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I am fairly new to statistics, so please forgive me for using probably the wrong vocabulary.

I have a dataset of locations with measurements of soil carbon SOC1mTHA. I want to study the predictors that best explain SOC1mTHA, among 15 potential predictors. I have used the glmulti package, and finally concluded that based on AICc of all potential model combinations, the best set of predictors is:

> global.model <-  glm(SOC1mTHA ~ CN + pH + MATcru + PETsplash + MAPcru + 
+                        ECM + PGB2, data= dat2)
> summary(global.model)

Call:
glm(formula = SOC1mTHA ~ CN + pH + MATcru + PETsplash + MAPcru + 
    ECM + PGB2, data = dat2)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-246.02   -78.72   -31.71   101.80   289.56  

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -165.25860  179.19404  -0.922 0.358695    
CN            35.42207    5.54562   6.387 5.85e-09 ***
pH             6.37200    1.80541   3.529 0.000639 ***
MATcru        18.86837    5.68337   3.320 0.001270 ** 
PETsplash     -0.87748    0.12285  -7.143 1.69e-10 ***
MAPcru         0.11970    0.02665   4.491 1.96e-05 ***
ECM           -1.86698    0.51799  -3.604 0.000496 ***
PGB2           2.76935    0.62110   4.459 2.22e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 14572.72)

    Null deviance: 4772480  on 104  degrees of freedom
Residual deviance: 1413554  on  97  degrees of freedom
AIC: 1314.3

Number of Fisher Scoring iterations: 2

However, when visualising the regressions of the individual predictors with visreg, it appears that one of the regressions is nonlinear. In particular, the relationship of soil pH and SOC1mTHA could be a humped-shaped gaussian curve around pH=7.0 (which makes sense in biological terms). Please, see attached figure.

visreg(global.model)

enter image description here

What would be the next step? Should I define global.model as composed by 6 linear regressions + 1 nonlinear regression? How could I do it? Sorry if my question is silly or to broad. Some readings that would help me figure out the solution are also welcome.

EDITED

Following the suggestion by @adibender I have changed the strategy, running model selection and GAM in one single step with select=TRUEin order to detect potential nonlinear predictors beyond pH.

I have therefore included the full model with all potential predictors in GAM:

> global.model2 <- gam(SOC1mTHA ~ s(CN) + s(pH) + s(MATcru) + s(PETsplash) + s(MAPcru) + 
+                        s(ECM) + s(PGB2) + s(moisture) + s(clay), 
+                      data= dat2, select = TRUE)
> summary(global.model2)

Family: gaussian 
Link function: identity 

Formula:
SOC1mTHA ~ s(CN) + s(pH) + s(MATcru) + s(PETsplash) + s(MAPcru) + 
    s(ECM) + s(PGB2) + s(moisture) + s(clay)

Parametric coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  286.743      3.703   77.44   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:
                   edf Ref.df      F  p-value    
s(CN)        5.285e-11      9  0.000 0.230133    
s(pH)        7.145e+00      9  2.841 0.000272 ***
s(MATcru)    7.475e+00      9 27.030  < 2e-16 ***
s(PETsplash) 2.308e+00      9  3.510 3.22e-09 ***
s(MAPcru)    7.441e+00      9 11.023 8.01e-16 ***
s(ECM)       5.550e+00      8 15.786  < 2e-16 ***
s(PGB2)      1.498e+00      8  1.987 2.80e-05 ***
s(moisture)  4.146e-11      9  0.000 0.610718    
s(clay)      1.935e+00      9  2.325 4.34e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Rank: 81/82
R-sq.(adj) =  0.969   Deviance explained = 97.9%
GCV = 2139.9  Scale est. = 1439.8    n = 105
  1. How do I know what are the important predictors kept by gam, similar to what I would obtain with glmulti, stepwise regression or a simple "best model" based on AIC? In other words, how do I know which predictors are unimportant, so I don't even need to gather data on them to predict SOC1mTHA?

  2. I have never used a gam model, so I am not sure how to interpret the results. What would be the form of my final model that can be used to predict SOC1mTHA based on the results of summary(global.model2)above?

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  • $\begingroup$ BTW, why are you using glm function to fit a linear model? $\endgroup$
    – adibender
    Oct 25 '17 at 11:23
  • $\begingroup$ It doesn't really matter, b/c glm is equivalent to lm if no family is specified. Just wondering if you maybe forgot to specify family. $\endgroup$
    – adibender
    Oct 25 '17 at 14:35
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    $\begingroup$ Is there some biological/chemical principle that can guide you with regard to the expected functional form of the response? If so, I would actually force that relationship by adjusting my predictors accordingly (fit $y$~$x^2$ instead of $y$~$x$, for instance). $\endgroup$
    – Josh
    Oct 25 '17 at 18:03
  • $\begingroup$ it appears that CN and moisture can be removed from the model, as the effective degrees of freedom are practically 0. The other model terms should be kept in the model. To predict you can use predict(model, newdata). To interpret the results you can plot the respective effects via plot(model). $\endgroup$
    – adibender
    Oct 27 '17 at 16:08
  • $\begingroup$ Note: do not modify your original question based on answers. Ask another question instead and link to the previous question. $\endgroup$
    – adibender
    Oct 27 '17 at 16:09
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Non-linear effects: If you have enough observations, you should be assuming potential non-linearity in all continuous covariates and fit a Generalized Additive Model (GAM) instead. If effects are linear, they will be estimated as such due to penalty.

To fit such models you can use mgcv::gam

library(mgcv)
global.model <- gam(SOC1mTHA ~ CN + s(pH) + MATcru + PETsplash + 
    MAPcru + ECM + PGB2, data= dat2)
plot(global.model)

Model/Variable selection: Note, if you additionally set select=TRUE in the call to gam, this will also perform model selection, as individual terms may not only be penalized towards a linear effect, but also towards 0 (see this reference).

Warning: No matter what type of model selection scheme you choose, standard inference will no longer be valid. E.g. the p-values displayed in your output are worthless, unless you adjust for previous AIC selection. This field of statistics is called Post Selection Inference (POSI), but there are few ready to use solutions.

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  • $\begingroup$ Thanks, your post goes in an interesting direction. Re the model selection analysis within gam, do you then suggest to include the full model in the gamfunction with s(pH)and then fit a new model including only the predictors with p<0.05 in the previous full-model gam? $\endgroup$
    – fede_luppi
    Oct 25 '17 at 19:32
  • $\begingroup$ @fede_luppi No, p-value based variable selection usually not a good idea. Best way is to put all continuous covariates in s() and specify select=TRUE, i.e. gam(y ~ s(x1) + s(x2) + s(x3) + ... , data=df, select=TRUE)`. The resulting model is your final model. Note, however, that p-values are no longer valid after variable selection. $\endgroup$
    – adibender
    Oct 25 '17 at 21:58
  • $\begingroup$ I have updated my original question to include your suggested approach. Thanks $\endgroup$
    – fede_luppi
    Oct 26 '17 at 21:04
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You can use the following formula to include a quadratic term for pH.

SOC1mTHA ~ CN + pH + I(pH^2) + MATcru + PETsplash + MAPcru + ECM + PGB2

If the AIC for the polynomial specification is smaller than for the linear specification, then you might want to use it.

Note that due to principle of marginality, if you include the quadratic term in your model, you would most likely also want to include the linear term.

Finally - as the commenter above noted - you might want to use lm() instead of glm.

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