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

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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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33 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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1answer
80 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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1answer
112 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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69 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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2answers
235 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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1answer
146 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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32 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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555 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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80 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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1answer
31 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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130 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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1answer
76 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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3answers
658 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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265 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
99 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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1answer
110 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 ...
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1answer
110 views

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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1answer
228 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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122 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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1answer
74 views

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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72 views

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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121 views

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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2answers
417 views

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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2answers
237 views

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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2answers
67 views

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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1answer
202 views

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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2k views

How does “stepwise regression” work?

I used the following R code to fit a probit model: ...
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1answer
185 views

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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1answer
195 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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1answer
525 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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1answer
354 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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2answers
774 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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1answer
197 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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1answer
222 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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1answer
544 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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1answer
60 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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1answer
185 views

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 ...
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3answers
710 views

Is it possible to have a variable significant in multiple regression but not significant in stepwise regression?

I have run a stepwise regression and found that some of the selected variables are not significant yet in a multiple regression with all variables included in the model those variables were ...
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1answer
522 views

fastbw with rule=“p” in R's rms package: why do results depend on number of covariates?

I've been trying to use the fastbw function from the rms package in R to perform logistic regression with backward selection, with p-values as exclusion criterion (I am well aware of the arguments ...
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1answer
204 views

Software implementation of stepwise regression after multiple imputation

Simple question, does anyone know of a package (R preferred, but I'll take anything, SAS, Stata, SPSS) which implements stepwise regression of multiply imputed datasets. I've read that it's possible ...
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146 views

Fast algorithm for variable selection

The (training) data contains 1280 observations with 1415 features. The test set has additional 380 observations. The data is sparse, that is, many of the variables has many zeros and few positive ...
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1answer
1k views

What is the difference between AIC() and extractAIC() in R?

The R documentation for either does not shed much light. All that I can get from this link is that using either one should be fine. What I do not get is why they are not equal. Fact: The stepwise ...
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1answer
287 views

Specifying add and drop thresholds for stepwise regression in R

I am running a stepwise regression using the F test as the criterion. Is there a way to explicitly set the add and drop thresholds (alpha levels) in R? The documentation does not make it clear.
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2answers
494 views

How to conduct predictor selection in a generalized linear mixed model?

I have 18 predictors in a binary generalized linear mixed model (repeated measurements, over a 1000 subjects). I would like to trim the model a bit and remove some noise and useless predictors. ...
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1answer
676 views

Stepwise regression vs. elastic net

I understand that Stepwise regression analysis has lots of limitations, including the assumption that the predictors are not highly correlated with each other. In fact, this limitation was the most ...
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2answers
300 views

R-code question: model selection based on individual significance in regression?

I'm trying to generate an R function that keeps relevant variables based on their absolute t-value (or p, whichever is easier in code). Basically what I want is to run one regression (1), retain all ...
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315 views

Stepwise Regression Models in JMP

In JMP, I am building a regression model by using "Analyze"->"Fit Model" and choosing "Stepwise" for the personality. Once I click "Run" in the "Model Specifications" window, I get the "Fit Stepwise" ...
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5answers
2k views

Detecting significant predictors out of 300 independent variables

In a dataset of two non-overlapping populations (patients & healthy, total $n=60$) I would like to find (out of $300$ independent variables) significant predictors for a continuous dependent ...
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961 views

Does a stepwise approach produce the highest $R^2$ model?

When using the forward stepwise approach to select variables, is the end model guaranteed to have the highest possible $R^2$? Said another way, does the stepwise approach guarantee a global optimum or ...