Tou Mou
Reputation: 1274
Unable to plot a matrix using ggplotr : Error in FUN(X[[i]], ...) : object 'Var1' not found
Good afternoon ,
Under R , i tried to plot the following :
m1 <-structure(list(Sensitivity = c(0, 0.701244813278008, 0.701244813278008,
0.705394190871369, 0.711297071129707, 0.711297071129707, 0.7125,
0.714876033057851), Specificity = c(0, 0.295454545454545, 0.295454545454545,
0.318181818181818, 0.347826086956522, 0.347826086956522, 0.355555555555556,
0.372093023255814), `Pos Pred Value` = c(0, 0.845, 0.845, 0.85,
0.85, 0.85, 0.855, 0.865), `Neg Pred Value` = c(0, 0.152941176470588,
0.152941176470588, 0.164705882352941, 0.188235294117647, 0.188235294117647,
0.188235294117647, 0.188235294117647), Precision = c(0, 0.845,
0.845, 0.85, 0.85, 0.85, 0.855, 0.865), Recall = c(0, 0.701244813278008,
0.701244813278008, 0.705394190871369, 0.711297071129707, 0.711297071129707,
0.7125, 0.714876033057851), F1 = c(0, 0.766439909297052, 0.766439909297052,
0.770975056689342, 0.774487471526196, 0.774487471526196, 0.777272727272727,
0.782805429864253), Prevalence = c(0, 0.845614035087719, 0.845614035087719,
0.845614035087719, 0.83859649122807, 0.83859649122807, 0.842105263157895,
0.849122807017544), `Detection Rate` = c(0, 0.592982456140351,
0.592982456140351, 0.596491228070175, 0.596491228070175, 0.596491228070175,
0.6, 0.607017543859649), `Detection Prevalence` = c(0, 0.701754385964912,
0.701754385964912, 0.701754385964912, 0.701754385964912, 0.701754385964912,
0.701754385964912, 0.701754385964912), `Balanced Accuracy ` = c(0,
0.498349679366277, 0.498349679366277, 0.511788004526594, 0.529561579043114,
0.529561579043114, 0.534027777777778, 0.543484528156833)), row.names = c("m_metric",
"X", "X.1", "X.2", "X.3", "X.4", "X.5", "X.6"), class = "data.frame")
print(m1)
print(colnames(m1))
library(ggplot2)
library(reshape2)
print(melt(m1))
ggplot(melt(m1), aes(x=Var1, y=value, col=Var2))+geom_line()+ labs(title = "TITLE", x = "X", y = "Y", color = "COLOR")
I got the following error :
No id variables; using all as measure variables
Error in FUN(X[[i]], ...) : object 'Var1' not found
Calls: <Anonymous> ... <Anonymous> -> f -> scales_add_defaults -> lapply -> FUN
Execution halted
I know that there is something i don't understand with melt(m1):
variable value
1 Sensitivity 0.0000000
2 Sensitivity 0.7012448
3 Sensitivity 0.7012448
4 Sensitivity 0.7053942
5 Sensitivity 0.7112971
6 Sensitivity 0.7112971
7 Sensitivity 0.7125000
8 Sensitivity 0.7148760
9 Specificity 0.0000000
10 Specificity 0.2954545
11 Specificity 0.2954545
12 Specificity 0.3181818
13 Specificity 0.3478261
14 Specificity 0.3478261
15 Specificity 0.3555556
16 Specificity 0.3720930
17 Pos Pred Value 0.0000000
18 Pos Pred Value 0.8450000
19 Pos Pred Value 0.8450000
20 Pos Pred Value 0.8500000
21 Pos Pred Value 0.8500000
22 Pos Pred Value 0.8500000
23 Pos Pred Value 0.8550000
24 Pos Pred Value 0.8650000
25 Neg Pred Value 0.0000000
26 Neg Pred Value 0.1529412
27 Neg Pred Value 0.1529412
28 Neg Pred Value 0.1647059
29 Neg Pred Value 0.1882353
30 Neg Pred Value 0.1882353
31 Neg Pred Value 0.1882353
32 Neg Pred Value 0.1882353
33 Precision 0.0000000
34 Precision 0.8450000
35 Precision 0.8450000
36 Precision 0.8500000
37 Precision 0.8500000
38 Precision 0.8500000
39 Precision 0.8550000
40 Precision 0.8650000
41 Recall 0.0000000
42 Recall 0.7012448
43 Recall 0.7012448
44 Recall 0.7053942
45 Recall 0.7112971
46 Recall 0.7112971
47 Recall 0.7125000
48 Recall 0.7148760
49 F1 0.0000000
50 F1 0.7664399
51 F1 0.7664399
52 F1 0.7709751
53 F1 0.7744875
54 F1 0.7744875
55 F1 0.7772727
56 F1 0.7828054
57 Prevalence 0.0000000
58 Prevalence 0.8456140
59 Prevalence 0.8456140
60 Prevalence 0.8456140
61 Prevalence 0.8385965
