Questions tagged [gamlss]
Generalized additive models for location, scale and shape (GAMLSS).
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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 (...
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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 ...
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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 ...
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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 ...
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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 ...
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fit GLM for weibull family [closed]
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 ...
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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 ...
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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: <...
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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 ...
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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 ...
user3410
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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 ...
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Is smoothing an appropriate solution to deal with model diagnostics in a GAMLSS?
I have just recently started using GAMLSS models (after being pointed in that direction in this question), and I'm wondering wether it's 'legit' to use smoothing (i.e. cubic splines in my case) to ...
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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 ...
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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 ...
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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 ...
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Regression with variance as outcome
Are there regression models where variance is the outcome, not mean? For instance, for interquartile range I may use quantile regression. But is there something similar for variance?
For example, let ...
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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 $x$...
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Are there better approaches than the weighted mean?
If I have a data set where the distribution from which the data are drawn changes, for example in the following plot, the data set is comprised of four normal distributions with the same mean ($\mu = ...
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How can GAMLSS relax the GLM exponential family assumption?
Generalized Additive Models for Location, Scale and Shape "relax the assumption of exponential families" in comparison to GLM's or GAM's. This is a direct quote from the paper by Rigby and ...
5
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0
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How do I account for temporal autocorrelation?
I am trying to fit a beta regression model using GAMLSS.
The data:
For each $y$, we have an indication of what patient $p$ it is, an indication of what month $m$ the observation took place, and at ...
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What does it mean to perform regression using a specific distribution?
When we specify the family= argument inside glm() in R, how is the distribution being used ...
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Zero-truncated Sichel distribution in R
I'm trying to fit the GAMLSS library's Sichel distribution to some zero-truncated data, but the only way to get the function to work is to include the zero-class anyway but give it a frequency of 0, ...
Jeff Tyzzer
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Is it possible to calculate variable confidence intervals, conditional on $\hat{Y}$ to address heteroscedasticity?
Estimating confidence intervals for non-normally distributed residuals can be accomplished using bootstrapping procedures, sandwich estimators or quantile regression.
But is there a way to calculate $...
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Why is Poisson regression different with glmer and gamlss?
I have a set of count data that seems to fit "Poisson" = not overdispersed, alpha = 0.
The problem is, I get different results using gamlss vs ...
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GAMLSS: model with interaction terms failed
I use gamlss method from library(gamlss) on my full models with interaction terms and try to reduce them with stepGAIC. There are 3 things I want to ask.
Do I have to specify a link for the model?
...
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Overall p-value for zero-inflated beta regression mixed model
I am analysing vegetation percentage cover data from grazed and ungrazed plots in R using a zero-inflated beta regression in package gamlss. Here are some example ...
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How to fit a regression for log-normal with gamlss
Since my original question was to R-code-specific I'm trying to rewrite it:
I want to make a regression where my dependent variable y should follow a log-normal-...
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Is there a way to add covariates to centile GAM curves in R?
Using the package "mgcv" I fitted a GAM to demonstrate head circumference changes over the lifetime for data stemming from two different countries;
...
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One inflated beta regression with random effects using GAMLSS
I am new to modelling percentage data, and I would be greatfull for some advice. I have proportion data (0,1] on a percentage of money sent by Player B to Player A. Participants received an amount of ...
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How to compare centiles from different models?
I am comparing the centiles from different GAMLSS models. Which model has a better performance. Why?
For model 1, the number of cases below 0.4 centile is 0.5. Is this possible? The number of cases ...
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What distribution has exactly three parameters for mean, variance, and skewness?
Common distributions usually fix their skewness. Beta distribution has two parameters to determine all of the mean, variance, and skewness.
Student-T's skewness can change by some definitions but it ...
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Johnson SU distribution parameters values from R/rugarch
I have fit a GARCH model where I set the distribution to the Johnson's SU. I don't fully understand the distribution parameters returned by the model.
To begin with, from Wikipedia, Johnson's SU is ...
