coef.ivreg: Methods for `"ivreg"` Objects

Description

Various methods for processing "ivreg" objects; for diagnostic methods, see ivregDiagnostics.

Usage

# S3 method for ivreg
coef(object, component = c("stage2", "stage1"), complete = TRUE, ...)
# S3 method for ivreg
vcov(object, component = c("stage2", "stage1"), complete = TRUE, ...)
# S3 method for ivreg
confint(
  object,
  parm,
  level = 0.95,
  component = c("stage2", "stage1"),
  complete = TRUE,
  vcov. = NULL,
  df = NULL,
  ...
)
# S3 method for ivreg
bread(x, ...)
# S3 method for ivreg
estfun(x, ...)
# S3 method for ivreg
vcovHC(x, ...)
# S3 method for ivreg
terms(x, component = c("regressors", "instruments", "full"), ...)
# S3 method for ivreg
model.matrix(
  object,
  component = c("regressors", "projected", "instruments"),
  ...
)
# S3 method for ivreg_projected
model.matrix(object, ...)
# S3 method for ivreg
predict(
  object,
  newdata,
  type = c("response", "terms"),
  na.action = na.pass,
  ...
)
# S3 method for ivreg
print(x, digits = max(3, getOption("digits") - 3), ...)
# S3 method for ivreg
summary(object, vcov. = NULL, df = NULL, diagnostics = NULL, ...)
# S3 method for summary.ivreg
print(
  x,
  digits = max(3, getOption("digits") - 3),
  signif.stars = getOption("show.signif.stars"),
  ...
)
# S3 method for ivreg
anova(object, object2, test = "F", vcov. = NULL, ...)
# S3 method for ivreg
update(object, formula., ..., evaluate = TRUE)
# S3 method for ivreg
residuals(
  object,
  type = c("response", "projected", "regressors", "working", "deviance", "pearson",
    "partial", "stage1"),
  ...
)
# S3 method for ivreg
Effect(focal.predictors, mod, ...)
# S3 method for ivreg
formula(x, component = c("complete", "regressors", "instruments"), ...)
# S3 method for ivreg
find_formula(x, ...)
# S3 method for ivreg
Anova(mod, test.statistic = c("F", "Chisq"), ...)
# S3 method for ivreg
linearHypothesis(
  model,
  hypothesis.matrix,
  rhs = NULL,
  test = c("F", "Chisq"),
  ...
)
# S3 method for ivreg
alias(object, ...)
# S3 method for ivreg
qr(x, ...)
# S3 method for ivreg
weights(object, type = c("variance", "robustness"), ...)

Arguments

object, object2, model, mod: An object of class "ivreg".
component: For terms, "regressors", "instruments", or "full"; for model.matrix, "projected", "regressors", or "instruments"; for formula, "regressors", "instruments", or "complete"; for coef and vcov, "stage2" or "stage1".
complete: If TRUE, the default, the returned coefficient vector (for coef()) or coefficient-covariance matrix (for vcov) includes elements for aliased regressors.
...: arguments to pass down.
parm: parameters for which confidence intervals are to be computed; a vector or numbers or names; the default is all parameters.
level: confidence level; the default is 0.95.
vcov.: Optional coefficient covariance matrix, or a function to compute the covariance matrix, to use in computing the model summary.
df: Optional residual degrees of freedom to use in computing model summary.
x: An object of class "ivreg" or "summary.ivreg".
newdata: Values of predictors for which to obtain predicted values.
type: For predict, one of "response" (the default) or "terms"; for residuals, one of "response" (the default), "projected", "regressors", "working", "deviance", "pearson", or "partial"; type = "working" and "response" are equivalent, as are type = "deviance" and "pearson"; for weights, "variance" (the default) for invariance-variance weights (which is NULL for an unweighted fit) or "robustness" for robustness weights (available for M or MM estimation).
na.action: na method to apply to predictor values for predictions; default is na.pass.
digits: For printing.
diagnostics: Report 2SLS "diagnostic" tests in model summary (default is TRUE). These tests are not to be confused with the regression diagnostics provided elsewhere in the ivreg package: see ivregDiagnostics.
signif.stars: Show "significance stars" in summary output.
test, test.statistic: Test statistics for ANOVA table computed by anova(), Anova(), or linearHypothesis(). Only test = "F" is supported by anova(); this is also the default for Anova() and linearHypothesis(), which also allow test = "Chisq" for asymptotic tests.
formula.: To update model.
evaluate: If TRUE, the default, the updated model is evaluated; if FALSE the updated call is returned.
focal.predictors: Focal predictors for effect plot, see Effect.
hypothesis.matrix, rhs: For formulating a linear hypothesis; see the documentation for linearHypothesis for details.

Description

Usage

Arguments

See Also