Skip to contents

Structured result container for linear model fits

Source: R/model-linear-reg.R
class_lm_object.Rd

An S7 class produced by LINEAR_REG pipelines. Not constructed manually — use define_model() |> prepare_model(LINEAR_REG) |> conclude() instead.

Inherits from anova_able, so it participates in anova() directly. Downstream packages can use it as a parent in S7::new_class().

Details

Constructor arguments (populated automatically by LINEAR_REG):

  • terms: model terms object.

  • df_residual: residual degrees of freedom.

  • deviance: residual sum of squares (RSS).

  • dispersion: mean squared error (MSE), i.e. rss / df_residual.

  • family: always "gaussian" for OLS.

  • fitted: numeric vector of fitted values.

  • residuals: numeric vector of model residuals.

  • beta: named numeric vector of coefficient estimates.

  • std_beta: named numeric vector of coefficient standard errors.

  • x_mat: model matrix stored as a flat numeric vector via as.numeric(stats::model.matrix(fit)). Required for single-model anova(); omit only if Type I ANOVA is not needed.

The following are computed automatically and do not need to be supplied:

  • statistic: per-coefficient t-statistics (beta / std_beta).

  • p_value: per-coefficient two-sided p-values.

  • coefficients: tibble with columns term, estimate, std_error, statistic, p_value.

  • fit_summary: two-column tibble (statistic, value) with R-squared, adjusted R-squared, sigma, n, residual df, F-statistic, df1, df2, and F p-value.

anova() protocol

class_lm_object supports two anova() modes:

  • Single model — Type I (sequential) ANOVA. Each term is tested against the model containing all preceding terms. Requires x_mat to be populated.

  • Multiple models — incremental F-test or LRT across nested models. Uses @deviance and @df_residual only; x_mat is not needed.

See also

anova(), LINEAR_REG

Examples

# Inheriting from class_lm_object in a downstream package:
my_lm = S7::new_class(
    "my_lm",
    parent = statim::class_lm_object
)

# Populating class_lm_object from a fitted lm (as done internally):
fit = lm(dist ~ speed, data = cars)
coef_tbl = summary(fit)$coefficients
rss = sum(fit$residuals^2)
df_res = fit$df.residual

obj = class_lm_object(
    terms = fit$terms,
    fitted = unname(fit$fitted.values),
    residuals = unname(fit$residuals),
    beta = coef_tbl[, 1],
    std_beta = coef_tbl[, 2],
    df_residual = df_res,
    deviance = rss,
    dispersion = rss / df_res,
    family = "gaussian",
    x_mat = as.numeric(stats::model.matrix(fit))
)

obj@coefficients
#> # A tibble: 2 × 5
#>   term        estimate std_error statistic  p_value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)   -17.6      6.76      -2.60 1.23e- 2
#> 2 speed           3.93     0.416      9.46 1.49e-12
obj@fit_summary
#> # A tibble: 9 × 2
#>   statistic         value
#>   <chr>             <dbl>
#> 1 R Squared      6.51e- 1
#> 2 Adj. R Squared 6.44e- 1
#> 3 Sigma          1.54e+ 1
#> 4 n              5   e+ 1
#> 5 df (residual)  4.8 e+ 1
#> 6 F-statistic    8.96e+ 1
#> 7 df1            1   e+ 0
#> 8 df2            4.8 e+ 1
#> 9 p-value        1.49e-12