Nonlinear logistic regression package in R -


is there r package performs nonlinear logistic regression?

in more words: have glm, can go glm (cbind (success, failure) ~ variable 1 + variable2, data = df, family = binomial (link = 'logit')), , can use nls go nls (y ~ * x^2 + b * x + c, data = df).

i'd have function take formula cbind (success, failure) ~ int - slo * x + gap / (1 + x / sca) (where x, success, , failure data , else parametres) binomial (link = 'logit') family, i.e. combine both things. i've been scouring google , haven't been able find that.

try gnlm::bnlr(). default link logit , can specify nonlinear function of data , parameters. include 2 answers depending on whether or not gap , sca data or parameters.

library(gnlm)

in case of gap , sca data:

## if gap , sca data: set.seed(1) dat <- data.frame(  x = rnorm(10),                   gap = rnorm(10),                   sca = rnorm(10),                     y = rbinom(10,1,0.4)) y_cbind = cbind(dat$y, 1-dat$y) attach(dat) bnlr(y=y_cbind, mu = ~ int - slo * x + gap / (1 + x / sca), pmu = c(0,0))

output:

call: bnlr(y = y_cbind, mu = ~int - slo * x + gap/(1 + x/sca), pmu = c(0,      0))  binomial distribution  response: y_cbind   log likelihood function: {     m <- plogis(mu1(p))     -sum(wt * (y[, 1] * log(m) + y[, 2] * log(1 - m))) }  location function: ~int - slo * x + gap/(1 + x/sca)  -log likelihood    2.45656  degrees of freedom 8  aic                4.45656  iterations         8   location parameters:      estimate      se int    -1.077  0.8827 slo    -1.424  1.7763  correlations:        1      2 1 1.0000 0.1358 2 0.1358 1.0000

in case gap , sca parameters:

## if gap , sca parameters: detach(dat) set.seed(2) dat <- data.frame(  x = rbinom(1000,1,0.3),                     y = rbinom(1000,1,0.4)) y_cbind = cbind(dat$y, 1-dat$y) attach(dat) bnlr(y=y_cbind, mu = ~ int - slo * x + gap / (1 + x / sca), pmu = c(0,0,0,1))

output:

call: bnlr(y = y_cbind, mu = ~int - slo * x + gap/(1 + x/sca), pmu = c(0,      0, 0, 1))  binomial distribution  response: y_cbind   log likelihood function: {     m <- plogis(mu1(p))     -sum(wt * (y[, 1] * log(m) + y[, 2] * log(1 - m))) }  location function: ~int - slo * x + gap/(1 + x/sca)  -log likelihood    672.9106  degrees of freedom 996  aic                676.9106  iterations         7   location parameters:      estimate      se int  -0.22189  2.1007 slo   0.03828  3.6051 gap  -0.20273  2.0992 sca   0.99885  0.3956  correlations:           1       2        3       4 1    1.0000   1.859  -0.9993 -281.45 2    1.8587   1.000  -1.8592  -82.06 3   -0.9993  -1.859   1.0000  281.64 4 -281.4530 -82.061 281.6443    1.00

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