Let's use the iris data set
data(iris)The Species variable is categorical and will serve as our response. Since log-reg is only appropriate for binary categorical variables, we create a new response setosa based on whether a given iris setosa or not.
iris1 <- iris %>% mutate(setosa = ifelse(Species == "setosa", 1, 0))Now we create the logistic regression model, with setosa as a function of Sepal.Width.
log_reg <- glm(setosa ~ Sepal.Width , data = iris1, family = "binomial")We create probability predictions from the model (note that the result would be log-odds if we omitted type = "response")
probs<-predict(log_reg, iris1, type = "response")Finally, we create our actual predictions using an ifelse statement. The classification threshhold is determined by the value we set on the right side of the >= inequality. Here, for example, we set our threshold at 0.253.
predicts<-ifelse( probs >= .253, 1, 0)
predicts## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## 1 0 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1
## 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
## 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1
## 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
## 1 0 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 0 0 0
## 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
## 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
## 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120
## 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0
## 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140
## 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 1 0 1
## 141 142 143 144 145 146 147 148 149 150
## 1 1 0 1 1 0 0 0 1 0