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Questions tagged [rlm]

rlm is an `R` function for robust fitting of linear models in package MASS.

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Using t-dist (Excel) to compute p values for robust (rlm in R) regression coefficients

A few questions have been asked in the past in relation to the fact that the rlm robust regression function in the MASS package for R does not provide p values for regression coefficients. This answer ...
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Robust regression - differences in approach (rlm and lmrob)?

I am looking to implement robust regression in R for large data (n=~500,000). The two options that come up are lmrob and rlm. ...
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Robust regression with Sandwich estimator

I understand that rlm (robust regression) addresses issues of outliers and influential observations, but does not address heteroskedasticity. I have come to learn ...
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2answers
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What is the default psi function for the rlm (robust regression) function? [closed]

What is the default psi function for the rlm (robust regression) function from MASS package? Is it a bisquare function? If not, may I know what psi function it gives out by default if I do not specify ...
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When is rlm preferred over lm and vice versa? Why R Square should not be considered for models built using rlm?

Both lm and rlm are used for multiple linear regression. rlm stands for robust multiple linear regression and so is considered to treat outliers in a better way. How exactly does rlm and lm work? ...
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how to assess importance of each predictor in robust linear regression

I have been using rlm() in the MASS library in R with the redescending weights (using MM or Tukey's biweight function). I wanted to find the importance of each predictor in the fitted model. Can ...
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Is a weighted $R^2$ in robust linear model meaningful for goodness of fit analysis?

I estimated a robust linear model in R with MM weights using the rlm() in the MASS package. `R`` does not provide an $R^2$ value ...
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1answer
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Prediction interval for robust regression with MM-estimator

In their book "Robust Statistics", Maronna et al. consider the following model for robust regression: $y_i = \beta x_i + u_i$, where $u_i$ are independent of the $x_i$, and are i.i.d, with finite ...
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Simultaneous heteroscedasticity and heavy tails in a regression model

I'm trying to create a prediction model using regression. This is the diagnostic plot for the model that I get from using lm() in R: What I read from the Q-Q plot is that the residuals have a heavy-...
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How does “rlm” in R decide its “w” weights?

Following user603's advice, I opened a new question thread for this question. For your reference, the original question is here: Pointers to understand "rlm" in R better? My question is: ...