Linear Regression models

A wrapper for lm enabling multiple linear models based on a grouping variable.

Usage

model_lm(data, yvar, neighbour = 0, linear.vars, verbose = FALSE, ...)

Arguments

data

Training data set on which models will be trained. Must be a data set of class tsibble.(Make sure there are no additional date or time related variables except for the index of the tsibble). If multiple models are fitted, the grouping variable should be the key of the tsibble. If a key is not specified, a dummy key with only one level will be created.

yvar

Name of the response variable as a character string.

neighbour

If multiple models are fitted: Number of neighbours of each key (i.e. grouping variable) to be considered in model fitting to handle smoothing over the key. Should be an integer. If neighbour = x, x number of keys before the key of interest and x number of keys after the key of interest are grouped together for model fitting. The default is neighbour = 0 (i.e. no neighbours are considered for model fitting).

linear.vars

A character vector of names of the predictor variables.

verbose

Logical; controls whether progress messages (model indices) are printed during fitting. Defaults to FALSE.

...

Other arguments not currently used.

Value

An object of class lmFit. This is a tibble with two columns:

key

The level of the grouping variable (i.e. key of the training data set).

fit

Information of the fitted model corresponding to the key.

Each row of the column fit is an object of class lm. For details refer stats::lm.

See also

model_smimodel, model_backward, model_gaim, model_ppr, model_gam

Examples

library(dplyr)
library(tibble)
library(tidyr)
library(tsibble)

# Simulate data
n = 1005
set.seed(123)
sim_data <- tibble(x_lag_000 = runif(n)) |>
  mutate(
    # Add x_lags
    x_lag = lag_matrix(x_lag_000, 5)) |>
  unpack(x_lag, names_sep = "_") |>
  mutate(
    # Response variable
    y = (0.9*x_lag_000 + 0.6*x_lag_001 + 0.45*x_lag_003)^3 + rnorm(n, sd = 0.1),
    # Add an index to the data set
    inddd = seq(1, n)) |>
  drop_na() |>
  select(inddd, y, starts_with("x_lag")) |>
  # Make the data set a `tsibble`
  as_tsibble(index = inddd)

# Predictor variables
linear.vars <- colnames(sim_data)[3:8]

# Model fitting
lmModel <- model_lm(data = sim_data,
                    yvar = "y",
                    linear.vars = linear.vars)
# Fitted model
lmModel$fit[[1]]
#> 
#> Call:
#> stats::lm(formula = as.formula(pre.formula), data = df_cat)
#> 
#> Coefficients:
#> (Intercept)    x_lag_000    x_lag_001    x_lag_002    x_lag_003    x_lag_004  
#>   -1.793753     2.770954     1.821332     0.048464     1.414372     0.038152  
#>   x_lag_005  
#>   -0.006478  
#>