How can I specify weight decay in a model fit by the mlogit?
The multinom() function of nnet allows you to specify weight decay for the model that is being fit, and mlogit uses this function behind the scenes to fit its models so I imagine that it should be possible to pass the decay argument to multinom, but have not so far found a way to do this.
So far I have attempted to simply pass a value in the model formula, like this.
library(mlogit)
set.seed(1)
data("Fishing", package = "mlogit")
Fishing$wts <- runif(nrow(Fishing)) #for some weights
Fish <- mlogit.data(Fishing, varying = c(2:9), shape = "wide", choice = "mode")
fit1 <- mlogit(mode ~ 0 | income, data = Fish, weights = wts, decay = .01)
fit2 <- mlogit(mode ~ 0 | income, data = Fish, weights = wts)
But the output is exactly the same:
identical(logLik(fit1), logLik(fit2))
[1] TRUE
mlogit() and nnet::multinom() both fit multinomial logistic models (predicting probability of class membership for multiple classes) but they use different algorithms to fit the model. nnet::multinom() uses a neural network to fit the model and mlogit() uses maximum likelihood.
Weight decay is a parameter for neural networks and is not applicable to maximum likelihood.
The effect of weight decay is keep the weights in the neural network from getting too large by penalizing larger weights during the weight update step of the fitting algorithm. This helps to prevent over-fitting and hopefully creates a more general model.
Consider using the pmlr function in the pmlr package. This function implements a "Penalized maximum likelihood estimation for multinomial logistic regression" when called with the default function parameter penalized = TRUE.
Related
I am using a glm model to predict my depending variable. For that I need to choose a family of the distribution of my variable. unfortunately the exponential distribution is not part of the available objects of the argument "family".
Now I don't know on how I can proceed with my research.
This is my model. Does anyone have an idea, what I can do?
model<-train(duration~., data = data, method='glm', family= ???, trControl =trainControl(method = "repeatedcv", repeats = 10)
The exponential distribution is in the gamma family with dispersion parameter fixed at 1, so family = Gamma(link="log") should provide you with what you need. When interpreting significance or standard error of the fitted coefficients assuming exponential, you specify the dispersion.
Since your example wasn't reproducible, an example using glm and summary is:
mdl <- glm(formula = duration ~., family = Gamma(link="log"), data = your_data)
summary(mdl, dispersion = 1)
I ran a conditional random forest regression model using the cforest function from the party package because I have both categorical and continuous predictor variables that are correlated with each other, and a continuous outcome variable.
Here is my code to run the conditional random forest model, obtain out-of-bag estimates, and estimate the permutation variable importance.
# 1. fit the random forest
crf <- party::cforest(Y ~ ., data = df,
controls = party::cforest_unbiased(ntree = 10000, mtry = 7))
# 2. obtain out-of-bag estimates
pred_oob <- as.data.frame(caret::predict(crf, OOB = T, newdata = NULL))
# 3. estimate permutation variable importance
vi <- permimp::permimp(crf, condition = T, threshold = 0.5, nperm = 1000, OOB = T,
mincriterion = 0)))
I would like to visualize the minimal depth distribution and calculate mean minimal depth similar to the output from the RandomForestExplainer package. However, the RandomForestExplainer package only takes in objects from the randomForest function in the randomForest package. It's not an option for me to use this function due to the nature of my data (described above).
I have been combing the internet and have not been able to find a solution. Can someone point me to a way to visualize the minimal depth distribution for all predictors and calculate the mean minimal depth?
I have longitudinal data from two surveys and I want to do a pre-post analysis. Normally, I would use survey::svyglm() or svyVGAM::svy_vglm (for multinomial family) to include sampling weights, but these functions don't account for the random effects. On the other hand, lme4::lmer accounts for the repeated measures, but not the sampling weights.
For continuous outcomes, I understand that I can do
w_data_wide <- svydesign(ids = ~1, data = data_wide, weights = data_wide$weight)
svyglm((post-pre) ~ group, w_data_wide)
and get the same estimates that I would get if I could use lmer(outcome ~ group*time + (1|id), data_long) with weights [please correct me if I'm wrong].
However, for categorical variables, I don't know how to do the analyses. WeMix::mix() has a parameter weights, but I'm not sure if it treats them as sampling weights. Still, this function can't support multinomial family.
So, to resume: can you enlighten me on how to do a pre-post test analysis of categorical outcomes with 2 or more levels? Any tips about packages/functions in R and how to use/write them would be appreciated.
