Hi,
Thanks for the response.
The warnings and errors can be reproduced with the data and code I
included in my first mailing list post. I will provide the full output
at the end of this post.
By sketchy, I mean having a higher likelihood of resulting in
overfitting. By more straightforward, I mean having a less steep
learning curve for implementation.
Thanks for your help!
> KNov <- read.table("Novelty_abr.txt", header = TRUE)
> KNov$Subject <- factor(KNov$Subject)
> glm1 <- glmmLasso(Activity~as.factor(Novelty) + as.factor(Valence) + STAIt +
> as.factor(ROI)
+ + as.factor(Valence):as.factor(ROI), list(Subject=~1), data = KNov, lambda=10)
> summary(glm1)
Call:
glmmLasso(fix = Activity ~ as.factor(Novelty) + as.factor(Valence) +
STAIt + as.factor(ROI) + as.factor(Valence):as.factor(ROI),
rnd = list(Subject = ~1), data = KNov, lambda = 10)
Fixed Effects:
Coefficients:
Estimate StdErr z.value p.value
(Intercept) 0.14047593 NA NA NA
as.factor(Novelty)R -0.06333466 NA NA NA
as.factor(Valence)N -0.03537854 NA NA NA
STAIt -0.00173351 NA NA NA
as.factor(ROI)B -0.00438142 NA NA NA
as.factor(ROI)H 0.00016285 NA NA NA
as.factor(Valence)N:as.factor(ROI)B -0.00739870 NA NA NA
as.factor(Valence)N:as.factor(ROI)H 0.00000000 NA NA NA
Random Effects:
StdDev:
Subject
Subject 0.05186835
> glm2 <- glmmLasso(Activity~as.factor(Novelty) + as.factor(Valence) + STAIt +
> as.factor(ROI)
+ + as.factor(Novelty):as.factor(Valence):as.factor(ROI),
list(Subject=~1), data = Nov7T, lambda=10)
Warning messages:
1: In split.default((1:ncol(X))[-inotpen.which], ipen) :
data length is not a multiple of split variable
2: In lambda_vec * sqrt(block2) :
longer object length is not a multiple of shorter object length
3: In lambda_vec * sqrt(block2) :
longer object length is not a multiple of shorter object length
4: In lambda_vec * sqrt(block2) :
longer object length is not a multiple of shorter object length
5: In lambda_vec * sqrt(block2) :
longer object length is not a multiple of shorter object length
6: In lambda_vec * sqrt(block2) :
longer object length is not a multiple of shorter object length
7: In lambda_vec * sqrt(block2) :
longer object length is not a multiple of shorter object length
8: In lambda_vec * sqrt(block2) :
longer object length is not a multiple of shorter object length
9: In lambda_vec * sqrt(block2) :
longer object length is not a multiple of shorter object length
> summary(glm2)
Call:
glmmLasso(fix = Activity ~ as.factor(Novelty) + as.factor(Valence) +
STAIt + as.factor(ROI) +
as.factor(Novelty):as.factor(Valence):as.factor(ROI),
rnd = list(Subject = ~1), data = Nov7T, lambda = 10)
Fixed Effects:
Coefficients:
Estimate
StdErr z.value p.value
(Intercept) -0.0562165
NA NA NA
as.factor(Novelty)R -0.0218362
NA NA NA
as.factor(Valence)N -0.0067723
NA NA NA
STAIt 0.0028832
NA NA NA
as.factor(ROI)BNST -0.0457882
NA NA NA
as.factor(ROI)Hip -0.0430477
NA NA NA
as.factor(Novelty)N:as.factor(Valence)E:as.factor(ROI)Amy 0.0000000
NA NA NA
as.factor(Novelty)R:as.factor(Valence)E:as.factor(ROI)Amy 0.0000000
NA NA NA
as.factor(Novelty)N:as.factor(Valence)N:as.factor(ROI)Amy 0.0164788
NA NA NA
as.factor(Novelty)R:as.factor(Valence)N:as.factor(ROI)Amy 0.0067723
NA NA NA
