statioVAR (version 0.1.3)

pooled: Pooled polynomial detrending for multivariate panel data

Description

Remove study-wide polynomial trend (up to cubic) plus optional cyclic effects from multivariate panel data by fitting a single OLS model on the pooled series. Trend terms up to the chosen degree are estimated; those whose two-sided t-tests are significant at alpha are retained, non-significant components are set to 0, and the resulting fitted values are subtracted from every observation of the raw series.

Usage

pooled(
  df,
  id_var,
  time_var = NULL,
  vars_to_detrend,
  poly_order = 1,
  cyc_vars = NULL,
  alpha = 0.05,
  miss_thresh = 0.30
)

Value

A named list with:

df

Tibble with added <var>_detrended columns.

coef_tables

Named list of coefficient tables (one per variable), with columns predictor, estimate, Std. Error, \(t\), \(p\), and a logical flag kept.

formula_str

Character string of the fitted model formula.

n_units

Integer: number of unique units (subjects).

Arguments

df

Data frame or tibble (long format).

id_var

Character string. Unit (subject) identifier column (required).

time_var

Character string. Numeric time index column (if NULL, then cyc_vars must be specified). If NULL, no polynomial time terms are included.

vars_to_detrend

Character vector. Column names to detrend (required).

poly_order

Integer in {1,2,3}. Maximum degree of the polynomial time trend tested (default: 1):

• 1 = linear only,

• 2 = linear + quadratic,

• 3 = linear + quadratic + cubic.

cyc_vars

Character vector. Column names (e.g. "weekend") for categorical cyclicity variables (if NULL, then time_var must be specified).

alpha

Numeric in (0,1). Significance threshold for retaining polynomial terms (default 0.05).

miss_thresh

Numeric in (0,1). Maximum allowed proportion of missing data per variable (default: 0.30).

Examples

dat <- data.frame(
id = rep(1:3, each=5),
time = rep(1:5, 3),
cyc = rep(c("A","B"), length.out=15),
y1 = rnorm(15, sd = 0.5) + seq(1,15)*1.0
)
res <- statioVAR::pooled(
df = dat,
id_var = "id",
time_var = "time",
vars_to_detrend = "y1",
poly_order = 2,
cyc_vars = "cyc",
alpha = 0.05,
miss_thresh = 0.30
)