[SciPy-user] predicting values based on (linear) models

Tim Michelsen timmichelsen@gmx-topmail...
Tue Jan 13 14:33:00 CST 2009


Hello,
I had to do several statistical computations lately. I therefore looked 
at the statistical language R since it seems to contain already many 
models and functionality.

Is there some function like "predict" [1] in Python?

Example:

      x <- rnorm(15)
      y <- x + rnorm(15)
      t = predict(lm(y ~ x))

      t => the predicted data determined by the linear model (comapare 
to scipy.stats.linregress)


How is this done by pure Python?


Are there many using Rpy (rpy2) to acces the statistical functionalities 
provided by R?
What are your experiences with this?

Programming in python seems to be more convenient than in R but lacking 
the vast statistics.


Thanks in advance,
Timmie


[1] predict is a generic function for predictions from the results of 
various model fitting functions. The function invokes particular methods 
which depend on the class of the first argument.

Most prediction methods which similar to fitting linear models have an 
argument newdata specifying the first place to look for explanatory 
variables to be used for prediction. Some considerable attempts are made 
to match up the columns in newdata to those used for fitting, for 
example that they are of comparable types and that any factors have the 
same level set in the same order (or can be transformed to be so).
  Time series prediction methods in package stats have an argument 
n.ahead specifying how many time steps ahead to predict.

Eample:

      x <- rnorm(15)
      y <- x + rnorm(15)
      t = predict(lm(y ~ x))

      t => the predicted data determined by the linear model (comapare 
to scipy.stats.linregress)



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