I tried to attach a decile value for each observation using the code below.However, it seems that the values are not correct. What can be the reason for that?
df<-read.table(text="pregnant glucose blood skin INSULIN MASS DIAB AGE CLASS predict_probability
1 106 70 28 135 34.2 0.142 22 0 0.15316285
1 91 54 25 100 25.2 0.234 23 0 0.05613959
4 136 70 0 0 31.2 1.182 22 1 0.54034794
9 164 78 0 0 32.8 0.148 45 1 0.64361578
3 173 78 39 185 33.8 0.970 31 1 0.79185196
11 136 84 35 130 28.3 0.260 42 1 0.31927737
0 141 84 26 0 32.4 0.433 22 0 0.41609308
3 106 72 0 0 25.8 0.207 27 0 0.10460090
9 145 80 46 130 37.9 0.637 40 1 0.67061324
10 111 70 27 0 27.5 0.141 40 1 0.16152296
",header=T)
deciles <- cut(df$predict_probability, breaks=c(quantile(df$predict_probability, probs = seq(0, 1, by = 0.10))),labels = 1:10, include.lowest=TRUE)
df1 <- cbind(df,deciles)
head(df1,10)
pregnant glucose blood skin INSULIN MASS DIAB AGE CLASS predict_probability deciles
1 1 106 70 28 135 34.2 0.142 22 0 0.15316285 3
2 1 91 54 25 100 25.2 0.234 23 0 0.05613959 1
3 4 136 70 0 0 31.2 1.182 22 1 0.54034794 7
4 9 164 78 0 0 32.8 0.148 45 1 0.64361578 8
5 3 173 78 39 185 33.8 0.970 31 1 0.79185196 10
6 11 136 84 35 130 28.3 0.260 42 1 0.31927737 5
7 0 141 84 26 0 32.4 0.433 22 0 0.41609308 6
8 3 106 72 0 0 25.8 0.207 27 0 0.10460090 2
9 9 145 80 46 130 37.9 0.637 40 1 0.67061324 9
10 10 111 70 27 0 27.5 0.141 40 1 0.16152296 4
Per Dason's proposal, here is the full answer to the question.
The quantile function should be taken out from the code so seq(0,1,by=0.1) should be passed directly to the cut function.
deciles <- cut(df$predict_probability, seq(0,1,by=0.1) ,labels = 1:10, include.lowest=TRUE)
df1 <- cbind(df,deciles)
head(df1,10)
pregnant glucose blood skin INSULIN MASS DIAB AGE CLASS predict_probability deciles
1 1 106 70 28 135 34.2 0.142 22 0 0.15316285 2
2 1 91 54 25 100 25.2 0.234 23 0 0.05613959 1
3 4 136 70 0 0 31.2 1.182 22 1 0.54034794 6
4 9 164 78 0 0 32.8 0.148 45 1 0.64361578 7
5 3 173 78 39 185 33.8 0.970 31 1 0.79185196 8
6 11 136 84 35 130 28.3 0.260 42 1 0.31927737 4
7 0 141 84 26 0 32.4 0.433 22 0 0.41609308 5
8 3 106 72 0 0 25.8 0.207 27 0 0.10460090 2
9 9 145 80 46 130 37.9 0.637 40 1 0.67061324 7
10 10 111 70 27 0 27.5 0.141 40 1 0.16152296 2