R - 训练和验证样本中的线性判别分析

Question

I am working with lda command to analyze a 2-column, 234 row dataset (x): column X1 contains the predictor variable (metric) and column X2 the independent variable (categorical, 4 categories). I would like to build a linear discriminant model by using 150 observations and then use the other 84 observations for validation. After a random partitioning of data i get x.build and x.validation with 150 and 84 observations, respectively. I run the following

fit = lda(x.build$X2~x.build$X1, data=x.build, na.action="na.omit")

Then I run predict command like this:

pred = predict(fit, newdata=x.validation)

From the reading of the commands description I thought that in pred$class I would get the classification of validation data according to the model built, but I get the classification of 150 observations instead of the 84 I intended to use as validation data. I don't really know what is happening, can someone please give me an example of how I should be conducting this analysis?

Thank you very much in advance.

Answer 1

Try this instead:

fit = lda(X2~X1, data=x.build, na.action="na.omit")
pred = predict(fit, newdata=x.validation)

If you use this formula x.build$X2~x.build$X1 when you build the model, predict expects x.build$X1 column in the validation data. Obviously there isn't one so you get prediction for training data.