nls function (RStudio)
90
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RStudio
nls function
Nls Function, supplied by RStudio, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/nls+function/nls+function/pm40597558-79-7-17
Average 90 stars, based on 1 article reviews
Nls Function, supplied by RStudio, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/nls+function/nls+function/pm40597558-79-7-17
Average 90 stars, based on 1 article reviews
nls function - by Bioz Stars,
2026-09
90/100 stars
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Software:Article Title: Deciphering Seasonal Patterns in Animal Feeding: A Mechanistic Approach to Analyzing the Restricted Growth of Iberian Pigs. Article Snippet: .. The lm and other:Article Title: 2b-RAD genotyping for population genomic studies of Chagas disease vectors: Rhodnius ecuadoriensis in Ecuador Article Snippet: # AlfI data set equation - logarithmic model y = 1/(a+b*ln(x)) did improve model fit nls_eqA = function(Alfeq, start=list(a=exp(3.60543), b=0.61202)){ m = nls(PolyLoci ~ a + b * log(Reads), start=start, data=Alfeq); eq <- substitute(italic(y) == a + b ~ ln ~italic((x)), list(a=format(coef(m)[1], digits=2), b=format(coef(m)[2], digits=2))) as.character(as.expression(eq)); } # BcgI data set equation - Geometric y = a*x^(b*x) did improve model fit nls_eqB = function(Bcgeq, start=list(a=exp(3.2973), b=1.3523)){ m = nls(PolyLoci ~ a * Reads ^ (b * Reads), start=start, data=Bcgeq); eq <- substitute(italic(y) == a ~ italic(x)^(b ~ italic(x)), list(a=format(coef(m)[1], digits=2), b=format(coef(m)[2], digits=2))) as.character(as.expression(eq)); } # CspCI data set equation - Exponential model y=a*exp(b*x) did improve model fit nls_eqC = function(Cspeq, start=list(a=exp(3.0635), b=2.3027)){ m = nls(PolyLoci ~ a * exp(b * Reads), start=start, data=Cspeq); eq <- substitute(italic(y) == a ~ italic(e)(b ~ italic(x)), list(a=format(coef(m)[1], digits=2), b=format(coef(m)[2], digits=2))) as.character(as.expression(eq)); } ================================================ Plot your data vs fitted nls models You might find some error when using nls function as stated in: Examples using geom_smooth(), particularly Article Title: Impact of pre-growth and storage conditions on the survival of Listeria monocytogenes in acidic baby fruit purees: Implications for food safety and consumer practices. Article Snippet: Fitting growth and Article Title: Determining the phosphorus release curve for Sunphase HT phytase in nursery pig diets Article Snippet: The model parameters were estimated using the nls function from the stat package in R (version 4.2.1 [June 23, 2022]; ) using the Article Title: Neural Tuning to Numerosity Relates to Perceptual Tuning in 3–6-Year-Old Children Article Snippet: The best fit for those data are obtained by using the nonlinear squares function (nls, “port” algorithm) in Article Title: Determining the phosphorus release curve for Smizyme TS G5 2,500 phytase from 500 to 2,500 FTU/kg in nursery pig diets Article Snippet: The model parameters were estimated using the nls function from the R Stats Package (Version 4.1.1 [2021-08-10]; ) using the Article Title: Measurement of mitochondrial H 2 O 2 production under varying O 2 tensions Article Snippet: The Article Title: Zebrafish as a model organism to study sporadic Alzheimer's disease: Behavioural, biochemical and histological validation. Article Snippet: Alzheimer’s disease (AD) is a global burden to the healthcare system with no viable treatment options till date.. Rodents and primates have been extensively used as models for understanding AD pathogenesis and identifying therapeutic targets.. However, the focus is now shifting towards developing alternate models. |