Questions tagged [gamlss]
Generalized additive models for location, scale and shape (GAMLSS).
71
questions
4
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1answer
41 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
37 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, ...
0
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1answer
36 views
Does this graph support an assumption of homoscedasticity?
Does this graphics support the assumption of homoscedasticity?
4
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1answer
82 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
votes
2answers
55 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
200 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) : ...
1
vote
2answers
73 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 ...
1
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1answer
51 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 ...
1
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0answers
13 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 ...
0
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0answers
54 views
Standard deviation of outcome in gamlss model with random intercepts in mean
I'm interested in a simple random-intercepts model:
$$Y_{ij} = \alpha_0 + \gamma_i + \epsilon_{ij}$$
where $\gamma_i \sim N(0, \sigma_{\gamma}^2)$ independently of $\epsilon_{ij} \sim N(0, \sigma_{\...
1
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0answers
71 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. ...
1
vote
1answer
151 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 ...
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0answers
14 views
When to use location-scale family general additive models with censoring versus parametric survival models?
When to use location-scale family general additive models with censoring (gamlss.cens) versus parametric survival models, i.e. weibull, exponential, etc? implemented i.e. in survreg of the survival ...
2
votes
2answers
83 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
votes
0answers
61 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 ...
6
votes
2answers
443 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 ...
5
votes
1answer
110 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 $x$...
1
vote
0answers
131 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
votes
1answer
137 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
votes
0answers
840 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
393 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 ...
3
votes
0answers
988 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
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1answer
380 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
557 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
votes
1answer
364 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
5k 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
343 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
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
votes
0answers
277 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
71 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
540 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
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
177 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
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 ...
1
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0answers
446 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
votes
2answers
9k 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
141 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 ...
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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
vote
1answer
500 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
votes
0answers
875 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 ...
6
votes
3answers
2k 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
352 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
130 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 ...
8
votes
3answers
5k 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
964 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 ...
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0answers
239 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
177 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
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0answers
2k 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
2k 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
288 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 ...
