Questions tagged [regression-strategies]

Regression Modeling Strategies

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1answer
27 views

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: ...
2
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1answer
89 views

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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1answer
37 views

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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0answers
7 views

How to interpret the effect of age on predicted wage for different Majors

I am conducting a multiple regression analyses to find the premiums on predicted log of wage associated with majoring in STEM vs non STEM degree. I take lnwage as the dependent variable and run ...
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1answer
37 views

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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1answer
17 views

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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0answers
7 views

Correlating variables taken from different samples

Considering a hypothetical example, we have samples from Twitter and Facebook across US counties during the same time period. Say we asked a different question on the different platforms. People on ...
1
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1answer
36 views

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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0answers
200 views

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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0answers
15 views

The procedure of adding interaction terms in regression models

What is the more sensible way to add interaction terms in regression models? I have a basic model which includes only the main effects. To add interactions to the basic model, do I add all of them at ...
2
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0answers
65 views

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 ...
3
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1answer
370 views

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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1answer
272 views

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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2answers
2k views

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 ...
6
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1answer
324 views

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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0answers
31 views

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 : ...
3
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0answers
89 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 ...
3
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1answer
168 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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2answers
164 views

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 ...
3
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0answers
89 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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1answer
77 views

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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0answers
33 views

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 ...
3
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1answer
424 views

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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0answers
20 views

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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0answers
21 views

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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1answer
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 (...
2
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1answer
116 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 ...
5
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1answer
934 views

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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2answers
770 views

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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0answers
66 views

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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0answers
80 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 ...
3
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1answer
1k 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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3answers
421 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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3answers
2k views

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 ...
4
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1answer
433 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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0answers
42 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 ...
0
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1answer
25 views

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 ...
4
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1answer
185 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 ...
2
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0answers
61 views

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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2answers
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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1answer
219 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 ...
4
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1answer
794 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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0answers
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, ...
2
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1answer
99 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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0answers
627 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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1answer
1k views

Does Regularized Logistic Regression Produce Calibrated Results?

It has been asked and addressed here that logistic regression modelling is calibrated already and there is no need for calibration of it. To me it seems the argument provided there does not follow ...
3
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2answers
1k views

Cox PH model: managing continuous variables and linearity assumption

In an epidemiological study, I'm using martingale plot to assess the linearity of continuous variables. Here are the Martingale Residuals (from Null Model) using R's ...
2
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1answer
1k views

Predictive Mean Matching as Single Imputation?

Multiple imputation is known to be advantageous compared to single imputation. However, in practice there are often non-statistical reasons why multiple imputation can not be used (e.g. the data ...
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0answers
30 views

Correlation Regression

I need some help to figure which statistical analysis will be useful for me. I have 15 variables and one dependent variable "Rate". I need to find out which of the variables have more impact on rate ...
0
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1answer
223 views

Variable with predictor name in Predict.rms

I would like to build regression models for a number of different exposure variables and show the results using ggplot.Predict of the ...

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