[R] Help using mapply to run multiple models

[Simon Kiss]

Dennis, how would your function be modified to allow it to be more flexible in future. 
I'm thinking like:
> f <- function(x='Dependent variable', y='List of Independent Variables', z='Data Frame')
> {
>    form <- as.formula(paste(x, y, sep = " ~ "))
>    glm(form, data =z)
> }

I tried that then using 
modlist <- lapply(xvars, f), but it didn't work. 

On 2013-12-18, at 3:29 AM, Dennis Murphy <djmuser at gmail.com> wrote:

> Hi:
> 
> Here's a way to generate a list of model objects. Once you have the
> list, you can write or call functions to extract useful pieces of
> information from each model object and use lapply() to call each list
> component recursively.
> 
> sample.df<-data.frame(var1=rbinom(50, size=1, prob=0.5),
>                      var2=rbinom(50, size=2, prob=0.5),
>                      var3=rbinom(50, size=3, prob=0.5),
>                      var4=rbinom(50, size=2, prob=0.5),
>                      var5=rbinom(50, size=2, prob=0.5))
> 
> # vector of x-variable names
> xvars <- names(sample.df)[-1]
> 
> # function to paste a variable x into a formula object and
> # then pass it to glm()
> f <- function(x)
> {
>    form <- as.formula(paste("var1", x, sep = " ~ "))
>    glm(form, data = sample.df)
> }
> 
> # Apply the function f to each variable in xvars
> modlist <- lapply(xvars, f)
> 
> To give you an idea of some of the things you can do with the list:
> 
> sapply(modlist, class)        # return class of each component
> lapply(modlist, summary)   # return the summary of each model
> 
> # combine the model coefficients into a two-column matrix
> do.call(rbind, lapply(modlist, coef))
> 
> 
> You'd probably want to rename the second column since the slopes are
> associated with different x variables.
> 
> Dennis
> 
> On Tue, Dec 17, 2013 at 5:53 PM, Simon Kiss <sjkiss at gmail.com> wrote:
>> I think I'm missing something.  I have a data frame that looks below.
>> sample.df<-data.frame(var1=rbinom(50, size=1, prob=0.5), var2=rbinom(50, size=2, prob=0.5), var3=rbinom(50, size=3, prob=0.5), var4=rbinom(50, size=2, prob=0.5), var5=rbinom(50, size=2, prob=0.5))
>> 
>> I'd like to run a series of univariate general linear models where var1 is always the dependent variable and each of the other variables is the independent. Then I'd like to summarize each in a table.
>> I've tried :
>> 
>> sample.formula=list(var1~var2, var1 ~var3, var1 ~var4, var1~var5)
>> mapply(glm, formula=sample.formula, data=list(sample.df), family='binomial')
>> 
>> And that works pretty well, except, I'm left with a matrix that contains all the information I need. I can't figure out how to use summary() properly on this information to usefully report that information.
>> 
>> Thank you for any suggestions.
>> 
>> *********************************
>> Simon J. Kiss, PhD
>> Assistant Professor, Wilfrid Laurier University
>> 73 George Street
>> Brantford, Ontario, Canada
>> N3T 2C9
>> 
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*********************************
Simon J. Kiss, PhD
Assistant Professor, Wilfrid Laurier University
73 George Street
Brantford, Ontario, Canada
N3T 2C9
Cell: +1 905 746 7606