R-INLA 不计算拟合边际值

Question

我遇到了 R INLA 未计算拟合边际值的问题。我首先使用自己的数据集获得它，并且能够按照此 book 中的示例重现它。 。我怀疑一定有一些配置需要更改，或者 INLA 可能无法在后台正常工作？无论如何，这是代码:

library("rgdal")
boston.tr <- readOGR(system.file("shapes/boston_tracts.shp",
                                 package="spData")[1])

#create adjacency matrices
boston.adj <- poly2nb(boston.tr)

W.boston <- nb2mat(boston.adj, style = "B") 
W.boston.rs <- nb2mat(boston.adj, style = "W") 

boston.tr$CMEDV2 <- boston.tr$CMEDV
boston.tr$CMEDV2 [boston.tr$CMEDV2 == 50.0] <- NA

#define formula
boston.form  <- log(CMEDV2) ~ CRIM + ZN + INDUS + CHAS + I(NOX^2) + I(RM^2) +
  AGE + log(DIS) + log(RAD) + TAX + PTRATIO + B + log(LSTAT)
boston.tr$ID <- 1:length(boston.tr)
#run model
boston.iid <- inla(update(boston.form, . ~. + f(ID, model = "iid")),
                   data = as.data.frame(boston.tr),
                   control.compute = list(dic = TRUE, waic = TRUE, cpo = TRUE),
                   control.predictor = list(compute = TRUE)
)

当我查看该模型的输出时，它表明拟合值已计算:

summary(boston.iid)

Call:
   c("inla(formula = update(boston.form, . ~ . + f(ID, model = \"iid\")), ", " data = as.data.frame(boston.tr), 
   control.compute = list(dic = TRUE, ", " waic = TRUE, cpo = TRUE), control.predictor = list(compute = TRUE))" 
   ) 
Time used:
    Pre = 0.981, Running = 0.481, Post = 0.0337, Total = 1.5 
Fixed effects:
              mean    sd 0.025quant 0.5quant 0.975quant   mode kld
(Intercept)  4.376 0.151      4.080    4.376      4.672  4.376   0
CRIM        -0.011 0.001     -0.013   -0.011     -0.009 -0.011   0
ZN           0.000 0.000     -0.001    0.000      0.001  0.000   0
INDUS        0.001 0.002     -0.003    0.001      0.006  0.001   0
CHAS1        0.056 0.034     -0.010    0.056      0.123  0.056   0
I(NOX^2)    -0.540 0.107     -0.751   -0.540     -0.329 -0.540   0
I(RM^2)      0.007 0.001      0.005    0.007      0.010  0.007   0
AGE          0.000 0.001     -0.001    0.000      0.001  0.000   0
log(DIS)    -0.143 0.032     -0.206   -0.143     -0.080 -0.143   0
log(RAD)     0.082 0.018      0.047    0.082      0.118  0.082   0
TAX          0.000 0.000     -0.001    0.000      0.000  0.000   0
PTRATIO     -0.031 0.005     -0.040   -0.031     -0.021 -0.031   0
B            0.000 0.000      0.000    0.000      0.001  0.000   0
log(LSTAT)  -0.329 0.027     -0.382   -0.329     -0.277 -0.329   0

Random effects:
  Name    Model
    ID IID model

Model hyperparameters:
                                          mean    sd 0.025quant 0.5quant 0.975quant   mode
Precision for the Gaussian observations 169.24 46.04      99.07   160.46     299.72 141.30
Precision for ID                         42.84  3.40      35.40    43.02      49.58  43.80

Deviance Information Criterion (DIC) ...............: -996.85
Deviance Information Criterion (DIC, saturated) ....: 1948.94
Effective number of parameters .....................: 202.49

Watanabe-Akaike information criterion (WAIC) ...: -759.57
Effective number of parameters .................: 337.73

Marginal log-Likelihood:  39.74 
CPO and PIT are computed

Posterior marginals for the linear predictor and
 the fitted values are computed

但是，当我尝试检查那些拟合的边际值时，什么也没有:

> boston.iid$marginals.fitted.values
NULL

有趣的是，我确实得到了后验的摘要，所以它们一定是通过某种方式计算的？

> boston.iid$summary.fitted.values
                         mean         sd 0.025quant 0.5quant 0.975quant     mode
fitted.Predictor.001 2.834677 0.07604927   2.655321 2.844934   2.959994 2.858717
fitted.Predictor.002 3.020424 0.08220780   2.824525 3.034319   3.149766 3.052558
fitted.Predictor.003 3.053759 0.08883760   2.841738 3.071530   3.188051 3.094010
fitted.Predictor.004 3.032981 0.09846662   2.801099 3.056692   3.175215 3.084842

关于我在通话中错误指定的内容的任何想法。我设置了 compute = T，这正是我在 R-INLA 论坛上看到的导致问题的原因。

Answer 1

开发人员intentionally disabled computing the marginals使模型更快。

要启用它，您可以将这些添加到 inla 参数中:

control.predictor=list(compute=TRUE)
control.compute=list(return.marginals.predictor=TRUE)

所以它看起来像这样:

boston.form  <- log(CMEDV2) ~ CRIM + ZN + INDUS + CHAS + I(NOX^2) + I(RM^2) +
  AGE + log(DIS) + log(RAD) + TAX + PTRATIO + B + log(LSTAT)
boston.tr$ID <- 1:length(boston.tr)

#run model
boston.iid <- inla(update(boston.form, . ~. + f(ID, model = "iid")),
                   data = as.data.frame(boston.tr),
                   control.compute = list(dic = TRUE, waic = TRUE, cpo = TRUE, return.marginals.predictor=TRUE),
                   control.predictor = list(compute = TRUE)
                   
)

boston.iid$summary.fitted.values
boston.iid$marginals.fitted.values