# [R] Obtaining p-values for coefficients from LRM function (package Design) - plaintext

Frank E Harrell Jr f.harrell at vanderbilt.edu
Sat Dec 13 22:24:43 CET 2008

```joris meys wrote:
>
> @David : I am aware of that, but this is far from the last model actually.
>
> @ Frank : I know the Anova procedure gives more relevant p-values, but
> the attempt is to order the terms by interaction type from low
> significance to high significance, based on their individual
> difference from zero (if I'm making any sense here). I use this merely
> as a quick guideline for model selection, the Anova I use later on for
> model evaluation.

Be sure to use the hierarchy principle, which anova.Design respects.

Beware of doing model selection on the basis of P-values, R-square,
partial R-square, AIC, BIC, regression coefficients, or Mallows' Cp.

Frank

>
> Therefore I would like to substract the p-values, as they're easier to
> interprete in that respect than the anova values. Or am I missing
> something?
>
> Kind regards
> Joris
>
> On Sat, Dec 13, 2008 at 8:44 PM, David Winsemius <dwinsemius at comcast.net> wrote:
>> On Dec 13, 2008, at 1:12 PM, joris meys wrote:
>>
>>> Sent this mail in rich text format before. Excuse me for this.
>>>
>>> ------------------------
>>> Dear all,
>>>
>>> I'm using the lrm function from the package "Design", and I want to
>>> extract the p-values from the results of that function. Given an lrm
>>> object constructed as follows :
>>>
>>> fit <- lrm(Y~(X1+X2+X3+X4+X5+X6+X7)^2, data=dataset)
>> That link could create a montrous interpretation problem.
>>
>>>
>>> I need the p-values for the coefficients printed by calling "fit".
>>>
>>> fit\$coef (gives a list of only the coefficients)
>>> fit\$pval, fit\$p, fit\$pvalue, fit\$p.value,... : nothing works
>>> str(fit) : no hints there
>>> fit[1,4] : gives dimension errors
>> If you want to see how Harrell does it, you can work through the code that
>> you get from:
>>
>> print.lrm
>>
>> The last element in the "stats" list is (1 - pchisq(z^2, 1), 4) ) where z
>> was defined as
>>
>> z <- cof/sqrt(vv)
>>
>> ... and those were obtained further up as:
>>
>> vv <- diag(x\$var)
>>    cof <- x\$coef
>>
>> So you could try seeing if this is satisfying:
>>
>> vv <- diag(fit\$var) ;
>> cof <- fit\$coef ;
>> z <- cof/sqrt(vv) ;
>> 1 - pchisq(z^2, 1)
>>
>> --
>> David Winsemius
>>
>>
>>>
>>> help files don't seem to give me a function that extracts them. Yet,
>>> they are calculated and printed, based on the Wald statistics. So they
>>> must be reachable.
>>>
>>> Anybody knows how?
>>>
>>> Kind regards
>>> Joris
>>>
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>>
>
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