Logistic Regression

amishrifle

New Member
Could someone explain the basics of logistic regression?

Thank you!

amishrifle

New Member
I'd like to know what kind of data it can be used on and what the results tell about the data

trinker

ggplot2orBust
Used pretty much the same as typical regression except the outcome variable is binary (0/1). The results usually tell us a model fit and how the predictors decrease/increase the odds of the outcome occurring.

gianmarco

TS Contributor
Hi!
Just to complement Trinker's useful summary: it happened to me to use LR in an article I wrote. I tried to summarize LR in layman's terms, citing further references.
The article is freely available here.

Hope it will help a bit
Regards
Gm

amishrifle

New Member
Thanks for the help guys! Could someone also please explain the basics of latent growth modeling?

pnwright

New Member
Gianmarco

Interesting stuff...I can hear my old arch tutor grumbling about processualists :-}

Also, thanks for clarifying a few points - I recently posted a GLM type question to the Applied Stats thread, and I am trying to figure these things out in my head too....still not sure I have got it quite straight yet, but I will, hopefully, get there. I assume that if you can test for differences between data and model, you could also test for differences between subgroups in the data? (My problem has presence/absence and categorical data)

Cheers

Paul

rogojel

TS Contributor
hi pnwright,
the logistic regression with categorical IVs will do something quite close to what you are interested in, if I understand correctly. It will create indicator variables for the categories you have, define one as a base and compare the outcome of each of the other categories to the outcome of the base variable you picked.

To make it concrete, imagine that you have 4 bait types, rabbit, chicken, lamb .. etc. When you do this type of analysis you will pick one bait type as the basis, say meat. The logistic regression model will give you an estimate of the ratio of odds of seeing an animal in the trap with meat as a bait and the odds of seeing an animal with chicken as a bait.. and so on for each type of bait. It does not compare each bait type to each but all of them to the one you picked as a base,

From the odds ratios you can go to an estimate of the probailities and get the probability of seeing an animal in the trap for each bait type.

regards
rogojel

hlsmith

Less is more. Stay pure. Stay poor.
Interesting example content rogojel. Perhaps the 4th bait could be bottle nosed dolphin.

rogojel

TS Contributor
yepp, it was pnwright´s original example in the other thread, I just could not remember the other bait types. did consider rat though.

pnwright

New Member
Thanks folks....I coded my baits 1, 2,3 etc., then coded the other column 1 or 0 if the camera trap too a picture of the mammal.

I've followed the example here: http://www.ats.ucla.edu/stat/r/dae/logit.htm

I *think* the output from the Wald's test showed that bait type was significant, and that one bait was significantly different to two others were not....but that's if I'm reading this all right

Paul
PS Baits no more exciting or endangered than chicken, rabbit and pigeon!! No dolphins were harmed in this field survey.....

gianmarco

TS Contributor
Gianmarco

Interesting stuff...I can hear my old arch tutor grumbling about processualists :-}
Hello,