Description Usage Arguments Value Examples
View source: R/compute_functionals_of_model.R
This only works for single-agent causes
1 | compute_logOR_single_cause(set_parameter)
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set_parameter |
True model parameters in an npLCM specification:
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a matrix of log odds ratio. See the example for a figure showing pairwise odds ratios for cases (upper right, solid lines) and controls (lower left, broken lines) as the first subclass weight increases from 0 to 1. Pairwise independence is represented by the dotted horizontal lines for reference.
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K.true <- 2 # no. of latent subclasses in actual simulation.
# If eta = c(1,0), effectively, it is K.true=1
J <- 5 # no. of pathogens.
N <- 500 # no. of cases/controls.
col_seq_cause <- c("#DB9D85","#A2B367","#47BEA2",
"#70B3DA","#CD99D8")#colorspace::rainbow_hcl(5, start = 30, end = 300)
subclass_mix_seq <- seq(0,1,by=0.05)
res <- array(NA,c(J,J,length(subclass_mix_seq)))
res_cond <- array(NA,c(J,J,length(subclass_mix_seq),J))
it <- layout(matrix(1:J^2,nrow=J,ncol=J,byrow=TRUE),
heights = rep(3,J),
widths = rep(3,J))
par(oma=c(8,10,8,3));
pch_seq_cause <- LETTERS[1:J]
lty_seq_cause <- 1+(1:J)
pch_pos_seq <- c(0.01); gap = 0.15
adj_seq <- c(0.15,0.5,0.85) # for roman numerals:
cex1 <- 2
cex_label1 <- 1
cex2 <- 2
cex_label2 <- 2
cex_margin_marks <- 2
for (scn in c(1,2,3)){
for (iter in seq_along(subclass_mix_seq)){
curr_mix <- subclass_mix_seq[iter]
lambda <- c(curr_mix,1-curr_mix)
eta <- c(curr_mix,1-curr_mix)
# if it is c(1,0),then it is conditional independence model, and
# only the first column of parameters in PsiBS, ThetaBS matter!
seed_start <- 20150923
# set fixed simulation sequence:
set.seed(seed_start)
if (scn == 3){
ThetaBS_withNA <- cbind(c(0.95,0.9,0.1,0.5,0.5),
c(0.95,0.1,0.9,0.5,0.5))
PsiBS_withNA <- cbind(c(0.4,0.4,0.05,0.2,0.2),
c(0.05,0.05,0.4,0.05,0.05))
}
if (scn == 2){
ThetaBS_withNA <- cbind(c(0.95,0.5,0.5,0.5,0.5),
c(0.95,0.5,0.5,0.5,0.5))
PsiBS_withNA <- cbind(c(0.4,0.4,0.05,0.2,0.2),
c(0.05,0.05,0.4,0.05,0.05))
}
if (scn == 1){
ThetaBS_withNA <- cbind(c(0.95,0.5,0.5,0.5,0.5),
c(0.95,0.5,0.5,0.5,0.5))
PsiBS_withNA <- cbind(c(0.3,0.3,0.15,0.2,0.2),
c(0.15,0.15,0.3,0.05,0.05))
}
# the following paramter names are set using names in the 'baker' package:
set_parameter0 <- list(
cause_list = c(LETTERS[1:J]),
etiology = c(0.5,0.2,0.15,0.1,0.05), #same length as cause_list
#etiology = rep(0.2,J), #same length as cause_list
pathogen_BrS = LETTERS[1:J],
meas_nm = list(MBS = c("MBS1")),
Lambda = lambda, #ctrl mix
Eta = t(replicate(J,eta)), #case mix, row number equal to Jcause.
PsiBS = PsiBS_withNA,
ThetaBS = ThetaBS_withNA,
Nu = N, # control size.
Nd = N # case size.
