ROC Curve using plotROC package and geom_roc(), transforming data to “M1 markers”











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I am attempting to plot a ROC curve using the plotROC package for ggplot2, but I am not sure how to transform the data I have into the M1 marker format. The documentation provided the following example:



# plotROC documentation example
library(plotROC)
library(ggplot2)
D.ex <- rbinom(200, size = 1, prob = .5)
M1 <- rnorm(200, mean = D.ex, sd = .65)
M2 <- rnorm(200, mean = D.ex, sd = 1.5)

D M1 M2
1 1.4995932 0.5508204
1 0.4181619 1.6339181
0 -0.3620614 -1.0428972
1 0.7991132 -1.6396751
0 0.9574047 2.1159753
1 1.3440595 1.3026485

test <- data.frame(D = D.ex, D.str = c("Healthy", "Ill")[D.ex + 1],
M1 = M1, M2 = M2, stringsAsFactors = FALSE)

ggplot(test, aes(d = D, m = M1)) +
geom_roc()


Sample ROC plot output by plotROC



My data is logistic regression scores on a test subset:



# Example starting point
test <- rbinom(200, size = 1, prob = 0.2)
scores.prob <- runif(200, min = 0 , max = 1)
scores.class <- ifelse(scores.prob > 0.5, 1, 0)

# Example generated data
test scores.prob scores.class
0 0.7323306 1
0 0.7860687 1
0 0.9535123 1
1 0.3082551 0
0 0.5762784 1
1 0.4613730 0


I would like to know what M1 is and how to transform my data to get that field.










share|improve this question
























  • I don't really understand it, but this paper: ncbi.nlm.nih.gov/pmc/articles/PMC3952000 has a whole bunch about ROC curves, M1 and M2. Could that help?
    – RAB
    Nov 22 at 7:24










  • also check out the ?StatRoc page as it goes into a bit more detail about the d and m parameters
    – RAB
    Nov 22 at 7:32






  • 1




    @user10626943 ?StatRoc was a great resource! Thank you for the fast response.
    – Nick
    Nov 22 at 17:18















up vote
2
down vote

favorite












I am attempting to plot a ROC curve using the plotROC package for ggplot2, but I am not sure how to transform the data I have into the M1 marker format. The documentation provided the following example:



# plotROC documentation example
library(plotROC)
library(ggplot2)
D.ex <- rbinom(200, size = 1, prob = .5)
M1 <- rnorm(200, mean = D.ex, sd = .65)
M2 <- rnorm(200, mean = D.ex, sd = 1.5)

D M1 M2
1 1.4995932 0.5508204
1 0.4181619 1.6339181
0 -0.3620614 -1.0428972
1 0.7991132 -1.6396751
0 0.9574047 2.1159753
1 1.3440595 1.3026485

test <- data.frame(D = D.ex, D.str = c("Healthy", "Ill")[D.ex + 1],
M1 = M1, M2 = M2, stringsAsFactors = FALSE)

ggplot(test, aes(d = D, m = M1)) +
geom_roc()


Sample ROC plot output by plotROC



My data is logistic regression scores on a test subset:



# Example starting point
test <- rbinom(200, size = 1, prob = 0.2)
scores.prob <- runif(200, min = 0 , max = 1)
scores.class <- ifelse(scores.prob > 0.5, 1, 0)

# Example generated data
test scores.prob scores.class
0 0.7323306 1
0 0.7860687 1
0 0.9535123 1
1 0.3082551 0
0 0.5762784 1
1 0.4613730 0


I would like to know what M1 is and how to transform my data to get that field.










share|improve this question
























  • I don't really understand it, but this paper: ncbi.nlm.nih.gov/pmc/articles/PMC3952000 has a whole bunch about ROC curves, M1 and M2. Could that help?
    – RAB
    Nov 22 at 7:24










  • also check out the ?StatRoc page as it goes into a bit more detail about the d and m parameters
    – RAB
    Nov 22 at 7:32






  • 1




    @user10626943 ?StatRoc was a great resource! Thank you for the fast response.
    – Nick
    Nov 22 at 17:18













up vote
2
down vote

favorite









up vote
2
down vote

favorite











I am attempting to plot a ROC curve using the plotROC package for ggplot2, but I am not sure how to transform the data I have into the M1 marker format. The documentation provided the following example:



# plotROC documentation example
library(plotROC)
library(ggplot2)
D.ex <- rbinom(200, size = 1, prob = .5)
M1 <- rnorm(200, mean = D.ex, sd = .65)
M2 <- rnorm(200, mean = D.ex, sd = 1.5)

