Questions tagged [gamlss]

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

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1answer
23 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 ...
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1answer
418 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 ...
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1answer
40 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
8 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 ...
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31 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_{\...
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37 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. ...
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1answer
75 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
13 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 ...
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2answers
52 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 ...
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2answers
396 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 ...
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0answers
55 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 ...
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1answer
106 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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1answer
27 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 ...
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20 views

How to calculate Z-scores using gamlss IG distribution

I am having difficulty calculating Z-scores using the values generated from a gamlss model that used the 'IG' (inverse Gaussian) distribution. The calculation for generating Z-scores using LMS data is ...
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0answers
107 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 ...
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1answer
873 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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3answers
15k 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 ...
2
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1answer
128 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, ...
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0answers
509 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-...
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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-...
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0answers
742 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 ...
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2answers
314 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 ...
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1answer
479 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
2k 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 ...
3
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0answers
868 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
294 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. ...
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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 ...
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1answer
4k 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 ...
5
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1answer
294 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
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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 ...
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2answers
2k 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 ...
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0answers
851 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 ...
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0answers
298 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 | ...
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1answer
762 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 ...
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3answers
4k 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 ...
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1answer
325 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 ...
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0answers
47 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 % ...
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0answers
415 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 ...
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2answers
127 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 ...
11
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1answer
4k 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 ...
12
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2answers
842 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 ...
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0answers
68 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 ...
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3answers
266 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 ...
4
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1answer
741 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? ...
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0answers
256 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 ...
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1answer
323 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 ...
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2answers
8k 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 ...
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0answers
421 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 ...
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0answers
242 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; ...
4
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3answers
675 views

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, ...