在gamm4R包中导电胶?-conductingGAM-GEEingamm4Rpackage?

I am trying to analyze some visual transect data of organisms to generate a habitat distribution model. Once organisms are sighted, they are followed as point data is collected at a given time interval. Because of the autocorrelation among these "follows," I wish to utilize a GAM-GEE approach similar to that of Pirotta et al. 2011, using packages 'yags' and 'splines' (http://www.int-res.com/abstracts/meps/v436/p257-272/). Their R scripts are shown here (http://www.int-res.com/articles/suppl/m436p257_supp/m436p257_supp1-code.r). I have used this code with limited success and multiple issues of models failing to converge.

我正试图分析一些生物的视觉横断面数据，以建立一个栖息地分布模型。一旦发现了生物，在给定的时间间隔内收集点数据时，就会跟踪它们。由于这些“接下来”之间的自相关，我希望使用与Pirotta等人类似的GAM-GEE方法，使用包'yags'和'splines' (http://www.int-res.com/abstracts/meps/v436/p257-272/)。它们的R脚本如下所示(http://www.int-res.com/articles/suppl/m436p257_supp/m436p257_supp1-code.r)。我使用这段代码的成功程度有限，而且模型的多个问题也不一致。

Below is the structure of my data:

以下是我的数据结构:

> str(dat2)

'data.frame':   10792 obs. of  4 variables:

 $ dist_slag       : num  26475 26340 25886 25400 24934 ...
 $ Depth           : num  -10.1 -10.5 -16.6 -22.2 -29.7 ...
$ dolphin_presence: int  0 0 0 0 0 0 0 0 0 0 ...


 $ block           : int  1 1 1 1 1 1 1 1 1 1 ...


> head(dat2)

  dist_slag    Depth dolphin_presence block
1  26475.47 -10.0934                0     1
2  26340.47 -10.4870                0     1
3  25886.33 -16.5752                0     1
4  25399.88 -22.2474                0     1



5  24934.29 -29.6797                0     1
6  24519.90 -26.2370                0     1

Here is the summary of my block variable (indicating the number of groups for which autocorrelation exists within each block

这是块变量的摘要(指示每个块中存在自相关的组的数量)

> summary(dat2$block)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00   39.00   76.00   73.52  111.00  148.00

However, I would like to use the package 'gamm4', as I am more familiar with Professor Simon Wood's packages and functions, and it appears gamm4 might be the most appropriate. It is important to note that the models have a binary response (organism presence of absence along a transect), and thus why I think gamm4 is more appropriate than gamm. In the gamm help, it provides the following example for autocorrelation within factors:

但是，我更喜欢使用“gamm4”这个包，因为我更熟悉Simon Wood教授的包和函数，看来gamm4可能是最合适的。需要注意的是，模型有一个二进制的响应(在传输过程中出现了不存在的生物)，因此我认为gamm4比gamm更合适。在gamm的帮助下，它提供了以下的例子:

## more complicated autocorrelation example - AR errors
## only within groups defined by `fac'
e <- rnorm(n,0,sig)
for (i in 2:n) e[i] <- 0.6*e[i-1]*(fac[i-1]==fac[i]) + e[i]
y <- f + e
b <- gamm(y~s(x,k=20),correlation=corAR1(form=~1|fac))

Following this example, the following is the code I used for my dataset

在这个示例之后，下面是我为数据集使用的代码

b <- gamm4(dolphin_presence~s(dist_slag)+s(Depth),random=(form=~1|block),  family=binomial(),data=dat)

However, by examining the output (summary(b$gam)) and specifically summary(b$mer)), I am either unsure of how to interpret the results, or do not believe that the autocorrelation within the group is being taken into consideration.

然而，通过检查输出(summary(b$gam)和特定的summary(b$mer))，我要么不确定如何解释结果，要么不相信组内的自相关被考虑在内。

> summary(b$gam)

Family: binomial 
Link function: logit 

Formula:
dolphin_presence ~ s(dist_slag) + s(Depth)

Parametric coefficients:


            Estimate Std. Error z value Pr(>|z|)   
    (Intercept)  -13.968      5.145  -2.715  0.00663 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Approximate significance of smooth terms:


               edf Ref.df Chi.sq  p-value    
s(dist_slag) 4.943  4.943  70.67 6.85e-14 ***
s(Depth)     6.869  6.869 115.59  <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 



