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I'm using matched pairs logistic regression (1-1 matched case-control; Hosmer and Lemeshow 2000) to model differences between vegetation selected at nest sites vs. paired random sites. To do this, I created a data frame that contained the difference in vegetation measurements between nest and random sites (so nest minus random) and used R to fit a logistic regression model, using a vector of all 1's as the 'Response' and a no-intercept model.

Here's the data frame (I only include 1 of the covariates, grass density, for the example):

nest<-structure(list(VerGR = c(1.380952381, 1.952380953, 2.666666667, 
-3.809523809, 2.428571428, 2.142857143, 0.142857143, 2.095238095, 
1.952380952, 3.333333334, 3.190476191, -2.857142857, 2.857142858, 
-1.666666667, 0.523809524, 4.761904762, 0.571428571, 2.238095238, 
-2.809523809, 0.857142857, 1.523809524, -2.476190476, -0.428571428, 
-5.190476191, 4.142857143, 2.857142858, -2.476190476, 4.095238096, 
1.428571428, 1.714285714, -2.80952381, 3.142857143, 2.809523809, 
7.238095238, 2.523809523, 2.333333333, -0.095238096, -0.095238096, 
-0.142857143, 4.047619048, 4.761904759, -1.285714285, -1.190476191, 
2.523809524, -2.095238095, -2, 4.761904761, 8.952380952, 1.095238096, 
5.666666666, -0.714285714, 0, 2.809523809, -0.238095239, 3.666666667, 
0.904761905, -4.952380952, -3.666666667, 2, -0.619047619, 4.523809524, 
1.523809524, 4.619047619, 6.142857143, 3.19047619, -2.190476191, 
-1.666666667, 2.714285714, -1.285714286, 2.857142857, 2.761904762, 
2.809523809, -7.142857139, -5.952380949, -1.19047619, 1.523809524, 
-0.38095238, 5.571428571, 5.238095239, 2.047619048, 7.857142857, 
0.61904761, 2.523809524, -1.190476191), Response = c(1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L)), .Names = c("VerGR", "Response"), class = "data.frame", row.names = c(NA, 
-84L))

And the no-intercept logistic regression models I am running:

grass.mod <- glm(Response ~ VerGR - 1, data=nest, family="binomial")
grass2.mod <- glm(Response ~ VerGR + I(VerGR^2) - 1, data=nest, family="binomial")

For the most part the models run fine, and give the same parameter estimates as models implemented using the 'clogit' function from the survival R package. The data set for the clogit models is slightly different, with Responses = 1 (nest) or = 0 (random point), and includes a column called 'PairID' to indicate nest-random pairs. Here's what the clogit models look like:

library(survival)
grass.mod.clog <- clogit(Response ~ VerGR + strata(PairID), data=full)
grass2.mod.clog <- clogit(Response ~ VerGR + I(VerGR^2) + strata(PairID), data=full)

But when I run the glm's, I get these 2 warnings if using a quadratic term:

Warning messages:
1: glm.fit: algorithm did not converge 
2: glm.fit: fitted probabilities numerically 0 or 1 occurred 

I'm able to satisfy the first warning if I use more iterations in the glm formula, but I'm not sure what is happening with the second warning. I would be glad to use the 'clogit' function (which works with quadratic terms), but I'm unsure how to create prediction plots to visually display the data when going that route. Any suggestions?

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migrated from stackoverflow.com Mar 9 '16 at 8:46

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  • 3
    $\begingroup$ You only have a response of "1" in this data sample. Therefore, you can perfectly classify without having to use the covariate. The estimates for the coefficients did not therefore converge under MLE and are meaningless here. $\endgroup$ – A. Webb Mar 8 '16 at 15:15
  • $\begingroup$ Using pairing in the sampling should prevent you from predicting a response. Only with the assumption that sampling from cases and controls is random can you construct the tables necessary to estimate a "response" using hte odds ratios and the Intercept term (or the base stratum). So I think this is a conceptual problem with your understanding of the two different methods and as such should go to CrossValidated.com. $\endgroup$ – DWin Mar 9 '16 at 0:47
  • $\begingroup$ Why would I being getting the same estimate though when using the 'clogit' function and the other approach? $\endgroup$ – Jason Mar 14 '16 at 21:06

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