as.factor(Novelty)N:as.factor(Valence)E:as.factor(ROI)BNST 0.0000000
NA NA NA
as.factor(Novelty)R:as.factor(Valence)E:as.factor(ROI)BNST 0.0000000
NA NA NA
as.factor(Novelty)N:as.factor(Valence)N:as.factor(ROI)BNST 0.0000000
NA NA NA
as.factor(Novelty)R:as.factor(Valence)N:as.factor(ROI)BNST 0.0000000
NA NA NA
as.factor(Novelty)N:as.factor(Valence)E:as.factor(ROI)Hip 0.0000000
NA NA NA
as.factor(Novelty)R:as.factor(Valence)E:as.factor(ROI)Hip 0.0000000
NA NA NA
as.factor(Novelty)N:as.factor(Valence)N:as.factor(ROI)Hip 0.0338616
NA NA NA
as.factor(Novelty)R:as.factor(Valence)N:as.factor(ROI)Hip 0.0000000
NA NA NA
Random Effects:
StdDev:
Subject
Subject 0.09132963
> glm3 <- glmmLasso(Activity~as.factor(Novelty) + as.factor(Valence) + STAIt +
> as.factor(ROI)
+ + as.factor(Valence):as.factor(ROI) + as.factor(Novelty):STAIt,
list(Subject=~1), data = Nov7T, lambda=10)
Error in rep(control$index[i], length.fac) : invalid 'times' argument
> summary(glm3)
Error in summary(glm3) : object 'glm3' not found
On Sat, Jul 16, 2016 at 12:51 PM, David Winsemius
<dwinsem...@comcast.net> wrote:
>
>> On Jul 16, 2016, at 9:29 AM, Walker Pedersen <w...@uwm.edu> wrote:
>>
>> Thank you for the input Brian and Ben.
>>
>> It is odd how it seems to handle a two way interaction fine (as long
>> as the continuous variable is not in the mix), but not a 3-way.
>
> You should post code and data to demonstrate what is "not being handled".
>>
>> In any case would anyone be able to give me a rundown of how I would
>> create a matrix/dummy variable for these interactions to input into
>> glmmLASSO?
>
> Your first question on this dataset June 17 to CrossValidated.com was closed
> because no reproducible example was offered. You then posted two further
> questions on StackOverflow and got guesses as to the solutions because you
> again posted no reproducible examples. One of those questions was given in
> this thread as a possible solution. IN the otehr one you did post some output
> that gave clues as to the arrangement of your data and suggested that the
> categorical data was relatively sparse:
>
> http://stackoverflow.com/questions/38132830/getting-p-values-for-all-included-parameters-using-glmmlasso
>
> Now you are getting advice that is similarly just speculation due to lack of
> code, data and output. You are unlikely to get further advice that addresses
> what ever problems you have vaguely described unless you post examples of
> code that is failing along with either a) the real data or b) R code that
> creates a simulation with covariate features resembling your data.
>>
>> Alternatively, is there a method for paring down a model that is a bit
>> less sketchy than simple backfitting, that you would expect to be more
>> straight forward software-wise?
>
> That appears incredibly vague. Exactly what is "sketchy"? And what would be
> "more straightforward"?
>
> --
> David.
>
>
>> Thanks!
>>
>> Walker
>>
>> UW-MKE
>>
>> On Thu, Jul 14, 2016 at 10:08 AM, Cade, Brian <ca...@usgs.gov> wrote:
>>> It has never been obvious to me that the lasso approach can handle
>>> interactions among predictor variables well at all. I'ld be curious to see
>>> what others think and what you learn.
