CorrMixed (version 1.0)

Fract.Poly: Fit fractional polynomials

Description

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.

Usage

Fract.Poly(Covariate, Outcome, S=c(-2,-1,-0.5,0,0.5,1,2,3), Max.M=5, Dataset)

Arguments

Covariate

The covariate to be considered in the models.

Outcome

The outcome to be considered in the models.

S

The set \(S\) from which each power \(p^{m}\) is selected. Default S={-2, -1, -0.5, 0, 0.5, 1, 2, 3}.

Max.M

The maximum order \(M\) to be considered for the fractional polynomial. This value can be \(5\) at most. When \(M=5\), then fractional polynomials of order \(1\) to \(5\) are considered. Default Max.M=5.

Dataset

A data.frame that should consist of multiple lines per subject ('long' format).

Value

Results.M1

The results (powers and AIC values) of the fractional polynomials of order \(1\).

Results.M2

The results (powers and AIC values) of the fractional polynomials of order \(2\).

Results.M3

The results (powers and AIC values) of the fractional polynomials of order \(3\).

Results.M4

The results (powers and AIC values) of the fractional polynomials of order \(4\).

Results.M5

The results (powers and AIC values) of the fractional polynomials of order \(5\).

References

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.

Examples

# NOT RUN {
# 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))
# }

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