Questions tagged [gamlss]
Generalized additive models for location, scale and shape (GAMLSS).
81
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1answer
13 views
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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0answers
29 views
Interpretation of zero-inflated beta regression
I have constructed a zero-inflated beta regression model in gamlss. I find the output of this model somewhat confusing to interpret and was hoping that someone may ...
3
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0answers
63 views
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, ...
2
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1answer
32 views
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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0answers
23 views
Obtain confidence interval for the mean in gamlss using emmeans?
In many distributions available in gamlss, the mu parameter does not correspond to the mean of the distribution. Would it be ...
1
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0answers
14 views
Residual autocorrelation in non-stationary (gaulss) model
I'm fitting a non-stationary model using mgcv (family: gaulss()) where the data have been collected at different points in space....
1
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1answer
33 views
meanBEINF vs predict(model, type = "response') in BEINF GAMLSS. and determining odds of predictor variable coefficient
A variation of this question has been asked, but certain items remain unanswered -
I am modeling the proportion of mortality (Prop) using a single continuous predictor variable which is temperature (...
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0answers
12 views
Mixed effects models in nlme vs gamlss
I fit mixed effects models in nlme::lme and gamlss with identical structure and compared the results. Because I used ...
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0answers
22 views
Propensity Score Weighting in GAMLSS
in a project of mine i want to use a propensity score weighted gamlss model. However, the gamlss user guide states "In general using weights that are not frequencies is not recommended unless the ...
0
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1answer
23 views
How to transform an uniform distribution into a generalized beta 2 distribution using gamlss, fitdist or other?
Not comfortable with math and just R beginner, I get stuck on the following problem:
Imagine that, for a variable ya (n=3000), the gamslss ...
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0answers
25 views
How to correctly specify and diagnose one-inflated beta regression mixed-models (using GAMLSS)
I would like to find out what variables influence/explain an efficiency score for an invasive species control method. As the score is defined on (0,1] and as some observations were not independent, I ...
5
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1answer
66 views
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 ...
0
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2answers
143 views
Beta regression fitted values
I have a beta regression model in R, have generated predicted (fitted) values based on my data, and plotted lines of those fitted values on a scatter plot of the actual data. I'm most used to GLMMs, ...
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1answer
41 views
Does this graph support an assumption of homoscedasticity?
Does this graphics support the assumption of homoscedasticity?
4
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1answer
96 views
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 = ...
5
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2answers
132 views
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 ...
0
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1answer
462 views
Post hoc analysis for gamlss model in R
I used a gamlss model for my data and would like to do some post hoc analysis afterwards. I tried the packages emmeans and ggemmeans but both of them give me an error: "Error in match.arg(type) : ...
2
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1answer
153 views
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 ...
0
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1answer
87 views
can a normal distribution have negative deviance?
I'm using GAMLSS to model a variable as normally distributed with mean and SD as linear functions of some parameters. Sometimes GAMLSS gives me negative global deviances, but in my limited ...
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0answers
19 views
To what degree can distribution fitting of a response variable inform GLM family and link selection?
This is more of a theoretical question. I have a response variable that is best described by the Box-Cox Power Exponential distribution, but there is no way to really "run a GLM" with this information ...
1
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1answer
191 views
R gamlss - fitting and simulating lognormal response
I believe I am making a mistake in parametrization in this case. My goal is fit a lognormal model to data using gamlss in R, then simulate from that fitted model. ...
2
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1answer
366 views
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 ...
2
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2answers
127 views
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 ...
3
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0answers
65 views
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 ...
0
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1answer
55 views
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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2answers
528 views
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 ...
6
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1answer
114 views
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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0answers
149 views
Confidence interval fit around GAMMA and BETA distribution
So currently I am using the new GAMLSS package and have plotted my model as shown in the photo. The blue line is my estimate (which I want to use at my point of estimate) when attempting to create a ...
2
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1answer
146 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, ...
2
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0answers
1k 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
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2answers
502 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 ...
4
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0answers
1k 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 ...
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1answer
500 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.
...
3
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0answers
629 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-...
5
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1answer
470 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 ...
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1answer
6k 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 ...
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0answers
424 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
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0answers
48 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 % ...
5
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0answers
306 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
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0answers
81 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
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1answer
632 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/...
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1answer
30 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 ...
2
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0answers
199 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
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1answer
3k 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 ...
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0answers
491 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 ...
10
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2answers
10k 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
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0answers
160 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 ...
3
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0answers
1k 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 ...
1
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1answer
603 views
How to model heteroscedasticity and get the coefficients?
I would like to model heteroscedasticity or better, the standard deviation of the mean as a function of x and retrieve the coefficients that says something about how standard deviation behaves with ...
2
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0answers
936 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 ...
