Use penalized regression

Method 2: Use the predictor variable to perfectly predict the response variable

We can use the penalized logistic regression such as lasso logistic regression or elastic-net regularization to handle the algorithm that did not converge warning. In order to perform penalized regression on the data, glmnet method is used which accepts predictor variable, response variable, response type, regression type, etc. Let’s look into the syntax of it-

Syntax: glmnet(x, y, family = “binomial”, alpha = 1, lambda = NULL)

where

x is predictor variable

y is response variable

family indicates the response type, for binary response (0,1) use binomial

alpha represents type of regression

1 is for lasso regression

0 is for ridge regression

Lambda defines the shrinkage

Below is the implemented penalized regression code

R

# import necessary libraries
library(glmnet)
 
# create random data which consists 
# of 50 numbers
x < - rnorm(50)
 
# create data with fifty 1's
y < - rep(1, 50)
 
# if x value is less than 0 the at that 
# index replace 1 with 0 in y
y[x < 0] < - 0
 
# fitting lasso regression model
glmnet(x, y, family="binomial", alpha=1, lambda=NULL)

How to Fix in R: glm.fit: algorithm did not converge

In this article, we will discuss how to fix the “glm.fit: algorithm did not converge” error in the R programming language.

glm.fit: algorithm did not converge is a warning in R that encounters in a few cases while fitting a logistic regression model in R. It encounters when a predictor variable perfectly separates the response variable. To get a better understanding let’s look into the code in which variable x is considered as the predictor variable and y is considered as the response variable. To produce the warning, let’s create the data in such a way that the data is perfectly separable.

Code that produces a warning:

The below code doesn’t produce any error as the exit code of the program is 0 but a few warnings are encountered in which one of the warnings is glm.fit: algorithm did not converge. This was due to the perfect separation of data. From the data used in the above code, for every negative x value, the y value is 0 and for every positive x, the y value is 1.

R

# create random data which consists
# of 50 numbers
x < - rnorm(50)
 
# create data with fifty 1's
y < - rep(1, 50)
 
# if x value is less than 0 the at that 
# index replace 1 with 0 in y
y[x < 0] < - 0
 
# create dataframe
data < - data.frame(x, y)
 
# first 6 rows
head(data)
 
# fitting logistic regression model
glm(y ~ x, data, family="binomial")

Output

x y

1 1.3295285 1

2 -0.9738028 0

3 0.6963700 1

4 -1.1586337 0

5 -1.1001865 0

6 -0.6252191 0

Call: glm(formula = y ~ x, family = “binomial”, data = data)

Coefficients:

(Intercept) x

-13.42 273.54

Degrees of Freedom: 49 Total (i.e. Null); 48 Residual

Null Deviance: 68.03

Residual Deviance: 1.436e-08 AIC: 4

Warning messages:

1: glm.fit: algorithm did not converge

2: glm.fit: fitted probabilities numerically 0 or 1 occurred

[Execution complete with exit code 0]

Tags:

#R How-to-Fix #R Language

How to fix the warning:

Method 2: Use the predictor variable to perfectly predict the response variable

Use penalized regression

R

How to Fix in R: glm.fit: algorithm did not converge

Code that produces a warning:

R

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