I know python and C++ but have very little experience with R. I'm supposed to figure out what my old coworker's script does - he hasn't been here for several years but I have his files. He has about 10 python files that pass data into a temp file and then into the next python script, which I'm able to track, but he has one R script that I don't understand because I don't know R.
The input to the R script is temp4.txt:
1.414442 0.0043
1.526109 0.0042
1.600553 0.0046
1.637775 0.0045
...etc
Where column 1 is the x-axis of a growth curve (time units) and column 2 is growth level (units OD600, which is a measure of cell density).
The R script is only 4 lines:
inp1 <- scan('/temp4.txt', list(0,0))
decay <- data.frame(t = inp1[[1]], amp = inp1[[2]])
form <- nls(amp ~ const*(exp(fact*t)), data=decay, start = list(const = 0.01, fact = 0.5))
summary(form)
The R script's output:
Parameters:
Estimate Std. Error t value Pr(>|t|)
const 2.293e-03 9.658e-05 23.74 <2e-16 ***
fact 7.106e-01 8.757e-03 81.14 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.002776 on 104 degrees of freedom
Correlation of Parameter Estimates:
const
fact -0.9905
Where the "fact" number is what he's pulling out in the next python script as the value to continue forward with analysis. Usually it's a positive value e.g. "6.649e-01 6.784e-01 6.936e-01 6.578e-01 6.949e-01 6.546e-01 0.6623768 0.6710339 6.952e-01 6.711e-01 6.721e-01 6.520e-01" but because temp file gets overwritten each time I only have one version of it with the negative value -0.9905 which he's throwing away negative values in the next python script.
I need to know what exactly he's doing to recreate it... I know the <- is passing data into an object, so it's the nls() function that's confusing me...
Thanks anyone who can explain the R for me.
The first line reads the data into R
The second line restructures the data into a data frame (a table structure that is used commonly in R, it will be passed as the data to nls in line 3).
This looks like older code, most modern coders would replace lines 1 and 2 with one call to read.table.
Line 3 fits a non-linear least squares equation to the data read in previously and line 4 prints the summary of the fit including the estimates of the parameters for the next python script to read.
The non-linear model that is being fit is an exponential growth curve and the fact parameter is a measure of the rate of growth.