glm

问题 I can use texreg to get beautiful output of glm to be used for knitr . Sometimes we need to convert the output of glm back to response using inverse link. I wonder how to get inverse link output with texreg . Something like texreg(exp(glm.D93)) . counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3,1,9) treatment <- gl(3,3) d.AD <- data.frame(treatment, outcome, counts) glm.D93 <- glm(counts ~ outcome + treatment, family = poisson()) library(texreg) texreg(glm.D93) which produces \begin

问题 I can use texreg to get beautiful output of glm to be used for knitr . Sometimes we need to convert the output of glm back to response using inverse link. I wonder how to get inverse link output with texreg . Something like texreg(exp(glm.D93)) . counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3,1,9) treatment <- gl(3,3) d.AD <- data.frame(treatment, outcome, counts) glm.D93 <- glm(counts ~ outcome + treatment, family = poisson()) library(texreg) texreg(glm.D93) which produces \begin

问题 I can use texreg to get beautiful output of glm to be used for knitr . Sometimes we need to convert the output of glm back to response using inverse link. I wonder how to get inverse link output with texreg . Something like texreg(exp(glm.D93)) . counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3,1,9) treatment <- gl(3,3) d.AD <- data.frame(treatment, outcome, counts) glm.D93 <- glm(counts ~ outcome + treatment, family = poisson()) library(texreg) texreg(glm.D93) which produces \begin

问题 I was running a bestglm analysis from the package bestglm : res.bestglm <- bestglm(Xy = offermodel, family = binomial, IC = "BIC", method = "exhaustive") summary(res.bestglm$BestModel) Morgan-Tatar search since family is non-gaussian. and I started getting this error: Error in model.frame.default(formula = y ~ 1, weights = weights, drop.unused.levels = TRUE) : could not find function "function (object, ...) \nobject" I inspected my model data and it looks good -- the right classes, no

问题 I found several questions on Stackoverflow regarding this topic (some of them without any answer) but nothing related (so far) with this error in regression. I'm, running a probit model in r with (I'm guessing) too many fixed effects (year and places): myprobit <- glm(factor(Y) ~ factor(T) + factor(X1) + factor(X2) + factor(X3) + factor(YEAR) + factor(PLACE), family = binomial(link = "probit"), data = DT) The PLACE variable has about 1000 unique values and YEAR 8 values. The dataset DT has 13

问题 Reading the description of glm in R it is not clear to me what the difference is between specifying a model offset in the formula, or using the offset argument. In my model I have a response y, that should be divided by an offset term w, and for simplicity lets assume we have the covariate x. I use log link. What is the difference between glm(log(y)~x+offset(-log(w))) and glm(log(y)~x,offset=-log(w)) 回答1: The two ways are identical. This can be seen in the documentation (the bold part): this

问题 I would like to plot a tree resulting from glmtree (partykit package). Unfortunately, the terminal nodes are overlapping and the labels of the graphs are no longer displayed properly. The code that generates the tree is this one: library(partykit) library(aVirtualTwins) data(sepsis) attach(sepsis) data <- cbind(y = survival, trt = as.factor(THERAPY), sepsis[,3:13]) formula <- as.formula(paste("y ~ trt", paste(names(sepsis[,3:13]), collapse = " + "), sep = " | ")) fit <- glmtree(formula, data,

问题 I have a set of GLMMs fitted with a binary response variable and a set of continuous variables, and I would like to get confidence intervals for each model. I've been using confint() function, at 95% and with the profile method, and it works without any problems if it is applied to a model with no interactions. However, when I apply confint() to a model with interactions (continuous*continuous), I've been getting this error: m1CI <- confint(m1, level=0.95, method="profile") Error in zeta

问题 I have constructed models in glmer and would like to predict these on a rasterStack representing the fixed effects in my model. my glmer model is in the form of: m1<-glmer(Severity ~ x1 + x2 + x3 + (1 | Year) + (1 | Ecoregion), family=binomial( logit )) As you can see, I have random effects which I don't have as spatial layer - for example 'year'. Therefore the problem is really predicting glmer on rasterStacks when you don't have the random effects data random effects layers. If I use it out

问题 I'm running a binomial GLM to predict the probability of a species occurrence, where I am training on one dataset and testing the model on another dataset: TrainingData<-read.csv("TrainingData.csv")[,-1] TrainingData[,1]<-as.factor(TrainingData[,1]) TrainingData[,4]<-as.factor(TrainingData[,4]) TestData<-read.csv("TestData.csv")[,-1] TestData[,1]<-as.factor(TestData[,1]) TestData[,4]<-as.factor(TestData[,4]) mod<-glm(presence~var1+var2+var3, family=binomial, data=TrainingData) probs=predict