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the robust scale is normally outputed by the routine you used to estimate the MM. For example in R:

library(robustbase)
data(coleman)
set.seed(0)
RlmST <- lmrob(log.light ~ log.Te, data = starsCYG)
RlmST$scale

There is no explicit formula to compute it: it's the result of an iterative scheme.

The robust scale is normally output by the routine you used to estimate the MM. For example, in R:

library(robustbase)
data(coleman)
set.seed(0)
RlmST <- lmrob(log.light ~ log.Te, data = starsCYG)
RlmST$scale

There is no explicit formula to compute it: it's the result of an iterative scheme.

the robust scale is normally outputed by the routine you used to estimate the MM. For example in R:

library(robustbase)
data(coleman)
set.seed(0)
RlmST <- lmrob(log.light ~ log.Te, data = starsCYG)
RlmST$scale

There is no explicit formula to compute it: it's the result of an iterative

the robust scale is normally outputed by the routine you used to estimate the MM. For example in R:

library(robustbase)
data(coleman)
set.seed(0)
RlmST <- lmrob(log.light ~ log.Te, data = starsCYG)
RlmST$scale

There is no explicit formula to compute it: it's the result of an iterative scheme.

the robust scale is normally outputed by the routine you used to estimate the MM. For example in R:

library(robustbase)
data(coleman)
set.seed(0)
RlmST <- lmrob(log.light ~ log.Te, data = starsCYG)
RlmST$scale

It's formula is simply:

$$(\sum_{i=1}^n w_i)^{-1}(\sum_{i=1}e_i^2w_i)$$

where $e_i$ ($w_i$) is the residual (weight) (from the MM fit) associated with the $i^{th}$ observation.

the robust scale is normally outputed by the routine you used to estimate the MM. For example in R:

library(robustbase)
data(coleman)
set.seed(0)
RlmST <- lmrob(log.light ~ log.Te, data = starsCYG)
RlmST$scale

There is no explicit formula to compute it: it's the result of an iterative