我是R的新手，当我要使用glm（）来估计逻辑模型时，它不会预测响应，但是对于我预测函数的每个输入，都会调用预测函数（如1）的非实际输出。
Code:
ex2data1R <- read.csv("/media/ex2data1R.txt")
x <-ex2data1R$x
y <-ex2data1R$y
z <-ex2data1R$z
logisticmodel <- glm(z~x+y,family=binomial(link = "logit"),data=ex2data1R)
newdata = data.frame(x=c(10),y=(10))
predict(logisticmodel, newdata, type="response")
Output:
> predict(logisticmodel, newdata, type="response")
1
1.181875e-11
Data(ex2data1R.txt) :
"x","y","z"
34.62365962451697,78.0246928153624,0
30.28671076822607,43.89499752400101,0
35.84740876993872,72.90219802708364,0
60.18259938620976,86.30855209546826,1
79.0327360507101,75.3443764369103,1
45.08327747668339,56.3163717815305,0
61.10666453684766,96.51142588489624,1
75.02474556738889,46.55401354116538,1
76.09878670226257,87.42056971926803,1
84.43281996120035,43.53339331072109,1
95.86155507093572,38.22527805795094,0
75.01365838958247,30.60326323428011,0
82.30705337399482,76.48196330235604,1
69.36458875970939,97.71869196188608,1
39.53833914367223,76.03681085115882,0
53.9710521485623,89.20735013750205,1
69.07014406283025,52.74046973016765,1
67.94685547711617,46.67857410673128,0
70.66150955499435,92.92713789364831,1
76.97878372747498,47.57596364975532,1
67.37202754570876,42.83843832029179,0
89.67677575072079,65.79936592745237,1
50.534788289883,48.85581152764205,0
34.21206097786789,44.20952859866288,0
77.9240914545704,68.9723599933059,1
62.27101367004632,69.95445795447587,1
80.1901807509566,44.82162893218353,1
93.114388797442,38.80067033713209,0
61.83020602312595,50.25610789244621,0
38.78580379679423,64.99568095539578,0
61.379289447425,72.80788731317097,1
85.40451939411645,57.05198397627122,1
52.10797973193984,63.12762376881715,0
52.04540476831827,69.43286012045222,1
40.23689373545111,71.16774802184875,0
54.63510555424817,52.21388588061123,0
33.91550010906887,98.86943574220611,0
64.17698887494485,80.90806058670817,1
74.78925295941542,41.57341522824434,0
34.1836400264419,75.2377203360134,0
83.90239366249155,56.30804621605327,1
51.54772026906181,46.85629026349976,0
94.44336776917852,65.56892160559052,1
82.36875375713919,40.61825515970618,0
51.04775177128865,45.82270145776001,0
62.22267576120188,52.06099194836679,0
77.19303492601364,70.45820000180959,1
97.77159928000232,86.7278223300282,1
62.07306379667647,96.76882412413983,1
91.56497449807442,88.69629254546599,1
79.94481794066932,74.16311935043758,1
99.2725269292572,60.99903099844988,1
90.54671411399852,43.39060180650027,1
34.52451385320009,60.39634245837173,0
50.2864961189907,49.80453881323059,0
49.58667721632031,59.80895099453265,0
97.64563396007767,68.86157272420604,1
32.57720016809309,95.59854761387875,0
74.24869136721598,69.82457122657193,1
71.79646205863379,78.45356224515052,1
75.3956114656803,85.75993667331619,1
35.28611281526193,47.02051394723416,0
56.25381749711624,39.26147251058019,0
