View source: R/TML.noncensored.R
TML.noncensored | R Documentation |
This function computes the truncated maximum likelihood regression estimate described in Marazzi and Yohai (2004). The error distribution is assumed to follow approximately a Gaussian or a log-Weibull distribution. The cut-off values for outlier rejection are fixed or adaptive.
TML.noncensored(formula, data, errors = "Gaussian", cu = NULL,
initial = "S",otp = "fixed", cov = "parametric",
input = NULL, control = list(), ...)
formula |
A |
data |
An optional data frame containing the variables in the model. If not
found in |
errors |
|
cu |
Preliminary minimal upper cut-off. The default is 2.5 in the Gaussian case and 1.855356 in the log-Weibull case. |
initial |
|
otp |
|
cov |
|
input |
Initial input estimates of location and scale.
|
control |
Control parameters. For the default values, see the function |
... |
If fastS=TRUE, parameters for |
TML.noncensored
returns an object of class "TML".
The function summary
can be used to obtain or print a summary of the results.
The generic extractor functions fitted
, residuals
and
weights
can be used to extract various elements of the value returned
by TML.noncensored
. The function update
can be used to update the model.
An object of class "TML" is a list with the following components:
th0 |
Initial coefficient estimates (S or input). |
v0 |
Initial scale (S or input). |
nit0 |
Reached number of iteration in |
th1 |
Final coefficient estimates. |
v1 |
Final scale (S or input). |
nit1 |
Number of iterations reached by the IRLS algorithm for the final estimates. |
tu,tl |
Final cut-off values. |
alpha |
Estimated proportion of retained observations. |
tn |
Number of retained observations. |
beta |
Consistency constant for scale. |
weights |
Vector of weights (0 for rejected observations, 1 for retained observations). |
COV |
Covariance matrix of the final estimates (th1[1],...,th1[p],v1) (where p=ncol(X)). |
residuals |
The residuals, that is response minus fitted values. |
fitted.values |
The fitted mean values. |
call |
The matched call. |
formula |
The formula supplied. |
terms |
The |
data |
The |
Marazzi A., Yohai V. (2004). Adaptively truncated maximum likelihood regression with asymmetric errors. Journal of Statistical Planning and Inference, 122, 271-291.
TML.noncensored.control
,
TML1.noncensored
, TML1.noncensored.control
,
TML.censored
## Not run:
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",control=list(fastS=TRUE))
summary(z)
# Truncated maximum likelihood regression with log-Weibull errors
w <- TML.noncensored(log(Cost)~log(LOS)+Adm+Ass+Age+Dst+Sex,
errors="logWeibull",otp="adaptive",control=list(fastS=TRUE))
summary(w)
## End(Not run)
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