## Stepwise regression using p-values to drop variables with nonsignificant p-values

stepwise regression p-value
stepwise regression in r
step function in r
stepwise logistic regression in r
alternatives to stepwise regression
variable selection in linear regression
variable selection in logistic regression
stepwise aic

I want to perform a stepwise linear Regression using p-values as a selection criterion, e.g.: at each step dropping variables that have the highest i.e. the most insignificant p-values, stopping when all values are significant defined by some threshold alpha.

I am totally aware that I should use the AIC (e.g. command step or stepAIC) or some other criterion instead, but my boss has no grasp of statistics and insist on using p-values.

If necessary, I could program my own routine, but I am wondering if there is an already implemented version of this.

Show your boss the following :

```set.seed(100)
x1 <- runif(100,0,1)
x2 <- as.factor(sample(letters[1:3],100,replace=T))

y <- x1+x1*(x2=="a")+2*(x2=="b")+rnorm(100)
summary(lm(y~x1*x2))
```

Which gives :

```            Estimate Std. Error t value Pr(>|t|)
(Intercept)  -0.1525     0.3066  -0.498  0.61995
x1            1.8693     0.6045   3.092  0.00261 **
x2b           2.5149     0.4334   5.802 8.77e-08 ***
x2c           0.3089     0.4475   0.690  0.49180
x1:x2b       -1.1239     0.8022  -1.401  0.16451
x1:x2c       -1.0497     0.7873  -1.333  0.18566
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
```

Now, based on the p-values you would exclude which one? x2 is most significant and most non-significant at the same time.

Edit : To clarify : This exaxmple is not the best, as indicated in the comments. The procedure in Stata and SPSS is AFAIK also not based on the p-values of the T-test on the coefficients, but on the F-test after removal of one of the variables.

I have a function that does exactly that. This is a selection on "the p-value", but not of the T-test on the coefficients or on the anova results. Well, feel free to use it if it looks useful to you.

```#####################################
# Automated model selection
# Author      : Joris Meys
# version     : 0.2
# date        : 12/01/09
#####################################
#CHANGE LOG
# 0.2   : check for empty scopevar vector
#####################################

# Function has.interaction checks whether x is part of a term in terms
# terms is a vector with names of terms from a model
has.interaction <- function(x,terms){
out <- sapply(terms,function(i){
sum(1-(strsplit(x,":")[] %in% strsplit(i,":")[]))==0
})
return(sum(out)>0)
}

# Function Model.select
# model is the lm object of the full model
# keep is a list of model terms to keep in the model at all times
# sig gives the significance for removal of a variable. Can be 0.1 too (see SPSS)
# verbose=T gives the F-tests, dropped var and resulting model after
model.select <- function(model,keep,sig=0.05,verbose=F){
counter=1
# check input
if(!is(model,"lm")) stop(paste(deparse(substitute(model)),"is not an lm object\n"))
# calculate scope for drop1 function
terms <- attr(model\$terms,"term.labels")
if(missing(keep)){ # set scopevars to all terms
scopevars <- terms
} else{            # select the scopevars if keep is used
index <- match(keep,terms)
# check if all is specified correctly
if(sum(is.na(index))>0){
novar <- keep[is.na(index)]
warning(paste(
c(novar,"cannot be found in the model",
"\nThese terms are ignored in the model selection."),
collapse=" "))
index <- as.vector(na.omit(index))
}
scopevars <- terms[-index]
}

# Backward model selection :

while(T){
# extract the test statistics from drop.
test <- drop1(model, scope=scopevars,test="F")

if(verbose){
cat("-------------STEP ",counter,"-------------\n",
"The drop statistics : \n")
print(test)
}

pval <- test[,dim(test)]

names(pval) <- rownames(test)
pval <- sort(pval,decreasing=T)

if(sum(is.na(pval))>0) stop(paste("Model",
deparse(substitute(model)),"is invalid. Check if all coefficients are estimated."))

# check if all significant
if(pval<sig) break # stops the loop if all remaining vars are sign.

# select var to drop
i=1
while(T){
dropvar <- names(pval)[i]
check.terms <- terms[-match(dropvar,terms)]
x <- has.interaction(dropvar,check.terms)
if(x){i=i+1;next} else {break}
} # end while(T) drop var

if(pval[i]<sig) break # stops the loop if var to remove is significant

if(verbose){
cat("\n--------\nTerm dropped in step",counter,":",dropvar,"\n--------\n\n")
}

#update terms, scopevars and model
scopevars <- scopevars[-match(dropvar,scopevars)]
terms <- terms[-match(dropvar,terms)]

formul <- as.formula(paste(".~.-",dropvar))
model <- update(model,formul)

if(length(scopevars)==0) {
warning("All variables are thrown out of the model.\n",
"No model could be specified.")
return()
}
counter=counter+1
} # end while(T) main loop
return(model)
}
```

Why stepAIC gives a model with insignificant variables in the , I would like to know what environmental variables allows to explain the But when I do a summary(model), some variables are not significant (according to pvalues). Stepwise approaches mean that you repeatedly test hypotheses, using the Burnham, Anderson 2011 - AIC model selection and multimodel inference in  Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Learn more R: Stepwise Regression using P-Values to Drop — setting the level?