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Maximum likelihood estimation under heteroskedasticity (and relation to OLS)
I have a question about MLE and how it relates to OLS. I know how to relate OLS and MLE when the noise is normal and homoskedastic. I can apply the same reason for heteroskedastic noise. My question ...
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How do gamboostLSS and gamlss packages predict outside range of x?
The mgcv package performs a linear extrapolation when the newdat argument of the predict ...
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0
answers
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How to interpret a n-related change in coverage for model (simulation study)
I have repeated measures data from n_subjects where each has n_obs number of measurements before and after some intervention, ...
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Case of a cubic spline, basis functions
I was studying the basis functions as describes in the Elements of Statistical Learning book on p.143. More precisely, I looked at the basis functions of that particular for cases. While the topleft ...
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How to build valid GAMLSS models?
Sorry for the following basic questions but it is important for me to get a feedback for my approach. I would like to create reference values for children in form of percentile curves (also called Z-...
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Methods for smearing multidimensional data
I have a dataset $X \in \mathbb{R}^{2p}$. Half of the parameters $X_{pre} \in \mathbb{R}^p$ represent true values without measurement biases or errors. The other half $X_{post} \in \mathbb{R}^p$ are ...
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What is Mu in zero/one beta inflated models? (gamlss (BEINF))
I am estimating a zero/one inflated beta regression model with gamlss (family BEINF). My dependent variable is [0,1] with a lot of 0s, quite some 1s, and some values in between. This means I assume ...
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Comparing categorical variable importance across groups; zero and one beta regression
I am attempting to compare behavioral responses across two species (one native and one invasive). Predictors run the range of types including continuous (size), discrete (day of trial) and ...
3
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Beta regression with random effects in R: different results in GAMLSS vs. glmmADMB
I am trying to fit a beta regression model to some repeated-measures data.
I fit the model both with the function glmmadmb() in the ...
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1
answer
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Is there a hypothesis test that tells us whether we should use GAM vs GLM?
Is there a hypothesis test that's ideally uniformly most powerful or metric that tells us whether we should use GAM vs GLM?
Does there exists some kind of metric i.e. AIC/BIC or loglikelihood to ...
user271077
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2
answers
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Is it possible to use location-scale family distributions for mixed effects modeling?
Is it possible to use location-scale family distributions for mixed modeling or generalized estimation equations? The only location-scale family package I know of is GAMLSS in R which is for additive ...
user271077
2
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1
answer
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Predict gamlss one-inflated beta model
How do you obtain predicted probabilities for the one-inflated component (nu model) of a one-inflated beta regression in gamlss?
I have built the following model
<...
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Mann Whitney test, unequal sample sizes, different=shaped continuous proportions plus many zeros
I was hoping someone could help, I am comparing proportional data in to two different groups. One group has a sample size of 22 and the other 530. The data are not normally distributed and have ...
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Is it possible to do meta-regression with GAMLSS?
I am about to conduct a meta-regression analysis to fit some age-related changes. Since the GAMLSS approach is highly suggested in the normal regression of such data, I want to apply GAMLSS to my ...
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GAMLSS Random Effect Coefficients
How do I extract the coefficients of my random effects in a Gamlss model?
Let's take a simple example of a sample of individuals with intercepts which are normally distributed. Additional normally ...
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1
answer
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How was the pdf of the generalized gamma distribution in GAMLSS reparametrized?
I am trying to bring together the definition of the Generalized Gamma distribution in R-package GAMLSS by Rigby and Stasinopoulos and the general definition of the Generalized Gamma distribution, ...
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1
answer
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Smoothing spline with dependent coordinates in R
I have a series of patients in whom I have measured the value of a certain blood quantity at several time points. However, the time points vary a lot and the number of measurements range between 2 and ...
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Weibull distributed GLM with GAMLSS good fit?
I have data from a plant disease screen in a greenhouse. 145 plants in a greenhouse were inoculated with bacteria over the course of three days, and the amount of bacteria in the plant assessed at a ...
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