How to fix the warning:
To overcome this warning we should modify the data such that the predictor variable doesn’t perfectly separate the response variable. In order to do that we need to add some noise to the data. Below is the code that won’t provide the algorithm did not converge warning.
R
# create random data which consists of# 50 numbersx <- 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 yy[x < 0] <- 0# create dataframe data <- data.frame(x, y)# first 6 rowshead(data) # add noisedata$x <- data$x + rnorm(50)# first 6 rows after data modificationhead(data) # fitting logistic regression modelglm(y ~ x, data, family = "binomial") |
Output
x y
1 -0.5787936 0
2 0.1105818 1
3 -0.5324901 0
4 0.6043288 1
5 -0.2479408 0
6 1.2583220 1
x y
1 0.06909437 0
2 2.01936841 1
3 0.08818184 0
4 0.22230790 1
5 0.19720200 0
6 1.44250592 1
Call: glm(formula = y ~ x, family = “binomial”, data = data)
Coefficients:
(Intercept) x
0.09985 1.97047
Degrees of Freedom: 49 Total (i.e. Null); 48 Residual
Null Deviance: 69.23
Residual Deviance: 40.85 AIC: 44.85
[Execution complete with exit code 0]
Here the original data of the predictor variable get changed by adding random data (noise). So it disturbs the perfectly separable nature of the original data. This process is completely based on the data. If the correlation between any two variables is unnaturally very high then try to remove those observations and run the model until the warning message won’t encounter.
Warning Handling
There are two ways to handle this glm.fit: the algorithm did not converge warning. They are listed below-
- Use penalized regression
- Use the predictor variable to perfectly predict the response variable
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 numbersx < - rnorm(50)# create data with fifty 1'sy < - rep(1, 50)# if x value is less than 0 the at that # index replace 1 with 0 in yy[x < 0] < - 0# create dataframedata < - data.frame(x, y)# first 6 rowshead(data)# fitting logistic regression modelglm(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]