Questions tagged [gamlss]
Generalized additive models for location, scale and shape (GAMLSS).
2
votes
1answer
53 views
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, ...
1
vote
0answers
306 views
Random effects in gamlss
I have a question regarding the gamlss package. I am attempting to fit a mixed effects model using the Befa Inflated distribution as follows
...
1
vote
2answers
138 views
Nonlinear regressor in GLM link function
Try to reproduce Robert E. McCulloch and Ruey S. Tsay’s paper Nonlinearity in High-Frequency Financial Data and Hierarchical Models with local market data.
the paper uses GLM to model high-frequency ...
0
votes
0answers
193 views
Specifying nested random factor in emmeans from a gamlss object
I am trying to use the package emmeans with a gamlss object with a mixed model using a beta distribution. I am unsure as to the best way to use the emmeans function to include my nested random effects....
3
votes
0answers
468 views
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 ...
0
votes
1answer
152 views
This is the right way to use dummy variables on GAMLSS package? [closed]
I want to identify if the response variable on the example data-set below is different between A and B groups.
...
2
votes
0answers
351 views
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-...
3
votes
1answer
128 views
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
votes
1answer
3k views
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 ...
1
vote
0answers
180 views
What is the correct way to fit sigma.formula in gamlss() to add a per-dependent variable variance?
I have data in the format:
| organism | treatment | replicate | response |
| --- | --- | --- | --- |
| A | X | 1 | 20 |
| A | X | ...
0
votes
0answers
40 views
Zero-inflated model does not produce zeroes in fitted values? [duplicate]
I have used GAMLSS to fit a zero-inflated model. However, when I then turn around and FIT that model, it produces absolutely no zeroes at all (even though the data used to fit the model is about 40 % ...
4
votes
0answers
189 views
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 ...
1
vote
0answers
53 views
Zero-inflated model underestimates the number of zeroes due to “near zero values”
I am using GAMLSS. The data looks like this:
So, I tried a zero-inflated beta model (beta because data lies in the [0,100] interval, and so I just divided by 100).
The estimates I got using GAMLSS ...
1
vote
1answer
309 views
Negative global deviance in gamlss?
What does a negative global deviance in gamlss mean?
From their book "Flexible Regression and Smoothing", draft is here:
http://www.gamlss.org/wp-content/uploads/2015/07/...
0
votes
0answers
19 views
Articles that work with covariates for mean, variance, and correlation simultaneously
Does anyone know of articles in which, in addition to modeling the mean parameter, are also modeled the variance and correlation parameters? I know the double generalized linear model, but they only ...
1
vote
0answers
78 views
What does 'additive framework' mean?
What does it signify in the context of non-linear relationships between multiple explanatory variables and dependent variable?
Just looking for an intuitive explanation.
In the context of GAMLSS ...
2
votes
1answer
1k views
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 ...
1
vote
0answers
261 views
Why do different negative binomial regression functions produce different coefficients, p-values
I have a dataset consisting of number of mutations per person. Covariates I think may be related are age and source of the DNA sample. I want to determine whether the disease the person has ...
9
votes
2answers
6k views
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 ...
3
votes
0answers
84 views
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 ...
1
vote
0answers
600 views
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 ...
3
votes
0answers
599 views
fitting mixture of bimodal distribution
I am trying to fit my data with a bimodal distribution using two beta distributions, however it seems to me that the two peaks are not captured very well. The reason that I notice from the data is ...
5
votes
3answers
1k views
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 ...
0
votes
1answer
244 views
Analyzing reaction time data by implementing GAMM analyses with non-normal distribution parameters
I have two questions, a conceptual one and a practical one (that are closely related). And just as a note, I'm not super familiar with this level of stats (much more comfortable with simple linear ...
4
votes
2answers
115 views
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 ...
7
votes
3answers
3k views
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 ...
9
votes
1answer
573 views
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 ...
1
vote
0answers
198 views
How to extract LMS constants from gamlss
I am trying to extract LMS constants from a gamlss object. I have the following gamlss object, where the best fitting family is the BCT.
...
2
votes
0answers
131 views
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 ...
4
votes
0answers
1k views
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-...
2
votes
0answers
893 views
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 ...
1
vote
3answers
218 views
How to choose correct distribution in R
I want to plot a probability distribution. I know the mean and think that a normal distribution would be a poor description of my beliefs. I either assume that the values smaller (or larger) than my ...
2
votes
0answers
292 views
Interpretation of Generalized Inverse Gaussian regression with GAMLSS
Background on my project:
I am comparing proteins (nodes) between a network representing protein interactions in metastatic patients v/s another network representing protein interactions in non-...
3
votes
1answer
284 views
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 ...
0
votes
2answers
991 views
Confidence intervals with gamlss package
My question regards the use of the gamlss package. I am using gamlss package to fit a dataset to a logistic function. There is ...
4
votes
1answer
584 views
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?
...
5
votes
2answers
1k views
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 ...
2
votes
1answer
680 views
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 ...
3
votes
0answers
214 views
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;
...
3
votes
0answers
2k views
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 ...
1
vote
0answers
303 views
Assessing the accuracy of zero-inflated beta regression models
I have fitted a zero-inflated beta regression model to my data in R, using the gamlss package.
However, I am unsure of how to assess the fit of the model to my data, i.e. finding a coefficient of ...
1
vote
1answer
212 views
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 ...
1
vote
2answers
981 views
Stepwise model selection using Generalized Akaike Information Criterion
I run a series of models using gamlss stepGAIC() model selection. The problem that I have is that in gamlss, ...
5
votes
2answers
6k views
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 ...
3
votes
3answers
689 views
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 $...
4
votes
1answer
2k views
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 ...
22
votes
3answers
13k views
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 ...
24
votes
1answer
3k views
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 (...
12
votes
2answers
792 views
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 ...
11
votes
1answer
3k views
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 ...
