search for: logistics

Dear R Helpers, I try to write a small package that based on nlme however my code does not work. R always appears this message: Error in eval(expr, envir, enclos) : object "y" not found where y is the response variable. Please help me out! This is my code: require(nlme) AMPmixed<-function(y, x, S1=c("asymptotic","logistic"), random,data,

Can someone clarify what I cannot make out from the documentation? The function 'clogit' in the 'survival' package is described as performing a "conditional logistic regression". Its return value is stated to be "an object of class clogit which is a wrapper for a coxph object." This suggests that its usefulness is confined to the sort of data which arise in

Hi all: My data has 3 variables: age(3levels : <30y=1 30-50y=2, >50y=3) gender(Male=0, Female=1) CD4 cell count(raw lab measurement) y(1:death 0:alive) I perform logistic regression to find out the factors that influence y. result<-glm(y ~ factor(age) + factor(gender) + CD4,family = binomial) >From the result,I can get OR(Odds Ratio) of gender via exp(Estimate of Female,

Hi, I wonder if anyone has written any code to implement the suggestions of Smart et al (2004) in the Bulletin of the Ecological Society of America for a new way of graphically presenting the results of logistic regression (see www.esapubs.org/bulletin/backissues/085-3/bulletinjuly2004_2column.htm#t ools1 for the full text)? I couldn't find anything relating to this sort of graphical

Dear R helpers, I am not a statistician and right now struggling with Richards curve. Wikipedia says (http://en.wikipedia.org/wiki/Generalised_logistic_function) The "generalized logistic curve or function", also known as Richard's curve is a widely-used and flexible sigmoid function for growth modelling, extending the well-known logistic curve. Now I am confused and will like to

Hi I can get all my features by doing this: > logistic.model = glm(similarity ~ ., family=binomial, data = cData[3001:3800,]) I can get the product of all my features by this: logistic.model = glm(similarity ~ . ^ 2, family=binomial, data = cData[3001:3800,]) I don't seem to be able to get polys by doing this: logistic.model = glm(similarity ~ poly(.,2), family=binomial, data

Dear list, first accept my apologies for asking a non-R question. Can anyone point me to a good reference on logistic regression? web or book references would be great. I am interested in the use and interpretation of dummy variables and prediction models. I checked the contributed section in the CRAN homepage but could not find anything (Julian Faraway?s "practical Regression and ANOVA

Hi I am working on corpora of automatically recognized utterances, looking for features that predict error in the hypothesis the recognizer is proposing. I am using the glm functions to do logistic regression. I do this type of thing: * logistic.model = glm(formula = similarity ~., family = binomial, data = data) and end up with a model: > summary(logistic.model) Call:

Hi all, I'm trying to discover the options available to me for logistic and linear regression. I'm doing some tests on a dataset and want to see how different flavours of the algorithms cope. So far for logistic regression I've tried glm(MASS) and lrm (Design) and found there is a big difference. Is there a list anywhere detailing the options available which details the specific

Hello, I can not get the function plot.logistic.fit.fnc() working....it returns "Error: could not find function "plot.logistic.fit.fnc"". Do I need to upload as specific package first? I am trying to check the fit of a mixed logistic model. Also, any advice for checking the assumption of independence in a mixed logistic model? Many thanks in advance, Sarah Foster

Hello, I'm trying to fit a logistic curve to data but I'm having a hard time discovering how. Every tutorial I've come across either assumes the logistic curve has 0<y<1 or assumes I have multiple categories of data I simply have two vectors, a and b, of equal length with no missing data, and I suspect they follow a logistic curve. The vectors are a<-c(39609, 39643,

 Hi, Is there any function available to do stepwise selection of variables in Conditional(matched) logistic regression( clogit)? step, stepwise  etc are failing in case of conditional logistic regression. Please help.  Thanks P.T. Subha [[alternative HTML version deleted]]

Hi, I'm not sure of the correct nomenclature or function for what I'm trying to do. I'm interested in calculated a logistic regression on a binary dependent variable (True,False). There are a few ways to easily do this in R. Both SVM and GLM work easily. The part that I want to add is "group wise" awareness. So that the algorithm computes the coefficients to maximize

Hello! I need to classify a data set with 19 variables and 9 classes using Logistic Regression(on R). I know that when we have only 2 classes we can use glm() to estimate the coefficients of the model. But I don?t understand how can I do a classification task with Logistic Regression on a data set with 9 classes! Does anybody know how can I estimate these coefficients (of a model with 9

Hi all, Where f(x) is a logistic function, I have data that follow: g(x) = f(x)*.5 + .5 How would you suggest I modify the standard glm(..., family='binomial') function to fit this? Here's an example of a clearly ill-advised attempt to simply use the standard glm(..., family='binomial') approach: ######## # First generate some data ######## #define the scale and location of

Hello: I am working with a stratified survey dataset with sampling weights and I want to use multiple imputation to help with missingness. 1. Is there a way to run an ordered logistic regression using both a multiply imputed dataset (i.e. from mice) and adjust for the survey characteristics using the weight variable? The Zelig package is able to do binary logistic regressions for survey

Hello everybody :-) I have some data that I want to model with a logistic regression, most of the independent variables are numeric and the only dependent is categorical, I was thinking that I could apply a logistic regression using glm but I wanted to deepen my knowledge of this so I tried to do some reading and found the "iris" dataset, now I would like to ask two things, first

I am looking for R packages that can make a Logistic Regression model with parameter estimation by Maximum Likelihood Estimation. Many thanks for helping out.

Hello useRs, I have a problem at hand which I'd think is fairly common amongst groups were R is being adopted for Analytics in place of SAS. Users would like to obtain results for logistic regression in R that they have become accustomed to in SAS. Towards this end, I was able to propose the Design package in R which contains many functions to extract the various metrics that SAS reports.

I am using the as.factor command to use with glm. When I use the command >maj <- as.factor(data.logistic$Majors) >maj I receive the following output: [1] M M N M M M M N N M M M N M M M M M M M M M M M N M N N M M N M M N M M M M M [40] N M N M M N M M M N M N M N M N N N M N M M M M M M N M N M M M M M N N M M M [79] M M M N N M M N M N M M M M M M M M M M M M M M M N M M M M M N