)
res[,,iter] <- round(compute_logOR_single_cause(set_parameter0),2)
for (pick in 1:J){
set_parameter <- set_parameter0
set_parameter$ThetaBS <- set_parameter0$PsiBS
set_parameter$ThetaBS[pick,] <- set_parameter0$ThetaBS[pick,]
set_parameter$etiology <- rep(0,J); set_parameter$etiology[pick] <- 1
res_cond[,,iter,pick] <- round(compute_logOR_single_cause(set_parameter),2)
}
}
ind <- sapply(c(0,0.5,1),function(x) which(subclass_mix_seq==x))
logOR_lim <- c(-2.15,2.15)
col_seq <- c("dodgerblue2","orange")
logOR_seq <- log(c(0.25,0.5,1,2,4))
pick_one <- 3
print(paste0("==Shading pairs of ",pch_seq_cause[pick_one]," and others.==="))
for (j in 1:J){
for (l in 1:J){
par(mar=c(0,0,0,0));
if (j==J){
par(mar=c(0,0,0,0))
}
if (l%%J==0){
par(mar=c(0,0,0,1))
}
if (l%%J==1){
par(mar=c(0,1,0,0))
}
if (!(j==l)){
plot(res[j,l,],type="l",xlab="",ylab="",
ylim=logOR_lim, lwd=5,
xaxt="n",
yaxt="n",
col=col_seq[1+(l>j)],
#lty=c(2,1)[1+(l>j)],
lty=1,
bty="n"
)
box(col="lightgray")
abline(h=0,col="lightgray",lwd=3,lty=3)
if (j<l){
matplot(res_cond[j,l,,],type="l",add=TRUE,pch=LETTERS[1:J],lwd=2,lty=2,
col=col_seq_cause)
}
lab_ord <- c(j,l); if (j>l){lab_ord <- rev(lab_ord)}
mtext(paste0("(",set_parameter$pathogen_BrS[lab_ord[1]],",",
set_parameter$pathogen_BrS[lab_ord[2]],")"),
side=3, adj=0.1,line=-2)
if (l%%J==1){
axis(2,at = logOR_seq,
labels = round(exp(logOR_seq),1),
las=2,cex.axis=cex1)
}
if (l%%J==0){
axis(4,at = logOR_seq,
labels = round(exp(logOR_seq),1),
las=2,cex.axis=cex1)
}
if (j==J){
axis(1,at=seq_along(subclass_mix_seq)[ind],
labels=rep("",length(ind)),cex.axis = cex1,las=1)
axis(1,at=seq_along(subclass_mix_seq)[ind]+c(1,rep(0,length(ind)-2),-1),
labels=subclass_mix_seq[ind],cex.axis = cex1,las=1,tick=FALSE)
}
if (j==1){
axis(3,at=seq_along(subclass_mix_seq)[ind],
labels=rep("",length(ind)),cex.axis = cex1,las=1)
axis(3,at=seq_along(subclass_mix_seq)[ind]+c(1,rep(0,length(ind)-2),-1),
labels=subclass_mix_seq[ind],cex.axis = cex1,las=1,tick=FALSE)
}
if (j==5 & l==1){
mtext(expression(atop("Odds Ratio","(log-scale)")), side = 2, line = 4,
cex=cex_label1, las=2)
}
if (j==5){
mtext(expression(lambda[o]),side=1,line=4,cex=cex_label1)
}
if ((j<l) && (l==pick_one | j==pick_one )){
# add shading cells for oen picked pathogen among cases:
color <- rgb(190, 190, 190, alpha=80, maxColorValue=255)
rect(par("usr")[1], par("usr")[3], par("usr")[2],
par("usr")[4], density = 100, col = color)
matplot(res_cond[j,l,,],type="l",add=TRUE,lwd=2,col=col_seq_cause,lty=lty_seq_cause)
for (ell in 1:J){
where_add_letter <- quantile(seq_along(subclass_mix_seq),pch_pos_seq+gap*ell)
points(where_add_letter, res_cond[j,l,where_add_letter,ell], pch=pch_seq_cause[ell])
}
mtext(paste0("(",set_parameter$pathogen_BrS[lab_ord[1]],",",
set_parameter$pathogen_BrS[lab_ord[2]],")"),
side=3, adj=0.1,line=-2)
}
}else{
plot(1, type="n", axes=FALSE, xlab="", ylab="", bty="n",
xlim=c(0,1),ylim=c(0,1))
if (j==3){
text(labels=expression(CASES%up%""),x=.7,
y=0.55,srt=-49,col=col_seq[2],cex=1.8,adj=0.5,font=4)
text(labels=expression(CONTROLS%down%""),x=.42,
y=0.38,srt=-49,col=col_seq[1],cex=1.8,adj=0.5,font=4)
}
if (j!=1 & j!=J){
dg <- par("usr")
segments(dg[1],dg[4],dg[2],dg[3], col='lightgray',lwd=3)
}
if (j==J){
legend("top",LETTERS[1:J],lty=2,col=col_seq_cause,cex = 1.5,lwd=2,
bty="n",horiz=FALSE)
}
}
}
}
}
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