# Illustrates how to get smoothed derivative estimates based on B-splines
# via wrapper function call to the fda package
# Author: Sy-Miin Chow
# Last modified: 11/4/2020
# The simulation model features:
# dx1(t)/dt = x2(t)
# dx2(t)/dt = eta*x1(t) + zeta*x2(t)

#Variables in this data set are: 
#ID = ID of participants
#x = true scores
#theTimes = time indices

#Loading simulated data generated using the linear oscillator model

#Structure the one indicator for n individuals into a matrix with n columns
#If different individuals have different number of rows, consider doing
#this step separately for each individual

n = length(unique(LinearOsc$ID)) #Number of subjects is 10
T = max(table(LinearOsc$ID)) #Number of time points is 100
out2 = matrix(LinearOsc$x,ncol=n,byrow=FALSE)
theTimes = LinearOsc$theTimes[1:T]
norder = 6 #Order of Bsplines - usually 2 higher than roughPenaltyMax
roughPenaltyMax = 4 #penalization order 
#  #lambdaLow, lambdaHi, lambdaBy = specify an interval of lambda (a positive smoothing parameter,
#                                 larger means more smoothing) to be tested from lambdaLow to
#                                 lambdaHi, as separated by lambdaBy
#isPlot = a binary flag on whether to plot the gcv values (0 = no, 1 = yes)
lambdaLow = 1e-10
lambdaHi = 2
lambdaBy = .1
isPlot = 1 #Whether to plot the average GCV values as a function of lambda values
matt = plotGCV(theTimes,norder,roughPenaltyMax,out2,lambdaLow, lambdaHi,lambdaBy,isPlot)

#Extract the lambda value that gives the minimum GCV
sp = matt[matt[,"GCV"] == min(matt[,"GCV"]),"lambda"]

#Extract smoothed level, first and second derivative estimates at the lambda value selected above
x = getdx(theTimes,norder,roughPenaltyMax,sp,out2,0)[[1]] #Smoothed level
dx = getdx(theTimes,norder,roughPenaltyMax,sp,out2,1)[[1]] #Smoothed 1st derivs
d2x = getdx(theTimes,norder,roughPenaltyMax,sp,out2,2)[[1]] #Smoothed 2nd derivs

#Put level and derivative estimates into a data frame
dxall = data.frame(time = rep(theTimes,n),
                   x = matrix(x,ncol=1,byrow=FALSE), 
                   dx = matrix(dx,ncol=1,byrow=FALSE),
                   d2x = matrix(d2x,ncol=1,byrow=FALSE))

g = lm(d2x~x+dx-1,data=dxall)

#Component + residuals plot to show the association between smoothed d2x and smoothed x
#after partialling out the effect of smoothed dx
oldpar <- par(mgp=c(2.5,0.5,0))
        ylab=expression(paste("Component+Residuals ", "  ",d^2,hat(eta)[i](t)/dt^2))

#Component + residuals plot to show the association between smoothed d2x and smoothed dx
#after partialling out the effect of smoothed x
oldpar <- par(mgp=c(2.5,0.5,0))
        xlab=expression(paste(d, hat(eta)[i](t)/dt)),
        ylab=expression(paste("Component+Residuals ", "  ",d^2,hat(eta)[i](t)/dt^2))

# ---- Plot of simple slopes and region of significance ----
g2 = lm(d2x~x+dx+x:dx-1,data=dxall) #Adding an interaction term to illustrate some
# functions for probing interaction effects
summary(g2) #In this case the data were generated without any interaction effect.
#With larger T or n, sometimes spurious interaction effects may be detected
#due to shared variability between x and dx.
theta_plot(g2, predictor = "x", moderator = "dx", 
           alpha = .05, jn = T, title0=" ",
           predictorLab = "x", moderatorLab = "dx")

# ---- Phase portrait ----
Osc <- function(t, y, parameters) {
  dy <- numeric(2)
  dy[1] <- y[2]
  dy[2] <- parameters[1]*y[1]+parameters[2]*dy[1]   

param <- coef(g)
dynr.flowField(Osc, xlim = c(-3, 3), 
                  ylim = c(-3, 3),
                  xlab="x", ylab="dx/dt",
                  main=paste0("Oscillator model"),
                  parameters = param, 
                  points = 15, add = FALSE,
IC <- matrix(c(-2, -2), ncol = 2, byrow = TRUE)  #Initial conditions
dynr.trajectory(Osc, y0 = IC,  
                   parameters = param,tlim=c(0,50))

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dynr documentation built on Oct. 17, 2022, 9:06 a.m.