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 <djmu...@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 <sjk...@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
>>
>> ______________________________________________
>> R-help@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
*********************************
Simon J. Kiss, PhD
Assistant Professor, Wilfrid Laurier University
73 George Street
Brantford, Ontario, Canada
N3T 2C9
Cell: +1 905 746 7606
______________________________________________
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