D M1 M2
1 1.4995932 0.5508204
1 0.4181619 1.6339181
0 -0.3620614 -1.0428972
1 0.7991132 -1.6396751
0 0.9574047 2.1159753
1 1.3440595 1.3026485

test <- data.frame(D = D.ex, D.str = c("Healthy", "Ill")[D.ex + 1],
M1 = M1, M2 = M2, stringsAsFactors = FALSE)

ggplot(test, aes(d = D, m = M1)) +
geom_roc()


Sample ROC plot output by plotROC



My data is logistic regression scores on a test subset:



# Example starting point
test <- rbinom(200, size = 1, prob = 0.2)
scores.prob <- runif(200, min = 0 , max = 1)
scores.class <- ifelse(scores.prob > 0.5, 1, 0)

# Example generated data
test scores.prob scores.class
0 0.7323306 1
0 0.7860687 1
0 0.9535123 1
1 0.3082551 0
0 0.5762784 1
1 0.4613730 0


I would like to know what M1 is and how to transform my data to get that field.










share|improve this question















I am attempting to plot a ROC curve using the plotROC package for ggplot2, but I am not sure how to transform the data I have into the M1 marker format. The documentation provided the following example:



# plotROC documentation example
library(plotROC)
library(ggplot2)
D.ex <- rbinom(200, size = 1, prob = .5)
M1 <- rnorm(200, mean = D.ex, sd = .65)
M2 <- rnorm(200, mean = D.ex, sd = 1.5)

D M1 M2
1 1.4995932 0.5508204
1 0.4181619 1.6339181
0 -0.3620614 -1.0428972
1 0.7991132 -1.6396751
0 0.9574047 2.1159753
1 1.3440595 1.3026485

test <- data.frame(D = D.ex, D.str = c("Healthy", "Ill")[D.ex + 1],
M1 = M1, M2 = M2, stringsAsFactors = FALSE)

ggplot(test, aes(d = D, m = M1)) +
geom_roc()


Sample ROC plot output by plotROC



My data is logistic regression scores on a test subset:



# Example starting point
test <- rbinom(200, size = 1, prob = 0.2)
scores.prob <- runif(200, min = 0 , max = 1)
scores.class <- ifelse(scores.prob > 0.5, 1, 0)

# Example generated data
test scores.prob scores.class
0 0.7323306 1
0 0.7860687 1
0 0.9535123 1
1 0.3082551 0
0 0.5762784 1
1 0.4613730 0


I would like to know what M1 is and how to transform my data to get that field.







r ggplot2 roc






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edited Nov 22 at 16:52

























asked Nov 22 at 3:56









Nick

134




134












  • I don't really understand it, but this paper: ncbi.nlm.nih.gov/pmc/articles/PMC3952000 has a whole bunch about ROC curves, M1 and M2. Could that help?
    – RAB
    Nov 22 at 7:24










  • also check out the ?StatRoc page as it goes into a bit more detail about the d and m parameters
    – RAB
    Nov 22 at 7:32






  • 1




    @user10626943 ?StatRoc was a great resource! Thank you for the fast response.
    – Nick
    Nov 22 at 17:18


















  • I don't really understand it, but this paper: ncbi.nlm.nih.gov/pmc/articles/PMC3952000 has a whole bunch about ROC curves, M1 and M2. Could that help?
    – RAB
    Nov 22 at 7:24










  • also check out the ?StatRoc page as it goes into a bit more detail about the d and m parameters
    – RAB
    Nov 22 at 7:32






  • 1




    @user10626943 ?StatRoc was a great resource! Thank you for the fast response.
    – Nick
    Nov 22 at 17:18
















I don't really understand it, but this paper: ncbi.nlm.nih.gov/pmc/articles/PMC3952000 has a whole bunch about ROC curves, M1 and M2. Could that help?
– RAB
Nov 22 at 7:24




I don't really understand it, but this paper: ncbi.nlm.nih.gov/pmc/articles/PMC3952000 has a whole bunch about ROC curves, M1 and M2. Could that help?
– RAB
Nov 22 at 7:24












also check out the ?StatRoc page as it goes into a bit more detail about the d and m parameters
– RAB
Nov 22 at 7:32




also check out the ?StatRoc page as it goes into a bit more detail about the d and m parameters
– RAB
Nov 22 at 7:32




1




1




@user10626943 ?StatRoc was a great resource! Thank you for the fast response.
– Nick
Nov 22 at 17:18




@user10626943 ?StatRoc was a great resource! Thank you for the fast response.
– Nick
Nov 22 at 17:18












1 Answer
1






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1
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library(plotROC) 
library(ggplot2)
test <- rbinom(200, size = 1, prob = 0.2)
scores.prob <- runif(200, min = 0 , max = 1)
test <- data.frame(D = test,
M1 = scores.prob, stringsAsFactors = FALSE)
ggplot(test, aes(d = D, m = M1)) +
geom_roc()


Your marker/predictor is the fitted values of your glm model. The ROC will give you the idea of how your model work (by means of AUC) and the best probability threshold (the ROC cutoff) for assigning persons to classes.
It is a usefull approach if you want to visualize the added value of fdifferent multivariate/univariate approaches.
Here a full example with the mtcars dataset. Hope it helps.



