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Support non-base modelling functions #3

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7 of 15 tasks
leeper opened this issue Dec 9, 2014 · 5 comments
Closed
7 of 15 tasks

Support non-base modelling functions #3

leeper opened this issue Dec 9, 2014 · 5 comments
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enhancement help wanted

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@leeper
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leeper commented Dec 9, 2014 •
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  • loess
    • predict() method uses se rather than se.fit
  • plm/pglm
    • no predict() method
  • MASS::polr()
    • has predict() method with type = c("class", "probs")
  • MASS::glm.nb()
    • should work out of the box
  • lme4 (lmer(), glmer(), ...)
    • lme4:::predict.merMod() has no se.fit argument
  • nlme
    • nlme:::predict.lme() has no se.fit or type arguments
    • nlme:::predict.gls() has no se.fit argument.
  • nls
    • requires setting model = TRUE in original call; and then getting term(object[["model"]])
  • lfe::felm()
  • survey
    • (implements alternative modelling functions for non-SRS survey datasets)
  • survival
    • coxph class has different type values for predict(): c("lp", "risk", "expected")
    • survival::survreg()
  • censReg
    • has a margEff() generic but no predict() method
  • nnet::multinom() or any nnet model generally
  • AER::ivreg()
  • quantreg::rq()
  • Document which model types have prediction() and marginal_effects()/margins() methods
@leeper leeper changed the title Support censReg Support non-base modelling functions Jul 28, 2016
This was referenced Jul 28, 2016
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@markdanese
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This looks like a great package. Thanks for putting it together. Thomas Lumley's survey package wraps glm for regression modeling when handling the weights needed for analyzing complex surveys. I don't know how hard it would be to get that package to work with margins. (Also, the survival package might be worth considering.)

@leeper
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leeper commented Aug 7, 2016

@markdanese Thanks and great suggestions. I've added them to the list.

leeper added a commit that referenced this issue Aug 22, 2016
@leeper
rm support for plm/pglm (#3)
12d79fb
@hughjonesd
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I would throw in a vote for nnet::multinom, the multinomial logit. This requires specifying which category of the DV you want to see marginal effects for.

@leeper
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leeper commented Aug 23, 2016

@hughjonesd Thanks. I've added it to the list.

leeper added a commit that referenced this issue Aug 27, 2016
@leeper
add loess support (#3)
d22708e
leeper added a commit that referenced this issue Aug 27, 2016
@leeper
add nls method for prediction() (#3)
db48de2
leeper added a commit that referenced this issue Aug 29, 2016
@leeper
add prediction() method for polr (#3)
920f57f
leeper added a commit that referenced this issue Aug 29, 2016
@leeper
add prediction() method for coxph models (#3)
56d4a56
leeper added a commit that referenced this issue Aug 30, 2016
@leeper
add prediction.ivreg() method (#3)
560995a
leeper added a commit that referenced this issue Aug 30, 2016
@leeper
add prediction.survreg() method (#3)
90092e8
leeper added a commit that referenced this issue Sep 1, 2016
@leeper
add prediction.gls() (#3)
86b8a88
@leeper leeper mentioned this issue Oct 30, 2016
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@leeper
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leeper commented Oct 30, 2016

I am moving this issue here: leeper/prediction#1

@leeper leeper closed this as completed Oct 30, 2016
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@leeper @hughjonesd @markdanese