This function makes the data balanced, i.e., each individual has the same time periods, by filling in or dropping observations
make.pbalanced( x, balance.type = c("fill", "shared.times", "shared.individuals"), ... ) ## S3 method for class 'pdata.frame' make.pbalanced( x, balance.type = c("fill", "shared.times", "shared.individuals"), ... ) ## S3 method for class 'pseries' make.pbalanced( x, balance.type = c("fill", "shared.times", "shared.individuals"), ... ) ## S3 method for class 'data.frame' make.pbalanced( x, balance.type = c("fill", "shared.times", "shared.individuals"), index = NULL, ... )
an object of class
character, one of
only relevant for
(p)data.frame and pseries objects are made balanced, meaning each
individual has the same time periods. Depending on the value of
balance.type, the balancing is done in different ways:
balance.type = "fill" (default): The union
of available time periods over all individuals is taken (w/o
NA values). Missing time periods for an individual are
identified and corresponding rows (elements for pseries) are
inserted and filled with
NA for the non–index variables
(elements for a pseries). This means, only time periods present
for at least one individual are inserted, if missing.
balance.type = "shared.times": The intersect of available time
periods over all individuals is taken (w/o
NA values). Thus, time
periods not available for all individuals are discarded, i. e., only time
periods shared by all individuals are left in the result).
balance.type = "shared.individuals": All available time periods
are kept and those individuals are dropped for which not all time periods
are available, i. e., only individuals shared by all time periods are left
in the result (symmetric to
The data are not necessarily made consecutive (regular time series
with distance 1), because balancedness does not imply
consecutiveness. For making the data consecutive, use
make.pconsecutive() (and, optionally, set argument
balanced = TRUE to make consecutive and balanced, see also
Examples for a comparison of the two functions.
Note: Rows of (p)data.frames (elements for pseries) with
values in individual or time index are not examined but silently
dropped before the data are made balanced. In this case, it cannot
be inferred which individual or time period is meant by the missing
value(s) (see also Examples). Especially, this means:
NA values in the first/last position of the original time
periods for an individual are dropped, which are usually meant to
depict the beginning and ending of the time series for that
individual. Thus, one might want to check if there are any
NA values in the index variables before applying
make.pbalanced, and especially check for
NA values in the
first and last position for each individual in original data and,
if so, maybe set those to some meaningful begin/end value for the
An object of the same class as the input
x, i.e., a
pdata.frame, data.frame or a pseries which is made balanced
based on the index variables. The returned data are sorted as a
stacked time series.
is.pbalanced() to check if data are balanced;
is.pconsecutive() to check if data are consecutive;
make.pconsecutive() to make data consecutive (and,
optionally, also balanced).
for two measures of unbalancedness,
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),
lagging (and leading) values of a 'pseries' object.
# take data and make it unbalanced # by deletion of 2nd row (2nd time period for first individual) data("Grunfeld", package = "plm") nrow(Grunfeld) # 200 rows Grunfeld_missing_period <- Grunfeld[-2, ] pdim(Grunfeld_missing_period)$balanced # check if balanced: FALSE make.pbalanced(Grunfeld_missing_period) # make it balanced (by filling) make.pbalanced(Grunfeld_missing_period, balance.type = "shared.times") # (shared periods) nrow(make.pbalanced(Grunfeld_missing_period)) nrow(make.pbalanced(Grunfeld_missing_period, balance.type = "shared.times")) # more complex data: # First, make data unbalanced (and non-consecutive) # by deletion of 2nd time period (year 1936) for all individuals # and more time periods for first individual only Grunfeld_unbalanced <- Grunfeld[Grunfeld$year != 1936, ] Grunfeld_unbalanced <- Grunfeld_unbalanced[-c(1,4), ] pdim(Grunfeld_unbalanced)$balanced # FALSE all(is.pconsecutive(Grunfeld_unbalanced)) # FALSE g_bal <- make.pbalanced(Grunfeld_unbalanced) pdim(g_bal)$balanced # TRUE unique(g_bal$year) # all years but 1936 nrow(g_bal) # 190 rows head(g_bal) # 1st individual: years 1935, 1939 are NA # NA in 1st, 3rd time period (years 1935, 1937) for first individual Grunfeld_NA <- Grunfeld Grunfeld_NA[c(1, 3), "year"] <- NA g_bal_NA <- make.pbalanced(Grunfeld_NA) head(g_bal_NA) # years 1935, 1937: NA for non-index vars nrow(g_bal_NA) # 200 # pdata.frame interface pGrunfeld_missing_period <- pdata.frame(Grunfeld_missing_period) make.pbalanced(Grunfeld_missing_period) # pseries interface make.pbalanced(pGrunfeld_missing_period$inv) # comparison to make.pconsecutive g_consec <- make.pconsecutive(Grunfeld_unbalanced) all(is.pconsecutive(g_consec)) # TRUE pdim(g_consec)$balanced # FALSE head(g_consec, 22) # 1st individual: no years 1935/6; 1939 is NA; # other indviduals: years 1935-1954, 1936 is NA nrow(g_consec) # 198 rows g_consec_bal <- make.pconsecutive(Grunfeld_unbalanced, balanced = TRUE) all(is.pconsecutive(g_consec_bal)) # TRUE pdim(g_consec_bal)$balanced # TRUE head(g_consec_bal) # year 1936 is NA for all individuals nrow(g_consec_bal) # 200 rows head(g_bal) # no year 1936 at all nrow(g_bal) # 190 rows
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