Displaying 4 results from an estimated 4 matches for "scal2".
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2010 Jan 13
1
Problem fitting a non-linear regression model with nls
Hi,
I'm trying to make a regression of the form :
formula <- y ~ Asym_inf + Asym_sup * ( (1 / (1 + (n1 * (exp( (tmid1-x)
/ scal1) )^(1/n1) ) ) ) - (1 / (1 + (n2 * (exp( (tmid2-x) / scal2)
)^(1/n2) ) ) ) )
which is a sum of the generalized logistic model proposed by richards.
with data such as these:
x <- c(88,113,128,143,157,172,184,198,210,226,240,249,263,284,302,340)
y <-
c(0.04,0.16,1.09,2.65,2.46,2.43,1.88,2.42,1.51,1.70,1.92,1.35,0.89,0.34,0.13,0.10)
I use the nls...
2004 Jul 28
0
Modelling compound logistic growth curves
...es into a series of logistic equations, I attempted to do the same thing
in R.
SIMULATED DATA
Time <- 1:200
pop.size <- SSlogis(Time,10,20,5) + SSlogis(Time,20,100,20) +
rnorm(length(Time))
MY ANALYSIS
results <- nls(size ~ SSlogis(Time, Asym1, xmid1, scal1) + SSlogis(Time,
Asym2, xmid2, scal2),
start = list(Asym1=5, xmid1=15, scal1=30, Asym2=25, xmid2=67, scal2=25))
THE RESULT
I get the error message:
Error in nls(size ~ SSlogis(Time, Asym1, xmid1, scal1) + SSlogis(Time, :
step factor 0.000488281 reduced below `minFactor' of 0.000976563
Assistance in doing this analysis w...
2004 Aug 16
0
Multiple logistic curves
...stic equations, I attempted to do the same thing
in R.
#SIMULATED DATA
Time <- 1:200
pop.size <- SSlogis(Time,10,20,5) + SSlogis(Time,20,100,20) +
rnorm(length(Time))
ts.plot(pop.size)
#MY ANALYSIS
results <- nls(pop.size ~ SSlogis(Time, Asym1, xmid1, scal1) + SSlogis(Time,
Asym2, xmid2, scal2),
start = list(Asym1=5, xmid1=15, scal1=30, Asym2=25, xmid2=60, scal2=25))
THE RESULT
I get the error message:
Error in nls(size ~ SSlogis(Time, Asym1, xmid1, scal1) + SSlogis(Time, :
step factor 0.000488281 reduced below `minFactor' of 0.000976563
Any hints in making this analysis w...
2010 Feb 03
1
comparison of parameters for nonlinear regression
Hi,
I have two series of data set (it's measurment of growth but under two
different conditions).
To model these data I use the same function which is :
formula <- y ~ Asym_inf + Asym_sup * ( (1 / (1 + (n1 * (exp( (tmid1-x)
/ scal1) )^(1/n1) ) ) ) - (1 / (1 + (n2 * (exp( (tmid2-x) / scal2)
)^(1/n2) ) ) ) )
After the estimation of the parameters thanks to "nls", I have 2 models.
The idea is I want to compare this 2 models and particularly I want to
know if the parameters are different are not beteween this 2 models. Is
there a way to do it simply in R?
Thanks in advanc...