similar to: Error in contrasts message when using logistic regression code.

Dear R-help, I have fitted a glm logistic function to dichotomous forced choices responses varying according to time interval between two stimulus. x values are time separation in miliseconds, and the y values are proportion responses for one of the stimulus. Now I am trying to extrapolate x values for the y value (proportion) at .25, .5, and .75. I have tried several predict parameters, and they

Dear All, probably a simple enough solution but don;t seem to be able to get my head around it...example based on a publicly available data set: mydata <- read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv") mylogit <- glm(admit ~ gre + gpa + rank, data = mydata, family = "binomial") library(effects) plot(allEffects(mylogit) ? ?

Gracias a todos por sus respuestas, perdón si no fui muy claro. Lo que intento replicar es un análisis realizado en Stata, tengo que hacer los mismos cálculos pero en el r. En Stata lo que se hizo fue: xi: logistic i.coord i.v11_sexo, vce (cluster red) *Vce (cluster clustvar) especifica que los errores estándar permiten la correlación intragrupo, relajando el requisito habitual de que las

I do not see an R-squared value after preforming the glm regression. Is there a separate command for this? Thanks -- View this message in context: http://r.789695.n4.nabble.com/Obtaining-R-squared-value-in-Logistic-Regression-tp4648954.html Sent from the R help mailing list archive at Nabble.com.

i am getting the following error Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : contrasts can be applied only to factors with 2 or more levels can any on e suggest how to rectify [[alternative HTML version deleted]]

Buenas tardes, desde Uruguay, quiero realizar una regresión logística,con la función GLM, pero ademas quiero designarle a las observaciones clusters. La idea es decirle al programa que las observaciones corresponden a diferentes clusters. Saludos! Sebastián. [[alternative HTML version deleted]]

Hello everyone, I have a variable with several categories and I want to convert this into dummy variables and do logistic regression on it. I used model.matrix to create dummy variables but it always picked the smallest one as the reference. For example, model.matrix(~.,data=as.data.frame(letters[1:5])) will code 'a' as '0 0 0 0'. But I want to code another category as

I have a data matrix with genes as columns and samples as rows. I want to create all possible gene ratios.Is there an elegant and fast way to do it in R and write it to a dataframe? Thanks for any help. Som. -- View this message in context: http://r.789695.n4.nabble.com/Calculating-all-possible-ratios-tp4627405.html Sent from the R help mailing list archive at Nabble.com. [[alternative HTML

Hi, I have been running the same code without problem for the last few days, changing data sets etc with no issue. Today I changed the covariates for the model and am now getting this error message: Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : contrasts can be applied only to factors with 2 or more levels Everything in the code looks the same to me, but I'm a

I am very new to R, so I apologize if this question is trivial. I have a row in my data of dates in the format mm/dd/yyyy; about 3500 rows. I am using this variable in a logistic regression model, and need to treat it as continuous, not a factor as r has decided it is. I tried the as.numeric function but it resulted in all NA's and the message: "NAs introduced by coercion " If

Hi everyone, I'm doing a logistic regression with an ordinal variable. I'd like to set the contrasts on the ordinal variable. However, when I set the contrasts, they work for ordinary linear regression (lm), but not logistic regression (lrm): ddist = datadist(bin.time, exp.loc) options(datadist='ddist') contrasts(exp.loc) = contr.treatment(3, base = 3, contrasts = TRUE) lrm.loc =

Hi, I've been trying to sort this out for the past week. I had a RedHat 7.3 desktop install with a disk dying on it. The desktop has two disks on it - one configured with /, /usr and /boot (this was the bad disk) and the second had /var and /home. I obtained a replacement disk and initially tried copying the /, /usr and /boot filesystems to the new disk but this ended up being a problem. In

Hi, I am doing a retrospective analysis on a cohort from a designed trial, and I am fitting the model fit<-glmD(survived ~ Covariate*Therapy + confounder,myDat,X=TRUE, Y=TRUE, family=binomial()) My covariate has three levels ("A","B" and "C") and therapy has two (treated and control), confounder is a continuous variable. Also patients were randomized to

I have survey data that I am working on. I need to make some multi-way tables and regression analyses on the data. After attaching the data, this is the code I use for tables for four variables (sweight is the weight variable): > a <- xtabs(sweight~research.area + gender + a2n2 + age) > tmp <- ftable(a) Is this correct? I don't think I need to use the strata and cluster

Is this a bug, or have I misunderstood the proper use of lm? Thanks, Whit code: x <- rnorm(50) y <- matrix(as.logical(round(runif(100),0)),ncol=2) NROW(x)==NROW(y) lm(x~y) > x <- rnorm(50) > y <- matrix(as.logical(round(runif(100),0)),ncol=2) > NROW(x)==NROW(y) [1] TRUE > lm(x~y) Error in "[[<-.data.frame"(`*tmp*`, nn, value = c(2, 1, 2, 1, 1, 1, 2, :

m1.logit <-lm(default ~ amt.fac + age.fac + duration + chk_acct + history + purpose + sav_acct + employment + install_rate + pstatus

Muy buenas, He usado muy poco R en mi vida, y ahora estoy intentando hacer una MANOVA, con dos variables independientes (Edad, que tiene 4 niveles y Genero que tiene 2) y 5 variables independientes. Le he puesto también que me haga el poshoc de las dos y la interacción. Decir también que me instalé el ULLRToolbox, que no se si tendrá algo que ver con el error que me da. Este es el código que uso

Hola: Probablemente está tomando 'Edad_Manova_18a54' y 'Genero2_1a2' como variables numéricas (integer). Deberías convertirlas en factor. Por ejemplo: Edad_Manova_18a54 <- factor(Edad_Manova_18a54) Genero2_1a2 <- factor(Genero2_1a2) Manova.fnc(datosPAS, variables=2:6, fac.inter=c('Edad_Manova_18a54','Genero2_1a2'),poshoc=c('todos')) Saludos,

Dear experts: Is it possible to create a new function based on stats:::model.matrix.default so that an alternative factor coding is used when the function is called instead of the default factor coding? Basically, I'd like to reproduce the results in 'mat' below, without having to explicitly specify my desired factor coding (identity matrices) in the 'contrasts.arg'. dd

Dear Bill, Thanks for pointing this difference out -- I was unaware of it. I think that the difference occurs in model.matrix.default(), which coerces character variables but not logical variables to factors. Later it treats both factors and logical variables as "factors" in that it applies contrasts to both, but unused factor levels are dropped while an unused logical level is not. I