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Cross-sectional averages by time (with optional leave-one-out)

Source: R/utils_avg.R
cross_sectional_avg.Rd

Computes cross-sectional averages (CSAs) of specified variables for each time period, optionally in a leave-one-out (LOO) fashion per observation. Supports unbalanced panels and observation weights.

Usage

cross_sectional_avg(
  data,
  id = NULL,
  time = NULL,
  vars,
  leave_out = FALSE,
  weights = NULL,
  suffix = "csa",
  return_mode = c("attach", "time"),
  na.rm = TRUE
)

Arguments

data

A data.frame or plm::pdata.frame.

id, time

Character scalar names of unit and time columns when data is a plain data.frame. If data is a pdata.frame, these are inferred from its index and can be omitted.

vars

Character vector of column names to average cross-sectionally.

leave_out

Logical; if TRUE, computes LOO means for each row: \(\bar{x}_{-i,t} = (\sum_{j \neq i} w_{jt} x_{jt}) / (\sum_{j \neq i} w_{jt})\). If FALSE, computes standard time means: \(\bar{x}_{t} = (\sum_j w_{jt} x_{jt}) / (\sum_j w_{jt})\).

weights

Optional. Either:

  • a numeric vector of length nrow(data), or

  • the name of a column in data with nonnegative weights.

If NULL, uses equal weights (1 for observed values).

suffix

Character suffix to append to CSA columns (default "csa").

return_mode

One of "attach" or "time".

  • "attach" returns the original data with CSA columns added.

  • "time" returns a unique-time table [time, csa_*].

na.rm

Logical; if TRUE, excludes NAs from sums and denominators. If FALSE, any NA in a time slice yields NA for that time's CSA for that variable.

Value

A data.frame:

  • If return_mode="attach": original data + CSA columns named paste0(suffix, "_", vars).

  • If return_mode="time": unique time rows with CSA columns.

Details

This is a standalone data utility. It does not configure the averages used by csdm(); use csdm_csa() for that purpose. Model fitting constructs averages from the evaluated model terms and its documented source sample, which can differ from averages of raw data columns produced here.

Efficiently computes, for each v in vars and time t, $$\bar v_t = \frac{\sum_i w_{it}\, 1_{\{v_{it}\text{ finite}\}}\, v_{it}} {\sum_i w_{it}\, 1_{\{v_{it}\text{ finite}\}}}$$ For leave_out=TRUE, each row's CSA excludes its own contribution; if the denominator becomes \(\le 0\) (e.g., only one finite observation at that time), the LOO mean is set to NA for that row/variable.