Questions tagged [gamlss]

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

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Identifying the appropriate model for determining the dimension with the most impact on poverty

I am working on a research to determine the dimension (health, unemployment, education and standard of living) with the most impact on poverty. The response variable is the decile score obtained for ...
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Error in post-hoc analysis on gamlss model with emmeans: "Error in V[idx, idx, drop = FALSE] : subscript out of bounds"

I am utilizing gamlss currently because of its flexibility in specifying 'rarer' data families but run into an error when trying to compare 2-by-2 differences in mu effects for my fixed model factors. ...
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Creating nonlinear centiles/reference charts with longitudinal data

I have a continuous dependent variable measured for several thousand subjects. The dependent variable has been measured one or more times per subject at nonuniform points in time. I am looking to ...
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How to improve the fit of a beta zero-inflated regression model (GAMLSS)?

I'm working with a response variable with values between 0.0 and 1.0. I have a lot of zero. Thus, I'm using beta zero-inflated regression model. Specifically, I'm using the function gamlss from the ...
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can daily count data use GAM ordered categorical family, proportional-odds model?

The observed response variable Y takes on one of K(=21) ordered categories. Here is a summary of my response data (count data: the number of hospital admission in each day), y has observations across ...
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Betabinomial (BB) regression in the gamlss package (R)

I'd be grateful for any help with beta-binomial (BB) regression in the gamlss package: predictions from the fitted model of "mu" values for each observation in my data seem reasonable. But ...
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Using previous outcome values as a covariate(t n-1, t n-2... ) in subsequent predictions

I am using GAMLSS to predict weight gain during pregnancy. I have upto 10 outcome measurements/person (weight difference from weight recorded at baseline over subsequent weight measurements at visit). ...
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How to use censoring at left point of zero in GAMLSS?

How do I apply GAMLSS tools to left-censored regression at a specific point? I know there exists gamlss.cens package, but it seems can not be censored at zero.
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gamlss plot check

I used the gamlss to fit the model. Bottom right panel: Normal QQPlot of the quantile residuals. The plots suggest a slight left skewness of the quantile residuals. My questions is should this be ...
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ZINB boosting model (gamboostLSS) seems off in R, what could be the reason?

I have a dataset containing counts (values from 0 to 30) and different covariates. This is what the distribution of the count data looks like: After fitting a gamlss boosting model in R (with the ...
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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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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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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 ...
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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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1 answer
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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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GAMLSS BEINF Model s(x) coefficient log odds to odds interpretation. after logit link transformation

I am fitting a BEINF beta inflated model using gamlss in R. The response is a continuous variable and I have a single continuous predictor covariate with a default ...
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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 ...
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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....
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1 answer
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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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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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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 ...
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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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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 ...
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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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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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GAM concurvity measures greater than 1

I am currently using a gam modelling approach to describe the effects of several variables (es_sum, nat_sum, sect_sum, and prop_spec) on another variable (btwn). Each of es_sum, nat_sum, sect_sum, and ...
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Does this graph support an assumption of homoscedasticity?

Does this graphics support the assumption of homoscedasticity?
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1 answer
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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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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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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) : ...
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1 answer
233 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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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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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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1 answer
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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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1 answer
654 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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2 votes
2 answers
147 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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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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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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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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1 answer
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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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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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2 votes
1 answer
178 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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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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2 answers
590 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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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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1 answer
602 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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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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5 votes
1 answer
584 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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8 votes
1 answer
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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1 vote
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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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