>>>
>>> Brian
>>>
>>> Brian S. Cade, PhD
>>>
>>> U. S. Geological Survey
>>> Fort Collins Science Center
>>> 2150 Centre Ave., Bldg. C
>>> Fort Collins, CO 80526-8818
>>>
>>> email: ca...@usgs.gov
>>> tel: 970 226-9326
>>>
>>>
>>> On Wed, Jul 13, 2016 at 2:20 PM, Walker Pedersen <w...@uwm.edu> wrote:
>>>>
>>>> Hi Everyone,
>>>>
>>>> I am having trouble running glmmLasso.
>>>>
>>>> An abbreviated version of my dataset is here:
>>>>
>>>> https://drive.google.com/open?id=0B_LliPDGUoZbVVFQS2VOV3hGN3c
>>>>
>>>> Activity is a measure of brain activity, Novelty and Valence are
>>>> categorical variables coding the type of stimulus used to elicit the
>>>> response, ROI is a categorical variable coding three regions of the
>>>> brain that we have sampled this activity from, and STAIt is a
>>>> continuous measure representing degree of a specific personality trait
>>>> of the subjects. Subject is an ID number for the individuals the data
>>>> was sampled from.
>>>>
>>>> Before glmmLasso I am running:
>>>>
>>>> KNov$Subject <- factor(KNov$Subject)
>>>>
>>>> to ensure the subject ID is not treated as a continuous variable.
>>>>
>>>> If I run:
>>>>
>>>> glm1 <- glmmLasso(Activity~as.factor(Novelty) + as.factor(Valence) +
>>>> STAIt + as.factor(ROI)
>>>> + as.factor(Valence):as.factor(ROI), list(Subject=~1), data = KNov,
>>>> lambda=10)
>>>> summary(glm1)
>>>>
>>>> I don't get any warning messages, but the output contains b estimates
>>>> only, no SE or p-values.
>>>>
>>>> If I try to include a 3-way interaction, such as:
>>>>
>>>> glm2 <- glmmLasso(Activity~as.factor(Novelty) + as.factor(Valence) +
>>>> STAIt + as.factor(ROI)
>>>> + as.factor(Novelty):as.factor(Valence):as.factor(ROI),
>>>> list(Subject=~1), data = Nov7T, lambda=10)
>>>> summary(glm2)
>>>>
>>>> I get the warnings:
>>>>
>>>> Warning messages:
>>>> 1: In split.default((1:ncol(X))[-inotpen.which], ipen) :
>>>> data length is not a multiple of split variable
>>>> 2: In lambda_vec * sqrt(block2) :
>>>> longer object length is not a multiple of shorter object length
>>>>
>>>> And again, I do get parameter estimates, and no SE or p-values.
>>>>
>>>> If I include my continuous variable in any interaction, such as:
>>>>
>>>> glm3 <- glmmLasso(Activity~as.factor(Novelty) + as.factor(Valence) +
>>>> STAIt + as.factor(ROI)
>>>> + as.factor(Valence):as.factor(ROI) + as.factor(Novelty):STAIt,
>>>> list(Subject=~1), data = Nov7T, lambda=10)
>>>> summary(glm3)
>>>>
>>>> I get the error message:
>>>>
>>>> Error in rep(control$index[i], length.fac) : invalid 'times' argument
>>>>
>>>> and no output.
>>>>
>>>> If anyone has an input as to (1) why I am not getting SE or p-values
>>>> in my outputs (2) the meaning of there warnings I get when I include a
>>>> 3-way variable, and if they are something to worry about, how to fix
>>>> them and (3) how to fix the error message I get when I include my
>>>> continuous factor in an interatction, I would be very appreciative.
>>>>
>>>> Thanks!
>>>>
>>>> Walker
>>>>
>>>> ______________________________________________
>>>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>>> PLEASE do read the posting guide
>>>> http://www.R-project.org/posting-guide.html
>>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>>
>>
>> ______________________________________________
>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>
> David Winsemius
> Alameda, CA, USA
>
______________________________________________
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