[英]Smoothed partial residuals of a covariate in a point process model in spatstat
I am using spatstat
to build point process models using the ppm
function but I have problems in validation, when I use the residual plot parres
to understand the effect of a covariate.我正在使用
spatstat
使用ppm
function 构建点过程模型,但是当我使用残差 plot parres
来理解协变量的影响时,我在验证中遇到了问题。
The model is composed of 1022 locations of bird occurrences (called ois.ppm
), the habitat availability (a raster called FB0lin
which has been normalized and log-transformed), the sampling effort (a raster called Nbdate
, normalized too) and the accessibility of places (a raster called pAccess
, normalized too) across the study area. model 由 1022 个鸟类出现位置(称为
ois.ppm
)、栖息地可用性(称为FB0lin
的栅格已标准化和对数转换)、采样工作量(称为Nbdate
的栅格,也已标准化)和可访问性组成整个研究区域的地点(称为pAccess
的栅格,也已归一化)。 The objective is to evaluate the fit of a Gibbs point process model with a Geyer process parameter, the habitat availability, the sampling effort and the accessibility.目的是评估 Gibbs 点过程 model 与 Geyer 过程参数、栖息地可用性、采样工作量和可达性的拟合度。 The
eps
function was also used to create a set of dummy points chosen along a grid with a 100 x 100 m resolution. eps
function 还用于创建一组沿网格选择的虚拟点,分辨率为 100 x 100 米。
The model used is: mod.ois.echlin = ppm(ois.ppp, ~ FB0lin + Nbdate + pAccess, interaction = Geyer(r=401,sat=9), eps=100)
使用的model是:
mod.ois.echlin = ppm(ois.ppp, ~ FB0lin + Nbdate + pAccess, interaction = Geyer(r=401,sat=9), eps=100)
Geyer parameter were identified using: rs=expand.grid(r=seq(1,1001, by=50), sat=1:40)
term.interlin=profilepl(rs, Geyer, ois.ppp,~FB0lin+Nbdate+pAccess)
Geyer 参数使用以下方法识别:
rs=expand.grid(r=seq(1,1001, by=50), sat=1:40)
term.interlin=profilepl(rs, Geyer, ois.ppp,~FB0lin+Nbdate+pAccess)
Then I use the parres function: res.FB0.echlin=parres(mod.ois.echlin, covariate="FB0lin")
plot(res.FB0.echlin,main="FB0 LinCost", legend=FALSE)
然后我使用 parres function:
res.FB0.echlin=parres(mod.ois.echlin, covariate="FB0lin")
plot(res.FB0.echlin,main="FB0 LinCost", legend=FALSE)
The problem is that the fitted values seems not to be optimal (see figure below).问题是拟合值似乎不是最优的(见下图)。 The fit curve should have lower values within interval confidence but is outside of this interval, which probably affect the quality of the point process model.
拟合曲线在置信区间内应该有较低的值,但在这个区间之外,这可能会影响点过程的质量 model。
My questions are then:我的问题是:
Figure: Smoothed partial residuals - FB0lin图:平滑的部分残差 - FB0lin
Any advice would be much appreciated.任何建议将不胜感激。
The diagnostic is working correctly.诊断工作正常。 It indicates that, as a function of the predictor variable
FB0lin
, the fitted model (represented by the dashed straight line) overestimates the true intensity of the model (represented by the thick black curve with grey confidence bands) by a constant amount.它表明,作为预测变量
FB0lin
的 function,拟合的 model(由虚直线表示)高估了 model(由带有灰色置信带的粗黑色曲线表示)的真实强度一个常数。 The linear relationship (linear dependence of the log intensity on the covariate) seems to be adequate, in the sense that you don't need to replace this linear relationship by a more complicated relationship (which is the main question for which the partial residuals are used).线性关系(对数强度对协变量的线性依赖性)似乎是足够的,因为您不需要用更复杂的关系替换这种线性关系(这是部分残差的主要问题)用过的)。 The diagnostic says that the model is adequate except that it is underestimating the log intensity by a constant amount, which means that it is underestimating the intensity by a constant factor.
诊断表明 model 是足够的,只是它低估了对数强度一个常数,这意味着它低估了一个常数因子的强度。 (This could be due to the way in which the other predictors
Nbdate
and pAccess
are involved in the model, or it could be due to the choice of interpoint interaction. To investigate that, you need to try other tools as discussed in Chapter 11 of the spatstat book .) (这可能是由于其他预测变量
Nbdate
和pAccess
参与 model 的方式,或者可能是由于选择了点间交互。要调查这一点,您需要尝试其他工具,如第 11 章中讨论的spatstat 书。)
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