similar to: logistic regression with response 0,1

I have a data set to be analyzed using to binary logistic regression. The data set is iin grouped form. My question is: how I can compute Hosmer-Lemeshow test and measures like sensitivity and specificity? Any suggestion will be greatly appreciated. Thank you Endy [[alternative HTML version deleted]]

Dear friends, I hope this email finds you all well. This is the dataset I am working with: dput(random_mod12_data2) structure(list(Index = c(1L, 5L, 11L, 3L, 2L, 8L, 9L, 4L), x = c(5, 13, 25, 9, 7, 19, 21, 11), n = c(500, 500, 500, 500, 500, 500, 500, 500), r = c(100, 211, 391, 147, 122, 310, 343, 176), ratio = c(0.2, 0.422, 0.782, 0.294, 0.244, 0.62, 0.686, 0.352)), row.names = c(NA, -8L),

Hi, I have my data the following way: y A B C 0 1 1 2 0 1 2 1 1 1 1 2 0 1 1 2 1 1 1 2 1 1 2 1 0 1 2 2 . . . And so on. How can I make my data look like the following: y A B C 2 1 1 2 1 1 2 1 0 1 2 2 . . . In other words how can I change my ungrouped cases into grouped cases? Thanks! Chris [[alternative

I'm sure this is probably simple, but I can't find an answer... I have a data frame (Galton's data on heights of parents and children), in grouped form, parent child frequency 73.5 72.2 1 73.5 73.2 3 72.5 68.2 1 72.5 69.2 2 72.5 70.2 1 72.5 71.2 2 72.5 72.2 7 72.5 73.2 2 72.5 74.2 4 71.5 65.2 1 71.5 66.2 3 71.5 67.2 4 71.5 68.2 3 71.5 69.2 5 71.5 70.2 10 ... and need the ungrouped

Dear R-Helpers, I'm working with immunoassay data and 5PL logistic model. I wanted to experiment with different forms of weighting and parameter selection, which is not possible in instrument software, so I turned to R. I am using R 2.14.2 under Win7 64bit, and the 'nls' library to fit the model - I started with the same model and weighting type (1/y) as in the instrument to see

I'm hoping the community knowledge can help me out here. I have found great value in R, especially using it to generate charts, but I am still scaling the learning curve in a number of ways. I am looking plot one grouped line and one ungrouped line in a lattice plot. I can plot one grouped line like this (the line's color in each panel becomes dependent on the newpool value):

Hello I am running R 1.8.1 on a Windows platform I am attempting to fit an ordinal logistic regression model, using the polr function, as described in Venables and Ripley. But when I try model4 <- polr(ypsxcat~committed + as.factor(sex) + as.factor(drugusey) + anycsw + as.factor(sex)*committed + as.factor(sex)*as.factor(drugusey)+as.factor(sex)*anycsw, data = duhray) I get a message

I have the following binary data set: Sex Response 0 1 0 159 162 1 4 37 My commands library(MASS) sib.glm=glm(sib~sex,family=binomial,data=sib.data) summary(sib.glm) The coefficients in the output are Estimate Std. Error z value Pr(>|z|) (Intercept) -3.6826 0.5062 -7.274 3.48e-13

Dear R-helpers I have a question related to logistic GAM models. Consider the following example: # Load package library(mgcv) # Simulation of dataset n <- 1000 set.seed(0) age <- rnorm(n, 50, 10) blood.pressure <- rnorm(n, 120, 15) cholesterol <- rnorm(n, 200, 25) sex <- factor(sample(c('female','male'), n,TRUE)) L <-

I'm trying to do a sunflowerplot of Galton's data, with both regression lines and data ellipses, and I must be doing something wrong, because the lines do not intersect at \bar{x}, \bar{y}. The problem is likely in the line for x.y, but I don't know how to specify that correctly. The data is read in grouped form( galton), and then ungrouped (galton2): galton <-

Hi, Trying to setup a logistic regression model. (Something new to me. I usually use SVM.) The person explaining the concept explained to me that I can include a "group" variable so that the probabilities predicted by the model will be "per group" Does this make sense to anyone? If so, how would I implement this? Using the glm or lrm function? Thanks! -N

Hi Folks, I have just started using dplyr and could use some help getting unstuck. It could well be that dplyr is not the package to be using, but let me just pose the question and seek your advice. Here is my basic data frame. head(h) subject ageGrp ear hearingGrp sex freq L2 Ldp Phidp NF SNR 1 HALAF032 A L A F 2 0 -23.54459 55.56005 -43.08282

It has always been my understanding that deviance for GLMs is defined by; D = -2(loglikelihood(model) - loglikelihood(saturated model)) and this can be calculated by (or at least usually is); D = -2(loglikelihood(model)) As is done so in the code for 'polr' by Brian Ripley (in the package 'MASS') where the -loglikehood is minimised using optim; res <-

Hi All, I have data with 400 individuals and the following information Grade: pass or fail coded as 1 for pass and 0 for fail Sex: male or female ( coded as 1 for male and 2 for female ) Age Teaching.method : can be 1,2,3 I want to fit a logistic regression where the outcome if (1=pass or 0 for fail) and the rest of the variables are the regressors. My question is that I am not sure

dear professor: I have a problem about the nomogram.I have got the result through analysing the dataset "exp2.sav" through multinominal logistic regression by SPSS 17.0. and I want to deveop the nomogram through R-Projject,just like this : > n<-100 > set.seed(10) > T.Grade<-factor(0:3,labels=c("G0", "G1", "G2","G3")) >

To whom this may concern, I would be very grateful if someone could give me some advice on where I am going wrong with a logistic regression I am trying to run. I am trying to run a logistic regression on an aggregated data set and have input the command: logistic.mod<-glm(x~Frequency+Location+Sex+Age.Group,family=binomial(link="logit"),data=earsag1.dat) where x is the count of my

Hi! Does anyone know how to do the test for goodness of fit of a logistic model (in rms package) after running fit.mult.impute? I am using the rms and Hmisc packages to do a multiple imputation followed by a logistic regression model using lrm. Everything works fine until I try to run the test for goodness of fit: residuals(type=c("gof")) One needs to specify y=T and x=T in the fit. But

I have used plor() to model a rather large 3-category dataset (~1500 data points, ~15 independent variables); from the resulting model (with a deviance slightly below the residual degrees of freedom), the training data are placed in only the two extreme categories. Though the result appears to indicate that there's only a relative 'narrow' bin for the medium group, [and when the

Dear all: "predict.glm" provides an example to perform logistic regression when the response variable is a tow-columned matrix. I find some paradox about the degree of freedom . > summary(budworm.lg) Call: glm(formula = SF ~ sex * ldose, family = binomial) Deviance Residuals: Min 1Q Median 3Q Max -1.39849 -0.32094 -0.07592 0.38220 1.10375

On Tue, 2018-06-05 at 10:05 -0700, Nick Desaulniers wrote: > Functions marked extern inline do not emit an externally visible > function when the gnu89 C standard is used. Some KBUILD Makefiles > overwrite KBUILD_CFLAGS. This is an issue for GCC 5.1+ users as without > an explicit C standard specified, the default is gnu11. Since c99, the > semantics of extern inline have changed