rminer (version 1.4.6)

delevels: Reduce, replace or transform levels of a data.frame or factor variable (useful for preprocessing datasets).

Description

Reduce, replace or transform levels of a data.frame or factor variable (useful for preprocessing datasets).

Usage

delevels(x, levels, label = NULL)

Arguments

x

factor with several levels or a data.frame. If a data.frame, then all factor attributes are transformed.

levels

character vector with several options:

• idf -- factor is transformed into a numeric vector using IDF transform.

• pcp or c("pcp",perc) -- factor is transformed using PCP transform. If perc is not provided, the default 0.1 value is used.

• any other values -- all level values are merged into a single factor level according to label.

Another possibility is to define a vector list, with levels[[i]] values for each factor of the data.frame (see example).

label

the new label used for all levels examples (if NULL then "_OTHER" is assumed).

Value

Returns a transformed factor or data.frame.

Details

The Inverse Document Frequency (IDF) uses f(x)= log(n/f_x), where n is the length of x and f_x is the frequency of x. The Percentage Categorical Pruned (PCP) merges all least frequent levels (summing up to perc percent) into a single level. When other values are used for levels, this function replaces all levels values with the single label value.

References

• PCP transform: L.M. Matos, P. Cortez, R. Mendes, A. Moreau. Using Deep Learning for Mobile Marketing User Conversion Prediction. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN 2019), paper N-19327, Budapest, Hungary, July, 2019 (8 pages), IEEE, ISBN 978-1-7281-2009-6. https://doi.org/10.1109/IJCNN.2019.8851888 http://hdl.handle.net/1822/62771

• IDF transform: L.M. Matos, P. Cortez, R. Mendes and A. Moreau. A Comparison of Data-Driven Approaches for Mobile Marketing User Conversion Prediction. In Proceedings of 9th IEEE International Conference on Intelligent Systems (IS 2018), pp. 140-146, Funchal, Madeira, Portugal, September, 2018, IEEE, ISBN 978-1-5386-7097-2. https://ieeexplore.ieee.org/document/8710472 http://hdl.handle.net/1822/61586

See Also

fit and imputation.

Examples

# NOT RUN {
### simples examples:
f=factor(c("A","A","B","B","C","D","E"))
print(table(f))
# replace "A" with "a":
f1=delevels(f,"A","a")
print(table(f1))
# merge c("C","D","E") into "CDE":
f2=delevels(f,c("C","D","E"),"CDE")
print(table(f2))
# merge c("B","C","D","E") into _OTHER:
f3=delevels(f,c("B","C","D","E"))
print(table(f3))

# }
# NOT RUN {
# larger factor:
x=factor(c(1,rep(2,2),rep(3,3),rep(4,4),rep(5,5),rep(10,10),rep(100,100)))
print(table(x))
# IDF: frequent values are close to zero and
# infrequent ones are more close to each other:
x1=delevels(x,"idf")
print(table(x1))
# PCP: infrequent values are merged
x2=delevels(x,c("pcp",0.1)) # around 10<!-- % -->
print(table(x2))

# example with a data.frame:
y=factor(c(rep("a",100),rep("b",20),rep("c",5)))
z=1:125 # numeric
d=data.frame(x=x,y=y,z=z,x2=x)
print(summary(d))

# IDF:
d1=delevels(d,"idf")
print(summary(d1))
# PCP:
d2=delevels(d,"pcp")
print(summary(d2))
# delevels:
L=vector("list",ncol(d)) # one per attribute
L[[1]]=c("1","2","3","4","5")
L[[2]]=c("b","c")
L[[4]]=c("1","2","3") # different on purpose
d3=delevels(d,levels=L,label="other")
print(summary(d3))
# }
# NOT RUN {
 # end dontrun 

# }