I would like to perform a model-based clustering using a mixture of ordinal logistic regressions (for outcome, not as concomitant model)
Does some one know if it implemented in R? For example, can I manage to use ordinal regression instead of multinomial in flexmix package?
Thanks a lot!
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I am trying to perform univariate analysis for multiple predictors independently for the outcome of an even in Cox regression model. I want to present these results (Hazard Ratios, HR) in a forest plot. Which package and method do you recommend? Thank you in advance.
I have only managed to do this for multivariate analysis.
I've fit a multinomial logistic regression using gam() and the mgcv package in R. I then used this model to make predictions on new data. The resulting predictions are probabilities because gam() doesn't have a type="class" option. I'd like to convert these probabilities to one of my three levels for my categorical response variable but I have two questions: 1) How should I determine where make the probability threshold between the three classes? and 2). How can I make a nested ifelse() statement that reclassifies the probabilities according to the thresholds?
I would like to run a cross-validation function like cv.glm on a logistic regression model built with speedglm on a large (millions of rows) data set. Does any such function exist?
I am finding that cv.glm (from boot package) and the train function from caret do not recognize speedglm models.
Thanks in advance.
I would like to compare two models using f-test fitting my data. For each model I performed Monte-Carlo simulation that provided statistical estimation for each model parameter and rms fit error. I would like to use f-test in R to determine which model is preferable.
Best to use the anova function.
anova(modle1, model2)
This preforms a model f test.
I am trying to analyze a longitudinal data with binary response using 3-level logistic regression. Hierarchical structure of my data looks like this example- students (level 1) are nested within a class(level 2) and the classes are nested within a school (level 3).
Can anyone suggest me some appropriate readings on the SAS or R codes for performing 3-level mixed effects logistic regression? I am not requesting helps regarding the theory behind 3-level logistic regression. Any examples with SAS or R codes would be of great help.
In R you can use the glmer() function from the lme4 package for mixed effects models. You specify the effects in the formula= option. For nested random effects you probably want something like:
model <- glmer(formula = response ~ (1|student|class/block), family=binomial)
There are some examples of multi-level models on R-sessions
and you can fit non-linear responses, such as binomial, with family=binomial.