fbetween-fwithin {collapse}

Fast Between (Averaging) and (Quasi-)Within (Centering) Transformations

Description

fbetween and fwithin are S3 generics to efficiently obtain between-transformed (averaged) or (quasi-)within-transformed (demeaned) data. These operations can be performed groupwise and/or weighted. B and W are wrappers around fbetween and fwithin representing the 'between-operator' and the 'within-operator'.

(B / W provide more flexibility than fbetween / fwithin when applied to data frames (i.e. column subsetting, formula input, auto-renaming and id-variable-preservation capabilities...), but are otherwise identical.)

Usage

fbetween(x, ...)
 fwithin(x, ...)
       B(x, ...)
       W(x, ...)

## Default S3 method:
fbetween(x, g = NULL, w = NULL, na.rm = .op[["na.rm"]], fill = FALSE, ...)
## Default S3 method:
fwithin(x, g = NULL, w = NULL, na.rm = .op[["na.rm"]], mean = 0, theta = 1, ...)
## Default S3 method:
B(x, g = NULL, w = NULL, na.rm = .op[["na.rm"]], fill = FALSE, ...)
## Default S3 method:
W(x, g = NULL, w = NULL, na.rm = .op[["na.rm"]], mean = 0, theta = 1, ...)

## S3 method for class 'matrix'
fbetween(x, g = NULL, w = NULL, na.rm = .op[["na.rm"]], fill = FALSE, ...)
## S3 method for class 'matrix'
fwithin(x, g = NULL, w = NULL, na.rm = .op[["na.rm"]], mean = 0, theta = 1, ...)
## S3 method for class 'matrix'
B(x, g = NULL, w = NULL, na.rm = .op[["na.rm"]], fill = FALSE, stub = .op[["stub"]], ...)
## S3 method for class 'matrix'
W(x, g = NULL, w = NULL, na.rm = .op[["na.rm"]], mean = 0, theta = 1,
  stub = .op[["stub"]], ...)

## S3 method for class 'data.frame'
fbetween(x, g = NULL, w = NULL, na.rm = .op[["na.rm"]], fill = FALSE, ...)
## S3 method for class 'data.frame'
fwithin(x, g = NULL, w = NULL, na.rm = .op[["na.rm"]], mean = 0, theta = 1, ...)
## S3 method for class 'data.frame'
B(x, by = NULL, w = NULL, cols = is.numeric, na.rm = .op[["na.rm"]],
  fill = FALSE, stub = .op[["stub"]], keep.by = TRUE, keep.w = TRUE, ...)
## S3 method for class 'data.frame'
W(x, by = NULL, w = NULL, cols = is.numeric, na.rm = .op[["na.rm"]],
  mean = 0, theta = 1, stub = .op[["stub"]], keep.by = TRUE, keep.w = TRUE, ...)

# Methods for indexed data / compatibility with plm:

## S3 method for class 'pseries'
fbetween(x, effect = 1L, w = NULL, na.rm = .op[["na.rm"]], fill = FALSE, ...)
## S3 method for class 'pseries'
fwithin(x, effect = 1L, w = NULL, na.rm = .op[["na.rm"]], mean = 0, theta = 1, ...)
## S3 method for class 'pseries'
B(x, effect = 1L, w = NULL, na.rm = .op[["na.rm"]], fill = FALSE, ...)
## S3 method for class 'pseries'
W(x, effect = 1L, w = NULL, na.rm = .op[["na.rm"]], mean = 0, theta = 1, ...)

## S3 method for class 'pdata.frame'
fbetween(x, effect = 1L, w = NULL, na.rm = .op[["na.rm"]], fill = FALSE, ...)
## S3 method for class 'pdata.frame'
fwithin(x, effect = 1L, w = NULL, na.rm = .op[["na.rm"]], mean = 0, theta = 1, ...)
## S3 method for class 'pdata.frame'
B(x, effect = 1L, w = NULL, cols = is.numeric, na.rm = .op[["na.rm"]],
  fill = FALSE, stub = .op[["stub"]], keep.ids = TRUE, keep.w = TRUE, ...)
## S3 method for class 'pdata.frame'
W(x, effect = 1L, w = NULL, cols = is.numeric, na.rm = .op[["na.rm"]],
  mean = 0, theta = 1, stub = .op[["stub"]], keep.ids = TRUE, keep.w = TRUE, ...)

