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: an object of class
"formula"(or one that can be coerced into that class): a symbolic description of the model to be fitted.
- data: an optional data frame, list or environment (or object coercible by
as.data.frameto 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
- subset: an optional vector specifying a subset of observations to be used in the fitting process.
- weights: an optional vector of weights to be used in the fitting process. Should be
NULLor 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.action: a function which indicates what should happen when the data contain
NAs. The default is set by the
options, and is
na.failif that is unset. The ‘factory-fresh’ default is
na.omit. Another possible value is
NULL, no action. Value
na.excludecan be useful.
- method: the method to be used; for fitting, currently only
method = "qr"is supported;
method = "model.frame"returns the model frame (the same as with
model = TRUE).
- model, x, y, q: If
TRUEthe corresponding components of the fit (the model frame, the model matrix, the response, the QR decomposition) are returned.
- singular.ok: If
FALSE(the default in S but not in R) a singular fit is an error.
- contrasts: an optional list.
- offset: this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be
NULLor a numeric vector or matrix of extents matching those of the response. One or more
offsetterms can be included in the formula instead or as well, and if more than one are specified their sum is used.
- …: additional arguments to be passed to the low level regression fitting functions.
Example Implementation of R lm() function:
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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