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

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Detecting outlying distributions of ratio data

I have a dataset consisting of hundreds of repeat observations on thousands of agents. Each observation is a ratio between two distance measures, A and B, where A is always larger than B. Thus, my ...
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84 views

Combining principal component regression and stepwise regression

I want to use a combination of principal component analysis (PCA) and stepwise regression to develop a predictor model. I have 5 independent variables (which are correlated among each other to ...
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21 views

Variable reduction techniques

I am researching variable reduction techniques for time series data. Atm I came up with expert judgement, Stepwise Regression (Forward), Stepwise Regression (Backward) and Granger Causality. Any ...
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42 views

R: Dynamic Regression with ARIMA model using xreg, make use of step function?

This might fit better here than on stackoverflow, I guess. I was trying to build a dynamic regression model with the dynlm package, but it did not work out. After reading this by Hyndman, I now ...
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88 views

Significant predictors of mpg in mtcars dataset in R

Which variables are significant predictors of mpg in mtcars dataset. When I perform following regression: ...
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2answers
112 views

What is the difference between VIF and stepwise regression?

What is the difference between the variance inflation factor (VIF) and stepwise regression as both help in detecting multicollinearity? What variables are different while running both techniques?
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48 views

Do I drop insignificant parameters from a model? Should I use stepwise regression?

I'm working on a project where we have a number of factors we believe might have a role on a survey result. It's my job to figure out if this is true. My boss suggested just doing correlations on each ...
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55 views

R: Why does step function of a Linear Modegives different AIC/BIC than AIC function?

I don't understand what I make or think wrong, but if one tries to evaluate the linear model of the data (which you can find in R in the Package AIC(stats)), then ...
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122 views

How to use residual analysis to remove the effect of confounding variables in a model in R

I want to find which soil variables better explain plant productivity, using a database that contains information for about 100 forests plots across Europe. These plots have only one species per plot, ...
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155 views

Stepwise logistic regression

I am working with a dataset of 1000 individuals, 200 of which are disease positive. I have run a logistic regression with 25 predictors to identify overall which variables are significantly ...
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1answer
77 views

What is the best lag length for auto correlation?

I am doing a monthly rainfall forecasting model. I have monthly data from 1998 to 2012. I found in previous research that they have used partial autocorrelations and stepwise regression as an input ...
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47 views

Validity of stepwise regression in DistLM

I have a set of nutrient fluxes data and I would like to know which environmental drivers explains the fluxes. I used DistLM and the marginal test showed that none ...
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14 views

Is there some analysis of block-greedy algorithms for feature selection or sparse approximation?

I consider the problem of sparse approximation, where one has a signal $\vec y = \sum_j \theta_j \cdot \phi_j(\vec x)$ using stepwise regression. One can use a greedy algorithm to solve it, e.g. ...
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95 views

GAMLSS: model with interaction terms failed

I use gamlss method from library(gamlss) on my full models with interaction terms and try to reduce them with stepGAIC. There are 3 things I want to ask. Do I have to specify a link for the model? ...
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2k views

Stepwise Model Selection in Logistic Regression in R

I'm implementing a logistic regression model in R and I have 80 variables to chose from. I need to automatize the process of variable selection of the model so I'm using the step function. I've no ...
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2answers
268 views

Should I remove non-significant variables from my regression model

I have run a multiple linear regression using stepwise regression to select the best model, however the best model returned has a non-significant variable. When I remove this the AIC value goes up ...
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1answer
318 views

dummy variables, interaction with continuous variable, and variable selection

I want to predict shop sales from a set of independent variables which consists of shop attributes like floor space, no. of stuff of a specific store (continuous variables) and also location of the ...
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143 views

step {stats} is too slow. Are there multicore solutions?

I am finding that trying to do a stepwise logistic regression is far too slow on my data set (6 hours). Is anyone aware of any faster solutions out there? Perhaps one that takes advantage of the ...
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38 views

When to plot different slopes for different treatments in ANCOVA

I am running an ANCOVA with a treatment of 3 levels and a continuous co-variate. Using step() in r, the simplest model includes the treatment factor and the co-variate, but not their interaction. To ...
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80 views

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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165 views

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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145 views

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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57 views

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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545 views

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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356 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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566 views

Backward selection for Cox model using R

I want to perform an exploratory Cox regression analysis of medical data using R. I am practicing using the pbc data from the survival function. Would you recommend performing a backward selection ...
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285 views

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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422 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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109 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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51 views

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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195 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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103 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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67 views

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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180 views

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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159 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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419 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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198 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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4k 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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724 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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3k views

How to perform stepwise regression without intercept?

I have to implement a regression model and I have about 30 variables in the model. Some of the variables do not have much influence on the model, but I need to use a formalized method for eliminating ...
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71 views

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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1answer
5k views

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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256 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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39 views

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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421 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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194 views

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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3k views

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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878 views

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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1answer
2k views

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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310 views

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 ...