我正在尝试在R中创建一个模型来说明我的调查。
该研究涉及许多解释变量，响应变量称为HUNTED，数据约为3个不同的时间PERIODS。
我的数据集太大，我只添加了一部分，以便您了解其分布情况。
我的问题：如果我像以前一样在脚本中保留变量PERIOD，程序会“理解”在PERIOD级别的名为“ “之前”的狩猎物种数量少于PERIOD级的“ DURING”（狩猎）物种吗？因此，因此DURING级别显得更为重要，还是我误解了结果？
Obs .：称为“ AFTER”的PERIOD级别未出现在控制台中，因为它的用法类似于基本级别。
我在控制台中得到的结果是：
Random effects:
Groups Name Variance Std.Dev.
Specie (Intercept) 1.728 1.314
Number of obs: 5622, groups: Specie, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.611e+01 2.510e+00 -6.420 1.36e-10 *
eco 9.337e-02 6.522e-02 1.432 0.152254
TrophicLevel 4.403e-01 3.561e-01 1.236 0.216324
age 8.676e-04 2.616e-03 0.332 0.740175
log(body_mass) 8.717e+00 1.495e+00 5.833 5.46e-09
habitat -5.238e-01 8.269e-02 -6.335 2.37e-10
ABUND 4.432e-01 4.938e-02 8.976 < 2e-16 *
BEFORE -2.850e-01 1.090e-01 -2.615 0.008932 **
DURING 3.478e-01 9.103e-02 3.821 0.000133 ***
taboo -1.057e+00 1.016e+00 -1.041 0.297926
我的代码
allspecies4<-read.csv(file= "dataGLMM.csv", header= TRUE, sep= ";" )
attach(allspecies4)
library(lme4)
allspecies4<- glmer(HUNTED~ eco+TrophicLevel+age+ log(body_mass)+
habitat+ABUND + PERIOD + tabu +(1|Specie),
data=dataGLMM, family=binomial)
summary(allspecies4)
示例数据
Specie body_mass TrophicLevel age eco habitat PERIOD HUNTED ABUND tabu
Cerc_mit 3.70 2 57 2 F DURING 0 2 1
Cerc_mit 3.70 2 57 2 F AFTER 0 3 1
Cerc_mit 3.70 2 57 2 F BEFORE 0 4 1
Cerc_mit 3.70 2 67 2 F DURING 1 2 1
Cerc_mit 3.70 2 67 2 F AFTER 0 2 1
Chlor_cyn 3.70 2 53 2 S DURING 0 3 0
Chlor_cyn 3.70 2 74 2 S DURING 0 3 0
Chlor_cyn 3.70 2 30 2 S DURING 0 4 0
Chlor_cyn 3.70 2 63 2 S DURING 0 4 0
Chlor_cyn 3.70 2 54 2 S DURING 0 3 0
Chlor_cyn 3.70 2 30 2 S DURING 0 4 0
Chlor_cyn 3.70 2 30 2 S DURING 0 3 0
Phil_mont 3.69 2 24 3 F DURING 0 3 0
Phil_mont 3.69 2 24 3 F BEFORE 0 4 0
Phil_mont 3.69 2 33 3 F AFTER 1 4 0
Phil_mont 3.69 2 33 3 F BEFORE 0 4 0
Phil_mont 3.69 2 33 3 F DURING 0 4 0
Phil_mont 3.69 2 43 3 F BEFORE 0 4 0
Phil_mont 3.69 2 43 3 F DURING 0 4 0