[R] almost logistic data evaluation

PIKAL Petr petr@p|k@| @end|ng |rom prechez@@cz
Tue Jun 9 14:29:27 CEST 2020

Hallo Patrick


Thanks. Actually „y“ is growing temperature, which, at some point, rise more rapidly due to exothermic reaction. This reaction starts and ends and proceed with some speed (hopefully different in each material). I hope to get starting point and speed of temperature rise by evaluating shape of curves.


I do not think left censoring could help. As seen from data plot at first „y“ is linearly growing but logistics curve needs to start from flat (left asymptote) and end as flat (right asymptote, AFAIK). With linear growth on left site simple logistics fail to model data correctly.


One option could be to estimate linear part and deduct it from the data and fit simple logistics model on deducted data. If this is the only way, I will do it but I, as always, first try to ask helpful and ingenious people on this list.





From: Patrick (Malone Quantitative) <malone using malonequantitative.com> 
Sent: Tuesday, June 9, 2020 2:05 PM
To: PIKAL Petr <petr.pikal using precheza.cz>
Subject: Re: [R] almost logistic data evaluation


Off-list because off-topic.


I didn't plot your data, but took your word that "They resemble logistics curve but they do not start as flat curve but
growing curve."


You also didn't say what your research question is. But if you're trying to model the growth, could it be *part* of a logistic curve, with a censoring point on the left? Maybe that helps with some avenues.


On Tue, Jun 9, 2020 at 7:21 AM PIKAL Petr <petr.pikal using precheza.cz <mailto:petr.pikal using precheza.cz> > wrote:

Dear all

I have several files with data like those.

> dput(temp)
temp <- structure(list(V1 = c(0L, 15L, 30L, 45L, 60L, 75L, 90L, 105L, 
120L, 135L, 150L, 165L, 180L, 195L, 210L, 225L, 240L, 255L, 270L, 
285L, 300L, 315L, 330L, 345L, 360L), V2 = c(98.68666667, 100.8, 
103.28, 107.44, 110.06, 114.26, 117.6, 121.04, 123.8533333, 126.66, 
129.98, 134.1866667, 139.04, 144.6, 152.08, 161.3, 169.8733333, 
176.6133333, 181.92, 186.0266667, 188.7533333, 190.7066667, 192.0533333, 
192.9933333, 193.3533333)), class = "data.frame", row.names = c(NA, 


They resemble logistics curve but they do not start as flat curve but
growing curve. Can you please give me some hints how to deal with such data?
I know that it is not strictly speaking R question but maybe somebody could
give me directions how to model such data and find model parameters.

I considered stepwise regression but it is not completely satisfactory.

Thanks beforehand
Petr Pikal
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Patrick S. Malone, Ph.D., Malone Quantitative
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