I am using Multinomial Model to calculate propability of a variable but when I type function pmultinom() in R, it informs could find function, could you help me what packages should I install in R?
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The documentation for the multinom() function from the nnet package in R says that it "[f]its multinomial log-linear models via neural networks" and that "[t]he response should be a factor or a matrix with K columns, which will be interpreted as counts for each of K classes." Even when I go to add a tag for nnet on this question, the description says that it is software for fitting "multinomial log-linear models."
Granting that statistics has wildly inconsistent jargon that is rarely operationally defined by whoever is using it, the documentation for the function even mentions having a count response and so seems to indicate that this function is designed to model count data. Yet virtually every resource I've seen treats it exclusively as if it were fitting a multinomial logistic regression. In short, everyone interprets the results in terms of logged odds relative to the reference (as in logistic regression), not in terms of logged expected count (as in what is typically referred to as a log-linear model).
Can someone clarify what this function is actually doing and what the fitted coefficients actually mean?
nnet::multinom is fitting a multinomial logistic regression as I understand...
If you check the source code of the package, https://github.com/cran/nnet/blob/master/R/multinom.R and https://github.com/cran/nnet/blob/master/R/nnet.R, you will see that the multinom function is indeed using counts (which is a common thing to use as input for a multinomial regression model, see also the MGLM or mclogit package e.g.), and that it is fitting the multinomial regression model using a softmax transform to go from predictions on the additive log-ratio scale to predicted probabilities. The softmax transform is indeed the inverse link scale of a multinomial regression model. The way the multinom model predictions are obtained, cf.predictions from nnet::multinom, is also exactly as you would expect for a multinomial regression model (using an additive log-ratio scale parameterization, i.e. using one outcome category as a baseline).
That is, the coefficients predict the logged odds relative to the reference baseline category (i.e. it is doing a logistic regression), not the logged expected counts (like a log-linear model).
This is shown by the fact that model predictions are calculated as
fit <- nnet::multinom(...)
X <- model.matrix(fit) # covariate matrix / design matrix
betahat <- t(rbind(0, coef(fit))) # model coefficients, with expicit zero row added for reference category & transposed
preds <- mclustAddons::softmax(X %*% betahat)
Furthermore, I verified that the vcov matrix returned by nnet::multinom matches that when I use the formula for the vcov matrix of a multinomial regression model, Faster way to calculate the Hessian / Fisher Information Matrix of a nnet::multinom multinomial regression in R using Rcpp & Kronecker products.
Is it not the case that a multinomial regression model can always be reformulated as a Poisson loglinear model (i.e. as a Poisson GLM) using the Poisson trick (glmnet e.g. uses the Poisson trick to fit multinomial regression models as a Poisson GLM)?
I'm looking to add VIF to logistic regression in R, using the export_summs function.
This is the current code:
export_summs(model1,model2, error_format = "({p.value})", exp = TRUE)
Any ideas?
Thanks.
You can use library car package and find vif(model1) or vif(model2) to find multicollinearity
This is how you can get VIF of a logistic regression model using jtools package in r. Your_glm_model is your logistic model that is returned from glm function.
summ(`Your_glm_model`, vifs = TRUE)
I am keen to implement a conditional (bivariate?) poisson regression in R to assess the change in rates of a variable (stratified by treatment condition) pre- / post- an intervention. Is anyone familiar with a package that runs this type of analysis?
Check out this "gnm" package in R. It has a function of gnm() where you can specify you model formula, family=poisson(), offset, dataset and strata id in "eliminate". Please read it.
I would like to use linear discriminant analysis model (lda) on my weighted data. In my data set, I have one column with weights which are not integers (I cant just replicate rows). lda function from MASS package does not allow me to use vector of weights for observations. Do you know, how deal with it ? I have tried also with mlr package but learner classif.lda still uses lda implementation from MASS package, so I get error:
Error in checkLearnerBeforeTrain(task, learner, weights) :
Weights vector passed to train, but learner 'classif.lda' does not support that!
Do you know how to solve this problem ?
I recently fitted a Graded Response Model to my data using R's latent trait modelling package. I tried to add my fitted Graded response model in mice package to impute missing data but i am failing to access the mice algorithm to edit. My question is does it make any difference if i use the polr() function in mice or if i use my graded response model that i fitted using latent trait modelling package to impute data since both these functions are derived from polr() function in R's mass package