[R] About stepwise regression problem

[pigpigmeow]

chris,
I'm not using lmer, i just use gam mixed with smoothing function and linear
function
and summary of the model, it shows
Family: gaussian 
Link function: log 

Formula: 
newNO2 ~ pressure + s(maxtemp, bs = "cr") + s(avetemp, bs = "cr") + 
    s(mintemp, bs = "cr") + RH + s(solar, bs = "cr") + s(windspeed, 
    bs = "cr") + s(transport, bs = "cr") 

Parametric coefficients: 
            Estimate Std. Error t value Pr(>|t|)     
(Intercept) 2.721513   0.049108  55.419   <2e-16 *** 
pressure    0.028988   0.019434   1.492    0.140     
RH          0.005228   0.009763   0.535    0.594     
--- 
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Approximate significance of smooth terms: 
               edf Ref.df     F p-value   
s(maxtemp)   6.346  7.276 1.223 0.29991   
s(avetemp)   1.000  1.000 0.226 0.63562   
s(mintemp)   1.908  2.396 1.066 0.35871   
s(solar)     3.797  4.490 2.164 0.07359 . 
s(windspeed) 5.305  6.341 2.346 0.03648 * 
s(transport) 7.234  7.984 2.807 0.00884 ** 
--- 
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

R-sq.(adj) =  0.307   Deviance explained = 49.1% 
GCV score = 61.136  Scale est. = 44.49     n = 105 

In the parametric  coefficients part, I see that Pr(>|t|)     which mean the
probablity greater than T-value. Is that probablity mean p-value?
In the Approximate significance of smooth terms part,  p-value column shows
the probability greater than F-value. 

I have the following question,
1.if I reject the variable term which has greater the p-value no matter the
variable term is smoothing term or linear term, is it correct to perform
stepwise regression.
 2. In my model
noxd<-gam(newNOX~pressure+maxtemp+s(avetemp,bs="cr")+s(mintemp,bs="cr")+s(RH,bs="cr")+s(solar,bs="cr")+s(windspeed,bs="cr")+s(transport,bs="cr"),family=gaussian
(link=log),groupD,methods=REML) , is it generalized additive mixed model?
3. what the different if I use other criteria such as AIC or BIC? 

Anyway, thank all of you!
  


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