Description Usage Arguments Details Value Note Author(s) See Also Examples

View source: R/tool_pdata.frame.R

This function checks the number of individuals and time observations in the panel and whether it is balanced or not.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
pdim(x, ...)
## Default S3 method:
pdim(x, y, ...)
## S3 method for class 'data.frame'
pdim(x, index = NULL, ...)
## S3 method for class 'pdata.frame'
pdim(x, ...)
## S3 method for class 'pseries'
pdim(x, ...)
## S3 method for class 'panelmodel'
pdim(x, ...)
## S3 method for class 'pgmm'
pdim(x, ...)
## S3 method for class 'pdim'
print(x, ...)
``` |

`x` |
a |

`...` |
further arguments. |

`y` |
a vector, |

`index` |
see |

`pdim`

is called by the estimation functions and can be also used
stand-alone.

An object of class `pdim`

containing the following
elements:

`nT` |
a list containing |

`Tint` |
a list containing two vectors (of type integer): |

`balanced` |
a logical value: |

`panel.names` |
a list of character vectors: |

Calling `pdim`

on an estimated `panelmodel`

object
and on the corresponding `(p)data.frame`

used for this
estimation does not necessarily yield the same result. When
called on an estimated `panelmodel`

, the number of
observations (individual, time) actually used for model
estimation are taken into account. When called on a
`(p)data.frame`

, the rows in the `(p)data.frame`

are
considered, disregarding any NA values in the dependent or
independent variable(s) which would be dropped during model
estimation.

Yves Croissant

`is.pbalanced()`

to just determine balancedness
of data (slightly faster than `pdim`

),

`punbalancedness()`

for measures of
unbalancedness,

`nobs()`

,
`pdata.frame()`

,

`pvar()`

to check for
each variable if it varies cross-sectionally and over time.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
# There are 595 individuals
data("Wages", package = "plm")
pdim(Wages, 595)
# Gasoline contains two variables which are individual and time
# indexes and are the first two variables
data("Gasoline", package="plm")
pdim(Gasoline)
# Hedonic is an unbalanced panel, townid is the individual index
data("Hedonic", package = "plm")
pdim(Hedonic, "townid")
# An example of the panelmodel method
data("Produc", package = "plm")
z <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp,data=Produc,
model="random", subset = gsp > 5000)
pdim(z)
``` |

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