
- 2,651
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7
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'Absolute' benchmarks of model performance on dataset
There are numerous techniques for benchmarking models, e.g. cross-validation and resampling. However, while these can easily be compared in a relative way between implemented models or against a ...
- 101
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24
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How should I approach statistical model development from rubric-based data?
Background:
I am currently working in a role where I work in Assessment and Selection of right-fit applicants for teaching roles at a partner organisation. We presently use a rubric with a few ...
- 101
3
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1
answer
230
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Difference between Parsimonious model vs Optimal model
As per my understanding, parsimonious regression model is the model that has less variables but with those variables I can describe the data best. Is it so?
Then ...
user386962
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99
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GAM selection via EDF vs. adding penalties
I have a generalized additive mixed model (GAMM) I'm using for modelling fish counts and many covariates to test (with different proportions of NA's in each covariate), but not enough data to include ...
- 1,653
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85
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Is AIC scale invariant for problems concerning the number of data points in regression?
I am trying to use Akaike Information Criterion with the small sample correction (AICc) as method for determining how many data points to use in a linear approximation of a non-linear function; the ...
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