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A class for panel series for which several useful computations and data transformations are available.
# S3 method for pseries
print(x, ...)# S3 method for pseries
as.matrix(x, idbyrow = TRUE, ...)
# S3 method for pseries
plot(
x,
plot = c("lattice", "superposed"),
scale = FALSE,
transparency = TRUE,
col = "blue",
lwd = 1,
...
)
# S3 method for pseries
summary(object, ...)
# S3 method for summary.pseries
plot(x, ...)
# S3 method for summary.pseries
print(x, ...)
Sum(x, ...)
# S3 method for default
Sum(x, effect, ...)
# S3 method for pseries
Sum(x, effect = c("individual", "time", "group"), ...)
# S3 method for matrix
Sum(x, effect, ...)
Between(x, ...)
# S3 method for default
Between(x, effect, ...)
# S3 method for pseries
Between(x, effect = c("individual", "time", "group"), ...)
# S3 method for matrix
Between(x, effect, ...)
between(x, ...)
# S3 method for default
between(x, effect, ...)
# S3 method for pseries
between(x, effect = c("individual", "time", "group"), ...)
# S3 method for matrix
between(x, effect, ...)
Within(x, ...)
# S3 method for default
Within(x, effect, ...)
# S3 method for pseries
Within(x, effect = c("individual", "time", "group", "twoways"), ...)
# S3 method for matrix
Within(x, effect, ...)
All these functions return an object of class pseries
or a matrix,
except:
between
, which returns a numeric vector or a matrix;
as.matrix
, which returns a matrix.
a pseries
or a matrix; or a summary.pseries
object,
further arguments, e. g., na.rm = TRUE
for
transformation functions like between
, see Details
and Examples.
if TRUE
in the as.matrix
method, the lines of
the matrix are the individuals,
plot arguments,
for the pseries methods: character string indicating the
"individual"
, "time"
, or "group"
effect, for Within
"twoways"
additionally; for non-pseries methods, effect
is a factor
specifying the dimension ("twoways"
is not possible),
Yves Croissant
The functions between
, Between
, Within
, and Sum
perform specific
data transformations, i. e., the between, within, and sum transformation,
respectively.
between
returns a vector/matrix containing the individual means (over
time) with the length of the vector equal to the number of
individuals (if effect = "individual"
(default); if effect = "time"
,
it returns the time means (over individuals)). Between
duplicates the values and returns a vector/matrix which length/number of rows
is the number of total observations. Within
returns a vector/matrix
containing the values in deviation from the individual means
(if effect = "individual"
, from time means if effect = "time"
), the so
called demeaned data. Sum
returns a vector/matrix with sum per individual
(over time) or the sum per time period (over individuals) with
effect = "individual"
or effect = "time"
, respectively, and has length/
number of rows of the total observations (like Between
).
For between
, Between
, Within
, and Sum
in presence of NA values it
can be useful to supply na.rm = TRUE
as an additional argument to
keep as many observations as possible in the resulting transformation.
na.rm is passed on to the mean()/sum() function used by these transformations
(i.e., it does not remove NAs prior to any processing!), see also
Examples.
# First, create a pdata.frame
data("EmplUK", package = "plm")
Em <- pdata.frame(EmplUK)
# Then extract a series, which becomes additionally a pseries
z <- Em$output
class(z)
# obtain the matrix representation
as.matrix(z)
# compute the between and within transformations
between(z)
Within(z)
# Between and Sum replicate the values for each time observation
Between(z)
Sum(z)
# between, Between, Within, and Sum transformations on other dimension
between(z, effect = "time")
Between(z, effect = "time")
Within(z, effect = "time")
Sum(z, effect = "time")
# NA treatment for between, Between, Within, and Sum
z2 <- z
z2[length(z2)] <- NA # set last value to NA
between(z2, na.rm = TRUE) # non-NA value for last individual
Between(z2, na.rm = TRUE) # only the NA observation is lost
Within(z2, na.rm = TRUE) # only the NA observation is lost
Sum(z2, na.rm = TRUE) # only the NA observation is lost
sum(is.na(Between(z2))) # 9 observations lost due to one NA value
sum(is.na(Between(z2, na.rm = TRUE))) # only the NA observation is lost
sum(is.na(Within(z2))) # 9 observations lost due to one NA value
sum(is.na(Within(z2, na.rm = TRUE))) # only the NA observation is lost
sum(is.na(Sum(z2))) # 9 observations lost due to one NA value
sum(is.na(Sum(z2, na.rm = TRUE))) # only the NA observation is lost
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