predict.rmm | R Documentation |
Predicted values based on RMM object
## S3 method for class 'rmm' predict(object, newdata, Rem_Choice_Set, Choice_Set_Code, fixed = TRUE, ...)
object |
Object of class inheriting from " |
newdata |
A data frame in which to look for variables with which to predict. |
Rem_Choice_Set |
List of choice sets remaining in the data. |
Choice_Set_Code |
Specifies the choice set of |
fixed |
If fixed=TRUE, the alternative with the highest prediction probability is determined as decision. Otherwise (fixed=FALSE), one of the alternatives is determined in proportion to the predictive probability. |
... |
further arguments passed to or from other methods. |
preict.rmm
produces a list of predictions, which contains decisions and probabilities.
data(Hotel_Long) # Before using the rmm function, the user must first use the rmm_shape function. rst_reshape <- rmm_reshape(data=Hotel_Long, idvar="Booking_ID", alts="Room_Type", asv="Price", resp="Purchase", min_obs=30) # Fitting a model rst_rmm <- rmm(rst_reshape, prop=0.7, model="cl") # Predictions Rem_Choice_Set <- rst_reshape$Rem_Choice_Set newdata1 <- data.frame(Price_1=c(232, 122, 524), Price_3=c(152, 531, 221), Price_4=c(163, 743, 192), Price_5=c(132, 535, 325), Price_7=c(136, 276, 673), Price_8=c(387, 153, 454), Price_9=c(262, 163, 326), Price_10=c(421, 573, 472)) predict(rst_rmm, newdata=newdata1, Rem_Choice_Set=Rem_Choice_Set, Choice_Set_Code=3, fixed=TRUE) newdata2 <- data.frame(Price_1=c(521, 321, 101, 234, 743), Price_5=c(677, 412, 98, 321, 382), Price_8=c(232, 384, 330, 590, 280)) predict(rst_rmm, newdata=newdata2, Rem_Choice_Set=Rem_Choice_Set, Choice_Set_Code=7, fixed=FALSE)
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