They just provide a quick visual summary. Lm(formula = asthma_sx ~ hazards * mutation_present, data = asthma) If you have categorical variables in your model, check first that they are correctly labeled as factors in your data with the str command: Factors will automatically be dummy-coded with the first level as the reference group. Important note: The lm function pays attention to the types of variables you include (e.g. numeric vs. factor) as they are encoded in your data frame. It’s in the stats package, which is included by default in base R and loaded at the beginning of any R session - so you don’t need to install anything or run library(stats) to be able to use its functions they’re all available to you by default when you open R or RStudio. To estimate a linear model in R, use the function lm. Here are the first five cases in the data: asthma_sx
The data include three variables: asthma_sx which gives a rating of severity of symptoms, mutation_present which indicates whether each patient has a genetic mutation associated with asthma (yes or no), and hazards which is a rating of the presence of environmental hazards in the patients home. 5*(hazards - mean(hazards)), 3 + 3*(hazards - mean(hazards)))Īsthma <- ame(asthma_sx, mutation_present, hazards)įor this example, we’ll be using some made-up data about asthma symptoms.
Mutation_present <- factor(sample(c("Y", "N"), size = n, replace = TRUE)) # This is the code to generate the made-up data
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