# Loading data
data(mtcars)
# Manual transmission (am = 1) depends on 1/4 mile time (qsec) and miles/(US) gallon (mpg)
glmfit <- glm(am ~ qsec + mpg, data = mtcars, binomial)
mtcars$fitted_am <- glmfit$fitted.values
# Loading packages
library(plotROC)
library(ggplot2)
library(pROC)
# Calculating ROC curve, AUC and threshold according to Youden index
rocfit <- roc(mtcars$am, mtcars$fitted_am)
auc(rocfit)
coords(rocfit, x = "b")
basicplot <- ggplot(mtcars, aes(d = am, m = fitted_am))
basicplot +
geom_roc() +
style_roc(theme = theme_grey) +
theme(axis.text = element_text(colour = "blue")) +
ggtitle("Automatic transmission prediction") +
scale_x_continuous("1 - Specificity", breaks = seq(0, 1, by = .1))
plot(rocfit)
prop.table(table(mtcars$am))





share|improve this answer



















  • 1




    The mtcars example helped a great deal and thank you for the quick response!
    – Nick
    Nov 22 at 17:21











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accepted










library(plotROC) 
library(ggplot2)
test <- rbinom(200, size = 1, prob = 0.2)
scores.prob <- runif(200, min = 0 , max = 1)
test <- data.frame(D = test,
M1 = scores.prob, stringsAsFactors = FALSE)
ggplot(test, aes(d = D, m = M1)) +
geom_roc()


Your marker/predictor is the fitted values of your glm model. The ROC will give you the idea of how your model work (by means of AUC) and the best probability threshold (the ROC cutoff) for assigning persons to classes.
It is a usefull approach if you want to visualize the added value of fdifferent multivariate/univariate approaches.
Here a full example with the mtcars dataset. Hope it helps.



# Loading data
data(mtcars)
# Manual transmission (am = 1) depends on 1/4 mile time (qsec) and miles/(US) gallon (mpg)
glmfit <- glm(am ~ qsec + mpg, data = mtcars, binomial)
mtcars$fitted_am <- glmfit$fitted.values
# Loading packages
library(plotROC)
library(ggplot2)
library(pROC)
# Calculating ROC curve, AUC and threshold according to Youden index
rocfit <- roc(mtcars$am, mtcars$fitted_am)
auc(rocfit)
coords(rocfit, x = "b")
basicplot <- ggplot(mtcars, aes(d = am, m = fitted_am))
basicplot +
geom_roc() +
style_roc(theme = theme_grey) +
theme(axis.text = element_text(colour = "blue")) +
ggtitle("Automatic transmission prediction") +
scale_x_continuous("1 - Specificity", breaks = seq(0, 1, by = .1))
plot(rocfit)
prop.table(table(mtcars$am))





share|improve this answer



















  • 1




    The mtcars example helped a great deal and thank you for the quick response!
    – Nick
    Nov 22 at 17:21















up vote
1
down vote



accepted










library(plotROC) 
library(ggplot2)
test <- rbinom(200, size = 1, prob = 0.2)
scores.prob <- runif(200, min = 0 , max = 1)
test <- data.frame(D = test,
M1 = scores.prob, stringsAsFactors = FALSE)
ggplot(test, aes(d = D, m = M1)) +
geom_roc()


Your marker/predictor is the fitted values of your glm model. The ROC will give you the idea of how your model work (by means of AUC) and the best probability threshold (the ROC cutoff) for assigning persons to classes.
It is a usefull approach if you want to visualize the added value of fdifferent multivariate/univariate approaches.
Here a full example with the mtcars dataset. Hope it helps.