30.05882244669796,49.59297386723685,0
44.66826172480893,66.45008614558913,0
66.56089447242954,41.09209807936973,0
40.45755098375164,97.53518548909936,1
49.07256321908844,51.88321182073966,0
80.27957401466998,92.11606081344084,1
66.74671856944039,60.99139402740988,1
32.72283304060323,43.30717306430063,0
64.0393204150601,78.03168802018232,1
72.34649422579923,96.22759296761404,1
60.45788573918959,73.09499809758037,1
58.84095621726802,75.85844831279042,1
99.82785779692128,72.36925193383885,1
47.26426910848174,88.47586499559782,1
50.45815980285988,75.80985952982456,1
60.45555629271532,42.50840943572217,0
82.22666157785568,42.71987853716458,0
88.9138964166533,69.80378889835472,1
94.83450672430196,45.69430680250754,1
67.31925746917527,66.58935317747915,1
57.23870631569862,59.51428198012956,1
80.36675600171273,90.96014789746954,1
68.46852178591112,85.59430710452014,1
42.0754545384731,78.84478600148043,0
75.47770200533905,90.42453899753964,1
78.63542434898018,96.64742716885644,1
52.34800398794107,60.76950525602592,0
94.09433112516793,77.15910509073893,1
90.44855097096364,87.50879176484702,1
55.48216114069585,35.57070347228866,0
74.49269241843041,84.84513684930135,1
89.84580670720979,45.35828361091658,1
83.48916274498238,48.38028579728175,1
42.2617008099817,87.10385094025457,1
99.31500880510394,68.77540947206617,1
55.34001756003703,64.9319380069486,1
74.77589300092767,89.52981289513276,1
让我知道我做错了什么？
答案 0 :(得分：7)
我没有看到任何问题。以下是x，y = 30,35,40,45,50,55,60,65,70,75,80,85,90,95,100的预测：
newdata = data.frame(x=seq(30, 100, 5) ,y=seq(30, 100, 5))
predict(logisticmodel, newdata, type="response")
1 2 3 4 5 6
2.423648e-06 1.861140e-05 1.429031e-04 1.096336e-03 8.357794e-03 6.078786e-02
7 8 9 10 11 12
3.320041e-01 7.923883e-01 9.670066e-01 9.955766e-01 9.994218e-01 9.999247e-01
13 14 15
9.999902e-01 9.999987e-01 9.999998e-01
您预测x = 10，y = 10，这超出了x，y值（30 - 100）的范围，但预测值为零，符合这些结果。当x和y为低（30-55）时，z的预测为零。当x和y为高（75-100）时，预测为一（或接近一）。如果将它们舍入到几位小数，可能更容易解释结果：
round(predict(logisticmodel, newdata, type="response") , 5)
1 2 3 4 5 6 7 8 9 10
0.00000 0.00002 0.00014 0.00110 0.00836 0.06079 0.33200 0.79239 0.96701 0.99558
11 12 13 14 15
0.99942 0.99992 0.99999 1.00000 1.00000
以下是预测类别并将结果与数据进行比较的简单方法：
predict <- ifelse(predict(logisticmodel, type="response")>.5, 1, 0)
xtabs(~predict+ex2data1R$z)
ex2data1R$z
predict 0 1
0 34 5
1 6 55
我们对原始数据使用了predict（），然后创建了一个规则，如果概率大于0.5，则选择1，如果不是，则选择0。然后我们使用xtabs（）将预测与数据进行比较。当z为0时，我们正确地预测零34次并错误地预测一次6次。当z为1时，我们正确地预测了55次并错误地预测零次5次。我们在89％的时间（34 + 55）/ 100 * 100是正确的。如果使用.45或.55作为截止值而不是.5。
，则可以探索预测的准确性答案 1 :(得分：2)
在我看来，一切都是正确的，你可以从R手册中读到：
newdata - 可选地，用于查找变量的数据框 哪个可以预测。如果省略，则使用拟合的线性预测变量。
如果您的数据框中包含1条记录，则只会为该记录生成预测。
有关详细信息，请参阅R manual/glm/predict
或只是在R控制台中，加载库glm put：
?glm
答案 2 :(得分：0)
您还可以使用以下命令制作混淆矩阵：
predict <- ifelse(predict(logisticmodel, type="response")>.5, 1, 0)
table(predict,ex2data1R$z)