[R] BCa Bootstrapped regression coefficients from lmrob function not working

[peter dalgaard]

> On 03 Jul 2016, at 13:47 , varin sacha via R-help <r-help at r-project.org> wrote:
> 
> Dear R-experts,
> 
> I am trying to calculate the bootstrapped (BCa) regression coefficients for a robust regression using MM-type estimator (lmrob function from robustbase package).
> 
> My R code here below is showing a warning message ([1] "All values of t are equal to 
> 22.2073014256803\n Can not calculate confidence intervals" NULL), I was wondering if it was because I am trying to fit a robust regression with lmrob function rather than a simple lm ? I mean maybe the boot.ci function does not work with lmrob function ? If not, I was wondering what was going on ?

You need to review your code. You calculate a,b,c,d in the global environment and create newdata as a subset of Dataset, then use a,b,c,d in the formula, but no such variables are in newdata. AFAICT, all your bootstrap fits use the _same_ global values for a,b,c,d hence give the same result 1000 times...

-pd


> 
> Here is the reproducible example
> 
> 
> Dataset = data.frame(PIBparHab=c(43931,67524,48348,44827,52409,15245,24453,57636,28992,17102,51495,47243,40908,22494,12784,48391,44221,32514,35132,46679,106022,9817,99635,38678,49128,12876,20732,17151,19670,41053,22488,57134,83295,10660),
> 
> QUALITESANSREDONDANCE=c(1082.5,1066.6,1079.3,1079.9,1074.9,1008.6,1007.5,1111.3,1108.2,1109.7,1059.6,1165.1,1026.7,1035.1,997.8,1044.8,1073.6,1085.7,1083.8,1021.6,1036.2,1075.3,1069.3,1101.4,1086.9,1072.1,1166.7,983.9,1004.5,1082.5,1123.5,1094.9,1105.1,1010.8),
> 
> competitivite=c(89,83,78,73,90,71,77,85,61,67,98,82,70,43,57,78,72,79,61,71,86,63,90,75,87,64,60,56,66,80,53,91,97,62),
> 
> innovation=c(56,52,53,54,57,43,54,60,47,55,58,62,52,35,47,59,56,56,45,52,58,33,57,57,61,40,45,41,50,61,50,65,68,34))
> 
> library("robustbase")
> newdata=na.omit(Dataset)
> a=Dataset$PIBparHab
> b=Dataset$QUALITESANSREDONDANCE
> c=Dataset$competitivite
> d=Dataset$innovation
> 
> fm.lmrob=lmrob(a~b+c+d,data=newdata)
> fm.lmrob
> 
> boot.Lmrob=function(formula,data,indices) {
> d=data[indices,]
> fit=lmrob(formula,data=d)
> return(coef(fit))
> }
> 
> library(boot)
> results=boot(data=newdata, statistic=boot.Lmrob, R=1000,formula=a~b+c+d)
> boot.ci(results, type= "bca",index=2)
> 
> 
> Any help would be highly appreciated,
> S
> 
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-- 
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Office: A 4.23
Email: pd.mes at cbs.dk  Priv: PDalgd at gmail.com