[R] How to construct a formula

[Hugh Morgan]

Thank you very much, that is exactly what I needed.

Hugh

On 01/22/2012 03:38 PM, Duncan Murdoch wrote:
> On 12-01-22 9:27 AM, Hugh Morgan wrote:
>> Hi,
>>
>> I need to construct a formula programaticly, and pass it to a function
>> such as the linear mixed model lme.  The help says it requires "a
>> two-sided linear formula object describing the fixed-effects part of the
>> model" but I do not know how to create this formula.  I have tried
>> various things using formula(x, ...), as.formula(object, env =
>> parent.frame()) and as.Formula(x, ...) but I cannot get it to work.  Can
>> anyone give me any pointers?
>>
>> What I want to do is pass particular columns to my mixed model depending
>> on the result of a previous test (anova) in a programatic way.  I could
>> use hard coded if statements as in the example below, but I want to be
>> able to do this in a more programatic way.  I hope the code below will
>> show what I am trying to do:
>>
>> Current code:
>>
>> data=read.csv("dataMini.csv", header=TRUE, sep=",", dec=".")
>> colnames(data)
>> library(nlme)
>>
>> if(weight_significant) {
>>       if(gender_significant) {
>>           if(weight_gender_interaction_significant) {
>>               model=lme(test_variable~Genotype + Weight + Gender +
>> Weight*Gender, random=~1|Assay.Date, data, na.action="na.exclude",
>> method="REML")
>>           } else {
>>               model=lme(test_variable~Genotype + Weight + Gender,
>> random=~1|Assay.Date, data, na.action="na.exclude", method="REML")
>>           }
>>       } else {
>> .... etc
>>
>> What I want to do:
>>
>> formulaObject = test_variable~Genotype
>> if(weight_significant) {
>>       formulaObject = formulaObject + Weight;
>> }
>> if(gender_significant) {
>>       formulaObject = formulaObject +  Gender;
>> }
>> if(weight_gender_interaction_significant) {
>>       formulaObject = formulaObject +  Weight*Gender;
>> }
>
> I think you don't really want that; you want to add those terms to the 
> right hand side of the formula.
>
> This function would do that:
>
> addterm <- function(fla, term) {
>   fla[[3]] <- call("+", fla[[3]], substitute(term))
>   fla
> }
>
> For example,
>
> > formulaObject
> test_variable ~ Genotype
> > addterm(formulaObject, Weight)
> test_variable ~ Genotype + Weight
>
> Duncan Murdoch
>
>
>> model=lme(formulaObject, random=~1|Assay.Date, data,
>> na.action="na.exclude", method="REML")
>
>


This email may have a PROTECTIVE MARKING, for an explanation please see:
http://www.mrc.ac.uk/About/Informationandstandards/Documentmarking/index.htm