Various methods for processing "ivreg" objects; for diagnostic methods,
see ivregDiagnostics.
# S3 method for class 'ivreg'
coef(object, component = c("stage2", "stage1"), complete = TRUE, ...)
# S3 method for class 'ivreg'
vcov(object, component = c("stage2", "stage1"), complete = TRUE, ...)
# S3 method for class 'ivreg'
bread(x, ...)
# S3 method for class 'ivreg'
estfun(x, ...)
# S3 method for class 'ivreg'
vcovHC(x, ...)
# S3 method for class 'ivreg'
terms(x, component = c("regressors", "instruments", "full"), ...)
# S3 method for class 'ivreg'
model.matrix(
object,
component = c("regressors", "projected", "instruments"),
...
)
# S3 method for class 'ivreg_projected'
model.matrix(object, ...)
# S3 method for class 'ivreg'
predict(
object,
newdata,
type = c("response", "terms"),
na.action = na.pass,
se.fit = FALSE,
interval = c("none", "confidence", "prediction"),
df = Inf,
level = 0.95,
weights,
...
)
# S3 method for class 'ivreg'
print(x, digits = max(3, getOption("digits") - 3), ...)
# S3 method for class 'ivreg'
update(object, formula., ..., evaluate = TRUE)
# S3 method for class 'ivreg'
residuals(
object,
type = c("response", "projected", "regressors", "working", "deviance", "pearson",
"partial", "stage1"),
...
)
# S3 method for class 'ivreg'
Effect(focal.predictors, mod, ...)
# S3 method for class 'ivreg'
formula(x, component = c("complete", "regressors", "instruments"), ...)
# S3 method for class 'ivreg'
find_formula(x, ...)
# S3 method for class 'ivreg'
alias(object, ...)
# S3 method for class 'ivreg'
qr(x, ...)
# S3 method for class 'ivreg'
weights(object, type = c("variance", "robustness"), ...)An object of class "ivreg".
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".
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.
An object of class "ivreg".
Values of predictors for which to obtain predicted values; if missing predicted (i.e., fitted) values are computed for the data to which the model was fit.
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 method to apply to predictor values for predictions; default is na.pass.
Compute standard errors of predicted values (default FALSE).
Type of interval to compute for predicted values: "none" (the default),
"confidence" for confidence intervals for the expected response, or "prediction" for
prediction intervals for future observations.
For predict, degrees of freedom for computing t-distribution confidence- or prediction-interval limits; the
default, Inf, is equivalent to using the normal distribution; if NULL,
df is taken from the residual degrees of freedom for the model.
These tests are not to be confused with the regression diagnostics provided elsewhere in the ivreg
package: see ivregDiagnostics.
for confidence or prediction intervals, default 0.95.
Either a numeric vector or a one-sided formula to provide weights for prediction
intervals when the fit is weighted. If weights and newdata are missing, the weights
are those used for fitting the model.
For printing.
To update model.
If TRUE, the default, the updated model is evaluated; if FALSE the updated call is returned.
Focal predictors for effect plot, see Effect.