The R lm() function is used to fit linear models for performing linear regression, single stratum analysis of variance, and analysis of covariance.
The usage of the lm() function in R is as follows:
lm(formula, data, subset, weights, na.action, method = "qr", model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE, contrasts = NULL, offset, …)
Here, the parameters/arguments are defined as:
"formula"
(or one that can be coerced into that class): a symbolic description of the model to be fitted. as.data.frame
to a data frame) containing the variables in the model. If not found in data
, the variables are taken from environment(formula)
, typically the environment from which lm
is called.NULL
or a numeric vector. If non-NULL, weighted least squares is used with weights weights
(that is, minimizing sum(w*e^2)
); otherwise ordinary least squares is used. NA
s. The default is set by the na.action
setting of options
, and is na.fail
if that is unset. The ‘factory-fresh’ default is na.omit
. Another possible value is NULL
, no action. Value na.exclude
can be useful.method = "qr"
is supported; method = "model.frame"
returns the model frame (the same as with model = TRUE
).TRUE
the corresponding components of the fit (the model frame, the model matrix, the response, the QR decomposition) are returned.FALSE
(the default in S but not in R) a singular fit is an error.NULL
or a numeric vector or matrix of extents matching those of the response. One or more offset
terms can be included in the formula instead or as well, and if more than one are specified their sum is used.The lm() function can be implemented in R according to the following example:
library(readxl) # Library for reading excel files ageandheight <- read_excel("ageandheight.xls", sheet = "Untitled1") # Upload the data lmHeight = lm(height~age, data = ageandheight) # Create linear regression model using lm summary(lmHeight) # Review the results
In the example above, you can substitute ageandheight.xls
to be any dataset that you want.
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