create a logistic regression on four parameters and predict for each in r

To create a logistic regression model in R, we can use the glm() function. Let's assume we have a dataset mydata with four variables (param1, param2, param3, param4) and a binary response variable (response).

# create a logistic regression model
model <- glm(response ~ param1 + param2 + param3 + param4, data = mydata, family = "binomial")

# view the summary of the model
summary(model)
180 chars
6 lines

To predict new data based on this model, we can use the predict() function. Let's say we have new data with the same four variables (newdata). We can use the type = "response" argument to get the predicted probability of the binary response variable.

# predict using the new data
predictions <- predict(model, newdata = newdata, type = "response")
97 chars
3 lines

Lastly, we can use data wrangling and visualization techniques to explore and understand our model and predictions.

# create a histogram of predicted probabilities
library(ggplot2)
ggplot(data.frame(predictions), aes(predictions)) +
  geom_histogram(bins = 20) +
  labs(x = "Predicted Probability", y = "Count", title = "Histogram of Predicted Probabilities")
244 chars
6 lines