On 4 Nov 2009, at 18:40, Gavin Simpson wrote:
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.
Yes - I see that polr() also doesn't produce p-values in the output
from
summary. You can use it to get "a" p-value for x if you use a LRT; fit
the model using polr() with and without 'x' and then supply both
fitted
models to the anova() method for polr objects.
I did think of that solution, but it's the last thing on my cards...
I'm trying to analyse 1.5 million markers, and fitting two models at a
time for every marker starts to be a bit on the heavy side. If all
else fails that's the way to go.
I will try lms() and hope for the best.
Sorry, I meant the lrm() function in package rms
Ta,
F
G
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
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%~%
--
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
--
%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%
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
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