The nls() function has a well documented (and discussed) different behavior compared to the lm()’s. Specifically you can’t just put an indexed column from a data frame as an input or output of the model.

> nls(data[,2] ~ c + expFct(data[,4],beta), data = time.data,
+ start = start.list)
Error in parse(text = x) : unexpected end of input in "~ "

The following will work, when we assign things as vectors.

> nls(y ~ c + expFct(x,beta), data = time.data,start = start.list)
#
# Formula: y ~ c + expFct(x,beta)
#
# Parameters:
# Estimate Std. Error t value Pr(>|t|)
# c 3.7850419 0.0042017 900.83 < 2e-16 ***
# beta 0.0053321 0.0003733 14.28 1.31e-12 ***
# ---
# Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#
# Residual standard error: 0.01463 on 22 degrees of freedom
#
# Number of iterations to convergence: 1
# Achieved convergence tolerance: 7.415e-06

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**Tags:** lm, nls, nonlinear, R, regression, statistics