我在观察不同地方蜜蜂的多样性--苹果园内外（安置），农场之间，以及品种之间。所有这三个变量对蜜蜂都很有效，但对所有的独居（“wildbees”），对于一些大黄蜂，我在应该看到结果的地方得到了NaN。我如何解决这个问题？我的数据点太少了吗？我只有不到500个陷阱，在那里我发现了~500只蜜蜂，~150只大黄蜂和~70只独居蜜蜂。很抱歉，如果我没有给予足够的信息来回答我的问题，这是我第一次使用这样的论坛！
另外，θ是什么意思？它不是我的变量

# This is what happened for inside vs outside

> p1 <- zeroinfl(Honeybee...29 ~ Placement, data = diversity, dist = "negbin")
> summary(p1)

Call:
zeroinfl(formula = Honeybee...29 ~ Placement, data = diversity, dist = "negbin")

Pearson residuals:
    Min      1Q  Median      3Q     Max 
-0.5872 -0.5872 -0.4080  0.1483 11.2744 

Count model coefficients (negbin with log link):
              Estimate Std. Error z value Pr(>|z|)    
(Intercept)    0.20377    0.11021   1.849   0.0645 .  
PlacementWild  0.09042    0.23433   0.386   0.6996    
Log(theta)    -0.73459    0.16043  -4.579 4.67e-06 ***

Zero-inflation model coefficients (binomial with logit link):
              Estimate Std. Error z value Pr(>|z|)
(Intercept)     -9.685     53.494  -0.181    0.856
PlacementWild    9.499     53.489   0.178    0.859
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Theta = 0.4797 
Number of iterations in BFGS optimization: 127 
Log-likelihood: -607.6 on 5 Df
> 
> p2 <- zeroinfl(Bumblebee ~ Placement, data = diversity, dist = "negbin")
> summary(p2)

Call:
zeroinfl(formula = Bumblebee ~ Placement, data = diversity, dist = "negbin")

Pearson residuals:
    Min      1Q  Median      3Q     Max 
-0.3761 -0.3761 -0.3289 -0.3289  5.9230 

Count model coefficients (negbin with log link):
              Estimate Std. Error z value Pr(>|z|)    
(Intercept)    -1.6686     0.1939  -8.607  < 2e-16 ***
PlacementWild   1.8530     0.3853   4.809 1.52e-06 ***
Log(theta)     -0.5619     0.5234  -1.074    0.283    

Zero-inflation model coefficients (binomial with logit link):
              Estimate Std. Error z value Pr(>|z|)
(Intercept)     -6.747     76.346  -0.088    0.930
PlacementWild    7.366     76.258   0.097    0.923
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Theta = 0.5701 
Number of iterations in BFGS optimization: 84 
Log-likelihood: -299.5 on 5 Df
> 
> p3 <- zeroinfl(Wildbee ~ Placement, data = diversity, dist = "negbin")
> summary(p3) 

Call:
zeroinfl(formula = Wildbee ~ Placement, data = diversity, dist = "negbin")

Pearson residuals:
    Min      1Q  Median      3Q     Max 
-0.3369 -0.3369 -0.3132 -0.3132  7.1499 

Count model coefficients (negbin with log link):
              Estimate Std. Error z value Pr(>|z|)    
(Intercept)    -1.7629     0.2065  -8.535  < 2e-16 ***
PlacementWild   0.2712     0.2817   0.963    0.336    
Log(theta)     -1.4738     0.2675  -5.510 3.58e-08 ***

Zero-inflation model coefficients (binomial with logit link):
              Estimate Std. Error z value Pr(>|z|)
(Intercept)    -12.886    325.692   -0.04    0.968
PlacementWild   -7.375        NaN     NaN      NaN
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Theta = 0.2291 
Number of iterations in BFGS optimization: 18 
Log-likelihood: -246.7 on 5 Df
Warning message:
In sqrt(diag(object$vcov)) : NaNs produced

这就是农场之间发生的事情

> f1 <- zeroinfl(Honeybee...29 ~ Farm, data = diversity, dist = "negbin")
> summary(f1)

Call:
zeroinfl(formula = Honeybee...29 ~ Farm, data = diversity, dist = "negbin")

Pearson residuals:
     Min       1Q   Median       3Q      Max 
-0.54910 -0.49323 -0.44511  0.01922  8.19537 

Count model coefficients (negbin with log link):
                Estimate Std. Error z value Pr(>|z|)    
(Intercept)       0.2768     0.1441   1.920   0.0548 .  
FarmFruktgården  -0.4622     0.3009  -1.536   0.1245    
FarmSando        -0.1801     0.2744  -0.656   0.5116    
Log(theta)       -0.9391     0.1857  -5.056 4.27e-07 ***

Zero-inflation model coefficients (binomial with logit link):
                Estimate Std. Error z value Pr(>|z|)
(Intercept)       -8.688     42.152  -0.206    0.837
FarmFruktgården    7.379     42.133   0.175    0.861
FarmSando          6.753     42.126   0.160    0.873
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Theta = 0.391 
Number of iterations in BFGS optimization: 32 
Log-likelihood: -612.9 on 7 Df
> 
> f2 <- zeroinfl(Bumblebee ~ Farm|Cultivar, data = diversity, dist = "negbin")
> summary(f2)

Call:
zeroinfl(formula = Bumblebee ~ Farm | Cultivar, data = diversity, dist = "negbin")

Pearson residuals:
    Min      1Q  Median      3Q     Max 
-0.4019 -0.3099 -0.3099 -0.2951 12.0230 

Count model coefficients (negbin with log link):
                Estimate Std. Error z value Pr(>|z|)    
(Intercept)      -1.9397     0.2679  -7.241 4.46e-13 ***
FarmFruktgården   0.1666     0.3705   0.450    0.653    
FarmSando         1.4289     0.3324   4.299 1.71e-05 ***
Log(theta)       -1.5096     0.2126  -7.102 1.23e-12 ***

Zero-inflation model coefficients (binomial with logit link):
                  Estimate Std. Error z value Pr(>|z|)
(Intercept)        -13.227    362.819  -0.036    0.971
CultivarDiscovery   -3.861        NaN     NaN      NaN
CultivarSummerred   -5.736   6888.605  -0.001    0.999
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Theta = 0.221 
Number of iterations in BFGS optimization: 14 
Log-likelihood: -296.3 on 7 Df