similar to: Binomial

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Hola, espero ser clara en el mensaje ya que es la primera vez que recurro a este tipo de ayudas, explico mi duda: Tengo un dataset con 4505 observaciones en el que la variable dependiente son presencias (n=97 y clasificadas como 1) y ausencias (n=4408 y clasificadas como 0). Mi primer paso fue realizar un GLM con una muestra compensada de ausencias y presencias para la variable dependiente, es

Hi all! I am trying to develop a plot a figure in which I would like to show the odds ratios obtained from a logistic model. I have tried with the dotplot option but no success. Could you help me? Is there any option when modelling the logistic model in R? Thank you in advance

Hi David, Ista and all, I have one related question Within one group I want to keep records conditionally. example within group A I want keep rows that have " x" values ranged between 15 and 30. group B I want keep rows that have " x" values ranged between 40 and 50. group C I want keep rows that have " x" values ranged between 60 and 75. DM <-

Hi Ashta, There are many ways to do it. Here is one: vars <- sapply(split(DM$x, DM$GR), var) DM[DM$GR %in% names(vars[vars > 0]), ] Best Ista On Wed, Dec 6, 2017 at 6:58 PM, Ashta <sewashm at gmail.com> wrote: > Thank you Jeff, > > subset( DM, "B" != x ), this works if I know the group only. > But if I don't know that group in this case "B", how

> On Dec 8, 2017, at 4:48 PM, Ashta <sewashm at gmail.com> wrote: > > Hi David, Ista and all, > > I have one related question Within one group I want to keep records > conditionally. > example within > group A I want keep rows that have " x" values ranged between 15 and 30. > group B I want keep rows that have " x" values ranged

> On Dec 6, 2017, at 4:27 PM, Ashta <sewashm at gmail.com> wrote: > > Thank you Ista! Worked fine. Here's another (possibly more direct in its logic?): DM[ !ave(DM$x, DM$GR, FUN= function(x) {!length(unique(x))==1}), ] GR x y 5 B 25 321 6 B 25 512 7 B 25 123 8 B 25 451 -- David > On Wed, Dec 6, 2017 at 5:59 PM, Ista Zahn <istazahn at gmail.com> wrote:

Hi, I am trying to construct a logistic regression model from my data (104 patients and 25 events). I build a full model consisting of five predictors with the use of penalization by rms package (lrm, pentrace etc) because of events per variable issue. Then, I tried to approximate the full model by step-down technique predicting L from all of the componet variables using ordinary least squares

Dear All, For logistic regression models: is it possible to use validate (rms package) to compute bias-corrected AUC, but have variable selection with AIC use step (or stepAIC, from MASS), instead of fastbw? More details: I've been using the validate function (in the rms package, by Frank Harrell) to obtain, among other things, bootstrap bias-corrected estimates of the AUC, when variable

Thank you Ista! Worked fine. On Wed, Dec 6, 2017 at 5:59 PM, Ista Zahn <istazahn at gmail.com> wrote: > Hi Ashta, > > There are many ways to do it. Here is one: > > vars <- sapply(split(DM$x, DM$GR), var) > DM[DM$GR %in% names(vars[vars > 0]), ] > > Best > Ista > > On Wed, Dec 6, 2017 at 6:58 PM, Ashta <sewashm at gmail.com> wrote: >> Thank

Hello, Try the following. keep <- list(A = c(15, 30), B = c(40, 50), C = c(60, 75)) sp <- split(DM$x, DM$GR) inx <- unlist(lapply(seq_along(sp), function(i) keep[[i]][1] <= sp[[i]] & sp[[i]] <= keep[[i]][2])) DM[inx, ] # GR x y #1 A 25 125 #2 A 23 135 #5 B 45 321 #6 B 47 512 #9 C 61 521 #10 C 68 235 Hope this helps, Rui Barradas On 12/9/2017 12:48 AM, Ashta

Dear all, I am using the publically available GustoW dataset. The exact version I am using is available here: https://drive.google.com/open?id=0B4oZ2TQA0PAoUm85UzBFNjZ0Ulk I would like to produce a nomogram for 5 covariates - AGE, HYP, KILLIP, HRT and ANT. I have successfully fitted a logistic regression model using the "glm" function as shown below. library(rms) gusto <-

Hi all R users I did a logistic regression with my binary variable Y (0/1) and 2 explanatory variables. Now I try to draw my nomogram with predictive value. I visited the help of R but I have problem to understand well the example. When I use glm fonction, I have a problem, thus I use lrm. My code is: modele<-lrm(Y~L+P,data=donnee) fun<- function(x) plogis(x-modele$coef[1]+modele$coef[2])

library(dplyr) DM <- read.table( text='GR x y A 25 125 A 23 135 . . . ) DM %>% filter((GR == "A" & (x >= 15) & (x <= 30)) | (GR == "B" & (x >= 40) & (x <= 50)) | (GR == "C" & (x >= 60) & (x <= 75))) On Fri, Dec 8, 2017 at 4:48 PM, Ashta <sewashm at gmail.com>

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

> On Dec 8, 2017, at 6:16 PM, David Winsemius <dwinsemius at comcast.net> wrote: > > >> On Dec 8, 2017, at 4:48 PM, Ashta <sewashm at gmail.com> wrote: >> >> Hi David, Ista and all, >> >> I have one related question Within one group I want to keep records >> conditionally. >> example within >> group A I want keep rows that

You could make numeric vectors, named by the group identifier, of the contraints and subscript it by group name: > DM <- read.table( text='GR x y + A 25 125 + A 23 135 + A 14 145 + A 35 230 + B 45 321 + B 47 512 + B 53 123 + B 55 451 + C 61 521 + C 68 235 + C 85 258 + C 80 654',header = TRUE, stringsAsFactors = FALSE) > > GRmin <- c(A=15, B=40, C=60) > GRmax <-

I am trying to plot a line graph for 3 or more regression lines abline(m1) abline(m2) abline(m3) Can I change the color of each line? if so how? Thanks in advance Ashta [[alternative HTML version deleted]]

Hi, I,ve a run a model like this mcrm<-glm(catroj~month,binomial) being catroj a binary response variable with two levels (infected and non infected) > anova(mcrm3,test="Chisq") Df Deviance Resid. Df Resid. Dev P(>|Chi|) NULL 520 149.81 mes 3 16.86 517 132.94 0.0007551 *** When I?m trying to do a post

lrm does some binning to make the calculations faster. The exact calculation is obtained by running f <- lrm(...) rcorr.cens(predict(f), DA), which results in: C Index Dxy S.D. n missing 0.96814404 0.93628809 0.03808336 32.00000000 0.00000000 uncensored Relevant Pairs Concordant Uncertain 32.00000000