Questions tagged [regression-strategies]

Regression Modeling Strategies

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Change per capita, logarithmic change or non logarithmic change?

I am currently working with Covid-19 key figures such as registered cases of infection and death. My data is a panel dataset across time and municipalities in Denmark, the set consists of several ...
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One-vs-rest vs one-vs-one multiclass probability validation: does it matter?

Now that I have figured out how rms::val.prob works to the extent that I have written my own Python implementation, I would like to extend that idea to multiple ...
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Walk through rms::val.prob

The val.prob function in the rms R package has similarities to the ...
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1 answer
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Walk through `rms::calibrate` for logistic regression

The calibrate function in the rms R package allows us to compare the probability values ...
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Can I interpret coefficients for "Year" as differences between years that are not explained by my predictors?

I am doing statistical analysis of a natural experiment that consists of multiple years of measurements. I have two independent variables that are physically related to ...
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Repeated measures ordinal outcomes

I have an outcome that is measured at two time points, baseline and 1 year after. The outcome is on an ordinal scale: 3 = better, 2 = somewhat better, 1 = somewhat worse, and 0 = worse. I'm interested ...
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Converting a code for 5-fold cross validation to stratified 5-fold cross validation for continuous target variable

Do you know how I can convert my code so it can do stratified 5-fold cross validation on a continuous target? df['score'] or y is a continuous variable. ...
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(Stratified) 5-fold cross validation for 2D tensor and real-valued target regression using sklean train_test_split method

In classification problem, when we want to do stratified 5-fold cross validation, we pick the target and use train_test_split using something like below: ...
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Meaning of interaction with %ia% in rms? Three-way interaction?

In this very illustrative post on evaluating added value of predictors by Frank Harrell, he codes a logistic regression model as such: ...
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Estimating regression optimism using the bootstrap

I am estimating optimism bias in for example risk predictions. A method for doing that is described by Frank Harrell and implemented in the R package rms. I am ...
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Articles about data analysis workflow

I am a non-statistician. I have to write a non-English article about my data analysis workflow for a particular epidemiological regression analysis that I conducted. The article will cover my workflow ...
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1 answer
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Effect size derived from LME longitudinal model: the statistical findings projected back down onto a group of people

I have been studying the change in a metric X with a linear mixed effect model. I have built this model in a multivariate setting, so I can see how each of my covariates (Time, sex, age) affect X. ...
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Modelling strategies for analyzing an effect of a predictor through higher hierarchical level

What strategies can be considered when a predictor's direct effect can not be measured directly due to unmeasured confounding? However, data has a hierarchical structure (patients within regions) that ...
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Applying count models with rate responses

How do you apply count models to data which is count in nature, but a rate in reality? In such cases, r can handle this to a certain extent, depending on the model, but what is the correct way to ...
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Boruta Algorithm for Logistic Regression?

Is it okay to use a Boruta algorithm to select features for a logistic regression? I read several sources, including the source package as well as this site explaining what Boruta does. My ...
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Logit Regression and F-test: Can I apply the f statistic when variables are non-normal and the output is binary?

I want to do a univariate analysis on a set of variables to see which predict a binary outcome. I want to discard some of them before performing logistic regression. I am trying to understand if I can ...
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External validation of a published Cox PH model

My aim is to externally validate a risk prediction model published in the medical literature that is based on a Cox regression model. I have a dataset with all the variables from the score. I read ...
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1 answer
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Brier score of calibrated probs is worse than non calibrated probs

The question is related to probability calibration and Brier score I have faced with the following issue. I have Random forest binary classifier and then I apply isotonic regression to calibration of ...
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3 votes
2 answers
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Handling missing data in logistic regression

I'm trying to do logistic regression, but I can't seem to get the results I want. I have 6 columns of data (one dependent and 5 independent binary variables) and about 100 rows. The problem with my ...
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1 answer
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probability calibration and Brier score

Assume that I have a binary classification problem. The outcome from classification I am mostly interested in is the well-calibrated probabilities. The first way to check this is the calibration plot (...
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Determine minimum data to start with building model

We have developed a basic Regression framework where we try to build models for over 100 configs(stored in a file). To run : ...
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105 views

OLR with rms: proportional odds assumption

I am fitting an ordinal logistic regression model with rms package. my data involves a three-level ordered outcome (see ...
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4 votes
1 answer
334 views

When LASSO selects only parts of a categorical variable?

I want to use LASSO to construct a model and then run a logistic regression on the variables LASSO selects. However, LASSO selects only parts of some categorical variables that I put into it. Does ...
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1 vote
2 answers
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Excluding the effect of control variables in the assessment of a logistic regression model

I have a logistic regression model with ten independent variables of which two are included as controls. While their inclusion is necessary for correctly assessing the coefficients of the other ...
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3 votes
0 answers
112 views

Using "moderately" correlated variables to select controls for a LASSO regression?

