Good day,
I am struggling with R and natural logarithm (ln). Firstly, I cannot find a ln(x) function in R. I have noticed that log(x) is the same as ln(x) (when using ln(x) with a calculator).
In R:
log(5) = 1.609438
And with a calculator:
ln(5) = 1.609438
log(5) = 0.69897
I'm trying to fit an equation in R (this is exactly how I found in the literature of 3 references):
y = a + b(x/305) + c(x/305)2 + d ln(305/x) + f ln2(305/x)
Is it correct to use the following syntax in R to use the equation?
y ~ a + b*(x/305) + c*((x/305)^2) + d*log(305/x) + f*(log(305/x))^2
The idea is to use this function with nls() in R. Thanks in advance!
To calculate the natural log in R, use the log() function. The default setting of this function is to return the natural logarithm of a value. But through a package called SciViews, you can use the ln() function, which also calculates the natural log in R.
log in R means the natural logarithm. This is the convention of mathematicians, since "common" logarithms have no mathematical interest. The "ln" abbreviation is something that was introduced to make things less confusing to students.
R log Functionlog(x) function computes natural logarithms (Ln) for a number or vector x by default. If the base is specified, log(x,b) computes logarithms with base b.
ln: Logarithms. To avoid confusion using the default log() function, which is natural logarithm, but spells out like base 10 logarithm in the mind of some beginneRs, we define ln() and ln1p() as wrappers for log()`` with default base = exp(1) argument and for log1p() , respectively.
In R, log
is the natural logarithm. In calculators, log usually means base 10 logarithm. To achieve that in R you can use the log10
function.
log(5)
## [1] 1.609438
log10
## [1] 0.69897(5)
As for your formula, it seems correct, since log
is the natural logarithm.
In addition I will point out that your model
y ~ a + b*(x/305) + c*((x/305)^2) + d*log(305/x) + f*(log(305/x))^2
is linear in the statistical sense of being linear in the coefficients; it doesn't need to be linear in x.
You don't need nls to fit this model, you could use lm().
But remember to look at the I() function to express terms like (x/305)^2.
ETA example:
aDF <- data.frame(x=abs(rnorm(100)), y=rnorm(100))
lm(y ~ 1 + I(x/305) + I((x/305)^2) + log(305/x) + I(log(305/x)^2), data=aDF)
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