linear_regression: linear_regression
In lxfjwj/lmpackage: lmpackage

Description Usage Arguments Details Value Examples

main function to return result of lm()

linear_regression(
  formula,
  data,
  subset,
  weights,
  model = TRUE,
  x = FALSE,
  y = FALSE,
  qr = TRUE,
  offset
)

`formula`	an object of class "formula": a symbolic description of the model to be fitted.
`data`	an optional data frame, list or environment containing the variables in the model. If not found in data, the variables are taken from environment(formula)
`subset`	optional vector specifying a subset of observations to be used in the fitting process.
`weights`	this can be used to specify an a priori known component weights of predictors to be included in the fitting. This should be NULL or a numeric vector or matrix of extents matching those of the response.
`model`	logical. If TRUE the the model frame is returned.
`x`	logical. If TRUE the the model matrix is returned.
`y`	logical. If TRUE the response is returned.
`qr`	logical. If TRUE the the QR is returned.
`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.

you can use this function to do linear regression and return a list of results.

'linear_regression' returns a list of results containing at least the following components:

coefficients: a named vector of coefficients

residuals: the residuals, that is response minus fitted values.

fitted.values: the fitted mean values.

rank: the numeric rank of the fitted linear model.

df.residual: the residual degrees of freedom.

call: the matched call.

y: if requested, the response used.

x: if requested, the model matrix used.

model: if requested (the default), the model frame used.

offset: if not null, a n*1 matrix containing the offset values.

weights: if not null, a n*1 matrix containing the weights values.

terms: the formula of fitted model.

#NOT RUN
data_range = matrix(c(0,0,0,1,1,1),3,2)
offsets_range = c(0,1)
weights_range = c(0,1)
coefficients = c(1,2,3)
data = simulate_data(10, 3, error_range = c(0,1), data_range, offsets_range, weights_range,coefficients)
model = linear_regression(V1~V2+V3+V4, data = data, weights = data$`(weights)`,offset = data$`(offsets)`)