[R] significance of random effect in mgcv gam

[William Shadish]

Thank you, Simon.

On 12/4/2013 1:02 AM, Simon Wood wrote:
>  > Question. Am I correct that p = .126 above can be taken as the
>  > p-value  associated with the random effect?
>
> - Yes. See
>
> http://biomet.oxfordjournals.org/content/100/4/1005.abstract
>
> for details of the approximate test used.
>
>
> On 03/12/13 20:09, William Shadish wrote:
>> Dear R-helpers,
>>
>> I would like to test whether a random effect is significant when
>> implemented with bs="re" in mgcv gam. For example, if I run:
>>
>> M3b <- gam(DVY  ~ s(SessIDX, fTX, bs = "re") + factor(TX),
>>             data = PCP,
>>             family = quasipoisson(link="log"), method="REML")
>> summary(M3b,all.p=TRUE)
>> gam.vcomp(M3b)
>>
>> I obtain the the following output:
>>
>>  > summary(M3b,all.p=TRUE)
>>
>> Family: quasipoisson
>> Link function: log
>>
>> Formula:
>> DVY ~ s(SessIDX, fTX, bs = "re") + factor(TX)
>>
>> Parametric coefficients:
>>              Estimate Std. Error t value Pr(>|t|)
>> (Intercept)   1.3282     0.2244   5.920 2.74e-07 ***
>> factor(TX)1  -1.0546     0.7210  -1.463     0.15
>> ---
>> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>>
>> Approximate significance of smooth terms:
>>                   edf Ref.df     F p-value
>> s(SessIDX,fTX) 1.052      2 1.138   0.126
>>
>> R-sq.(adj) =  0.388   Deviance explained = 39.5%
>> REML score =  37.67  Scale est. = 1.4172    n = 54
>>  > gam.vcomp(M3b)
>>
>> Standard deviations and 0.95 confidence intervals:
>>
>>                    std.dev      lower     upper
>> s(SessIDX,fTX) 0.07842742 0.01095655 0.5613865
>> scale          1.19029872 0.97816911 1.4484316
>>
>> Rank: 2/2
>>
>> Question. Am I correct that p = .126 above can be taken as the p-value
>> associated with the random effect?
>>
>> Thanks.
>>
>> Will Shadish
>>
>
>

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William R. Shadish
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