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