Re: [R] Combining estimates from multiple regressions

in text, not HTML. > > Cheers, > Bert > Bert Gunter > > "Data is not information. Information is not knowledge. And knowledge > is certainly not wisdom." >-- Clifford Stoll > > > On Wed, Jun 24, 2015 at 3:27 AM, James Shaw wrote: >> I am interested

[R] Combining estimates from multiple regressions

I am interested in using quantile regression to fit the following model at different quantiles of a response variable: (1) y = b0 + b1*g1 + b2*g2 + B*Z where b0 is an intercept, g1 and g2 are dummy variables for 2 of 3 independent groups, and Z is a matrix of covariates to be adjusted for in the

[R] Problems using quantile regression (rq) to model GLD random variables in R

Everyone: I am working on a simulation of the efficiencies of regression estimators when applied to model a specific form of highly skewed data. The outcome variable (y) is being simulated from a generalized lambda distribution (GLD) to reflect the characteristics (mean, variance, skewness, kurto

[R] How to estimate shape parameters for generalized lambda distribution (GLD) in R

Is anyone aware of an R package that enables one to estimate the shape parameters (lambda3 and lambda4) for the generalized lambda distribution (GLD) based on known mean, variance, skewness, and kurtosis? I am aware of a package for generating data from a generalized lambda distribution. However,

Re: [R] A question regarding RFreak

: > > > On 18.04.2011 23:41, James Shaw wrote: >> >> I am using robreg.evol (part of the RFreak package) to fit models via >> least trimmed squares (LTS) regression and am encountering the >> following error message when attempting to access the coefficients: >

[R] A question regarding RFreak

I am using robreg.evol (part of the RFreak package) to fit models via least trimmed squares (LTS) regression and am encountering the following error message when attempting to access the coefficients: Error in fit1$coef : $ operator not defined for this S4 class. It appears to me that robreg.evol

[R] m out of n bootstrap

Can anyone confirm the formula for the m out of n bootstrap variance estimator? rq.boot applies a deflation factor directly to the bootstrap estimates. Presumably, the SE of the estimate of interest is then taken to be the SD of the deflated estimates. I have read Bickel's and others' papers on

Re: [R] Robust variance estimation with rq (failure of the bootstrap?)

nce to favor one over the > other. > > Also, I can't justify (to myself) why skew would hamper the quality of > bootstrap variance estimates. I wonder how it affects the sandwich > variance estimate... > > Best, > Matt > > On Mon, 2011-02-28 at 17:50 -0600, James

[R] Robust variance estimation with rq (failure of the bootstrap?)

I am fitting quantile regression models using data collected from a sample of 124 patients. When modeling cross-sectional associations, I have noticed that nonparametric bootstrap estimates of the variances of parameter estimates are much greater in magnitude than the empirical Huber estimates der

[R] Quantile regression (rq) and complex samples

I am new to R and am interested in using the program to fit quantile regression models to data collected from a multi-stage probability sample of the US population. The quantile regression package, rq, can accommodate person weights. However, it is not clear to me that boot.rq is appropriate for