Trying to create a BN using BNlearn, but I keep getting an error;
Error in check.data(data, allowed.types = discrete.data.types) : variable Variable1 must have at least two levels.
It gives me that error for every of my variable, even though they're all factors and has more than 1 levels, As you can see - in this case my variable "model" has 4 levels
As I can't share the variables and dataset, I've created a small set and belonging code to the data set. I get the same problem. I know I've only shared 2 variables, but I get the same error for all the variables.
library(tidyverse)
library (bnlearn)
library(openxlsx)
DataFull <- read.xlsx("(.....)/test.xlsx", sheet = 1, startRow = 1, colNames = TRUE)
set.seed(600)
DataFull <- as_tibble(DataFull)
DataFull$Variable1 <- as.factor(DataFull$Variable1)
DataFull$TargetVar <- as.factor(DataFull$TargetVar)
DataFull <- na.omit(DataFull)
DataFull <- droplevels(DataFull)
DataFull <- DataFull[sample(nrow(DataFull)),]
Data <- DataFull[1:as.integer(nrow(DataFull)*0.70)-1,]
Datatest <- DataFull[as.integer(nrow(DataFull)*0.70):nrow(DataFull),]
nrow(Data)+nrow(Datatest)==nrow(DataFull)
FocusVar <- as.character("TargetVar")
BN.naive <- naive.bayes(Data, FocusVar)
Using str(data)
, I can see that the variable has 2 or more levels already:
str(Data)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 27586 obs. of 2 variables:
$ Variable1: Factor w/ 3 levels "Small","Medium",..: 2 2 3 3 3 3 3 3 3 3 ...
$ TargetVar: Factor w/ 2 levels "Yes","No": 1 1 1 1 1 1 2 1 1 1 ...
Link to data set: https://drive.google.com/open?id=1VX2xkPdeHKdyYqEsD0FSm1BLu1UCtOj9eVIVfA_KJ3g
bnlearn
expects a data.frame
: doesn't work with tibbles
, So keep your data as a data.frame
by omitting the line DataFull <- as_tibble(DataFull)
Example
library(tibble)
library (bnlearn)
d <- as_tibble(learning.test)
hc(d)
Error in check.data(x) : variable A must have at least two levels.
In particular, it is the line from bnlearn:::check.data
if (nlevels(x[, col]) < 2)
stop("variable ", col, " must have at least two levels.")
In a standard data.frame
,learning.test[,"A"]
returns a vector and so nlevels(learning.test[,"A"])
works as expected, however, by design, you cannot extract vectors like this from tibbles
: d[,"A"])
is still a tbl_df
and not a vector hence nlevels(d[,"A"])
doesn't work as expected, and returns zero.
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