glm

I have a quasibinomial glm with two continuous explanatory variables (let's say "LogPesticide" and "LogFood") and an interaction. I would like to calculate the LC50 of the pesticide with confidence intervals at different amounts of food (e. g. the minimum and maximum food value). How can this be achieved? Example: First I generate a data set. mydata <- data.frame( LogPesticide = rep(log(c(0, 0.1, 0.2, 0.4, 0.8, 1.6) + 0.05), 4), LogFood = rep(log(c(1, 2, 4, 8)), each = 6) ) set.seed(seed=16) growth <- function(x, a = 1, K = 1, r = 1) { # Logistic growth function. a = position of turning point

I am trying to predict values over time (Days in x axis) for a glmer model that was run on my binomial data. Total Alive and Total Dead are count data. This is my model, and the corresponding steps below. full.model.dredge<-glmer(cbind(Total.Alive,Total.Dead)~(CO2.Treatment+Lime.Treatment+Day)^3+(Day|Container)+(1|index), data=Survival.data,family="binomial") We have accounted for overdispersion as you can see in the code (1:index). We then use the dredge command to determine the best fitted models with the main effects (CO2.Treatment, Lime.Treatment, Day) and their corresponding interactions.

I am currenlty computing glm models off a huge data data set. Both glm and even speedglm take days to compute. I currently have around 3M observations and altogether 400 variables, only some of which are used for the regression. In my regression I use 4 integer independent variables ( iv1 , iv2 , iv3 , iv4 ), 1 binary independent variable as factor ( iv5 ), the interaction term ( x * y , where x is an integer and y is a binary dummy variable as factor). Finally, I have fixed effects along years ff1 and company ids ff2 . I have 15 years and 3000 conmpanies. I have introduced the fixed effects

I am using R to replicate a study and obtain mostly the same results the author reported. At one point, however, I calculate marginal effects that seem to be unrealistically small. I would greatly appreciate if you could have a look at my reasoning and the code below and see if I am mistaken at one point or another. My sample contains 24535 observations, the dependent variable "x028bin" is a binary variable taking on the values 0 and 1, and there are furthermore 10 explaining variables. Nine of those independent variables have numeric levels, the independent variable "f025grouped" is a factor

I'm trying to create a model using the MCMCglmm package in R. The data are structured as follows, where dyad, focal, other are all random effects, predict1-2 are predictor variables, and response 1-5 are outcome variables that capture # of observed behaviors of different subtypes: dyad focal other r present village resp1 resp2 resp3 resp4 resp5 1 10101 14302 0.5 3 1 0 0 4 0 5 2 10405 11301 0.0 5 0 0 0 1 0 1 … So a model with only one outcome (teaching) is as follows: prior_overdisp_i <- list(R=list(V=diag(2),nu=0.08,fix=2), G=list(G1=list(V=1,nu=0.08), G2=list(V=1,nu=0.08), G3=list(V=1,nu=0.08

When I use the following R code, model_glm=glm(V1~. , data=xx,family="binomial"); save(file="modelfile",model_glm); The size of modelfile will be as much as the data, which will be 1gig in my case. How can I remove the data part in the result of model_glm, so I can only save a small file. Setting model = FALSE in your call to glm should prevent the model.frame from being returned. Also setting y = FALSE will prevent the response vector from being returned. x = FALSE is the default setting and prevents the model.matrix from being returned. This combination should shrink the size of your glm

I'm working on a project that would show the potential influence a group of events have on an outcome. I'm using the glmnet() package, specifically using the Poisson feature. Here's my code: # de <- data imported from sql connection x <- model.matrix(~.,data = de[,2:7]) y <- (de[,1]) reg <- cv.glmnet(x,y, family = "poisson", alpha = 1) reg1 <- glmnet(x,y, family = "poisson", alpha = 1) **Co <- coef(?reg or reg1?,s=???)** summ <- summary(Co) c <- data.frame(Name= rownames(Co)[summ$i], Lambda= summ$x) c2 <- c[with(c, order(-Lambda)), ] The beginning imports a large amount of data from my

I want to find Lethal Dose ( LD50 ) with its confidence interval in R . Other softwares line Minitab, SPSS, SAS provide three different versions of such confidence intervals. I could not find such intervals in any package in R (I also used findFn function from sos package). How can I find such intervals? I coded for one type of intervals based on Delta method (as not sure about it correctness) but would like to use any established function from R package. Thanks MWE: dose <- c(10.2, 7.7, 5.1, 3.8, 2.6, 0) total <- c(50, 49, 46, 48, 50, 49) affected <- c(44, 42, 24, 16, 6, 0) finney71 <- data