glm

The Problem I trained a linear regression in R to predict this.target from city , variables in data frame data . This trainig is done on a subset of the data, which is specified by train.index . model = glm('data[, this.target] ~ data$city', data = data, subset = train.index) I am trying to test this model on the held out data, which is specified by test.index . predictions = predict(model, data[test.index, ]) For whatever reason, this second step creates an error and a warning. Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels) : invalid type (NULL) for

When I run a binomial regression in R with an independed factor variable consisting of three levels "Higher" , "Middle" and "Lower" of which I want to change the reference category using relevel I get this error: “Error in relevel.ordered(cbsnivcat3, "Lower") : 'relevel' only for factors” I have checked whether cbsnivcat3 is a factor > is.factor(data$cbsnivcat3) [1] TRUE > levels(data$cbsnivcat3) [1] "Higher" "Middle" "Lower" > t1m4=glm(tertiary ~ relevel(cbsnivcat3, "Lower") , family = binomial, data = data) Error in relevel.ordered(cbsnivcat3, "Lower") : 'relevel' only for factors but the

I am conducting a log binomial regression in R. I want to control for covariates in the model (age and BMI- both continuous variables) whereas the dependent variable is Outcome(Yes or No) and independent variable is Group (1 or 2). fit<-glm(Outcome~Group, data=data.1, family=binomial(link="log")) and it works fine. When I try putting age in the model, it still works fine. However, when I put BMI in the model, it gives me the following: Error: no valid set of coefficients has been found: please supply starting values I have been tried different combination of starting values such as: fit<-glm

I have a data set called data which has 481 092 rows. I split data into two equal halves: The first halve (row 1: 240 546) is called train and was used for the glm() ; the second halve (row 240 547 : 481 092) is called test and should be used to validate the model; Then I started the regression: testreg <- glm(train$returnShipment ~ train$size + train$color + train$price + train$manufacturerID + train$salutation + train$state + train$age + train$deliverytime, family=binomial(link="logit"), data=train) Now the prediction: prediction <- predict.glm(testreg, newdata=test, type="response") gives

I'm extremely stuck at the moment as I am trying to figure out how to calculate the probability from my glm output in R. I know the data is very insignificant but I would really love to be shown how to get the probability from an output like this. I was thinking of trying inv.logit() but didn't know what variables to put within the brackets. The data is from occupancy study. I'm assessing the success of a hair trap method versus a camera trap in detecting 3 species (red squirrel, pine marten and invasive grey squirrel). I wanted to see what affected detection (or non detection) of the various