This dataset has been re-packaged for convenience from https://github.com/paulhendricks/titanic
Passenger ID
Passenger Survival Indicator
Passenger Class
Name
Sex
Age
Number of Siblings/Spouses Aboard
Number of Parents/Children Aboard
Ticket Number
Passenger Fare
Cabin
Port of Embarkation
Dummy variable for Sex="female"
A data frame with 1309 rows and 12 variables:
https://www.kaggle.com/c/titanic/data
fit <- pls("Survived ~ Age + Female + Age:Female",
data = titanic, ordered = "Survived")
#> Removing missing data using listwise deletion...
pls_predict(fit, benchmark = "acc")
#> PlsSemPredict object
#> Available fields: $Y, $X.cont, $X.cont.pred, $X.ord, $X.ord.pred, $benchmark
#>
#> Y [714 x 4] (head)
#> Survvd Age Female Ag:Fml
#> 1 -0.367 -0.530 -0.759 0.495
#> 2 1.086 0.571 1.317 0.845
#> 3 0.876 -0.255 1.317 -0.242
#> 4 1.033 0.365 1.317 0.574
#> 5 -0.601 0.365 -0.759 -0.184
#> 7 -0.943 1.673 -0.759 -1.176
#>
#> X.cont [714 x 3] (head)
#> Survvd Age Female
#> 1 -0.643 -0.530 -0.759
#> 2 0.930 0.571 1.317
#> 3 0.930 -0.255 1.317
#> 4 0.930 0.365 1.317
#> 5 -0.643 0.365 -0.759
#> 7 -0.643 1.673 -0.759
#>
#> X.cont.pred [714 x 3] (head)
#> Survvd Age Female
#> 1 -0.367 -0.530 -0.759
#> 2 1.086 0.571 1.317
#> 3 0.876 -0.255 1.317
#> 4 1.033 0.365 1.317
#> 5 -0.601 0.365 -0.759
#> 7 -0.943 1.673 -0.759
#>
#> X.ord [714 x 3] (head)
#> Survvd Age Female
#> 1 1.000 -0.530 -0.759
#> 2 2.000 0.571 1.317
#> 3 2.000 -0.255 1.317
#> 4 2.000 0.365 1.317
#> 5 1.000 0.365 -0.759
#> 7 1.000 1.673 -0.759
#>
#> X.ord.pred [714 x 3] (head)
#> Survvd Age Female
#> 1 1.000 -0.530 -0.759
#> 2 2.000 0.571 1.317
#> 3 2.000 -0.255 1.317
#> 4 2.000 0.365 1.317
#> 5 1.000 0.365 -0.759
#> 7 1.000 1.673 -0.759
#>
#> Benchmark summary
#> acc: n=1, mean=0.780, median=0.780, min=0.780, max=0.780
#> variable value
#> Survived 0.780
#>