CODEWITHSUNDEEP
rstatisticspredictionlmpredict
MMDG
MMDG

Reputation: 83

R: Limit/Set values of predicted results from linear model

New to R.

Looking to limit the range of values that can be predicted.

df.Train <- data.frame(S=c(1,2,2,2,1),L=c(1,2,3,3,1),M=c(400,450,400,700,795),V=c(423,400,555,600,800),G=c(4,3.2,2,2.7,3.4), stringsAsFactors=FALSE)
m.Train <- lm(G~S+L+M+V,data=df.Train)
df.Test <- data.frame(S=c(1,2,1,2,1),L=c(1,2,3,1,1),M=c(400,450,500,800,795),V=c(423,475,555,600,555), stringsAsFactors=FALSE)
round(predict(m.Train, df.Test, type="response"),digits=1)
#seq(0,4,.1) #Predicted values should fall in this range

I've experimented with the predict() options but no luck. Is there an option in predict? Should I be limiting it in the model?

Thank you

Upvotes: 1

Views: 5719

Answers (1)

LyzandeR
LyzandeR

Reputation: 37879

There are ways to transform your response variable, G in this occasion but there needs to be a good reason to do this. For example, if you want the output to be probabilities between 0 and 1 and your response variable is binary (0,1) then you need a logistic regression.

It all comes down to what data you have and whether a model / transformation of the response variable would be appropriate. In your example you do not specify what the data is and therefore we cannot say anything about which model or which transformation to use.

Setting the above on the side, if you really care about the prediction and do not care about the model or the transformation (but why wouldn't you care?) it looks like your data could use a quasipossion generalised linear model which might provide the output you need:

df.Train <- data.frame(S=c(1,2,2,2,1),L=c(1,2,3,3,1),M=c(400,450,400,700,795),V=c(423,400,555,600,800),G=c(4,3.2,2,2.7,3.4), stringsAsFactors=FALSE)
m.Train <- glm(G~S+L+M+V,data=df.Train, family=quasipoisson)
df.Test <- data.frame(S=c(1,2,1,2,1),L=c(1,2,3,1,1),M=c(400,450,500,800,795),V=c(423,475,555,600,555), stringsAsFactors=FALSE)

> predict(m.Train, df.Test, type="response")
       1        2        3        4        5 
4.000000 2.840834 3.062754 3.615447 4.573276 
#probably not as good as you want

The model is using a log link by default which ensures the values will be positive. There is no guarantee that the model will not predict values greater than 4 but since you fed it values of less than 4 (your G variable) then chances are that most of the predictions will follow that distribution (like in this example). You might then need to consider how to treat predictions that go above 4.

In general you should consider carefully which model to choose and which response transformation. The poison model above for example is usually used for count data. However, you should never manipulate predictions on your own so if you choose the lm model in the end make sure you use the predictions it gives.

EDIT

It looks like in your case a non-linear regression might be what you need. The problem using a linear model like lm is that predictions can be greater than the max of the observed cases and less than the min of the observed cases. In which case doing a linear regression might not be appropriate. There are algorithms that will never predict a value greater than the max or less than the min. Such a case might be better suited in your case. One of these algorithms is the k-nearest neighbor for example:

library(FNN)
> knn.reg(df.Train[1:4], test=df.Test[1:4], y=df.Train[5], k=3)
Prediction:
[1] 3.066667 3.066667 3.066667 2.700000 3.100000

As you can see the predictions will never go above 4. That said knn is a local solution algorithm so again you need to research whether this is a good approach or not for your problem and your data. In terms of predictions though it definitely confirms your conditions. Knn is a very easy to understand algorithm that relies on distances between points to calculate predictions.

Hope it helps :)

Upvotes: 2

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