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Latest Discussions

• Models with weight

  Hi I'm trying to find a correlation between some X predictors and a continuous response variable strongly skewed on the right. The database contains 140 instances only. First of all I transformed the response variable by using the function lnx+1. Then I normalized all the x in the range 0 and 1, reduced the dimensionality with the screening option and then removed the correlated predictors X. At t...

  FR60

  Discussions |

  Aug 9, 2020 2:10 PM

  2744 views | 3 replies

• How to do 2-stage least squares (2SLS) / Instrumental Variable regression / Heckman self-selection model

  Hi there, Is it possible to do a two-stage least squares model or IV regression or Heckman self-selection model in JMP? Is it also possible to get the inverse Mills ratio? Thanks in advance! 

  Heckman123

  Discussions |

  Aug 7, 2020 6:59 AM

  2603 views | 1 replies

• Standard betas for logistic regression

  I would like to compare strength of effect in my logistic regression model.  In least squares models I can bring up and compare standardized beta coefficients.  Can this be done with logistic models?  (I don't see the option in "columns" option).  Or, is there another method to compare the strength of effect of each of my terms in the model? ThanksFishguy

  fishguy

  Discussions |

  Jul 28, 2020 8:27 AM

  4807 views | 4 replies

• How do I fix base level for indicator function parameterization

  If my data has 1's and 0's in a column for a X and I am running a regression of Y on X,  how can I force JMP to select one of those as baseline values (instead of JMP picking it based on its internal rule) in creating indicator function parameterization? Thanks

  goutam

  Discussions |

  Jul 21, 2020 7:53 AM

  1579 views | 1 replies

• How do I auto-calculate precision and F1 score for models on imbalanced data?

  I like that JMP's prints a confusion matrix and ROC for the logistic (Generalized) regression, but some important metrics seem to be missing from the results: precision and F1 score. Is there a way to auto-include these in the output? Or, have it be computed as "automatically" as possible when I re-run a new classificaiton model. I am trying to avoid manually recomputing these with each new model....

  Oleg

  Discussions |

  Sep 28, 2017 3:40 PM

  20698 views | 16 replies