I give below some data sets with binomial and multinomial outcomes:
library(data.table)
set.seed(1)
data_long <- data.table(
id=rep(1:5,2),
time=c(rep("Pre",5),rep("Post",5)),
outcome1=sample(c("Yes","No"),10,replace=T),
outcome2=sample(c("Low","Medium","High"),10,replace=T),
outcome3=rnorm(10),
group=rep(sample(c("Man","Woman"),5,replace=T),2),
weight=rep(c(1,0.5,1.5,0.75,1.25),2)
)
data_wide <- dcast(data_long, id~time, value.var = c('outcome1','outcome2','outcome3','group','weight'))[, `:=` (weight_Post = NULL, group_Post = NULL)]
EDIT
As I said below in the comments, I've been using lmer and glmer with variables used to calculate the weights as predictors. It happens that glmer returns a lot of problems (convergence, high eigenvalues...), so I give another look at #ThomasLumley answer in this post and others (https://stat.ethz.ch/pipermail/r-help/2012-June/315529.html | https://stats.stackexchange.com/questions/89204/fitting-multilevel-models-to-complex-survey-data-in-r).
So, my question is now if a can use participants id as clusters in svydesign
library(survey)
w_data_long_cluster <- svydesign(ids = ~id, data = data_long, weights = data_long$weight)
summary(svyglm(factor(outcome1) ~ group*time, w_data_long_cluster, family="quasibinomial"))
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.875e+01 1.000e+00 18.746 0.0339 *
groupWoman -1.903e+01 1.536e+00 -12.394 0.0513 .
timePre 5.443e-09 5.443e-09 1.000 0.5000
groupWoman:timePre 2.877e-01 1.143e+00 0.252 0.8431
and still interpret groupWoman:timePre as differences in the average rate of change/improvement in the outcome over time between sex groups, as if I was using mixed models with participants as random effects.
Thank you once again!
A linear model with svyglm does not give the same parameter estimates as lme4::lmer. It does estimate the same parameters as lme4::lmer if the model is correctly specified, though.
Generalised linear models with svyglm or svy_vglm don't estimate the same parameters as lme4::glmer, as you note. However, they do estimate perfectly good regression parameters and if you aren't specifically interested in the variance components or in estimating the realised random effects (BLUPs) I would recommend just using svy_glm.
Another option if you have non-survey software for random effects versions of the models is to use that. If you scale the weights to sum to the sample size and if all the clustering in the design is modelled by random effects in the model, you will get at least a reasonable approximation to valid inference. That's what I've seen recommended for Bayesian survey modelling, for example.
I am using the Caret package in R for training logistic regression model for a binary classification problem. I have been able to get the results, accuracy, etc., but I also want the importance of the variables (in decreasing order of importance). I used varImp() function. But according to the documentation, the importance depends on the class :
"For most classification models, each predictor will have a separate variable importance for each class (the exceptions are classification trees, bagged trees and boosted trees)."
How can I get the variable importance for each class ?
Thank you
For the first part, have you tried:
round(importance(myModel$finalModel), 2)
For putting that in decreasing order:
imp <- round(importance(myModel$finalModel), 2)
dfimp <- data.frame(feature = rownames(imp), MeanDecreaseGini = as.numeric(imp))
dfimp[order(dfimp$MeanDecreaseGini, decreasing = T),]
I want to use a mixed model without a random intercept but with a correlation structure. The reason is to get the AIC to help choose the best correlation structure (e.g., autoregressive versus compound symmetry). So it is essentially a GEE, but GEEs don't allow estimation of the AIC. They are also called covariance pattern models.
The code below simulates random data with a compound symmetry correlation. The model fits both a random intercept and a variance-covariance matrix. Is there any way to switch off the random intercept?
library(MASS)
library(nlme)
Sigma = toeplitz(c(1,0.5,0.5,0.5))
data = data.frame(mvrnorm(n=10, mu=1:4, Sigma=Sigma))
data$id = 1:nrow(data)
long = reshape(data, direction='long', varying=list(1:4), v.names='Y')
cs = corCompSymm(0.5, form = ~ 1 | id)
model = lme(Y~time , random=list(~1|id), data=long, correlation=cs)
summary(model)
If you are solely interested in comparing correlation structures, then I am pretty sure your goal could be served by a generalized least squares model fit with gls:
model = gls(Y~time, data=long, correlation=cs)
summary(model)
AIC(model)
Otherwise, a linear mixed effects model fit with lme must have random effects specified.