# Data normalization for lme mixed model_R

I have a set of data and I am going to apply the lme mixed model. However, the data are not normally distributed (see the graph below).

I tried (log, sqrt, zscore) and box-cox transformations and even the graphs look better, Kolmogorov-Smirnov test gave me a max p-value=0.008.

The residual plot for the lme model (used data were transformed with box-cox) is attached below and

Lilliefors (Kolmogorov-Smirnov) normality test p-value = 9.909e-12 To my knowledge, the residuals plot should not have any pattern and I can not see any on my plot, but why the normality tests (Kolmogorov-Smirnov / Shapiro) gave me values <0.05?

My question is:

What also can I do to normalize my data and use them for mixed model (lme)?

• (if OP is still around ...) is the density plot based on the residuals or on the response variable? – Ben Bolker Mar 21 '18 at 2:37

## 2 Answers

A few points of general advice:

1) The plot you have of residuals vs. index isn't particularly useful for assessing the normality of the residuals. It is more useful to use a q-q plot or a histogram.

2) Don't rely on statistical tests (Shapiro-Wilk, Anderson–Darling, Kolmogorov–Smirnov, et al.) to determine if data or residuals are normally-distributed. They are sensitive to sample size. If you have a lot of data, they are likely to find a significant deviation from normal even if that deviation is small.

Here is a small example of a q-q plot and histogram in R. The chosen transformation doesn't work all that well for this example.

library(nlme)

data(mtcars)

mtcars$mpg_trans = mtcars$mpg ^ 1.3

model = lme(mpg_trans ~ disp + cyl, random=~1|gear,
data=mtcars,
method="REML")

x = residuals(model)

hist(x, prob=TRUE, col="darkgray")
Range = seq(min(x), max(x), length = length(x))
Norm = dnorm(Range, mean = mean(x), sd = sd(x))
lines(Range, Norm, col = "blue", lwd = 2) qqnorm(x)
qqline(x, col="red") plot(predict(model), residuals(model)) You can try to apply the Johnson transformation to normalize a data.

# require
library(Johnson)
#Applying Johnson transformation
your_data_JT<-RE.Johnson(your_data)
# check p-value of the Anderson-Darling test
your_data_JT\$p