| make.pbalanced {plm} | R Documentation |
This function makes the data balanced, i.e. each individual has the same time periods
## S3 method for class 'pdata.frame'
make.pbalanced(x, balance.type = c("fill", "shared"), ...)
## S3 method for class 'pseries'
make.pbalanced(x, balance.type = c("fill", "shared"), ...)
## S3 method for class 'data.frame'
make.pbalanced(x, balance.type = c("fill", "shared"), index = NULL, ...)
x |
an object of class |
balance.type |
character, one of |
index |
only relevant for |
... |
further arguments. |
(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 two 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":
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 (only time periods shared among
all individuals are left in the result).
The data are not necessarily made consecutive (regularly 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 NA 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 time series.
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.
Kevin Tappe
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).
punbalancedness 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),
lag for lagged (and leading) values of a 'pseries' object.
pseries, data.frame, pdata.frame.
# 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)$balanced # check if balanced: FALSE
make.pbalanced(Grunfeld_missing_period) # make it balanced (by filling)
make.pbalanced(Grunfeld_missing_period, balance.type = "shared") # (shared periods)
nrow(make.pbalanced(Grunfeld_missing_period))
nrow(make.pbalanced(Grunfeld_missing_period, balance.type = "shared"))
# more complex data:
# First, make data unbalanced (and non-consecutive)
# by deletion of 2nd time period (year 1936) for all individuals
# and more time periods for first individual only
Grunfeld_unbalanced <- Grunfeld[Grunfeld$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