cenmle {NADA} | R Documentation |
Regression by Maximum Likelihood (ML) Estimation for left-censored ("nondetect" or "less-than") data. This routine computes regression estimates of slope(s) and intercept by maximum likelihood when data are left-censored. It will compute ML estimates of descriptive statistics when explanatory variables following the ~ are left blank. It will compute ML tests similar in function and assumptions to two-sample t-tests and analysis of variance when groups are specified following the ~. It will compute regression equations, including multiple regression, when continuos explanatory variables are included following the ~. It will compute the ML equivalent of analysis of covariance when both group and continuous explanatory variables are specified following the ~. To avoid an appreciable loss of power with regression and group hypothesis tests, a probability plot of residuals should be checked to ensure that residuals from the regression model are approximately gaussian.
cenmle(obs, censored, groups, ...)
obs |
Either a numeric vector of observations or a formula. See examples below. |
censored |
A logical vector indicating TRUE where an observation in `obs' is censored (a less-than value) and FALSE otherwise. |
groups |
A factor vector used for grouping `obs' into subsets. |
... |
Additional items that are common to this function and the survreg
function from the `survival' package. The most important of which
is `dist' (distribution to use). See Details below.
|
This routine is a front end to the survreg
routine in the
survival
package.
There are many additional options that are supported and documented
in survfit
. Only a few have application to the geosciences.
The most important option is `dist' which specifies the distributional model to use in the regression. The default is `lognormal'.
Also supported is a `gaussian' or a normal distribution. The use of a gaussian distribution requires an interval censoring context for left-censored data. Luckily, this routine automatically does this for you – simply specify `gaussian' and the correct manipulations are done.
If any other distribution is specified besides lognormal or gaussian, the return object is a raw survreg object – it is up to the user to `do the right thing' with the output (and input for that matter).
If you are using the formula interface: The censored
and
groups
parameters are not specified – all information is provided
via a formula as the obs
parameter. The formula must have a
Cen
object as the response on the left of the ~
operator and,
if desired, terms separated by + operators on the right.
See Examples below.
a cenmle
object.
Methods defined for cenmle
objects are provided for
mean
, median
, sd
.
Lopaka(Rob) Lee <rclee@usgs.gov>
Helsel, Dennis R. (2005). Nondectects and Data Analysis; Statistics for censored environmental data. John Wiley and Sons, USA, NJ.
Cen
,
mean-methods
,
sd-methods
,
median-methods
,
summary-methods
# Create a MLE regression object data(TCEReg) obs = TCEReg$TCEConc censored = TCEReg$TCECen mycenmle = cenmle(obs, censored) summary(mycenmle) median(mycenmle) mean(mycenmle) sd(mycenmle) # With groupings groups = as.factor(TCEReg$LandUse) cenmle(obs, censored, groups) # Formula interface -- no groups cenmle(Cen(obs, censored)) # assume a gaussian distribution #cenmle(Cen(obs, censored), dist="gaussian") # Formula interface -- a complicated regression attach(TCEReg) cenmle(Cen(TCEConc, TCECen)~PopDensity+PctIndLU+Depth)