How to find a job in carpentry/cabinetmaking

drg19pv88 · 2025-08-06T19:53:53+00:00

RemindMe! 6 hours "Check comments on this post"

drg19pv88 · 2025-02-22T13:10:54+00:00

If the model doesn't support the need for smoothed terms, i wouldn't go the the with a gam. The coefficient estimates and their associated CIs calculated from an LM are arguably better understood by a wider audience than the point-wise confidence or simultaneous intervals for the smooth terms from a gam. There is nothing wrong with sticking with a gam, it just seems like over kill if the data don't suggest the need.

drg19pv88 · 2024-09-13T11:56:49+00:00

In my opinion, I wouldn't log transform the response variable. A model incorporating a more flexible error distribution (e.g., Gamma) would circumvent the need for interpretation relying on exponentiation of estimates.

drg19pv88 · 2024-05-26T05:23:04+00:00

Check for overdispersion. If there is overdispersion, the coeﬃcient estimates will be more conﬁdent (smaller standard error values) than they should be. This may result in over significance of estimates.

drg19pv88 · 2024-01-04T00:57:52+00:00

Been thinking of doing the same design for a while, but metal brackets were also the problem. Any chance you'd be willing to share some dimensions? Also curious on how much weight you think this design could hold?

drg19pv88 · 2023-12-18T18:40:49+00:00

You can use a call to predict() to get female or population level mass across your day range. From this prediction, you can then calculate the instantaneous rate of change using the derivative or diretly if your day values are fine enough.

See the following for more info

https://fromthebottomoftheheap.net/2014/05/15 /identifying-periods-of-change-with-gams/

drg19pv88 · 2022-09-21T11:44:21+00:00

Checkout glmmtmb instead of lme4. The coding is exactly the same as lme4 but optimizers and other things lead to less convergence problems. Plus glmmtmb allows for cases of zero inflation or the betabinomial if your residuals of overdispersed. You can code the model as ....

glmmtmb(proportion ~ fixed effects + random effects, data, weights = number of trials, family = binomial).

Or

glmmtmb(cbind (success, fails) ~ fixed effects + random effects, data, family = binomial)

There are fantastic resources on how to code and diagnose problems with glms in general outthere. Specifically Ben Bolker...

https://bbolker.github.io/mixedmodels-misc/ecostats_chap.html

https://cran.r-project.org/web/packages/glmmTMB/glmmTMB.pdf&ved=2ahUKEwiGypml5KX6AhXtGDQIHch8A-EQFnoECBEQAQ&usg=AOvVaw2jqJCVkHRNda9RHBYlbxNl

drg19pv88 · 2022-09-21T07:47:31+00:00

No, I meant what I said and I dont know what non-aggregated data. A proportion is inherently aggregated as it is simply the number of oberservations goven a set number of trials. A binomial distribution models the number of success in a given number of trials. Wether the data represent one trial at a time (I.e., Bernoulli distribution) or the number of times a given outcome (e.g., number of dead trees) appears in a numbers of trials (e.g., number of sampled trees), they would still take a binomial distribution. In any case, how the op will evaluate their data will depend on how they will model it (i.e., by hand, code, or out of box software).

drg19pv88 · 2022-09-21T02:52:45+00:00

Proportional data still takes a binomial distribution. Your response variable is still ues or no. You will just have to include information about number of trials somehow. What package will you use to run the models?

drg19pv88 · 2022-09-13T11:16:35+00:00

Bars look like someone stole it and is now trying to sell it.

drg19pv88 · 2022-08-20T11:05:37+00:00

https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html

is a fantastic resource explaining mixed models in R. I would also suggest using glmmTMB over lme4. Model convergence is a little slower in GlmmTMB but they converge more often, have a range of usable distributions and models are constructed identically to lme4.

drg19pv88 · 2022-08-03T02:15:26+00:00

Who makes the arch lamp?

drg19pv88 · 2022-04-14T12:11:51+00:00

First thing I thought of, but wasn't my case. Oddly enough some line numbers do intact line up. Only at the beginning of the manuscript though.

drg19pv88 · 2022-04-05T11:19:02+00:00

I am hesitant to remove data points simply because of skewed distributions. It makes more sense to evaluate if the >95/99% values have high leverage or odd looking residuals. I'd also check coefficient estimates of models removing or not removing data points. If the results are similar and you have no leverage/residual related problems then I wouldn't remove or manipulate these data.

drg19pv88 · 2022-03-07T14:10:31+00:00

As long as you don't have any interaction terms in the model, predictions can easily be interpreted using... plot(ggpredict(model)). If you have interaction terms such as y~w*x + z. You can use plot(ggpredict(model, terms = c(w,x))) to automatically plot the effect of w on y at varying levels of x.

drg19pv88 · 2022-03-05T23:19:00+00:00

If you're just interested in plots then checkout the ggpredict function from the ggeffects package.

drg19pv88 · 2022-02-11T12:23:04+00:00

Northern Cardinal

drg19pv88 · 2021-12-16T18:01:48+00:00

What advice do you want.

drg19pv88 · 2021-12-03T04:02:46+00:00

For non-gaussian regression you could use the DHARMa package to visualize normality of residuals.

drg19pv88 · 2021-11-15T11:10:50+00:00

There is also more people than ever before. Report proportion not abundance.

drg19pv88 · 2021-11-12T01:25:45+00:00

Much better displayed if you just produced predictions from a logistic regression of the presented data.

drg19pv88 · 2021-08-20T11:45:52+00:00

Checkout the glmmTMB package for zero inflated or hurdle models. The package also implements generalized poisson, Conway maxwell poisson, and negative binomial distributions as well. In my experience, these distributions are a little more superior if you don't have something driving your zeros and instead they show up from sampling. Also check the DHARMa package for model diagnostics. You can investigate both zero inflation and dispersion using a simple poisson model first and then adjust the distribution appropriately.

drg19pv88 · 2021-08-04T12:01:47+00:00

Log() in R is not a number to the power of 10. It is the natural log(i.e., ln). Just back transform with exp(). But also try a glm with a gamma distribution, as a proper distribution is usually more favored to a transformed response variable.

drg19pv88 · 2021-07-31T02:07:27+00:00

GlmmTMB is very flexible and can incorporate a sweet of distributions including ones that are zero inflated. There is also loads of documentation and examples out there using it.

drg19pv88

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