# Loading data
data(mtcars)
# Manual transmission (am = 1) depends on 1/4 mile time (qsec) and miles/(US) gallon (mpg)
glmfit <- glm(am ~ qsec + mpg, data = mtcars, binomial)
mtcars$fitted_am <- glmfit$fitted.values
# Loading packages
library(plotROC)
library(ggplot2)
library(pROC)
# Calculating ROC curve, AUC and threshold according to Youden index
rocfit <- roc(mtcars$am, mtcars$fitted_am)
auc(rocfit)
coords(rocfit, x = "b")
basicplot <- ggplot(mtcars, aes(d = am, m = fitted_am))
basicplot +
geom_roc() +
style_roc(theme = theme_grey) +
theme(axis.text = element_text(colour = "blue")) +
ggtitle("Automatic transmission prediction") +
scale_x_continuous("1 - Specificity", breaks = seq(0, 1, by = .1))
plot(rocfit)
prop.table(table(mtcars$am))





share|improve this answer



















  • 1




    The mtcars example helped a great deal and thank you for the quick response!
    – Nick
    Nov 22 at 17:21













up vote
1
down vote



accepted







up vote
1
down vote



accepted






library(plotROC) 
library(ggplot2)
test <- rbinom(200, size = 1, prob = 0.2)
scores.prob <- runif(200, min = 0 , max = 1)
test <- data.frame(D = test,
M1 = scores.prob, stringsAsFactors = FALSE)
ggplot(test, aes(d = D, m = M1)) +
geom_roc()


Your marker/predictor is the fitted values of your glm model. The ROC will give you the idea of how your model work (by means of AUC) and the best probability threshold (the ROC cutoff) for assigning persons to classes.
It is a usefull approach if you want to visualize the added value of fdifferent multivariate/univariate approaches.
Here a full example with the mtcars dataset. Hope it helps.



# Loading data
data(mtcars)
# Manual transmission (am = 1) depends on 1/4 mile time (qsec) and miles/(US) gallon (mpg)
glmfit <- glm(am ~ qsec + mpg, data = mtcars, binomial)
mtcars$fitted_am <- glmfit$fitted.values
# Loading packages
library(plotROC)
library(ggplot2)
library(pROC)
# Calculating ROC curve, AUC and threshold according to Youden index
rocfit <- roc(mtcars$am, mtcars$fitted_am)
auc(rocfit)
coords(rocfit, x = "b")
basicplot <- ggplot(mtcars, aes(d = am, m = fitted_am))
basicplot +
geom_roc() +
style_roc(theme = theme_grey) +
theme(axis.text = element_text(colour = "blue")) +
ggtitle("Automatic transmission prediction") +
scale_x_continuous("1 - Specificity", breaks = seq(0, 1, by = .1))
plot(rocfit)
prop.table(table(mtcars$am))





share|improve this answer














library(plotROC) 
library(ggplot2)
test <- rbinom(200, size = 1, prob = 0.2)
scores.prob <- runif(200, min = 0 , max = 1)
test <- data.frame(D = test,
M1 = scores.prob, stringsAsFactors = FALSE)
ggplot(test, aes(d = D, m = M1)) +
geom_roc()


Your marker/predictor is the fitted values of your glm model. The ROC will give you the idea of how your model work (by means of AUC) and the best probability threshold (the ROC cutoff) for assigning persons to classes.
It is a usefull approach if you want to visualize the added value of fdifferent multivariate/univariate approaches.
Here a full example with the mtcars dataset. Hope it helps.



# Loading data
data(mtcars)
# Manual transmission (am = 1) depends on 1/4 mile time (qsec) and miles/(US) gallon (mpg)
glmfit <- glm(am ~ qsec + mpg, data = mtcars, binomial)
mtcars$fitted_am <- glmfit$fitted.values
# Loading packages
library(plotROC)
library(ggplot2)
library(pROC)
# Calculating ROC curve, AUC and threshold according to Youden index
rocfit <- roc(mtcars$am, mtcars$fitted_am)
auc(rocfit)
coords(rocfit, x = "b")
basicplot <- ggplot(mtcars, aes(d = am, m = fitted_am))
basicplot +
geom_roc() +
style_roc(theme = theme_grey) +
theme(axis.text = element_text(colour = "blue")) +
ggtitle("Automatic transmission prediction") +
scale_x_continuous("1 - Specificity", breaks = seq(0, 1, by = .1))
plot(rocfit)
prop.table(table(mtcars$am))






share|improve this answer














share|improve this answer



share|improve this answer








edited Nov 22 at 8:53

























answered Nov 22 at 8:18









paoloeusebi

622413




622413








  • 1




    The mtcars example helped a great deal and thank you for the quick response!
    – Nick
    Nov 22 at 17:21














  • 1




    The mtcars example helped a great deal and thank you for the quick response!
    – Nick
    Nov 22 at 17:21








1




1




The mtcars example helped a great deal and thank you for the quick response!
– Nick
Nov 22 at 17:21




The mtcars example helped a great deal and thank you for the quick response!
– Nick
Nov 22 at 17:21


















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