Why not try using the `step()` function specifying your testing method?

For example, for backward elimination, you type only a command:

```step(FullModel, direction = "backward", test = "F")
```

and for stepwise selection, simply:

```step(FullModel, direction = "both", test = "F")
```

This can display both the AIC values as well as the F and P values.

Stepwise regression in R – Critical p-value, As I explained in my comment on your other question, step uses AIC rather than p​-values. However, for a single variable at a time, AIC does correspond to using  Stepwise regression adds or removes predictor variables based on their p values. The first step is to determine what p value you want to use to add a predictor variable to the model or to remove a predictor variable from the model. A common approach is to use the following: p value to enter = P enter = 0.15.

Here is an example. Start with the most complicated model: this includes interactions between all three explanatory variables.

```model1 <-lm (ozone~temp*wind*rad)
summary(model1)

Coefficients:
Estimate Std.Error t value Pr(>t)
(Intercept) 5.683e+02 2.073e+02 2.741 0.00725 **
temp          -1.076e+01 4.303e+00 -2.501 0.01401 *
wind          -3.237e+01 1.173e+01 -2.760 0.00687 **
temp:wind      2.377e-01 1.367e-01 1.739 0.08519
```

The three-way interaction is clearly not significant. This is how you remove it, to begin the process of model simplification:

```model2 <- update(model1,~. - temp:wind:rad)
summary(model2)
```

Depending on the results, you can continue simplifying your model:

```model3 <- update(model2,~. - temp:rad)
summary(model3)
...
```

Alternatively you can use the automatic model simplification function `step`, to see how well it does:

```model_step <- step(model1)
```

Step-wise regression using p values to drop variables with non , I want to perform a stepwise linear Regression using p-values as a selection using p values to drop variables with non-significant p values. I want to use R to perform a stepwise linear Regression using p-values as a selection criterion e.g. at each step dropping variables that have the highest i.e. the most insignificant p-values, stopping when all values are significant defined by some treshold alpha.

Package rms: Regression Modeling Strategies has `fastbw()` that does exactly what you need. There is even a parameter to flip from AIC to p-value based elimination.

Limitations of P-Values and R-squared for Stepwise Regression , Problems with P-Values and R2 in Stepwise Regression for Risk Adjustment the addition of a variable with a nonsignificant or significant p-value. Here, R2 dropped by 18%, yet MHSUD net compensation improved by 2%  after performing a stepwise selection based on the AIC criterion, it is misleading to look at the p-values to test the null hypothesis that each true regression coefficient is zero. Indeed, p-values represent the probability of seeing a test statistic at least as extreme as the one you have, when the null hypothesis is true.

If you are just trying to get the best predictive model, then perhaps it doesn't matter too much, but for anything else, don't bother with this sort of model selection. It is wrong.

Use a shrinkage methods such as ridge regression (in `lm.ridge()` in package MASS for example), or the lasso, or the elasticnet (a combination of ridge and lasso constraints). Of these, only the lasso and elastic net will do some form of model selection, i.e. force the coefficients of some covariates to zero.

See the Regularization and Shrinkage section of the Machine Learning task view on CRAN.

Model Selection, In this week, we'll explore multiple regression, which allows us to model numerical One stepwise model selection method is backwards elimination. full model, then we drop the variable with the highest p-value and refit a smaller model. And once again we can see that mom's work status has a non-​significant p-value. In the presence of correlated variables, the standard errors on model coefficients are inflated, making the tests of significance for those variables too conservative (thereby increasing there p-values). As a result, stepwise variable selection based on p-values would result in what are actually useful predictors to be omitted from a model

Variable Selection in Multiple Regression, When we fit a multiple regression model, we use the p-value in the ANOVA table In this video, we introduce some classical approaches to variable selection, but significant terms) and backward selection (for removing nonsignificant terms). Stepwise selection and All Possible Models is provided in the "Read About It"  In this webpage we describe a different approach to stepwise regression based on the p-values of the regression coefficients. The algorithm we use can be described as follows where x 1, …, x k are the independent variables and y is the dependent variable: 0. Establish a significance level.

3.2 Model selection, In Chapter 2 we briefly saw that the inclusion of more predictors is not for free: there Data: n observations and p = n - 1 predictors set.seed(123456) n <- 5 p <- n - 1 df procedure that usually gives good results: the stepwise model selection​. p-value: < 2.2e-16 summary(mod2) ## ## Call: ## lm(formula = medv ~ age +​  Stepwise selection of models using AIC adapts the critical p-value in an implicit way and using higher p-values allows inclusion of predictors with weaker effects, although the best results use model averaging.

Stepwise Regression using P-Values, Has anbody created a stepwise model based on P-Values rather than /​3701170/stepwise-regression-using-p-values-to-drop-variables-with. Regression analysisis a form of inferential statistics. The p-values help determine whether the relationships that you observe in your samplealso exist in the larger population. The p-value for each independent variable tests the null hypothesisthat the variable has no correlationwith the dependent variable.