Hello, community!
A linear model, and one independent variable may have many different groups data(different measure methods), and put one group data at one time? How could i loop the regression model(like GLM or others)? iris data could be example data.
Any help would be appreciated.
Need a formal reproducible example here to work with but sounds like the map
and walk
families of functions from purrr
could help here (i.e.purrr::imap_dfr(...)
)
Here is an example for linear model using the iris dataset you suggested. I used a for loop:
Load packages
library(tidyverse)
library(agricolae)
iris data
head(iris)
Data Wrangling
iris <- iris %>%
relocate(Species,.before = Sepal.Length)
Fit the model with a for loop
for (i in 2:ncol(iris)) {
print(paste('---',names(iris[i]),'---')) # This adds the traits names
model <- lm(iris[[i]]~.,data=iris) # This fit the linear model
print(anova(model)) # This print the anova table
}
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