The stepwise-regression tag has no wiki summary.
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1answer
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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
50 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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0answers
57 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
171 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 ...
0
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1answer
85 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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0answers
39 views
Nested spline based learning algorithms using stepwise model selection
I am interested in setting up a general forward model selection algorithm for simulating outcomes in multiple imputation.
I am binning the outcome into deciles (or possibly a more granular level ...
0
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1answer
117 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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0answers
92 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 ...
8
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1answer
326 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 ...
2
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1answer
176 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.
2
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2answers
179 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. ...
3
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1answer
316 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 ...
2
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2answers
244 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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0answers
148 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
931 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 ...
2
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3answers
534 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 ...
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2answers
2k views
Is there a way to use cross validation to do variable/feature selection in R?
I have a data set with about 70 variables that I'd like to cut down. What I'm looking to do is use CV to find most useful variables in the following fashion.
1) Randomly select say 20 variables.
...
18
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4answers
3k views
Algorithms for automatic model selection
I would like to implement an algorithm for automatic model selection.
I am thinking of doing stepwise regression but anything will do (it has to be based on linear regressions though).
My problem ...
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2answers
2k views
Assessing the effect of adding a variable using stepwise forward logistic regression using Stata?
I'd really appreciate help using Stata to perform a manual stepwise forward logistic regression.
I have 37 biologically plausible, statistically significant categorical variables linked to disease ...
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4answers
2k views
What are modern, easily used alternatives to stepwise regression?
I have a dataset with around 30 independent variables and would like to construct a GLM to explore the relationship between them and the dependent variable.
I am aware that the method I was taught ...
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0answers
999 views
Forcing variable selection to keep certain predictor in R
I am looking for a variable selection technique in R to reduce the number of my regression predictors, where I can force the method to keep a specific variable within the model. Here is a toy example ...
2
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1answer
943 views
Incorporating random effects in the logistic regression formula in R
I'm trying to find the best model based on AIC using the stepwise (direction = both) model selection in R using the stepAIC in MASS package.
This is the script i ...
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1answer
488 views
How to plot AIC values when using the leaps package?
Does anybody know how to plot all AIC values for different size models, when using the command regsubsets from the package ...
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1answer
223 views
Multiple regression with no origin and mix of directly entered and stepwise entered variables using R
I am running a regression equation and I want to enter in 12 indepdendent variables then stepwise enter 7 more independent variables and not have an origin.
DV is ...
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3answers
9k views
AIC or p-value: which one to choose for model selection?
I'm brand new to this R thing but am unsure which model to select.
I did a stepwise forward regression selecting each variable based on the lowest AIC. I came up with 3 models that I'm unsure which ...
9
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1answer
2k views
Estimating R-squared and statistical significance from penalized regression model
I am using the R package penalized to obtain shrunken estimates of coefficients for a dataset where I have lots of predictors and little knowledge of which ones are important. After I've picked tuning ...
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2answers
2k views
Regression selection using all possible subsets selection and automatic selection techniques
Given the dataset cars.txt, we want to formulate a good regression model for the Midrange Price using the variables Horsepower, Length, Luggage, Uturn, Wheelbase, and Width. Both:
using all possible ...
8
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1answer
466 views
Sane stepwise regression?
Suppose I want to build a binary classifier. I have several thousand features and only a few 10s of samples. From domain knowledge, I have a good reason to believe that the class label can be ...
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0answers
362 views
Discrepancy between stepwise and nominal logistic regression results in JMP
I have carried out a stepwise logistic regression in JMP. Then (using the proper button in the program window), I have chosen to build a nominal logistic regression model using (only) the variables ...
4
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3answers
732 views
Can you use heteroskedastic time series variables within a regression model?
We are working on a multivariate linear regression model. Our objective is to forecast the quarterly % growth in mortgage loans outstanding.
The independent variables are:
1) Dow Jones level.
2) % ...
10
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5answers
1k views
Stepwise logistic regression and sampling
I am fitting a stepwise logistic regression on a set of data in SPSS. In the procedure, I am fitting my model to a random subset that is approx. 60% of the total sample, which is about 330 cases.
...
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1answer
530 views
R: How to create a function from a model?
I am using an automatic model selection procedure, "step". The model of depart (the biggest possible) is a polynom, say of the 4th degree.
...
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2answers
2k views
Interpreting the step output in R
In R, the step command is supposedly intended to help you select the input variables to your model, right?
The following comes from
...
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2answers
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Interpreting the drop1 output in R
In R, the drop1command outputs something neat.
These two commands should get you some output:
example(step)#-> swiss
...
