r remove insignificant coefficient in output
To remove insignificant coefficients in R, you can follow these steps:
- Fit the model: Start by fitting the model using a suitable regression function, such as
lm()
for linear regression orglm()
for generalized linear models. For example, let's say you have a linear regression model calledmodel
:
model <- lm(y ~ x1 + x2 + x3, data = your_data)
- Extract p-values: Use the
summary()
function to obtain the summary of the fitted model and extract the p-values for each coefficient. The p-values indicate the significance of each coefficient. Lower p-values suggest more significant coefficients. For example:
summary_model <- summary(model)
p_values <- summary_model$coefficients[, "Pr(>|t|)"]
Set significance level: Determine the significance level that you want to use to decide whether a coefficient is significant or not. The most commonly used significance level is 0.05, which corresponds to a 5% threshold. You can choose a different threshold based on your requirements.
Identify insignificant coefficients: Compare the p-values of the coefficients with the chosen significance level. If a p-value is greater than the significance level, it indicates that the corresponding coefficient is not statistically significant and can be considered insignificant. You can use the comparison operator
<
to identify the insignificant coefficients. For example:
insignificant_coefficients <- p_values >= 0.05
- Remove insignificant coefficients: Use the subset function to remove the insignificant coefficients from the model. You can subset the model formula by excluding the insignificant coefficients. For example:
model <- update(model, formula = . ~ . - x2 - x3)
This will update the model by removing the x2
and x3
variables.
- Refit the model: Finally, refit the model using the updated formula to get the final model without the insignificant coefficients. For example:
final_model <- lm(formula(model), data = your_data)
Now, final_model
will only include the significant coefficients.
By following these steps, you can remove the insignificant coefficients from your regression model in R.