62 Prevalence 0.8385965
63 Prevalence 0.8421053
64 Prevalence 0.8491228
65 Detection Rate 0.0000000
66 Detection Rate 0.5929825
67 Detection Rate 0.5929825
68 Detection Rate 0.5964912
69 Detection Rate 0.5964912
70 Detection Rate 0.5964912
71 Detection Rate 0.6000000
72 Detection Rate 0.6070175
73 Detection Prevalence 0.0000000
74 Detection Prevalence 0.7017544
75 Detection Prevalence 0.7017544
76 Detection Prevalence 0.7017544
77 Detection Prevalence 0.7017544
78 Detection Prevalence 0.7017544
79 Detection Prevalence 0.7017544
80 Detection Prevalence 0.7017544
81 Balanced Accuracy 0.0000000
82 Balanced Accuracy 0.4983497
83 Balanced Accuracy 0.4983497
84 Balanced Accuracy 0.5117880
85 Balanced Accuracy 0.5295616
86 Balanced Accuracy 0.5295616
87 Balanced Accuracy 0.5340278
88 Balanced Accuracy 0.5434845
I already did another try but it doesn't give the expected plots :
ggplot(melt(m1), aes(x=variable, y=value, col=variable))+geom_line()+ labs(title = "TITLE", x = "X", y = "Y", color = "COLOR")
For example , the plot of sensitivity should be something like this :
plot(melt(m1)[1:16,2],type="s")
The expected plot should be :
Thank you for help !
Upvotes: 0
Views: 139
Answers (2)
Tou Mou
Reputation: 1274
I had found a possible solution :
m1 <-structure(list(Sensitivity = c(0, 0.701244813278008, 0.701244813278008,
0.705394190871369, 0.711297071129707, 0.711297071129707, 0.7125,
0.714876033057851), Specificity = c(0, 0.295454545454545, 0.295454545454545,
0.318181818181818, 0.347826086956522, 0.347826086956522, 0.355555555555556,
0.372093023255814), `Pos Pred Value` = c(0, 0.845, 0.845, 0.85,
0.85, 0.85, 0.855, 0.865), `Neg Pred Value` = c(0, 0.152941176470588,
0.152941176470588, 0.164705882352941, 0.188235294117647, 0.188235294117647,
0.188235294117647, 0.188235294117647), Precision = c(0, 0.845,
0.845, 0.85, 0.85, 0.85, 0.855, 0.865), Recall = c(0, 0.701244813278008,
0.701244813278008, 0.705394190871369, 0.711297071129707, 0.711297071129707,
0.7125, 0.714876033057851), F1 = c(0, 0.766439909297052, 0.766439909297052,
0.770975056689342, 0.774487471526196, 0.774487471526196, 0.777272727272727,
0.782805429864253), Prevalence = c(0, 0.845614035087719, 0.845614035087719,
0.845614035087719, 0.83859649122807, 0.83859649122807, 0.842105263157895,
0.849122807017544), `Detection Rate` = c(0, 0.592982456140351,
0.592982456140351, 0.596491228070175, 0.596491228070175, 0.596491228070175,
0.6, 0.607017543859649), `Detection Prevalence` = c(0, 0.701754385964912,
0.701754385964912, 0.701754385964912, 0.701754385964912, 0.701754385964912,
0.701754385964912, 0.701754385964912), `Balanced Accuracy ` = c(0,
0.498349679366277, 0.498349679366277, 0.511788004526594, 0.529561579043114,
0.529561579043114, 0.534027777777778, 0.543484528156833)), row.names = c("m_metric",
"X", "X.1", "X.2", "X.3", "X.4", "X.5", "X.6"), class = "data.frame")
rownames(m1)<-NULL
print(m1)
print(colnames(m1))
library(ggplot2)
library(reshape2)
print(melt(m1))
V1=nrow(melt(m1))/length(which(melt(m1)[,"variable"] == "Sensitivity"))
melted=cbind(Var1=rep(which(melt(m1)[,"variable"] == "Sensitivity"),V1),melt(m1))
ggplot(melted, aes(x=Var1, y=value, group=variable, color=variable))+geom_line()+ labs(title = "TITLE", x = "X", y = "Y", color = "COLOR")
plot(melt(m1)[1:16,2],type="s")
This gives :
Upvotes: 0
Pedro Alencar
Reputation: 1089
First you have to create another column with the name of each model. Maybe with the names of the rows:
m1$model <- rownames(m1)
m1 <- melt(m1)
Then you can plot as a bar plot:
ggplot(m1, aes(x=variable, y=value, fill=model))+
geom_bar(stat = 'identity', position = 'dodge')+
labs(title = "TITLE", x = "X", y = "Y", color = "COLOR")
Upvotes: 1
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