[R-sig-ME] Help understanding an error Line Search Fails

[Bill Poling]

Just found your tutorial, wow!

I am going to see if I can figure out how or if my data is applicable to this process.

https://bbolker.github.io/mixedmodels-misc/ecostats_chap.html

Thanks again

WHP

From: Ben Bolker <bbolker using gmail.com>
Sent: Thursday, December 6, 2018 11:20 AM
To: Bill Poling <Bill.Poling using zelis.com>; r-sig-mixed-models using r-project.org
Subject: Re: [R-sig-ME] Help understanding an error Line Search Fails


You could ask your question on r-help or StackOverflow or
CrossValidated (https://stats.stackexchange.com). r-help is mostly for R
questions (obviously). StackOverflow might be best, as this is
primarily a programming question (and you're already looking on SO for
answers ...)

There's a whole lot of information & advice on constructing
reproducible examples here:

https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example

If you choose to post on SO or CV, make sure to read the info on "how
to post"; for R-help, read https://www.r-project.org/posting-guide.html

good luck,
Ben Bolker


On 2018-12-06 7:36 a.m., Bill Poling wrote:
> Good morning Ben and thank you for your response.
>
> Yes, had not considered this a sig-mixed-models question but was unsure of where to start my questions.
>
> How would I make available a reproducible example for further help?
>
> If you suggest that this may be more suitable data for mixed model I would like to pursue that.
>
> Appreciate your help Sir, thank you.
>
> WHP
>
>
>
>
> From: R-sig-mixed-models <mailto:r-sig-mixed-models-bounces using r-project.org> On Behalf Of Ben Bolker
> Sent: Wednesday, December 5, 2018 12:48 PM
> To: mailto:r-sig-mixed-models using r-project.org
> Subject: Re: [R-sig-ME] Help understanding an error Line Search Fails
>
>
> As far as I can tell none of the model types you're using fall under
> the category of "mixed models" (linear/generalized linear models with
> data identified in known groups that are to be estimated by some form
> of shrinkage estimator/"random effect"). (Please feel free to correct me!)
>
> By the way, I don't think it makes any sense to use "ProviderID" as a
> *numeric* predictor variable ... that (and ProductID) are places where
> you *might* actually want to use a mixed model.
>
> This looks like more of a CrossValidated question - note that you'll
> have to provide a *reproducible* example in order to get help ...
>
> cheers
> Ben Bolker
>
> On 2018-12-05 12:45 p.m., Bill Poling wrote:
>>
>>
>> Good afternoon. I hope I have provided enough info to get my question answered.
>>
>>> I am running windows 10 -- R3.5.1 -- RStudio Version 1.1.456
>>
>>
>> Using caret package I have been comparing models using my data, a training subset N=17357.
>>
>> I have run PLS, RDA, GLM, and Boosted Logit based on a couple of tutorials.
>>
>> http://dataaspirant.com/2017/01/19/support-vector-machine-classifier-implementation-r-caret-package/
>>
>> https://cran.r-project.org/web/packages/caret/vignettes/caret.html
>>
>> https://topepo.github.io/caret/model-training-and-tuning.html
>>
>> However, when I get to trying svmLinear or svmRadial they both produce error: line search fails -1.614732 -0.257144 0.00001920624 0.00001369617 -0.00000001857456 -0.00000001542947 -0.000000000000568072
>>
>> I have done some googling research but cannot find a definitive answer as to why this model does not work with my data but the other models do?
>>
>> https://stackoverflow.com/questions/43267209/line-search-fails-when-training-a-model-using-caret
>>
>> https://stackoverflow.com/questions/15895897/line-search-fails-in-training-ksvm-prob-model
>>
>>
>>
>> Any advice would be appreciated.
>>
>> Thank you
>>
>> WHP
>>
>> str(training)
>>
>> # 'data.frame':17357 obs. of 7 variables:
>> # $ SavingsReversed: num 0 0 0 0 0 ...
>> # $ productID : num 3 3 3 3 3 1 3 3 3 1 ...
>> # $ ProviderID : num 113676 114278 114278 114278 114278 ...
>> # $ ModCnt : num 0 1 1 1 1 1 1 0 0 1 ...
>> # $ B2 : num -1 -1 -1 -1 -1 -1 7 9 9 -1 ...
>> # $ B1a : num 1 1 1 1 1 1 26 26 26 3 ...
>> # $ EditnumberI : Factor w/ 2 levels "Bad","Good": 1 2 2 2 2 2 1 1 2 2 ...
>>
>>
>> head(training, n=25)
>>
>> # SavingsReversed productID ProviderID ModCnt B2 B1a EditnumberI
>> # 1 0.00 3 113676 0 -1 1 Bad
>> # 5 0.00 3 114278 1 -1 1 Good
>> # 6 0.00 3 114278 1 -1 1 Good
>> # 7 0.00 3 114278 1 -1 1 Good
>> # 8 0.00 3 114278 1 -1 1 Good
>> # 10 0.00 1 114278 1 -1 1 Good
>> # 12 128.25 3 116641 1 7 26 Bad
>> # 13 159.60 3 116641 0 9 26 Bad
>> # 14 0.00 3 116641 0 9 26 Good
>> # 15 0.00 1 117280 1 -1 3 Good
>> # 16 1622.55 3 117439 1 9 26 Good
>> # 17 60.07 3 117439 1 9 26 Good
>> # 18 0.00 3 117439 0 -1 3 Good
>> # 19 190.00 3 117962 0 9 26 Good
>> # 20 372.66 3 119316 0 1 26 Bad
>> # 22 0.00 3 120431 1 -1 1 Good
>> # 25 0.00 3 121319 1 7 26 Bad
>> # 26 18.79 3 121319 1 7 26 Bad
>> # 27 23.00 3 121319 1 7 26 Bad
>> # 28 18.79 3 121319 1 7 26 Bad
>> # 29 0.00 3 121319 1 7 26 Bad
>> # 30 25.86 3 121319 2 7 26 Bad
>> # 31 14.00 3 121319 1 7 26 Bad
>> # 36 113.00 3 121545 1 1 26 Bad
>> # 37 197.20 3 121545 1 9 26 Bad
>>
>>
>> anyNA(training)
>> #[1] FALSE
>>
>> My scripts
>>
>> ctrl <- trainControl(
>> method = "repeatedcv",
>> repeats = 3,
>> classProbs = TRUE,
>> summaryFunction = twoClassSummary
>> )
>>
>> set.seed(123)
>> svm_Linear <- train(EditnumberI ~., data = training,
>> method = "svmLinear",
>> trControl = ctrl,
>> preProcess = c("center", "scale"),
>> tuneLength = 10,
>> metric="ROC")
>> #warnings()
>> svm_Linear
>>
>>
>>
>> set.seed(123)
>> svm_Radial <- train(EditnumberI ~., data = training,
>> method = "svmRadial",
>> trControl = ctrl,
>> preProcess = c("center", "scale"),
>> tuneLength = 10,
>> metric="ROC")
>> #warnings()
>> svm_Radial
>>
>>
>>
>> line search fails -1.614732 -0.257144 0.00001920624 0.00001369617 -0.00000001857456 -0.00000001542947 -0.000000000000568072
>>
>>
>>
>> WHP
>>
>>
>>
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>>
>
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