R-sq.(adj) =  0.317glmer.ML score =  10504  Scale est. = 1         n = 10792
> 

> summary(b$mer)
Generalized linear mixed model fit by the Laplace approximation 


   AIC   BIC logLik deviance
 10514 10551  -5252    10504
Random effects:
 Groups Name         Variance Std.Dev.
 Xr     s(dist_slag) 1611344  1269.39 
 Xr.0   s(Depth)       98622   314.04 
Number of obs: 10792, groups: Xr, 8; Xr.0, 8



Fixed effects:
                 Estimate Std. Error z value Pr(>|z|)   
X(Intercept)      -13.968      5.145  -2.715  0.00663 **
Xs(dist_slag)Fx1  -35.871     33.944  -1.057  0.29063   
Xs(Depth)Fx1        3.971      3.740   1.062  0.28823   


---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Correlation of Fixed Effects:
            X(Int) X(_)F1
Xs(dst_s)F1  0.654       
Xs(Dpth)Fx1 -0.030  0.000
> 

How do I ensure that autocorrelation is indeed being accounted for within each unique value of the "block" variable? What is the simplest way to interpret the output for "summary(b$mer)"?

如何确保在“块”变量的每个惟一值中确实存在自相关?解释“摘要”输出的最简单方法是什么?

The results do differ from a normal gam (package mgcv) using the same variables and parameters without the "correlation=..." term, indicating that something different is occurring.

结果与使用相同变量和参数的普通gam (package mgcv)不同，没有“correlation=…”项，这表明发生了一些不同的事情。

However, when I use a different variable for the correlation term (season), I get the SAME output:

但是，当我对相关项(季节)使用不同的变量时，得到的输出是相同的:

> dat2 <- data.frame(dist_slag = dat$dist_slag, Depth = dat$Depth, dolphin_presence = dat$dolphin_presence,

+ block = dat$block, season=dat$season)
 > head(dat2)
      dist_slag    Depth dolphin_presence block season
1  26475.47 -10.0934                0     1      F
2  26340.47 -10.4870                0     1      F

3  25886.33 -16.5752                0     1      F
4  25399.88 -22.2474                0     1      F
5  24934.29 -29.6797                0     1      F
6  24519.90 -26.2370                0     1      F

> summary(dat2$season)

   F    S 
3224 7568 


> b <- gamm4(dolphin_presence~s(dist_slag)+s(Depth),correlation=corAR1(1, form=~1 |   season), family=binomial(),data=dat2)
> summary(b$gam)

Family: binomial 
Link function: logit 


Formula:
dolphin_presence ~ s(dist_slag) + s(Depth)

Parametric coefficients:
            Estimate Std. Error z value Pr(>|z|)   
    (Intercept)  -13.968      5.145  -2.715  0.00663 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 


Approximate significance of smooth terms:
               edf Ref.df Chi.sq  p-value    
s(dist_slag) 4.943  4.943  70.67 6.85e-14 ***
s(Depth)     6.869  6.869 115.59  <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 


R-sq.(adj) =  0.317glmer.ML score =  10504  Scale est. = 1         n = 10792
> summary(b$mer)
Generalized linear mixed model fit by the Laplace approximation 
   AIC   BIC logLik deviance

 10514 10551  -5252    10504
Random effects:
 Groups Name         Variance Std.Dev.
 Xr     s(dist_slag) 1611344  1269.39 
 Xr.0   s(Depth)       98622   314.04 
Number of obs: 10792, groups: Xr, 8; Xr.0, 8


Fixed effects:
                 Estimate Std. Error z value Pr(>|z|)   
X(Intercept)      -13.968      5.145  -2.715  0.00663 **
Xs(dist_slag)Fx1  -35.871     33.944  -1.057  0.29063   
Xs(Depth)Fx1        3.971      3.740   1.062  0.28823   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Correlation of Fixed Effects:
            X(Int) X(_)F1
Xs(dst_s)F1  0.654       
Xs(Dpth)Fx1 -0.030  0.000
> 

I just want to make sure it is correctly allowing for correlation within each value for the "block" variable. How do I formulate the model to say that autocorrelation can exist within each single value for block, but assume independence among blocks?

我只是想确保它正确地允许“块”变量的每个值之间的相关性。如何构造模型来表示块的每个单独值中都可以存在自相关，而假定块之间是独立的?

On another note, I am also receiving the following warning message after model completion for larger models (with many more variables than 2):

另一方面，在模型完成后，我也收到了以下警告信息(有更多的变量多于2):

Warning message:
 In mer_finalize(ans) : false convergence (8)

1 个解决方案