run_lm: lm() for pipes - data as first argument

In LukasWallrich/rNuggets: Useful Helper Functions for (Academic) Data Analysis

lm() for pipes - data as first argument

Description

Within a dplyr-pipe, running lm() is often complicated be the placing of the data argument. This wrapper places data first and allows to run standardized models.

Usage

run_lm(df, formula, std = FALSE, rename_std = FALSE, ...)

Arguments

df	Data for modeling
formula	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’.
std	Logical. Should variables be standardised? This is only applied to numeric variables, factors are left unchanged so that their coefficients remain interpretable.
rename_std	Logical. Should standardised variables be indicated by _sd suffix
...	Arguments passed on to stats::lm 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 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’, na.action a 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. 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, see below). model logicals. If TRUE the corresponding components of the fit (the model frame, the model matrix, the response, the QR decomposition) are returned. x logicals. If TRUE the corresponding components of the fit (the model frame, the model matrix, the response, the QR decomposition) are returned. y logicals. If TRUE the corresponding components of the fit (the model frame, the model matrix, the response, the QR decomposition) are returned. qr logicals. If TRUE the corresponding components of the fit (the model frame, the model matrix, the response, the QR decomposition) are returned. singular.ok logical. If FALSE (the default in S but not in R) a singular fit is an error. contrasts an optional list. See the contrasts.arg of model.matrix.default. offset this 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.

Details

Note that the model call in the lm-object is replaced by the call to this function - that means that update() cannot be used.

Source

After experiencing an issue with passing weights, I rewrote this based on the code suggested by "Vandenman" here https://stackoverflow.com/questions/38683076/ellipsis-trouble-passing-to-lm

References

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()'

LukasWallrich/rNuggets documentation built on Aug. 26, 2022, 11:03 a.m.