# Methods for grouped data frame / compatibility with dplyr:

## S3 method for class 'grouped_df'
fbetween(x, w = NULL, na.rm = .op[["na.rm"]], fill = FALSE,
         keep.group_vars = TRUE, keep.w = TRUE, ...)
## S3 method for class 'grouped_df'
fwithin(x, w = NULL, na.rm = .op[["na.rm"]], mean = 0, theta = 1,
        keep.group_vars = TRUE, keep.w = TRUE, ...)
## S3 method for class 'grouped_df'
B(x, w = NULL, na.rm = .op[["na.rm"]], fill = FALSE,
  stub = .op[["stub"]], keep.group_vars = TRUE, keep.w = TRUE, ...)
## S3 method for class 'grouped_df'
W(x, w = NULL, na.rm = .op[["na.rm"]], mean = 0, theta = 1,
  stub = .op[["stub"]], keep.group_vars = TRUE, keep.w = TRUE, ...)

Arguments

x

a numeric vector, matrix, data frame, 'indexed_series' ('pseries'), 'indexed_frame' ('pdata.frame') or grouped data frame ('grouped_df').

g

a factor, GRP object, or atomic vector / list of vectors (internally grouped with group) used to group x.

by

B and W data.frame method: Same as g, but also allows one- or two-sided formulas i.e. ~ group1 or var1 + var2 ~ group1 + group2. See Examples.

w

a numeric vector of (non-negative) weights. B/W data frame and pdata.frame methods also allow a one-sided formula i.e. ~ weightcol. The grouped_df (dplyr) method supports lazy-evaluation. See Examples.

cols

B/W (p)data.frame methods: Select columns to scale using a function, column names, indices or a logical vector. Default: All numeric columns. Note: cols is ignored if a two-sided formula is passed to by.

na.rm

logical. Skip missing values in x and w when computing averages. If na.rm = FALSE and a NA or NaN is encountered, the average for that group will be NA, and all data points belonging to that group in the output vector will also be NA.

effect

plm methods: Select which panel identifier should be used as grouping variable. 1L takes the first variable in the index, 2L the second etc. Index variables can also be called by name using a character string. If more than one variable is supplied, the corresponding index-factors are interacted.

stub

character. A prefix/stub to add to the names of all transformed columns. TRUE (default) uses "W."/"B.", FALSE will not rename columns.

fill

option to fbetween/B: Logical. TRUE will overwrite missing values in x with the respective average. By default missing values in x are preserved.

mean

option to fwithin/W: The mean to center on, default is 0, but a different mean can be supplied and will be added to the data after the centering is performed. A special option when performing grouped centering is mean = "overall.mean". In that case the overall mean of the data will be added after subtracting out group means.

theta

option to fwithin/W: Double. An optional scalar parameter for quasi-demeaning i.e. x - theta * xi.. This is useful for variance components ('random-effects') estimators. see Details.

keep.by, keep.ids, keep.group_vars

B and W data.frame, pdata.frame and grouped_df methods: Logical. Retain grouping / panel-identifier columns in the output. For data frames this only works if grouping variables were passed in a formula.

keep.w

B and W data.frame, pdata.frame and grouped_df methods: Logical. Retain column containing the weights in the output. Only works if w is passed as formula / lazy-expression.

...

arguments to be passed to or from other methods.

Details

Without groups, fbetween/B replaces all data points in x with their mean or weighted mean (if w is supplied). Similarly fwithin/W subtracts the (weighted) mean from all data points i.e. centers the data on the mean.

With groups supplied to g, the replacement / centering performed by fbetween/B | fwithin/W becomes groupwise. In terms of panel data notation: If x is a vector in such a panel dataset, xit denotes a single data-point belonging to group i in time-period t (t need not be a time-period). Then xi. denotes x, averaged over t. fbetween/B now returns xi. and fwithin/W returns x - xi.. Thus for any data x and any grouping vector g: B(x,g) + W(x,g) = xi. + x - xi. = x. In terms of variance, fbetween/B only retains the variance between group averages, while fwithin/W, by subtracting out group means, only retains the variance within those groups.

The data replacement performed by fbetween/B can keep (default) or overwrite missing values (option fill = TRUE) in x. fwithin/W can center data simply (default), or add back a mean after centering (option mean = value), or add the overall mean in groupwise computations (option mean = "overall.mean"). Let x.. denote the overall mean of x, then fwithin/W with mean = "overall.mean" returns x - xi. + x.. instead of x - xi.. This is useful to get rid of group-differences but preserve the overall level of the data. In regression analysis, centering with mean = "overall.mean" will only change the constant term. See Examples.

If theta != 1, fwithin/W performs quasi-demeaning x - theta * xi.. If mean = "overall.mean", x - theta * xi. + theta * x.. is returned, so that the mean of the partially demeaned data is still equal to the overall data mean x... A numeric value passed to mean will simply be added back to the quasi-demeaned data i.e. x - theta * xi. + mean.

