Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [gamlss]

Generalized additive models for location, scale and shape (GAMLSS).

Questions about gamlss models—Generalized Additive Models for Location, Scale and Shape, and implementations such as R package gamlss and related packages, or implementations in other languages (there is an implementation in java).

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What diagnostic plots exists for quantile regression?

Following on my question for OLS, I wonder: what diagnostic plots exists for quantile regression? (and are there R implementation of them?) A quick google search already came up with the worm plot (which I have never heard about before), and I'd be…
Tal Galili
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Regression modelling with unequal variance

I would like to fit a linear model (lm) where the residuals variance is clearly dependent on the explanatory variable. The way I know to do this is by using glm with the Gamma family to model the variance, and then put its inverse into the weights…
Tal Galili
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Parametric modelling of variance of count data

I am looking to model some data, but I am not sure what type of model I can use. I have count data, and I want a model that will give parametric estimates of both the mean and the variance of the data. That is, I have various predictive factors…
Brian Diggs
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fit GLM for weibull family

I am trying to fit generalized linear model for weibull family, but when I try it in R, it gives an error. I know that weibull does not fit in exponential family, but I have read some research articles about fitting GLM for weibull family. If anyone…
NiroshaR
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Convert SAS NLMIXED code for zero-inflated gamma regression to R

I'm trying to run a zero-inflated regression for a continuous response variable in R. I'm aware of a gamlss implementation, but I'd really like to try out this algorithm by Dale McLerran that is conceptually a bit more straightforward.…
a11msp
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Significance of (GAM) regression coefficients when model likelihood is not significantly higher than null

I am running a GAM-based regression using the R package gamlss and assuming a zero-inflated beta distribution of the data. I have only a single explanatory variable in my model, so it's basically: mymodel = gamlss(response ~ input, family=BEZI).…
a11msp
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Simulate linear regression with heteroscedasticity

I am trying to simulate a dataset that matches empirical data that I have, but am unsure how to estimate the errors in the original data. The empirical data includes heteroscedasticity, but I am not interested in transforming it away, but rather…
user44796
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Prediction interval for a future proportion of successes under Binomial setting

Suppose I fit a Binomial regression and obtain the point estimates and variance-covariance matrix of the regression coefficients. That will allow me to get a CI for the expected proportion of successes in a future experiment, $p$, but I need a CI…
James
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How to model zero inflated, over dispersed poisson time series?

I am trying to model weekly disease counts in 25 different regions within 1 country over a ten year period as influenced by temperature. The data is zero inflated and over dispersed. I am most familiar with Stata but I don't think that there is…
user3410
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Zero inflated beta regression using gamlss for vegetation cover data

My goal is to analyse vegetation cover data. The way the data collection works is that you throw a quadrat (0.5m x 0.5m) in a sample plot and estimate the percent cover of the target species. Here is an example: df <- structure(list(site =…
Stefan
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Are Random Forests more powerful than generalized linear models?

I have never used Random Forests, but I have read some about it. Until now I have used GLM/GAMLSS extensively. I would like to know: What are the advantages that RF provides over GLM/GAMLSS? What are the disadvantages of using Random Forests? I am…
maximusdooku
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Modelling zero-inflated proportion data in R using GAMLSS

I am new to the gamlss package and would like to check that I am using the correct family for proportion data (tree species cover after treatment), which is bounded between zero and one. According to the documentation, the correct distribution…
Alex Picard
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Create Spline from Coefficients and Knots in GAMLSS

In the R package GAMLSS, it is possible to model a random variable $Y$ as a smoothed non-parametric function of some predictor $x$. One option for such a function is the penalised spline using y~pb(x). This outputs a list of coefficients and knots…
LBogaardt
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Smoothed Moments as Function of Predictor

Setup Let $x$ describe a continuous predictor variable (e.g. age). Let $Y$ be a random variable (e.g. height) which is some function of $x$. The data consists of $n$ points, each a combination of $x$ and $y$ (e.g. $data_i = (age_i, height_i)$). The…
LBogaardt
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Overdispersed poisson or negative binomial regression

I am performing a GLM on count data (insurance claims) and I wish to compare Overdispersed Poisson Regression (ODP) against Negative Binomial regression. would know whether there is a practical index (AIC, logLik) that in standard R could support…
Giorgio Spedicato
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