Kira (version 1.0.6)

plot_curve: Graphics of the results of the classification process

Description

Return graphics of the results of the classification process.

Usage

plot_curve(data, type = "ROC", title = NA, xlabel = NA, ylabel = NA,  
           posleg = 3, boxleg = FALSE, axis = TRUE, size = 1.1, grid = TRUE, 
           color = TRUE, classcolor = NA, savptc = FALSE, width = 3236, 
           height = 2000, res = 300, casc = TRUE)

Value

ROC or PRC curve.

Arguments

data

Data with x and y coordinates.

type

ROC (default) or PRC graphics type.

title

Title of the graphic, if not set, assumes the default text.

xlabel

Names the X axis, if not set, assumes the default text.

ylabel

Names the Y axis, if not set, assumes the default text.

posleg

0 with no caption,
1 for caption in the left upper corner,
2 for caption in the right upper corner,
3 for caption in the right lower corner (default),
4 for caption in the left lower corner.

boxleg

Puts the frame in the caption (default = TRUE).

axis

Put the diagonal axis on the graph (default = TRUE).

size

Size of the points in the graphs (default = 1.1).

grid

Put grid on graphs (default = TRUE).

color

Colored graphics (default = TRUE).

classcolor

Vector with the colors of the classes.

savptc

Saves graphics images to files (default = FALSE).

width

Graphics images width when savptc = TRUE (defaul = 3236).

height

Graphics images height when savptc = TRUE (default = 2000).

res

Nominal resolution in ppi of the graphics images when savptc = TRUE (default = 300).

casc

Cascade effect in the presentation of the graphic (default = TRUE).

Author

Paulo Cesar Ossani

See Also

results

Examples

data(iris) # data set

data  <- iris
names <- colnames(data)
colnames(data) <- c(names[1:4],"class")

#### Start - hold out validation method ####
dat.sample = sample(2, nrow(data), replace = TRUE, prob = c(0.7,0.3))
data.train = data[dat.sample == 1,] # training data set
data.test  = data[dat.sample == 2,] # test data set
class.train = as.factor(data.train$class) # class names of the training data set
class.test  = as.factor(data.test$class)  # class names of the test data set
#### End - hold out validation method ####


dist = "euclidean" 
# dist = "manhattan"
# dist = "minkowski"
# dist = "canberra"
# dist = "maximum"
# dist = "chebyshev"

k = 1
lambda = 5

r <- (ncol(data) - 1)
res <- knn(train = data.train[,1:r], test = data.test[,1:r], class = class.train, 
           k = 1, dist = dist, lambda = lambda)

resp <- results(orig.class = class.test, predict = res$predict)

message("Mean squared error:"); resp$mse
message("Mean absolute error:"); resp$mae
message("Relative absolute error:"); resp$rae
message("Confusion matrix:"); resp$conf.mtx  
message("Hit rate: ", resp$rate.hits)
message("Error rate: ", resp$rate.error)
message("Number of correct instances: ", resp$num.hits)
message("Number of wrong instances: ", resp$num.error)
message("Kappa coefficient: ", resp$kappa)
# message("Data for the ROC curve in classes:"); resp$roc.curve 
# message("Data for the PRC curve in classes:"); resp$prc.curve
message("General results of the classes:"); resp$res.class

dat <- resp$roc.curve; tp = "roc"; ps = 3
# dat <- resp$prc.curve; tp = "prc"; ps = 4

plot_curve(data = dat, type = tp, title = NA, xlabel = NA, ylabel = NA,  
           posleg = ps, boxleg = FALSE, axis = TRUE, size = 1.1, grid = TRUE, 
           color = TRUE, classcolor = NA, savptc = FALSE,
           width = 3236, height = 2000, res = 300, casc = FALSE)