In medicine we often have a disease status as an outcome variable and a lot of independent variables in which we want to see if there is some connection. Traditionally, baseline characteristics such ...
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1 answer
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How to validate Generalized Least Square model for longitudinal response

I have a dataset with body weights before and in the follow-up visits after surgery, for a group of patients with obesity. Our goal is to fit a model to predict weight loss throughout the follow-up. ...
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Combining mean differences in regressors and significant prediction and moderation in multiple regression

I am analyzing a survey about career choices with an eye toward demonstrating sex differences in: 1) the means for factors that might be related to career choice (e.g., differences by sex in ...
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Conducting a subgroup analysis with regression modeling

I'm conducting a survival analysis using Cox Proportional Hazards regression to identify prognostic factors for cancer patients. My covariates include information such as age, sex, tumor location etc. ...
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how to choose the best logit model [duplicate]

I have two logit regression models with different AIC. I'm using R. my first model has significant variables and AIC 192.7436. And my second model has 1 non-significant variables but with smaller AIC ...
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I have a confusion between using 4 different general linear models and 1 singular ones. I have provided with the codes and outputs

I want to check the effect on mass of crickets, I have a fixed linear effect (AltitudeAge), fixed quadratic effect (AltitudeAge^2), random effects (Nymph IDs, population and the incubators they are ...
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1 vote
1 answer
1k views

Number of Covariates in Cox PH Model and Overfitting

I have a small time to event dataset (N=20) where patients are given one of two drugs (drug) at varying doses (...
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2 votes
1 answer
183 views

Fitting model again after variable selection

This question has been asked quite a bit in other contexts (doing LASSO then OLS on selected variables for example), but I'm unsure about how to proceed for this case. Suppose I have a set of 50 ...
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1 answer
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Problems from having too many interactions in a regression?

Excluding the 'dummy variable trap', are the problems from including too many interaction terms in a regression any different from the problems of including too many continuous or binary variables in ...
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21 votes
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Does LASSO suffer from the same problems stepwise regression does?

Stepwise algorithmic variable-selection methods tend to select for models which bias more or less every estimate in regression models ($\beta$s and their SEs, p-values, F statistics, etc.), and are ...
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Kaplan Meier Diagnostic Utility

I'm trying to understand a paper that claims to have identified a gene expression signature that can distinguish primary from metastatic tumors. The authors stratify their data into patients with and ...
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111 views

Logistic regression with repeated mesures and unique outcome

I have one independent continuous and time-dependent variable X, repeatedly measured (from 1 to 4 times) in different patients during some period of time. My dependent variable Y is binary and is ...
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3 votes
1 answer
2k views

Interpretting Cox Regression ANOVA

I'm having difficulty interpreting the results from anova() in the rms package. My confusion arises from what information the <...
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2 votes
3 answers
533 views

Interaction between dependent and independent variable

I am conducting a multiple linear regression on data from a cross-sectional study, and I suspect that there is an interaction between my dependent variable (a disease risk marker) and one independent ...
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9 votes
3 answers
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How to reduce predictors the right way for a logistic regression model

So I have been reading some books (or parts of them) on modeling (F. Harrell's "Regression Modeling Strategies" among others), since my current situation right now is that I need to do a logistic ...
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5 votes
1 answer
593 views

How to perform cross validation in semi-supervised learning

Suppose in semi-supervised learning, we have labeled set $X_L$ and unlabeled set $X_U$ Is it ok to validate model performance on labeled data only? How to do cross-validation in transductive learning,...
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1 vote
0 answers
49 views

Method for testing interaction in regression analysis

I am running a linear model with 6 explanatory variables (5 in classes, 1 quantitative), and would like to test which interaction(s) are eventually significant. I think an ascendant stepwise method ...
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1 answer
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Trying to test if an increased rate of use decreases total product

TL-DR: The higher the rate of production on my expendable unit, the less overall product it seems to produce in its lifetime. I want to know how best to model this or 20 pages I could read which would ...
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4 votes
1 answer
199 views

Why do we even bother running regression models?

I'm working through regression with Intro to Statistical Learning by Hastie, Witten, James and Tibshirani. They break down regression into stages: data cleaning and processing, model building and ...
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2 votes
0 answers
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What is the best way to choose interactions between continuous variables for Logistic Regression?

I have a logistic regression model that I am working on for a school project. I have about 55 predictors, all of which are continuous. I am relatively new to the idea of "interactions" between ...
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8 votes
2 answers
1k views

What is the problem with $p > n$?

I know that this is the solving system of linear equation problem. But my question is why it is a problem the number of observation is lower than the number of predictors how can that thing happen? ...
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1 vote
1 answer
269 views

recommended approach for building an ANOVA model

Despite reading several online references, including the full Wikipedia article on "ANOVA", I'm still confused at the recommended process taken to build the most statistically significant linear or ...
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4 votes
1 answer
1k views

Bias corrected calibration curve (regression modelling strategies)

I have a question regarding calibration plot for a binary logistic regression model (calibrate) in the rms(regression modelling strategies) package. The Bias-corrected curve (see below) shows if the ...
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0 answers
70 views

Is Wald statistic sufficient to show no association?

When reporting the results of multivariable regression analyses, I would normally provide either $\beta$ with 95% confidence interval, or the estimated effect size (e.g. between Q1 and Q3). However, ...
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  • 315
2 votes
1 answer
151 views

how can I obtain a beta value for three way interaction term in a logistic regression

I am using the RMS package in R to conduct a logistic regression that contains a three-way interaction. As part of my modelling approach, I have conducted chunk tests of the interaction (using Wald ...
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1 vote
0 answers
700 views

How to apply a model built using Multiple Imputation to predict on dataset with missing data?

I understand that Professor Harrell recommends using the target variable in Multiple Imputation. An example using aregImpute of the rms package is in his lecture notes: http://hbiostat.org/doc/rms....
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