"Stepwise regression includes regression models in which the choice of predictive variables is carried out by an automatic procedure." [Wikipedia]

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Selection of regressors into a regression model

Why is it that backward selection/elimination as compared to forward selection of regressors, is often less adversely affected by the correlative structure of regressors?
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Linear regresson lm or stepwise regression here using R?

It is a basic question but I could not find clear answer on my reading. I am trying to find independent predictors of Infant.Mortality in data frame 'swiss' in R. ...
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What is the basis of setting critical p-value value in stepwise regression?

In statistical software like MINITAB and SAS, the default alpha value (critical p-value) is set as 0.15. I would like to know (1) if there is any statistic basis to set it as 0.15 and (2) if this is a ...
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stepwise regression with constraints

Is it possible to do stepwise regression with adding constraints on coefficients to be positive only? I am using Matlab's stepwisefit function to do this exercise. Thanks for your help
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covariate selection for a cox model by Lasso using glmnet

I would like to use model selection through shrinkage (Lasso) using glmnet. So far I did the following: ...
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87 views

(Automated) feature selection in multiple regression with categorical variables

I need a general guide on what are the appropriate approaches to automated feature selection in multiple regression with categorical variables. In my case, I have several numeric and categorical ...
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Why is Lasso regression for high dimensional data better than Stepwise AIC?

I know Lasso eventually set some parameters to zero, acting like variable selection. I also read from paper talking about automated variable selection method like Stepwise AIC can be troublesome. So ...
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50 views

Highly correlated predictors in backward stepwise regression?

I know that it's not right to enter variables having multicollinearity (high correlation) into a regression analysis. But if I'm using backward stepwise regression could I add all the highly ...
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50 views

How to run main effects and interactions in a stepwise regression?

I am using multiple regression with the backward elimination method. I have one control variable (social desirable responding) and four predictor variables (gender and three self-esteem constructs). ...
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Logistics Regression Stoped at Step 0 with SAS Enterprise Miner

I use some insurance quote data with demographic data as variables and the target is binary (0,1). The total observations is around 50,000 and the variables are around 60. The demographic data is ...
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53 views

Stepwise regression modeling using multiply imputed data sets

After multiply imputing data, it is natural to estimate regression models on the data. When multiple predictors are available, sometimes stepwise regression is used for model building (forward ...
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58 views

Non-significant factors after stepwise regression [duplicate]

I have run a stepwise regression on R. However, the summary of the final model includes some factors that are not significant. Why have these factors not been removed? Should I remove these from my ...
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Significance of varibles after stepwise regression

I did stepwise regression with my multiple regression model and using AIC as a measure of fit with the step function in R. Afterwards some variables that the ...
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R cv.glm returns NaN for stepwise-generated regression model

I'm trying to run K-fold cross-validation on a multiple regression model that was generated via the step function in R. However, the call to ...
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104 views

Interaction in stepwise regression analysis

I did a stepwise regrssion analysis to predict energy expenditure using the variables, height, weight, age, gender and energy intake. The final model contains the variables gender and weight. Now does ...
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184 views

Stepwise binary logit regression - help for bootstrapping in Stata

I am running a stepwise binary logit regression in Stata using 14 independent variables. Two of the independent variables are dummies (assuming a value of 0 or 1). I've tested the independent ...
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95 views

Same p-value when comparing two GLM

This is my first question, please should I write something wrong correct me. I have a question when comparing two GLMs after applying stepwise selection. What I've always heard is that stepwise ...
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728 views

Stepwise regression in R – Critical p-value

What is the critical p-value used by the step() function in R for stepwise regression? I assume it is 0.15, but is my assumption correct? How can I change the ...
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234 views

Stepwise regression in R with both direction

How does the stepwise regression method work for both direction in R with the step() function. I would think that one variable ...
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How do you extract confidence intervals and OR out of the step() function in R?

I've been wondering something for a while. If you run a simple regression model in R and then perform a step-wise selection (it doesn't have to be the way I typed the code below), how do you extract ...
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using stepAIC of MASS package to select variables with a significance level of 5% in R project

First of all, sorry i am new about this and any helps are really welcome. I am reading a reaserch paper where the authors report: Stepwise forward regression (Zar 1996) was used to select the most ...
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134 views

Stepwise meta-regression with R (metafor)

I am using the "metafor" package to do a multivariate meta-regression in "R". I have 6 predictors and I am able to run the full model (all the predictors simultaneously in the model) just fine. ...
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R not testing certain variables in forward stepwise regression?

FullModel<- (lm(Fubar~.-Foo-Bar,data=BarFoo)) NullModel<-(lm(Fubar~1)) step(NullModel,scope=formula(FullModel),direction="forward",k=log(nrow(BarFoo))) When ...
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202 views

Superiority of LASSO over forward selection/backward elimination in terms of the cross validation prediction error of the model

I obtained three reduced models from a original full model using forward selection backward elimination L1 penalization technique (LASSO) For the models obtained using forward selection/backward ...
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Linear model predictor selection. Which method to use ?

From what I understand, there are 3 main types of predictor selection method for linear models, namely, 1 Subset Selection, 2 Shrinkage and 3 Dimension Reduction. The subset selection includes the ...
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Variable is significant through stepwise regression but not in final model's summary; which should I report?

