Arcsine transformation in r
I'm trying to compare herd size and pregnancy with percentage of heads up- which is supposed to be a 2 way anova, but need to transform the data first. r/statistics - D How do you think we should address utter BS. **Error in sign(mydata) : non-numeric argument to mathematical function** The arcsine transformation is a helpful variance stabalizing transformation when your. (for any pregnant in herd column, I have used pregnant and no clear evidence- should I make them numeric, as 1 and 0s or something similar?) trans.arcsine<-function(mydata) Non-numeric variable(s) in data frame: Herd.size, .herd.**
I also tried (as suggested help in other answers): trans.arcsine<-asin(sign(mydata)*sqrt(abs(mydata)))
Arcsine transformation in r how to#
Which I am unsure in how to change/ interpret. The arcsine transformation, not generally recommended for data sets having values from 0 to 20 or 80 to 100, was as effective in correcting non-normality. What is the correct way to transform this data - i.e. However, when I use transf.arcsine in R on a dataset ranging from -1 to 1, NaNs are produced because of the square-rooting of a negative number. In asin(sqrt(mydata$percentage.of.heads.up.at.halfway)) : NaNs produced** According to the Handbook of Biological Statistics, the arcsine squareroot transformation is used for proportional data, constrained at -1 and 1. Mydatatrans<-asin(sqrt(mydata$percentage.of.heads.up.at.halfway)) Length(percentage.of.heads.up.at.halfway) glmer( ) from the lme4 package in R (Bates et al. Shapiro.test(mydata$percentage.of.heads.up.at.halfway) Key words: arcsine transformation binomial generalized linear mixed models logistic regression.
The arcsine transformation (also called the arcsine square root. best option to transform your data are the -> arcsine transformation. Label = c("Large", "Small"), class = "factor"),Īny.pregnant.in.herd.
Arcsine transformation in r code#
My code (using csv: HS.PvsPERC): mydata<-read.csv(file.choose()) I need to arcsine transform my data, but as I've never done this before I'm not sure if the code I'm using is right, and therefore I don't know how to address errors when they occur.