Questions tagged [gamlss]
Generalized additive models for location, scale and shape (GAMLSS).
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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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Catgeorical variable coefficients interpretation
if I have the following regression model in gamlss (or if it's a general concept)
Y ~ var1
Where Y ~ beta, and var1=(g1,g2,g3) g1 is the reference level, what does it mean that the intercept is ...
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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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Standard errors in GAMLSS vs LMER
Why do the standard errors for gamlss are much lower than the standard errors from lmer?
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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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How to find the appropriate model to apply (GAMLSS)/Approving statistical thinking
I need to compute percentile curves using the LMS method (from the GAMLSS package/models) with Age and Height as predictors.
What is the best way to determine which equation (with which transformation ...
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Choosing whether to eliminate or keep a predictor in a GAMLSS model
I need to calculate the centile curve for y using a GAMLSS model with age and height as predictors.
The plots below depict the relationship between log(y) and each of the independent variables.
I ...
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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" [closed]
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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qBCCGo function in GAMLSS package (quantile function)
I am working with GAMLSS technique and I use LMS (with three parameters of mean,variation and skewness) method. I found the follwoing formula to calculate the lower limit of normal as the link ...
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Censoring in GAMLSS
I have some observations $y$ which have only few negative values $y_i\in (-\infty,0)$ and mostly non-negativ values, i.e. $y_i\in [0,\infty)$. I would like to estimate a censored logistic ...
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Need some help to identify the appropriate distribution
The residual plots of the following models are not normal, while the distribution of residuals between (-3 to 3) is normal in model m17. The worm plot of all equations appears to be correct. The goal ...
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Find the more appropriate fit by using term.plot in GAMLSS
I'm comparing several GAMLSS models and was wondering how I might use the term.plot to identify the best model/fit. What type of information can we get from this graph? Can I argue that the model with ...
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A question about applying GAMLSS models
I want to make the growth chart by using age and height as predictors. As the scatter plots show a nonlinear relationship between age and y, I need to use the P-spline to make the statistical equation....
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How to compare centiles from different models?
I am comparing the centiles from different GAMLSS models. Which model has a better performance. Why?
For model 1, the number of cases below 0.4 centile is 0.5. Is this possible? The number of cases ...
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Proportion/Ratio response variable
I need some advice to understand the following code. We are working with lung data and usually we use LMS technique to calculate the reference curves. In order to calcualte the curves with LMS method ...
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Robust options to fit GLM or GAM for overdispersed Poisson counts (quasipoisson or negative binomial) in R
It appears glmrob from the robustbase library does not support quasipoisson or ...
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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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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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Is it possible to do meta-regression with GAMLSS?
I am about to conduct a meta-regression analysis to fit some age-related changes. Since the GAMLSS approach is highly suggested in the normal regression of such data, I want to apply GAMLSS to my ...
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GAMLSS vs VGAM for percentile curves (Growth chart)
It seems that there are two methods and packages in R to calculate the Percentile curves based on LMS (Lambda for the skew, Mu for the median, and Sigma for the generalized coefficient of variation; ...
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How to choose gam base functions, family and splines and how to deal with non-convergence with gam (fam=ziplss, mgcv)?
I am new to this forum and modelling in general. I will try to give a full overview of issue, so bear with me. I am modelling a the habitat use of herbivore species. I have observational data from ...
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How to obtain the p-values of a gamlssMX model?
I am working with a dataset that includes a binary target variable (0 or 1).
I have built a model with the gamlssMX() function included on the "gamlss.mx" package to explain a continuous ...
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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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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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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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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 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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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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What's a good distribution for high-variability right-skewed proportion data?
My data is in the interval $(0,1)$.
I am interested in regression using GAM or GAMLSS. The distribution should preferably be available in the packages mgcv or gamlss.
However, so far, I have used ...
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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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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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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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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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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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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 ...
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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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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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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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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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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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Does this graph support an assumption of homoscedasticity?
Does this graphics support the assumption of homoscedasticity?
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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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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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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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