Multiple non linear regression in R program

Question

I am trying to use a logistic model of the form

Y = exp(ao + a1fi1....)/(1 + exp(a0 + a1fi1 ....)

for multiple non linear regression in R, The dependent variable Y is a row consisting of about 500 values and there are 33 independent variables X1, X2, X3.....X33

I am reading my data from an EXCEL file:

data1<-read.csv(file.choose(), header=TRUE)

which populates R with my data. I performed linear regression with the lm() function using input:

results<- lm(Y~ X1 + X2....X33, data = data1)

which worked perfectly fine and now I am trying to use the self starting logistic function of the form:

 nls(Y ~ SSlogis(x, Asym, xmid, scal), data1)

for non linear regression; however, I do not seem to be applying the function properly. Thus my question is how would I use this function to perform multiple non linear regression analysis for my dataset?? Thank you for any help you can provide.

"I do not seem to be applying the function properly"... makes you say that? Do you get an error? A warning? If so, what do they say? Does your R session crash? Does your computer freeze? Do you get a result but it just doesn't seem right? — Gregor Thomas
Also, let's be sure you need nls with SSlogis. Do you have binary data (Y takes two distinct values)? If so, use glm with family = binomial, not nls with SSlogis. — Gregor Thomas
Your question is a bit unclear. The gnls function from package nlme allows you to model the parameters of a nonlinear model with linear relationships to covariates. — Roland
Yes My Y function is 1 and 0. Thus I used the function: glm(formula = Y ~ X1 + X2, family = binomial, data = data1) . The estimated coefficients matched those in a published paper that were calculated with SAS — MC2016
Thank you everyone for the help!! the glm() function was what I should have been using!! — MC2016

CSV CSV · Accepted Answer · 2017-03-27T22:46:21

You simply choose the type of the model when doing regression. The codes following should help. (I used a online dataset for example)

    mydata <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv")
    model <- glm(admit ~ .,
    family=binomial(link='logit'),
    data= my data)

Then you can use following code to get more info about your model

    fit
    fit$resample
    fit$results
    fit$finalModel

Multiple non linear regression in R program

1 Answers