我是一名生物学研究生，在下一期问题上我已经将我的车轮旋转了大约30个小时 . 总之，我想从我生成的glm二元逻辑回归模型中绘制估计概率的数字 . 我已经完成了模型选择，验证等工作，现在我只是想制作数字 . 我没有问题为我选择的模型绘制概率曲线，但我真正感兴趣的是产生一个数字，当另一个预测变量保持不变时，该数字显示预测变量的二元结果的概率 .
我无法弄清楚如何将此常量值仅分配给其中一个预测变量并绘制另一个变量的概率 . 最终我想生成类似于我附加的原始示例desired output的数字 . 我承认我是R的新手，我当然感谢大家的时间，但我已经筋疲力尽在线搜索，还没有找到适当解释的方法或解决方案 . 这是与我的问题相关的最接近的信息，但是我发现这个解释含糊不清，但它没有提供一个例子，用于在绘制另一个预测值的概率时为一个预测值分配一个常数值 . https://stat.ethz.ch/pipermail/r-help/2010-September/253899.html
下面我提供了一个模拟数据集和我的进度 . 非常感谢您的专业知识，我相信解决方案和代码示例对于使用逻辑回归的其他生态学家会有所帮助 .
模拟数据集显示了蜥蜴冬季的生存结果 . 预测变量是“质量”和“深度” .
x<-read.csv('logreg_example_data.csv',header = T)
x
survival mass depth
1 0 4.294456 262
2 0 8.359857 261
3 0 10.740580 257
4 0 10.740580 257
5 0 6.384678 257
6 0 6.384678 257
7 0 11.596380 270
8 0 11.596380 270
9 0 4.294456 262
10 0 4.294456 262
11 0 8.359857 261
12 0 8.359857 261
13 0 8.359857 261
14 0 7.920406 258
15 0 7.920406 258
16 0 7.920406 261
17 0 10.740580 257
18 0 10.740580 258
19 0 38.824960 262
20 0 9.916840 239
21 1 6.384678 257
22 1 6.384678 257
23 1 11.596380 270
24 1 11.596380 270
25 1 11.596380 270
26 1 23.709520 288
27 1 23.709520 288
28 1 23.709520 288
29 1 38.568970 262
30 1 38.568970 262
31 1 6.581013 295
32 1 6.581013 298
33 1 0.766564 269
34 1 5.440803 262
35 1 5.440803 262
36 1 19.534710 252
37 1 19.534710 259
38 1 8.359857 263
39 1 10.740580 257
40 1 38.824960 264
41 1 38.824960 264
42 1 41.556970 239
#Dataset name is x
# time to run the glm model
model1<-glm(formula=survival ~ mass + depth, family = "binomial", data=x)
model1
summary(model1)
#Ok now heres how i predict the probability of a lizard "Bob" surviving the winter with a mass of 32.949 grams and a burrow depth of 264 mm
newdata<-data.frame(mass = 32.949, depth = 264)
predict(model1, newdata, type = "response")
# the lizard "Bob" has a 87.3% chance of surviving the winter
#Now lets assume the glm. model was robust and the lizard was endangered,
#from all my research I know the average burrow depth is 263.9 mm at a national park
#lets say i am also interested in survival probabilities at burrow depths of 200 and 100 mm, respectively
#how do i use the valuable glm model produced above to generate a plot
#showing the probability of lizards surviving with average burrow depths stated above
#across a range of mass values from 0.0 to 100.0 grams??????????
#i know i need to use the plot and predict functions but i cannot figure out how to tell R that i
#want to use the glm model i produced to predict "survival" based on "mass" when the other predictor "depth" is held at constant values of biological relevance
#I would also like to add dashed lines for 95% CI