Re: [R] Conditioning plots (wth coplot function) with logistic regression curves

, Kiyoshi   From: Michael Friendly Cc: "r-help@r-project.org" Sent: Sunday, September 22, 2013 7:46:04 PM Subject: Re: Conditioning plots (wth coplot function) with logistic regression curves On 9/21/2013 11:12 PM, Kiyoshi Sasaki wrote: > I hav

[R] Conditioning plots (wth coplot function) with logistic regression curves

I have been trying to produce a conditional plot using coplot function (http://stat.ethz.ch/R-manual/R-devel/library/graphics/html/coplot.html) for a binary response ("Presence" in my case) variable and one continuous variable ("Overstory") given a specific levels of the other continuous variabl

[R] Histogram with two groups on the same graph (not on separate panels)

I have been trying to produce a histogram that has two groups (male and female snakes) on the same graph (either superimposed or each frequency bar appears side by side). I found a couple of functions for superimposed histogram written by other people. The below is the codes I used for my data c

[R] Appropriate tests for logistic regression with a continuous predictor variable and Bernoulli response variable

I have a data with binary response variable, repcnd (pregnant or not) and one predictor continuous variable, svl (body size) as shown below. I did Hosmer-Lemeshow test as a goodness of fit (as suggested by a kind “R-helper” previously). To test whether the predictor (svl, or body size) has

Re: [R] Different goodness of fit tests leads to contradictory conclusions

e: [R] Different goodness of fit tests leads to contradictory conclusions To: "Kiyoshi Sasaki" Cc: r-help@r-project.org Date: Wednesday, July 7, 2010, 10:08 AM The first two options are GOF-tests for ungrouped data, the latter two can only be used for grouped data. According to

[R] Different goodness of fit tests leads to contradictory conclusions

I am trying to test goodness of fit for my legalistic regression using several options as shown below.  Hosmer-Lemeshow test (whose function I borrowed from a previous post), Hosmer–le Cessie omnibus lack of fit test (also borrowed from a previous post), Pearson chi-square test, and deviance

[R] Why do s appear in the list of predictor variables in logistic regression using 'step' or 'stepAIC' function?

Would anyone help me solve my problem with R, please? I am very new to R. I am doing logistic regression analysis on the presence/absence of salamanders using several predictor variables, as shown below. I have checked my data, but I didn't find any 'NA' or empty cells. When I used step() or s