summary.DiSSMod: Summarizing Discrete Sample Selection Model Fits

Description

summary method for a class "DiSSMod".

Usage

# S3 method for DiSSMod
summary(object, ...)
# S3 method for summary.DiSSMod
print(x, digits = max(3, getOption("digits") -
  3), ...)

Arguments

object

an object of class "DiSSMod" made by the function DiSSMod.

…

additional control argument is as follows.

level: an option for controlling the significance level of confidence interval. It has to be given in probability between 0 and 1. Initial level is set to \(1 - \alpha = 0.95\).

an object of class "summary.DiSSMod".

digits

a numeric number of significant digits.

Value

The function summary.DiSSMod returns a list of summary statistics of the fitted discrete sample selection model given in object.

The components, which are not duplicated from the object, are as follows:

z.value_response

Z statistics (normal distribution) for coefficients of response equation.

z.value_selection

Z statistics (normal distribution) for coefficients of selection equation.

CI_alpha

confidence interval of the parameter alpha.

level

a numeric value between 0 and 1 for controlling the significance level of confidence interval. Initial level is set to \(1 - \alpha = 0.95\).

Details

If standard equals TRUE, summary also additionally returns summary statistics of standardized results. Otherwise, it just returns summary statistics as similar statistics as the generic function summary.

Examples

Run this code

# NOT RUN {
# example continued from DiSSMod
set.seed(45)
data(DoctorRWM, package = "DiSSMod")
n0 <- 600
set.n0 <- sample(1:nrow(DoctorRWM), n0)
reduce_DoctorRWM <- DoctorRWM[set.n0,]
result0 <- DiSSMod(response = as.numeric(DOCVIS > 0) ~ AGE + INCOME_SCALE + HHKIDS + EDUC + MARRIED,
                   selection = PUBLIC ~ AGE + EDUC + FEMALE,
                   data = reduce_DoctorRWM, resp.dist="bernoulli", select.dist = "normal",
                   alpha = seq(-5.5, -0.5, length.out = 21), standard = TRUE)

summary(result0, level = 0.90)

data(CreditMDR, package = "DiSSMod")
n1 <- 600
set.n1 <- sample(1:nrow(CreditMDR), n1)
reduce_CreditMDR <- CreditMDR[set.n1,]
result1 <- DiSSMod(response = MAJORDRG ~ AGE + INCOME + EXP_INC,
                   selection = CARDHLDR ~ AGE + INCOME + OWNRENT + ADEPCNT + SELFEMPL,
                   data = reduce_CreditMDR, resp.dist="poi", select.dist = "logis",
                   alpha = seq(-0.3, 0.3,length.out = 21), standard = FALSE, verbose = 1)

summary(result1)

# }