summary.plm.list

0th

Percentile

Summary for plm objects

The summary method for plm objects generates some more information about estimated plm models.

Keywords
regression
Usage
# S3 method for plm.list
summary(object, ...)

# S3 method for summary.plm.list coef(object, eq = NULL, ...)

# S3 method for summary.plm.list print( x, digits = max(3, getOption("digits") - 2), width = getOption("width"), ... )

# S3 method for plm summary(object, vcov = NULL, ...)

# S3 method for summary.plm print( x, digits = max(3, getOption("digits") - 2), width = getOption("width"), subset = NULL, ... )

Arguments
object

an object of class "plm",

further arguments.

eq

the selected equation for list objects

x

an object of class "summary.plm",

digits

number of digits for printed output,

width

the maximum length of the lines in the printed output,

vcov

a variance--covariance matrix furnished by the user or a function to calculate one (see Examples),

subset

a character or numeric vector indicating a subset of the table of coefficients to be printed for "print.summary.plm",

Details

The summary method for plm objects (summary.plm) creates an object of class c("summary.plm", "plm", "panelmodel") that extends the plm object it is run on with various information about the estimated model like (inferential) statistics, see Value. It has an associated print method (print.summary.plm).

Value

An object of class c("summary.plm", "plm", "panelmodel"). Some of its elements are carried over from the associated plm object and described there (plm()). The following elements are new or changed relative to the elements of a plm object:

fstatistic

'htest' object: joint test of significance of coefficients (F or Chi-square test) (robust statistic in case of supplied argument vcov, see pwaldtest() for details),

coefficients

a matrix with the estimated coefficients, standard errors, t--values, and p--values, if argument vcov was set to non-NULL the standard errors (and t-- and p--values) in their respective robust variant,

vcov

the "regular" variance--covariance matrix of the coefficients (class "matrix"),

rvcov

only present if argument vcov was set to non-NULL: the furnished variance--covariance matrix of the coefficients (class "matrix"),

r.squared

a named numeric containing the R-squared ("rsq") and the adjusted R-squared ("adjrsq") of the model,

df

an integer vector with 3 components, (p, n-p, p*), where p is the number of estimated (non-aliased) coefficients of the model, n-p are the residual degrees of freedom (n being number of observations), and p* is the total number of coefficients (incl. any aliased ones).

See Also

plm() for estimation of various models; vcovHC() for an example of a robust estimation of variance--covariance matrix; r.squared() for the function to calculate R-squared; stats::print.power.htest() for some information about class "htest"; fixef() to compute the fixed effects for "within" (=fixed effects) models and within_intercept() for an "overall intercept" for such models; pwaldtest()

Aliases
  • summary.plm.list
  • coef.summary.plm.list
  • print.summary.plm.list
  • summary.plm
  • print.summary.plm
Examples
# NOT RUN {
data("Produc", package = "plm")
zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
          data = Produc, index = c("state","year"))
summary(zz)

# summary with a funished vcov, passed as matrix, as function, and
# as function with additional argument
data("Grunfeld", package = "plm")
wi <- plm(inv ~ value + capital,
          data = Grunfeld, model="within", effect = "individual")
summary(wi, vcov = vcovHC(wi))
summary(wi, vcov = vcovHC)
summary(wi, vcov = function(x) vcovHC(x, method = "white2"))

# extract F statistic
wi_summary <- summary(wi)
Fstat <- wi_summary[["fstatistic"]]

# extract estimates and p-values
est <- wi_summary[["coefficients"]][ , "Estimate"]
pval <- wi_summary[["coefficients"]][ , "Pr(>|t|)"]

# print summary only for coefficent "value"
print(wi_summary, subset = "value")

# }
Documentation reproduced from package plm, version 2.2-5, License: GPL (>= 2)

Community examples

Looks like there are no examples yet.