Timeline for Identify maximum in quadratic regression
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| Aug 9 at 16:52 | comment | added | Nick Cox | I hope someone can follow that. I am not a routine user of R or such models. | |
| Aug 9 at 16:47 | comment | added | a.henrietty |
I am thinking of a model such as lmer(Y ~ X + I (X^2) + random(1 | patient)) , @NickCox. Y is expected to decrease linearly (disease severity will only get worse over time), while X, the biological variable, will increase as the disease worsens, but will decrease after a certain disease stage is passed.
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| Aug 9 at 16:33 | comment | added | Nick Cox | Your question about testing can't be answered well without an explicit mention of whatever model you fitted. Indeed what is X and what is Y still seems confused to me. | |
| Aug 9 at 16:28 | comment | added | a.henrietty | also thanks for your comment, @whuber . | |
| Aug 9 at 16:27 | comment | added | a.henrietty | Thanks for your answer, @Nick Cox. Regarding the multi-panel nature, the dataset contains data from patients and some of them were tested over multiple years. I wanted to do something as an LMM to incorporate random effects (and possibly covariates). How could I statistically test the maximum of x? | |
| Aug 9 at 16:06 | comment | added | whuber♦ | To avoid the problems with estimating the standard error of $-\hat b_1/(2\hat b_2),$ I suggest analyzing the question of testing the contrast $2(x-x^*)\hat b_2 + \hat b_1$ where $x^*$ is the estimated peak location and $x$ is any other location. Intuitively, values of $x$ for which this test does not reject the null hypothesis are consistent with being a possible peak location, thereby producing fiducial limits for $x^*.$ This is sometimes known as inverse regression., q.v. | |
| Aug 9 at 15:55 | history | answered | Nick Cox | CC BY-SA 4.0 |