GLM in R, Understanding warum omitance of coefficient

Question

Ich verwende R, um glm für Blattlaus- und Parasitoiddaten zu erstellen. Diese ist das Modell Ich bin mit

Aphid_glm.full8 = glm(Field.Abundance~Region-1+Total_Rainfall+Species+GS+Date, family=gaussian()) 

bekomme ich folgende Ausgabe

Aphid_glm.full8 = glm(Field.Abundance~Region-1+Total_Rainfall+Species+GS+Date, family=gaussian()) 
Error in eval(expr, envir, enclos) : object 'Field.Abundance' not found 
> summary(Aphid_glm.full8) 

Call: 
glm(formula = Field.Abundance ~ Region + Date + Total_Rainfall + 
    Species + GS, family = gaussian()) 

Deviance Residuals: 
    Min  1Q Median  3Q  Max 
-61.947 -8.323 0.085 4.574 164.910 

Coefficients: (3 not defined because of singularities) 
          Estimate Std. Error t value Pr(>|t|)  
(Intercept)     65.5787 17.7445 3.696 0.000284 *** 
RegionTarlee    -12.9239 10.8321 -1.193 0.234251  
RegionWalkers Flat   20.2769 12.6896 1.598 0.111655  
Date13/09/2016    -7.0521 13.6629 -0.516 0.606329  
Date13/10/2016    -9.4719 10.3875 -0.912 0.362956  
Date15/09/2016    9.8287 10.3489 0.950 0.343405  
Date16/11/2016    29.6553 14.9223 1.987 0.048266 * 
Date17/11/2016    16.2882 11.9715 1.361 0.175194  
Date19/10/2016    21.8028 13.2087 1.651 0.100398  
Date2/12/2016    18.5352 16.1257 1.149 0.251770  
Date21/09/2016    33.2361 10.2939 3.229 0.001456 ** 
Date25/10/2016    4.9734 10.4278 0.477 0.633932  
Date26/08/2016    2.4930 11.6788 0.213 0.831188  
Date26/10/2016    -2.0381 10.1118 -0.202 0.840473  
Date3/11/2016    31.8712 15.4324 2.065 0.040206 * 
Date3/12/2016    24.8508 26.6299 0.933 0.351858  
Date30/09/2016    -23.8682 12.9022 -1.850 0.065814 . 
Date31/08/2016    23.7204 15.4324 1.537 0.125878  
Date4/12/2016    22.6700 26.6299 0.851 0.395633  
Date5/10/2016     8.8952 10.9998 0.809 0.419677  
Date7/09/2016    34.4688 11.2740 3.057 0.002541 ** 
Date7/10/2016      NA   NA  NA  NA  
Date9/11/2016      NA   NA  NA  NA  
Total_Rainfall     NA   NA  NA  NA  
SpeciesRhopalosiphum_maidis -11.5408  4.0173 -2.873 0.004512 ** 
SpeciesRhopalosiphum_padi -9.3599  4.0173 -2.330 0.020817 * 
GS       -0.8864  0.2699 -3.284 0.001212 ** 
--- 
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for gaussian family taken to be 592.0688) 

    Null deviance: 221574 on 221 degrees of freedom 
Residual deviance: 117230 on 198 degrees of freedom 
AIC: 2071.8 

Number of Fisher Scoring iterations: 2 

Wo jedoch unter ‚Region‘ sollte ich drei Variablen bekommen, aber ich bin nur zwei bekommen, die gleiche für 'Species'

Meine Frage ist: Warum bekomme ich nicht die drei Ausgaben für Region und Arten in der Analyse-Ausgabe? Ist es einfach, dass es diese Variablen nicht erkennt? kaum veränderte alles von Ihrem Codes

https://1drv.ms/u/s!Ao4jj6OKiTAYgQmlpCxh1GiylZkx

Answer 1

Mit folgenden Codes, erhalte ich drei Regionen in Koeffizienten, die ich habe.

library(data.table) # to read data 
data_in <- fread("FYPT.csv") 
# remove spaces from column names 
colnames(data_in) <- gsub("[[:space:]]", "_", colnames(data_in)) 

# run glm model 
Aphid_glm.full8 = glm(Field_Abundance ~ Region-1+Total_Rainfall+Species+GS+Date, data = data_in, family=gaussian()) 

Output

> Aphid_glm.full8 

Call: glm(formula = Field_Abundance ~ Region - 1 + Total_Rainfall + 
Species + GS + Date, family = gaussian(), data = data_in) 

Coefficients: 
      RegionMundulla     RegionTarlee   RegionWalkers Flat    Total_Rainfall SpeciesRhopalosiphum_maidis 
       107.8518      76.7179      85.9857      -0.6504      -11.5408 
SpeciesRhopalosiphum_padi       GS    Date13/09/2016    Date13/10/2016    Date15/09/2016 
       -9.3599      -0.8864      6.3452      -0.4970      27.3883 
     Date16/11/2016    Date17/11/2016    Date19/10/2016    Date2/12/2016    Date21/09/2016 
       12.6160      -8.4253      14.6489      -22.5672      30.1144 
     Date25/10/2016    Date26/08/2016    Date26/10/2016    Date3/11/2016    Date3/12/2016 
       -5.0420      -15.1967      4.0753      18.0837      -16.2516 
     Date30/09/2016    Date31/08/2016    Date4/12/2016    Date5/10/2016    Date7/10/2016 
       26.4692      30.0939      -18.4325      25.1540       NA 
      Date7/9/2016    Date9/11/2016 
        NA       NA 

Degrees of Freedom: 222 Total (i.e. Null); 198 Residual 
Null Deviance:  261500 
Residual Deviance: 117200 AIC: 2072