I used generalized linear mixed models (with the glmmADMB package) to identify environmental factors related to parasite abundance in rodents. I used stepwise ...
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Backward stepwise regression with cross validation in R

I would like to do model selection using backward stepwise procedure and cross validation. https://www.otexts.org/fpp/5/3 I have used stepAIC in ...
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AIC values and their use in stepwise model selection for a simple linear regression

The Wikipedia article for AIC says the following (emphasis added): As an example, suppose that there were three models in the candidate set, with AIC values 100, 102, and 110. Then the second ...
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Variable selection for regression - the subselect package

No regular here will be unaware of the perils of using stepwise and similar automatic methods for variable selection in regression analysis. But preferred alternatives, such as the lasso or ...
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Interaction effect in stepwise regression

I am trying to creat a multiple regression model with a forward stepwise procedure. Predictors are air temperature, soil temperature, PAR and snow depth. I also want to see if there are some ...
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302 views

Stepwise regression, moderation effects, main effects

I have a simple model: $A$ is hypothesized to be a predictor / regressor / explanatory / input variable $B$ is hypothesized to be the response / regressand / explained / outcome variable So, the ...
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182 views

Model estimation procedure using backward elimination

I have run a multiple linear model using Minitab. The result showed that all variables are not significant. So, I use backward elimination. Lastly, I found that ...
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Numeric example of data for special case of stepwise linear regression

Stepwise Regression works as follows if I'm correct: fit the initial model add the variable which has its f-stat larger than a in-threshold and repeat step 2. if there are no candidates to enter - ...
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Does full subset selection regression model building suffer from the same handicaps as stepwise regression?

Let's assume $p$ potential predictor variables $X_1,...,X_p$ and a single dependent variable $Y$. Now I evaluate the performance of all possible linear models considering all possible combinations of ...
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How to build the best regression model using stepwise regression?

I have a data set that contains rental cost (Dependent variable) and I have another data sets that are potential predictors like city, area, No. of rooms etc. (Independent variables). I have ...
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Generalized linear mixed models: model selection

This question/topic came up in a discussion with a colleague and I was looking for some opinions on this: I am modeling some data using a random effects logistic regression, more precisely a random ...
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Logistic regression, SPSS ignores my reference category and assumes another one

I am modelling logistic regressions in SPSS, the same model for different countries (well, with slight differences in the independent variables set due to collinearity diagnosis and stepwise results). ...
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Does the number of IV after stepwise model selection depend on the amount of data?

I have 3 different DV that I try to model with 3 distinct models (linear mixed models) using the same set of IV. I found that the DV that I have the least amount of data for also has the lowest number ...
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Alternatives to stepwise discriminant analysis for feature selection on hyperspectral data

I am new to R and to hyperspectral data analysis. However, in my research, I have found that many warn against using Stepwise discriminant analysis (using Wilk's Lambda or Mahalanobis distance) for ...
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How does “stepwise regression” work?

I used the following R code to fit a probit model: ...
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Implement Forward, Backward, Step and LASSO in VB .NET

My client wants me to implement Variable selection methods i.e. Forward, Backward, Step and LASSO in VB .Net platform including p-value and AIC. I have no idea about the steps involved to calculate ...
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246 views

Variable selection for linear regression using robust or least squares estimation

I have a data set consisting of one continuous response variable and about 70 predictors. Using this data, I want to construct a linear regression model. However, I don't know what predictors are ...
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643 views

What does an infinite AIC mean and what can be done about it?

I have a question about performing stepwise regression. I realize that there are issues with using stepwise methods, but I have about 30 or so predictors and have constructed an ...
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391 views

Interpretation of insignificant predictors in logistic regression model

First I should explain what I did, and it might not be right. I have a variable that represents a test outcome, it might be positive or negative. I have a set of observations of one important ...
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924 views

Interpretation of coefficients in multiple regression without intercept

I am trying to interpret the SPSS output from a multiple hierarchical regression where the intercept has been eliminated because it is not significant. I have read previous discussions about ...
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227 views

Stepwise versus L2 regularized logistic regression: dataset-specific performance

I have two data sets from different collections. The second data set is smaller. They were both analyzed with the same methods in order to derive feature sets of 10-30 features each. Each feature set ...
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240 views

The dangers of stepwise variable selection in regression

This paper discusses some of the dangers of using stepwise variable selection procedures: http://www.auburn.edu/~tds0009/Articles/Whittingham%20et%20al.%202006.pdf I'm struggling to understand ...
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661 views

Stepwise introduction of predictors to mixed-effects models

As the title says, what I'd like to do is stepwise introduction of predictor variables to a mixed-effects model. I'm going to first say what I'd be doing if it were stepwise linear regression, just to ...
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63 views

Estimation Technique

My panel regression model is as follows: $$Y_{it}= PS_{it}+PF_{it}+EF_{it}+ e_{it}$$ where $i$ : country $t$ : year $Y_{it}$ : GDP per capita $PS_{it}$ : Political stability $PF_{it}$ : ...
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How to perform step() when n < p in R?

I am trying to perform stepwise regression for variable selection in R. In matlab, the stepwisefit function is able to work in ...