[R] vglm(), t values and p values

[Federico Calboli]

On 4 Nov 2009, at 18:11, Gavin Simpson wrote:
> Is there a particular reason for choosing a VGLM here? My reading of
> your post suggests the response is an univariate, ordered factor and
> VGLMs are especially for multivariate responses. In which case, can  
> you
> not use polr() in package MASS that comes with R or the lms() function
> in the rms package (available from CRAN).

I have used polr() but:

1) seems to be even more reluctant to give me at least the t value for  
my marker: for the very same data I used to understand what was going  
on vglm does give a t value for x (my marker), polr does not. As much  
as my data is *dire* I still need to use it and get as many p-values  
as I can (ideally one for each marker). The goodness of the model is  
going to be assessed, perversely, on the whole sheabang of p-values  
and  their aderence to a null distribution.

Additionally, while extracting the t value is a piece of cake with  
polr(), the p-value I get a nowhere close to a null distribution.

I will try lms() and hope for the best.
>
> I haven't really used either of these functions in earnest, but one or
> both may provide the p-values you desire, out of the box.

I hope so!

Thanks,

F


>
> HTH
>
> G
>
>>
>> My response variable is the severity of diseases, going from 0 to 5  
>> (the
>> severity is actually an ordered factor).
>>
>> The independent variables are: 1 genetic marker, time of medical  
>> observation,
>> age, sex. What I *need* is a p-value for the genetic marker.  
>> Because I have ~1.5
>> million markers I'd rather not faffing around too much.
>>
>> My model is:
>>
>>> mod.vglm = vglm(disease.status ~ x + time + age + sex, family =
>> cumulative(par = T))
>>
>> where x is my genetic marker, coded as 0/1/2, time is days of  
>> medical observation.
>>
>>> summary(mod.vglm) works:
>>
>> Call:
>> vglm(formula = disease.status ~ x + time + age + sex, family =  
>> cumulative(par = T))
>>
>> Pearson Residuals:
>>                    Min       1Q   Median       3Q     Max
>> logit(P[Y<=1]) -0.6642 -0.28704 -0.18329 -0.11681  3.8919
>> logit(P[Y<=2]) -2.5580 -0.48080 -0.23315  0.47388  2.5983
>> logit(P[Y<=3]) -2.1565 -0.56961  0.22089  0.44349 10.7964
>> logit(P[Y<=4]) -3.3175  0.13064  0.20117  0.43176 12.5233
>>
>> Coefficients:
>>                     Value Std. Error  t value
>> (Intercept):1 -2.4460e+00 4.2791e-01  -5.7162
>> (Intercept):2 -7.1078e-01 4.1628e-01  -1.7074
>> (Intercept):3  3.7619e-01 4.1545e-01   0.9055
>> (Intercept):4  1.7467e+00 4.2092e-01   4.1496
>> x              4.1421e-01 1.9762e-01   2.0959
>> time          -3.6021e-04 3.0387e-05 -11.8540
>> age           -2.6115e-05 9.2504e-06  -2.8232
>> sexM           1.0188e-01 1.2491e-01   0.8156
>>
>> Number of linear predictors:  4
>>
>> Names of linear predictors:
>> logit(P[Y<=1]), logit(P[Y<=2]), logit(P[Y<=3]), logit(P[Y<=4])
>>
>> Dispersion Parameter for cumulative family:   1
>>
>> Residual Deviance: 2475.937 on 3460 degrees of freedom
>>
>> Log-likelihood: -1237.969 on 3460 degrees of freedom
>>
>> #######################
>>
>> So here are my questions:
>>
>> 1) I need to get the t value for x, so I can use "1 - pt(tvalue,1)"  
>> to find some
>> sort of probability value for x. That's not trivial. Additionally,  
>> I assume df
>> for x is 1, hence I plan to use  "1 - pt(tvalue,1)", though I might  
>> well be
>> wrong. In any case getting the darned t value seems impossible
>>
>> 2) because of the difficulty of getting (1), it there a way of  
>> getting vglm() to
>> spit out a p-value for x please?
>>
>> I do recon many people might scoff at my crass desire for a p- 
>> value, but I'm
>> dealing with some dire phenotype in a whole genome analysis where  
>> the *only*
>> thing that matters are p-values. I have to be quite unsophysticated  
>> I'm afraid.
>>
>> Best,
>>
>> Federico
>>
>>
> -- 
> %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%
> Dr. Gavin Simpson             [t] +44 (0)20 7679 0522
> ECRC, UCL Geography,          [f] +44 (0)20 7679 0565
> Pearson Building,             [e] gavin.simpsonATNOSPAMucl.ac.uk
> Gower Street, London          [w] http://www.ucl.ac.uk/~ucfagls/
> UK. WC1E 6BT.                 [w] http://www.freshwaters.org.uk
> %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%
>

--
Federico C. F. Calboli
Department of Epidemiology and Public Health
Imperial College, St. Mary's Campus
Norfolk Place, London W2 1PG

Tel +44 (0)20 75941602   Fax +44 (0)20 75943193

f.calboli [.a.t] imperial.ac.uk
f.calboli [.a.t] gmail.com