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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 ...
361 views

### 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. ...
129 views

### 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 ...
593 views

### 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 ...
185 views

### 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? ...
165 views

### 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 ...
10k views

### 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 ...
2k views

### 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 ...