Check if data are balanced
This function checks if the data are balanced, i.e., if each individual has the same time periods
is.pbalanced(x, ...) ## Default S3 method: is.pbalanced(x, y, ...) ## S3 method for class 'data.frame' is.pbalanced(x, index = NULL, ...) ## S3 method for class 'pdata.frame' is.pbalanced(x, ...) ## S3 method for class 'pseries' is.pbalanced(x, ...) ## S3 method for class 'panelmodel' is.pbalanced(x, ...) ## S3 method for class 'pgmm' is.pbalanced(x, ...)
x |
an object of class |
... |
further arguments. |
y |
(only in default method) the time index variable (2nd index variable), |
index |
only relevant for |
Balanced data are data for which each individual has the same time periods.
The returned values of the is.pbalanced(object)
methods are identical
to pdim(object)$balanced
. is.pbalanced
is provided as a short
cut and is faster than pdim(object)$balanced
because it avoids those
computations performed by pdim
which are unnecessary to determine the
balancedness of the data.
A logical indicating whether the data associated with
object x
are balanced (TRUE
) or not
(FALSE
).
punbalancedness()
for two measures of
unbalancedness, make.pbalanced()
to make data
balanced; is.pconsecutive()
to check if data are
consecutive; make.pconsecutive()
to make data
consecutive (and, optionally, also balanced).pdim()
to check the dimensions of a 'pdata.frame'
(and other objects), pvar()
to check for individual
and time variation of a 'pdata.frame' (and other objects),
pseries()
, data.frame()
,
pdata.frame()
.
# take balanced data and make it unbalanced # by deletion of 2nd row (2nd time period for first individual) data("Grunfeld", package = "plm") Grunfeld_missing_period <- Grunfeld[-2, ] is.pbalanced(Grunfeld_missing_period) # check if balanced: FALSE pdim(Grunfeld_missing_period)$balanced # same # pdata.frame interface pGrunfeld_missing_period <- pdata.frame(Grunfeld_missing_period) is.pbalanced(Grunfeld_missing_period) # pseries interface is.pbalanced(pGrunfeld_missing_period$inv)
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