Fract.Poly {CorrMixed} | R Documentation |
Fits regression models with m
terms of the form X^{p}
, where the exponents p
are selected from a small predefined set S
of both integer and non-integer values.
Fract.Poly(Covariate, Outcome, S=c(-2,-1,-0.5,0,0.5,1,2,3), Max.M=5, Dataset)
Covariate |
The covariate to be considered in the models. |
Outcome |
The outcome to be considered in the models. |
S |
The set |
Max.M |
The maximum order |
Dataset |
A |
Results.M1 |
The results (powers and AIC values) of the fractional polynomials of order |
Results.M2 |
The results (powers and AIC values) of the fractional polynomials of order |
Results.M3 |
The results (powers and AIC values) of the fractional polynomials of order |
Results.M4 |
The results (powers and AIC values) of the fractional polynomials of order |
Results.M5 |
The results (powers and AIC values) of the fractional polynomials of order |
Wim Van der Elst, Geert Molenberghs, Ralf-Dieter Hilgers, & Nicole Heussen
Van der Elst, W., Molenberghs, G., Hilgers, R., & Heussen, N. (2015). Estimating the reliability of repeatedly measured endpoints based on linear mixed-effects models. A tutorial. Submitted.
# Open data
data(Example.Data)
# Fit fractional polynomials, mox. order = 3
FP <- Fract.Poly(Covariate = Time, Outcome = Outcome,
Dataset = Example.Data, Max.M=3)
# Explore results
summary(FP)
# best fitting model (based on AIC) for m=3,
# powers: p_{1}=3, p_{2}=3, and p_{3}=2
# Fit model and compare with observed means
# plot of mean
Spaghetti.Plot(Dataset = Example.Data, Outcome = Outcome,
Time = Time, Id=Id, Add.Profiles = FALSE, Lwd.Me=1,
ylab="Mean Outcome")
# Coding of predictors (note that when p_{1}=p_{2},
# beta_{1}*X ** {p_{1}} + beta_{2}*X ** {p_{1}} * log(X)
# and when p=0, X ** {0}= log(X) )
term1 <- Example.Data$Time**3
term2 <- (Example.Data$Time**3) * log(Example.Data$Time)
term3 <- Example.Data$Time**2
# fit model
Model <- lm(Outcome~term1+term2+term3, data=Example.Data)
Model$coef # regression weights (beta's)
# make prediction for time 1 to 47
term1 <- (1:47)**3
term2 <- ((1:47)**3) * log(1:47)
term3 <- (1:47)**2
# compute predicted values
pred <- Model$coef[1] + (Model$coef[2] * term1) +
(Model$coef[3] * term2) + (Model$coef[4] * term3)
# Add predicted values to plot
lines(x = 1:47, y=pred, lty=2)
legend("topright", c("Observed", "Predicted"), lty=c(1, 2))