This runs lm() after standardising all continuous variables, while leaving factors intact.
lm_std(formula, data = NULL, weights = NULL, ...)an object of class "formula" (or one that
can be coerced to that class): a symbolic description of the
model to be fitted. The details of model specification are given
under ‘Details’.
an optional data frame, list or environment (or object
coercible by 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.
an optional vector of weights to be used in the fitting
process. Should be 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. See also ‘Details’,
Arguments passed on to stats::lm
na.actiona function which indicates what should happen
when the data contain NAs. 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.
methodthe 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, see below).
model,x,y,qrlogicals. If TRUE the corresponding
components of the fit (the model frame, the model matrix, the
response, the QR decomposition) are returned.
singular.oklogical. If FALSE (the default in S but
not in R) a singular fit is an error.
contrastsan optional list. See the contrasts.arg
of model.matrix.default.
offsetthis can be used to specify an a priori known
component to be included in the linear predictor during fitting.
This should be 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. See model.offset.
In the model call, the weights variable will always be called .weights. This might
pose a problem when you update the model later on, for the moment the only workaround
is to rename the weights variable accordingly (or to fix it and contribute a PR on
Github).
See (Fox, 2015) for an argument why dummy variables should never
be standardised. If you want to run a model with all variables standardised,
one option is QuantPsyc::lm.beta()
lm_std(Sepal.Length ~ Sepal.Width + Species, iris)
#>
#> Call:
#> lm(formula = Sepal.Length ~ Sepal.Width + Species, data = data)
#>
#> Coefficients:
#> (Intercept) Sepal.Width Speciesversicolor Speciesvirginica
#> -1.371 0.423 1.762 2.351
#>