为什么我在运行以下代码时会得到 NaN？我正在对每个协变量的物种密度进行距离抽样分析。我的任何其他协变量都没有发生这种情况？
#umf2总结
> summary(umf2)
unmarkedFrameDS Object
line-transect survey design
Distance class cutpoints (m): 0 5 10 15 20 25 30 35
64 sites
Maximum number of distance classes per site: 7
Mean number of distance classes per site: 7
Sites with at least one detection: 8
Tabulation of y observations:
0 1 2
434 12 2
Site-level covariates:
transect length_m length number
01_Cungha_T1 : 1 Min. : 996 Min. :0.996 Min. : 1.00
02_Capicada_T1: 1 1st Qu.:1061 1st Qu.:1.061 1st Qu.:16.75
03_Caghode_T1 : 1 Median :1098 Median :1.099 Median :32.50
04_Caghode_T2 : 1 Mean :1126 Mean :1.126 Mean :32.50
05_Cafal_T1 : 1 3rd Qu.:1167 3rd Qu.:1.167 3rd Qu.:48.25
06_Muna_T1 : 1 Max. :1758 Max. :1.758 Max. :64.00
(Other) :58
y x d_forest_1 d_all_roads_1
Min. :11.10 Min. :-15.16 Min. : 0 Min. : 5.0
1st Qu.:11.19 1st Qu.:-15.08 1st Qu.: 0 1st Qu.: 180.3
Median :11.25 Median :-15.02 Median : 10 Median : 534.8
Mean :11.27 Mean :-15.00 Mean : 2751 Mean : 811.5
3rd Qu.:11.34 3rd Qu.:-14.94 3rd Qu.: 2893 3rd Qu.:1145.9
Max. :11.42 Max. :-14.77 Max. :17242 Max. :3666.7
#拟合先验模型集：所有道路，危险。
m.haz.2.allroads <- distsamp(~1 ~d_all_roads_1, umf2, keyfun="hazard", output="density", unitsOut="kmsq")
#预测到所有道路的距离
> m.allroads2 <- data.frame(d_all_roads_1=seq(5.0000, 3666.6775, length=64))
> allroads.pred2 <- predict(m.haz.2.allroads, type="state", newdata=m.allroads2, appendData=TRUE)
There were 50 or more warnings (use warnings() to see the first 50)
> allroads.pred2
Predicted SE lower upper d_all_roads_1
1 1.979158 0.23962464 1.561069 2.509221 5.00000
2 2.041778 0.23667781 1.626820 2.562582 63.12187
3 2.106379 0.23300058 1.695819 2.616337 121.24373
4 2.173025 0.22849261 1.768321 2.670350 179.36560
5 2.241779 0.22303220 1.844623 2.724444 237.48746
...
10 2.619589 0.17424708 2.299396 2.984369 528.09679
11 2.702472 0.15742845 2.410881 3.029331 586.21865
12 2.787978 0.13611667 2.533561 3.067943 644.34052
13 2.876189 0.10742754 2.673157 3.094641 702.46238
14 2.967191 0.06136485 2.849323 3.089934 760.58425
15 3.061072 NaN NaN NaN 818.70611
16 3.157924 NaN NaN NaN 876.82798
17 3.257839 NaN NaN NaN 934.94984
18 3.360917 NaN NaN NaN 993.07171
19 3.467255 NaN NaN NaN 1051.19357
...
60 12.434748 NaN NaN NaN 3434.19004
61 12.828180 NaN NaN NaN 3492.31190
62 13.234061 NaN NaN NaN 3550.43377
63 13.652784 NaN NaN NaN 3608.55563
64 14.084755 NaN NaN NaN 3666.67750
如果需要任何进一步的信息来帮助我解决这个问题，请告诉我，非常感谢。