[R] defining a formula method for a weighted lm()
Martin Maechler
maechler at stat.math.ethz.ch
Thu Jan 6 20:16:48 CET 2011
>>>>> Michael Friendly <friendly at yorku.ca>
>>>>> on Thu, 06 Jan 2011 09:33:25 -0500 writes:
> No one replied to this, so I'll try again, with a simple example. I
> calculate a set of log odds ratios, and turn them into a data frame as
> follows:
>> library(vcdExtra)
>> (lor.CM <- loddsratio(CoalMiners))
> log odds ratios for Wheeze and Breathlessness by Age
> 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64
> 3.695261 3.398339 3.140658 3.014687 2.782049 2.926395 2.440571 2.637954
>>
>> (lor.CM.df <- as.data.frame(lor.CM))
> Wheeze Breathlessness Age LOR ASE
> 1 W:NoW B:NoB 25-29 3.695261 0.16471778
> 2 W:NoW B:NoB 30-34 3.398339 0.07733658
> 3 W:NoW B:NoB 35-39 3.140658 0.03341311
> 4 W:NoW B:NoB 40-44 3.014687 0.02866111
> 5 W:NoW B:NoB 45-49 2.782049 0.01875164
> 6 W:NoW B:NoB 50-54 2.926395 0.01585918
> 7 W:NoW B:NoB 55-59 2.440571 0.01452057
> 8 W:NoW B:NoB 60-64 2.637954 0.02159903
> Now I want to fit a linear model by WLS, LOR ~ Age, which can do directly as
>> lm(LOR ~ as.numeric(Age), weights=1/ASE, data=lor.CM.df)
> Call:
> lm(formula = LOR ~ as.numeric(Age), data = lor.CM.df, weights = 1/ASE)
> Coefficients:
> (Intercept) as.numeric(Age)
> 3.5850 -0.1376
> But, I want to do the fitting in my own function, the simplest version is
> my.lm <- function(formula, data, subset, weights) {
> lm(formula, data, subset, weights)
> }
> But there is obviously some magic about formula objects and evaluation
> environments, because I don't understand why this doesn't work.
>> my.lm(LOR ~ as.numeric(Age), weights=1/ASE, data=lor.CM.df)
> Error in model.frame.default(formula = formula, data = data, subset =
> subset, :
> invalid type (closure) for variable '(weights)'
>>
Yes, the "magic" has been called "standard non-standard evaluation"
for a while (since August 2002, to be precise),
and the http://developer.r-project.org/ web page has had two
very relevant links since then, namely those mentioned in the
following two lines there:
----------------------------
# Description of the nonstandard evaluation rules in R 1.5.1 and some suggestions. (updated). Also an R function and docn for making model frames from multiple formulas.
# Notes on model-fitting functions in R, and especially on how to enable all the safety features.
----------------------------
For what you want, I think (but haven't tried) the second link, which is
http://developer.r-project.org/model-fitting-functions.txt
is still very relevant.
Many many people (package authors) had to use something like
that or just directly taken the lm function as an example..
{{ but then probably failed the more subtle points on how to
program residuals() , predict() , etc functions which you can
also learn from model-fitting-functions.txt}}
> A second question: Age is a factor, and as.numeric(Age) gives me 1:8.
> What simple expression on lor.CM.df$Age would give me either the lower
> limits (here: seq(25, 60, by = 5)) or midpoints of these Age intervals
> (here: seq(27, 62, by = 5))?
With
data(CoalMiners, package = "vcd")
here are some variations :
> (Astr <- dimnames(CoalMiners)[[3]])
[1] "25-29" "30-34" "35-39" "40-44" "45-49" "50-54" "55-59" "60-64"
> sapply(lapply(strsplit(Astr, "-"), as.numeric), `[[`, 1)
[1] 25 30 35 40 45 50 55 60
> sapply(lapply(strsplit(Astr, "-"), as.numeric), `[[`, 2)
[1] 29 34 39 44 49 54 59 64
> sapply(lapply(strsplit(Astr, "-"), as.numeric), mean)
[1] 27 32 37 42 47 52 57 62
Or use the 2-row matrix and apply(*, 1) to that :
> sapply(strsplit(Astr, "-"), as.numeric)
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 25 30 35 40 45 50 55 60
[2,] 29 34 39 44 49 54 59 64
Regards,
Martin Maechler, ETH Zurich
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