[R] Error using mgcv package

[spime]

Dear Mr. Oksanen,

First of all thanks for your reply. I have solved this problem in this way.
My data consists of some categorical(CAT..) predictors and also some
numerical variables(NUM..) have only {0,1} 0r {0,1,2,3} values. For applying
GAM i just didnot consider their splines. I had came to this decision
because when i tested the same data on S-PLUS, i got an error regarding the
applicability of s(...) function on the predictors less than 4 different
values. I dont know whether gam() of S-PLUS and gam() of mgcv(R) are same or
not. anyway, thanks for your kind reply.

bye


Jari Oksanen wrote:
> 
> spime <sabya23 <at> gmail.com> writes:
> 
>> 
>> 
>> Hi all,
>> 
>> I need some solution in the following problem. The following error
>> appears
>> when i use "mgcv" package for implementing GAM. But the same formula
>> works
>> fine in "gam" package.
>> 
>> > model.gam <- gam(formula = RES ~
>> > CAT01+s(NUM01,5)+CAT02+CAT03+s(NUM02,5)+CAT04+
>> + CAT05+s(NUM03,5)+CAT06+CAT07+s(NUM04,5)+CAT08+s(NUM05,5)+CAT09+
>> + CAT10+s(NUM06,5)+CAT11+NUM07+CAT12+CAT13,
>> + family = binomial(link = logit), data = train.data,na.action =
>> na.exclude,
>> + control = list(epsilon = 0.001,bf.epsilon = 0.001, maxit = 50, 
>> + bf.maxit = 10, trace = F))
>> 
>> Error in terms.formula(reformulate(term[i])) : 
>>         invalid model formula in ExtractVars
>> 
> It seems that nobody answered this (in public). 
> 
> It seems that function s() in mgcv is defined as:
> 
> s(..., k = -1, fx = FALSE, bs = "tp", m = 0, by = NA) 
> 
> (Like you see reading its help ?s). The function definition starts with
> "...",
> and after three dots you cannot use positional arguments, but you must
> give the
> full argument name. Try replacing s(NUM01, 5) with s(NUM01, k=5). See also
> help
> in mgcv (?s pointing to ?choose.k) for interpreting argument 'k' which is
> not
> directly degrees of freedom.
> 
> There may be other problems, but this probably fixes tha one you reported
> above.
> 
> cheers, jari oksanen
> 
>> And after deleting df's 
>> 
>> model.gam <- gam(formula = RES ~
>> CAT01+s(NUM01)+CAT02+CAT03+s(NUM02)+CAT04+
>> + CAT05+s(NUM03)+CAT06+CAT07+s(NUM04)+CAT08+s(NUM05)+CAT09+
>> + CAT10+s(NUM06)+CAT11+NUM07+CAT12+CAT13,
>> + family = binomial(link = logit), data = train.data)
>> 
>> Error in smooth.construct.tp.smooth.spec(object, data, knots) : 
>>         A term has fewer unique covariate combinations than specified
>> maximum degrees of freedom
>> 
>> Can anybody show me some light in this case!!!
>> 
>> Thanks in advance.
> 
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