Now in the case of a linear panel model y_{it} = \beta_0 + \beta_1 X_{it} + u_{it} with u_{it} = \alpha_i + \epsilon_{it}. If \alpha_i \neq \alpha = const. (there exists individual heterogeneity), then pooled OLS is at least inefficient and inference on \beta_1 is invalid. If E[\alpha_i|X_{it}] = 0 (mean independence of individual heterogeneity \alpha_i), the variance components or 'random-effects' estimator provides an asymptotically efficient FGLS solution by estimating a transformed model y_{it}-\theta y_{i.} = \beta_0 + \beta_1 (X_{it} - \theta X_{i.}) + (u_{it} - \theta u_{i.}), where \theta = 1 - \frac{\sigma_\alpha}{\sqrt(\sigma^2_\alpha + T \sigma^2_\epsilon)}. An estimate of \theta can be obtained from the an estimate of \hat{u}_{it} (the residuals from the pooled model). If E[\alpha_i|X_{it}] \neq 0, pooled OLS is biased and inconsistent, and taking \theta = 1 gives an unbiased and consistent fixed-effects estimator of \beta_1. See Examples.

Value

fbetween/B returns x with every element replaced by its (groupwise) mean (xi.). Missing values are preserved if fill = FALSE (the default). fwithin/W returns x where every element was subtracted its (groupwise) mean (x - theta * xi. + mean or, if mean = "overall.mean", x - theta * xi. + theta * x..). See Details.

References

Mundlak, Yair. 1978. On the Pooling of Time Series and Cross Section Data. Econometrica 46 (1): 69-85.

See Also

fhdbetween/HDB and fhdwithin/HDW, fscale/STD, TRA, Data Transformations, Collapse Overview

Examples

## Simple centering and averaging
head(fbetween(mtcars))
head(B(mtcars))
head(fwithin(mtcars))
head(W(mtcars))
all.equal(fbetween(mtcars) + fwithin(mtcars), mtcars)

## Groupwise centering and averaging
head(fbetween(mtcars, mtcars$cyl))
head(fwithin(mtcars, mtcars$cyl))
all.equal(fbetween(mtcars, mtcars$cyl) + fwithin(mtcars, mtcars$cyl), mtcars)

head(W(wlddev, ~ iso3c, cols = 9:13))    # Center the 5 series in this dataset by country
head(cbind(get_vars(wlddev,"iso3c"),     # Same thing done manually using fwithin..
      add_stub(fwithin(get_vars(wlddev,9:13), wlddev$iso3c), "W.")))

## Using B() and W() for fixed-effects regressions:

# Several ways of running the same regression with cyl-fixed effects
lm(W(mpg,cyl) ~ W(carb,cyl), data = mtcars)                     # Centering each individually
lm(mpg ~ carb, data = W(mtcars, ~ cyl, stub = FALSE))           # Centering the entire data
lm(mpg ~ carb, data = W(mtcars, ~ cyl, stub = FALSE,            # Here only the intercept changes
                        mean = "overall.mean"))
lm(mpg ~ carb + B(carb,cyl), data = mtcars)                     # Procedure suggested by
# ..Mundlak (1978) - partialling out group averages amounts to the same as demeaning the data
plm::plm(mpg ~ carb, mtcars, index = "cyl", model = "within")   # "Proof"..

# This takes the interaction of cyl, vs and am as fixed effects
lm(W(mpg) ~ W(carb), data = iby(mtcars, id = finteraction(cyl, vs, am)))
lm(mpg ~ carb, data = W(mtcars, ~ cyl + vs + am, stub = FALSE))
lm(mpg ~ carb + B(carb,list(cyl,vs,am)), data = mtcars)

# Now with cyl fixed effects weighted by hp:
lm(W(mpg,cyl,hp) ~ W(carb,cyl,hp), data = mtcars)
lm(mpg ~ carb, data = W(mtcars, ~ cyl, ~ hp, stub = FALSE))
lm(mpg ~ carb + B(carb,cyl,hp), data = mtcars)       # WRONG ! Gives a different coefficient!!

## Manual variance components (random-effects) estimation
res <- HDW(mtcars, mpg ~ carb)[[1]]  # Get residuals from pooled OLS
sig2_u <- fvar(res)
sig2_e <- fvar(fwithin(res, mtcars$cyl))
T <- length(res) / fndistinct(mtcars$cyl)
sig2_alpha <- sig2_u - sig2_e
theta <- 1 - sqrt(sig2_alpha) / sqrt(sig2_alpha + T * sig2_e)
lm(mpg ~ carb, data = W(mtcars, ~ cyl, theta = theta, mean = "overall.mean", stub = FALSE))

# A slightly different method to obtain theta...
plm::plm(mpg ~ carb, mtcars, index = "cyl", model = "random")