| psacf {collapse} | R Documentation |
psacf, pspacf and psccf compute (and by default plot) estimates of the auto-, partial auto- and cross- correlation or covariance functions for panel series. They are analogues to acf, pacf and ccf.
psacf(x, ...)
pspacf(x, ...)
psccf(x, y, ...)
## Default S3 method:
psacf(x, g, t = NULL, lag.max = NULL, type = c("correlation", "covariance","partial"),
plot = TRUE, gscale = TRUE, ...)
## Default S3 method:
pspacf(x, g, t = NULL, lag.max = NULL, plot = TRUE, gscale = TRUE, ...)
## Default S3 method:
psccf(x, y, g, t = NULL, lag.max = NULL, type = c("correlation", "covariance"),
plot = TRUE, gscale = TRUE, ...)
## S3 method for class 'data.frame'
psacf(x, by, t = NULL, cols = is.numeric, lag.max = NULL,
type = c("correlation", "covariance","partial"), plot = TRUE, gscale = TRUE, ...)
## S3 method for class 'data.frame'
pspacf(x, by, t = NULL, cols = is.numeric, lag.max = NULL,
plot = TRUE, gscale = TRUE, ...)
# Methods for indexed data / compatibility with plm:
## S3 method for class 'pseries'
psacf(x, lag.max = NULL, type = c("correlation", "covariance","partial"),
plot = TRUE, gscale = TRUE, ...)
## S3 method for class 'pseries'
pspacf(x, lag.max = NULL, plot = TRUE, gscale = TRUE, ...)
## S3 method for class 'pseries'
psccf(x, y, lag.max = NULL, type = c("correlation", "covariance"),
plot = TRUE, gscale = TRUE, ...)
## S3 method for class 'pdata.frame'
psacf(x, cols = is.numeric, lag.max = NULL,
type = c("correlation", "covariance","partial"), plot = TRUE, gscale = TRUE, ...)
## S3 method for class 'pdata.frame'
pspacf(x, cols = is.numeric, lag.max = NULL, plot = TRUE, gscale = TRUE, ...)
x, y |
a numeric vector, 'indexed_series' ('pseries'), data frame or 'indexed_frame' ('pdata.frame'). |
g |
a factor, |
by |
data.frame method: Same input as |
t |
a time vector or list of vectors. See |
cols |
data.frame method: Select columns using a function, column names, indices or a logical vector. Note: |
lag.max |
integer. Maximum lag at which to calculate the acf. Default is |
type |
character. String giving the type of acf to be computed. Allowed values are "correlation" (the default), "covariance" or "partial". |
plot |
logical. If |
gscale |
logical. Do a groupwise scaling / standardization of |
... |
further arguments to be passed to |
If gscale = TRUE data are standardized within each group (using fscale) such that the group-mean is 0 and the group-standard deviation is 1. This is strongly recommended for most panels to get rid of individual-specific heterogeneity which would corrupt the ACF computations.
After scaling, psacf, pspacf and psccf compute the ACF/CCF by creating a matrix of panel-lags of the series using flag and then correlating this matrix with the series (x, y) using cor and pairwise-complete observations. This may require a lot of memory on large data, but is done because passing a sequence of lags to flag and thus calling flag and cor one time is much faster than calling them lag.max times. The partial ACF is computed from the ACF using a Yule-Walker decomposition, in the same way as in pacf.
An object of class 'acf', see acf. The result is returned invisibly if plot = TRUE.
Time Series and Panel Series, Collapse Overview
## World Development Panel Data head(wlddev) # See also help(wlddev) psacf(wlddev$PCGDP, wlddev$country, wlddev$year) # ACF of GDP per Capita psacf(wlddev, PCGDP ~ country, ~year) # Same using data.frame method psacf(wlddev$PCGDP, wlddev$country) # The Data is sorted, can omit t pspacf(wlddev$PCGDP, wlddev$country) # Partial ACF psccf(wlddev$PCGDP, wlddev$LIFEEX, wlddev$country) # CCF with Life-Expectancy at Birth psacf(wlddev, PCGDP + LIFEEX + ODA ~ country, ~year) # ACF and CCF of GDP, LIFEEX and ODA psacf(wlddev, ~ country, ~year, c(9:10,12)) # Same, using cols argument pspacf(wlddev, ~ country, ~year, c(9:10,12)) # Partial ACF ## Using indexed data: wldi <- findex_by(wlddev, iso3c, year) # Creating a indexed frame PCGDP <- wldi$PCGDP # Indexed Series of GDP per Capita LIFEEX <- wldi$LIFEEX # Indexed Series of Life Expectancy psacf(PCGDP) # Same as above, more parsimonious pspacf(PCGDP) psccf(PCGDP, LIFEEX) psacf(wldi[c(9:10,12)]) pspacf(wldi[c(9:10,12)])