summary.TML: Summarizing Truncated Maximum Likelihood regression

Description

Summary and print methods for R object of class "TML" and print method for the summary object. Further, methods fitted(), residuals(), weights() or update() work (via the default methods), and coef(), vcov() have explicitly defined TML methods.

Usage

# S3 method for TML
summary(object, ...)
# S3 method for TML
print(x, digits = max(3, getOption("digits") - 3), ...)
# S3 method for TML
coef(object, ...)
# S3 method for TML
vcov(object, ...)
# S3 method for summary.TML
print(x, digits = max(3, getOption("digits") - 3),
  signif.stars = getOption("show.signif.stars"), ...)

Value

An object of class "summary.TML" is a list with the following components:

call: The component from object.
terms: The component from object.
residuals: The component from object.
fitted.values: The component from object.
tn: The component from object.
coefficients: The matrix of coefficients, standard errors, t-values and p-values. Aliased coefficients are omitted.
aliased: Named logical vector showing if the original coefficients are aliased.
df: Degrees of freedom, a 3-vector (p, n-p, p*), the last being the number of non-aliased coefficients.
sigma: The final scale estimate from object.
cutoff.values: A vector of the final lower and upper cut-off values from object.

Arguments

object: An object of class "TML", usually, a result of a call to TML.noncensored or TML.censored.
...: Potentially more arguments passed to methods.
digits: Number of digits for printing, see digits in options.
x: An object of class "TML" or "summary.TML".
signif.stars: Logical indicating if the P-values should be visualized by so called "significance stars".

Details

summary.TML returns an object of class "summary.TML".

print.TML returns a printed summary of object of class "TML".

print.summary.TML tries to be smart about formatting the coefficients, standard errors, etc, and gives "significance stars" if signif.stars is TRUE (as per default when options where not changed).

coef.TML returns the final coefficient estimates (value th1 of a "TML" object), and vcov.TML returns the covariance matrix of the final estimates (value CV1 of a "TML" object).

Examples

Run this code

if (FALSE) {
     data(D243)
     Cost <- D243$Cost                             # Cost (Swiss francs)
     LOS  <- D243$LOS                              # Length of stay (days)
     Adm  <- D243$Typadm; Adm <- (Adm==" Urg")*1   # Type of admission 
                                                   # (0=on notification, 1=Emergency)
     Ass  <- D243$Typass; Ass <- (Ass=="P"   )*1   # Type of insurance 
                                                   # (0=usual, 1=private)
     Age  <- D243$age                              # Age (years)
     Dst  <- D243$dest;   Dst <- (Dst=="DOMI")*1   # Destination 
                                                   # (1=Home, 0=another hospital)
     Sex  <- D243$Sexe;   Sex <- (Sex=="M"   )*1   # Sex (1=Male, 0=Female)

     # Truncated maximum likelihood regression with Gaussian errors
     z    <- TML.noncensored(log(Cost)~log(LOS)+Adm+Ass+Age+Dst+Sex, otp="adaptive", 
             cov="nonparametric", control=list(fastS=TRUE))

     z                  # -> print.TML(....)
     sumz <- summary(z) # -> summary.TML(....)
     sumz               # -> print.summary.TML(....)
     coef(z)            # -> coef.TML(....)
     vcov(z)            # -> vcov.TML(....)
}