Sending again as I pressed the wrong button
Hi
Your model is overfitted for the number of points - the fitted values bear
no resemblance to a line fitting the data - ?too many x values to properly
predict
? splines etc
library(locfit)
?locfit
If you want coefs of sort.
You have to do a nls on each group to get the values.
You have 2 ways to do this
1 do nls and predict y values separately as below an plot them in a panel
function
2 do nls and predict within the panel function
> res_nlsA <-
+ nls(y ~ SSgompertz(time, Asym = 100, b2, b3), data = subset(foo, char ==
"A"),
+ start = list( b2 = c(7,7), b3 = c(0.9, 0.9)))
There were 50 or more warnings (use warnings() to see the first 50)
> res_nlsA
Nonlinear regression model
model: y ~ SSgompertz(time, Asym = 100, b2, b3)
data: subset(foo, char == "A")
b21 b22 b31 b32
4.6761 7.6280 0.9111 0.9000
residual sum-of-squares: 10.59
Number of iterations to convergence: 32
Achieved convergence tolerance: 8.001e-06
> res_nlsB <-
+ nls(y ~ SSgompertz(time, Asym = 100, b2, b3), data = subset(foo, char ==
"B"),
+ start = list( b2 = c(7,7), b3 = c(0.9, 0.9)))
> res_nlsB
Nonlinear regression model
model: y ~ SSgompertz(time, Asym = 100, b2, b3)
data: subset(foo, char == "B")
b21 b22 b31 b32
9.5231 86.6421 0.8164 0.6582
residual sum-of-squares: 36.26
Number of iterations to convergence: 10
Achieved convergence tolerance: 1.37e-06
>
> predict(res_nlsB, newdata = list(time = seq(5,60,2.5)))
[1] 3.162527 2.321040 28.575719 62.814774 63.489626 94.416582
84.809570 99.292612 94.199502 99.912322 97.856129 99.989162 99.217093
99.998661 99.715346
[16] 99.999835 99.896669 99.999980 99.962512 99.999997 99.986402
100.000000 99.995068
Warning messages:
1: In b3^x :
longer object length is not a multiple of shorter object length
2: In -b2 * .expr2 :
longer object length is not a multiple of shorter object length
> predict(res_nlsB, newdata = list(time = seq(5,60,5)))
[1] 3.162527 26.638924 63.489626 98.000694 94.199502 99.969171
99.217093 99.999529 99.896669 99.999993 99.986402 100.000000
> predict(res_nlsA, newdata = list(time = seq(0,55,5)))
[1] 0.9314904 1.1062577 15.8281748 20.7951276 48.3512870 57.8358643
75.0914782 82.6202586 89.3216331 93.5601696 95.6459638 97.7058236
xyplot(y ~ time, data = foo,
groups = char,
panel = panel.superpose,
panel.groups = function(x, y, ...,group.number){
panel.xyplot(x,y, ...);
xy.nls <-
nls(y ~ SSgompertz(x, Asym = 100, b2, b3),
start = list( b2 =7, b3 = 0.9))
#print(summary(xy.nls))
if (group.number == 1){
xy.pre <- predict(xy.nls, newdata = list(time =
seq(0,55,5)))
print(xy.pre)
llines( seq(5,60,5), xy.pre, col = "blue")
} else {
xy.pre <- predict(xy.nls, newdata = list(time =
seq(5,60,5)))
print(xy.pre)
llines( seq(5,60,5), xy.pre, col = "magenta")
}
})
Regards
Duncan
Duncan Mackay
Department of Agronomy and Soil Science
University of New England
Armidale NSW 2351
Email: home: mac...@northnet.com.au
-----Original Message-----
From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of François
Collin
Sent: Wednesday, 5 August 2015 22:24
To: r-help@r-project.org
Subject: [R] Lattice: raw data and prediction of a non linear fitted
function
Dear all,
I have a question about lattice use. I would like to use it to represent:
- my raw data as points,
- and the results of a non linear fit as a line,
- having 2 groups data (and so 2 colors).
However, as I have few raw data, I would like to increase the number of
points to smooth the line which correspond to the fit.
So my questions are:
- How can I use the group argument to make new predictions inside panel
argument ?
- How can I use this prediction inside the panel argument to draw the fit
per group?
Hereafter a minimal example:
#==================================================
library(lattice)
set.seed(2)
# Dummy dataframe
foo <- data.frame(
time = seq(0, 60, 5),
char = sample(c("A", "B"), size = 13, replace = TRUE)
);
# Simulated response vector according a Gompertz function + rnorn(0, 5)
foo$y <- SSgompertz(foo$time, Asym = 100, b2 = ifelse(foo$char == 'A', 6,
10),
b3 = ifelse(foo$char == "A", .91, .8)) +
rnorm(nrow(foo), mean=0, sd = 5);
# Non-linear fit on simulation data
res_nls <- nls(
y ~ SSgompertz(time, Asym = 100, b2[char], b3[char]), data = foo,
start = list( b2 = c(7,7), b3 = c(0.9, 0.9)));
# My problem
xyplot(y ~ time, groups = char, data = foo,
panel = function(x, y, groups=groups, model = model, ...){
panel.xyplot(x,y, groups = groups, ...);
newdata <- data.frame(time = x, char = groups);
newdata$y <- predict(model, newdata = newdata);
panel.superpose(x = newdata$time, y=newdata$y, groups = groups, ...,
panel.groups = function(x,y, col, col.symbol, ...){
panel.lines(x,y, col.line = col.symbol)
})
}, model = res_nls);
#==================================================
Many thanks,
Francois
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