On Tue, 2011-07-12 at 10:12 -0400, Heiman, Thomas J. wrote:
> Hi,
>
> Hopefully I got the formatting down.. I am trying to do a lasso regression
> using the lars package with the following data (the data files is in .csv
> format):
>
> V1 V2 V3 V4
> V5 V6 V7 V8 V9
> 1 FastestTime WinPercentage PlacePercentage ShowPercentage
> BreakAverage FinishAverage Time7Average Time3Average Finish
> 2 116.9 0.14285715 0.14285715 0.2857143
> 4.428571 3.2857144 117.557144 117.76667 5.0
> 3 116.22857 0.2857143 0.42857143 0.14285715
> 6.142857 2.142857 116.84286 116.8 2.0
> 4 116.41428 0.0 0.14285715 0.2857143
> 5.714286 3.7142856 117.24286 117.166664 4.0
> 5 115.8 0.5714286 0.0 0.2857143
> 2.142857 2.5714285 116.21429 116.53333 6.0
>
> #load Data
> crs<-
> read.csv("file:///C:/temp/Horse//horseracing.csv<file:///C:\temp\Horse\horseracing.csv>",
> na.strings=c(",", "NA", "", "?"), encoding="UTF-8")
>
> # # define x and y
> x= x<-crs[,9] #predictor variables
> y= y<-crs[1:8,] #response variable
>
>
> library(lars)
> cv.lars(x, y, K=10, trace=TRUE, plot.it = TRUE,se = TRUE, type="lasso")
>
> and I get:
>
> LASSO sequence
> Error in one %*% x : requires numeric/complex matrix/vector arguments
>
> Any idea on what I am doing wrong? Thank you!!
Row 1 contains character data, the variable names. Are you missing a
`header = TRUE` (this is the default in `read.csv()`), or do you have
several header lines?
I also think you have the response/predictors back to front there;
otherwise, why would you need to shrink the coefficient and select from
a model with a single predictor?
HTH
G
> Sincerely,
>
> tom
>
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