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With Cox survival regression models, what you get are estimated log-hazard differences or hazard ratios. Plots like those you show are log-hazard differences from some chosen reference condition. … lwd.se, col = col.se) } The difference between rcs() (restricted cubic spline) and pspline() (penalized smoothing spline) is outlined on this page, along with several other ways to model nonlinear …

If your quantile functions are linear in predictors, transformation leaves you with nonlinear functions. … If you had a quantile version of a GLM, or some form of nonlinear-quantile regression, perhaps you can implement a log-link, but I'm not sure I've seen any offhand. …

Are there nonlinear patterns in your data? Do the polynomials actually do a good job of fitting the data as you have here? … I tend to prefer splines for nonlinear patterns of data, but more information about that point may be helpful. …

Regression assumptions are usually about the latter. … I recommend the R package ordinal to explore all these possibilities of scale and nominal effects with a variety of link functions. …

A typical best practice is to fit all the data, in your case absorbance versus toxicant concentration, with a nonlinear least squares regression model that captures the underlying dynamics. … Using this package takes some care, however, as fitting a nonlinear regression only works reliably if you have good starting estimates for the model parameters. …

I would like to model the effect of temperature on the daily movement patterns of the animals using a regression model in lme4 The model: TOTAL_DIST ~ TEMP.MAX + (1 | COLLARID) Has the following features … If it is nonlinear then you can add nonlinear terms, or regression splines for TEMP.MAX …

Because normal and logistic cumulative probability curves are nonlinear, the effect of any predictor on probability is nonlinear and depends on all predictors. … See discussions at Discrete-Time Event History (Survival) Model in R and book Tutz, G., & Schmid, M. (2016). Modeling discrete time-to-event data. …

As the comment from @user11852 suggests, you can incorporate multiple forms of seasonality (and how those seasonalities may change over time) using the cyclic cubic regression basis in the {mgcv} package … There are better ways to handle nonlinear splines and generate good forecasts, which I have made available in the {mvgam} R package. …

That's why I say in Regression Modeling Strategies that using the data to select the model is almost as bad as not doing so. …

For a binary response you can use logistic regression. In SAS that would be PROC LOGISTIC. … If I understand correctly there are several approaches for proportional/fractional outcomes: Tobit (with PROC QLIM or NLMIXED), NLS (nonlinear least squares, with PROC NLMIXED), fractional logit (PROC …

Edit You asked about nonlinear effects in the comments. There are a couple things you could consider. … J., & Inger, R. (2018). A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ, 6, e4794. https://doi.org/10.7717/peerj.4794 Meteyard, L., & Davies, R. A. …

This is easiest to do using structural equation modeling, but can also be accomplished in R with nlme. …

To combat the error distribution assumption, you can consider using ordinal regression. The R package ordinal has functions “clmm()” and “clmm2()” that allow random intercepts and slopes. … Beta regression with random effects can be done in package glmmTMB. …

If the response variable is binary (0/1, presence/absence) there's really not much you can do other than some form of logistic regression (in R, glm(..., family="binomial"); a negative binomial model, … A GAM is a generalized additive model, which can account for nonlinear patterns in continuous variables. You can fit these easily with mgcv::gam(). It might be worth considering this option. …

Transformation may be necessary if data is heavily skewed, nonlinear, or has other features that make ML problematic (other estimation methods may be used to counteract this, but I would educate yourself … If you are new to statistics, I would highly recommend going through the primers that come with the book to educate yourself on some of the underpinnings of SEM that are important (psychometrics, regression …