From baron@cattell.psych.upenn.edu  Sat Sep 14 18:49:39 2002
From: baron@cattell.psych.upenn.edu (baron@cattell.psych.upenn.edu)
Date: Sat, 14 Sep 2002 19:49:39 +0200 (MET DST)
Subject: p.s. regarding stripchart missing-data report (PR#2019)
Message-ID: <200209141749.TAA07722@pubhealth.ku.dk>


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In case you want this - and feel free to delete this - here are the
data for the bug I just reported concerning missing data in
stripchart.

You need to run hrp2.R.  Please excuse the extraneous junk.  This was
written mostly by a grad student who was learning R in the process
(Andrea Gurmankin).  She doesn't like to delete things.

Jon


--oyUTqETQ0mS9luUI
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t2 <- read.table("hrp2.data")
# t1 <- t2[-c(1,2,11,19,20,54,83)] # less than 6 on log scale
t1 <- t2[-c(52,124),] # less than 5

ns <- nrow(t1) # number of Ss remaining

# ----------- define sub-matrices and summary variables

t1[which(t1[,1]==0),1] <- NA
t1[which(t1[,4]==0),4] <- NA
t1[which(t1[,12]==0),12] <- NA
t1[which(t1[,13]==0),13] <- NA
t1[which(t1[,15]==0),15] <- NA
t1[which(t1[,17]==0),17] <- NA
t1[which(t1[,18]==0),18] <- NA
t1[which(t1[,19]==0),19] <- NA
t1[which(t1[,20]==0),20] <- NA
t1[which(t1[,31]==7),31] <- NA
t1[which(t1[,12]==21),12] <- NA
t1[which(t1[,34]==0),34] <- NA
t1[which(t1[,37]==0),37] <- NA
t1[which(t1[,38]==0),38] <- NA
t1[which(t1[,39]=="y"),39] <- 1
t1[which(t1[,41]==5),41] <- NA
t1[which(t1[,43]==5),43] <- NA
t1[which(t1[,44]==5),44] <- NA
t1[which(t1[,46]==3),46] <- NA
t1[which(t1[,50]==5),50] <- NA
t1[which(t1[,51]==5),51] <- NA
t1[which(t1[,52]==5),52] <- NA
t1[which(t1[,53]==5),53] <- NA
t1[which(t1[,56]==5),56] <- NA
t1[which(t1[,58]==4),58] <- NA
t1[which(t1[,59]==4),59] <- NA
t1[which(t1[,60]==3),60] <- NA
t1[which(t1[,60]==11),60] <- 1
t1[which(t1[,61]==3),61] <- NA
t1[which(t1[,64]==5),64] <- NA
t1[which(t1[,65]==5),65] <- NA
t1[which(t1[,66]==5),66] <- NA
t1[which(t1[,67]==5),67] <- NA
t1[which(t1[,71]==3),71] <- NA
t1[which(t1[,72]==11),72] <- 1
t1[which(t1[,72]==4),72] <- NA
t1[which(t1[,74]==0),74] <- NA
t1[which(t1[,74]=="n"),74] <- 2
t1[which(t1[,75]=="n"),75] <- 2
t1[which(t1[,76]=="y"),76] <- 1
t1[which(t1[,77]==11),77] <- 1
t1[which(t1[,77]=="y"),77] <- 1
t1[which(t1[,78]==11),78] <- 1
t1[which(t1[,78]=="y"),78] <- 1
t1[which(t1[,79]=="n"),79] <- 2
t1[which(t1[,80]=="n"),80] <- 2
t1[which(t1[,81]=="y"),81] <- 1
t1[which(t1[,82]=="y"),82] <- 1
t1[which(t1[,87]==0),87] <- NA
t1[which(t1[,88]==0),88] <- NA
t1[which(t1[,90]==0),90] <- NA
t1[which(t1[,96]==13),96] <- NA
t1[which(t1[,100]==13),100] <- NA
t1[which(t1[,101]==11),101] <- 1
t1[which(t1[,101]==110),101] <- NA
t1[which(t1[,105]==0),105] <- NA
t1[which(t1[,113]==110),113] <- NA
t1[which(t1[,115]==0),115] <- NA
t1[which(t1[,213]==5),213] <- NA
t1[which(t1[,214]==6),214] <- NA
t1[which(t1[,215]==11),215] <- 1
t1[which(t1[,217]==0),217] <- NA
t1[which(t1[,218]==24),218] <- NA
t1[which(t1[,222]==23),222] <- NA
t1[which(t1[,227]==5),227] <- NA
t1[which(t1[,231]==5),231] <- NA
t1[which(t1[,236]==5),236] <- NA
t1[which(t1[,242]==12),242] <- NA
t1[which(t1[,243]==42),243] <- NA
t1[which(t1[,245]==41),245] <- NA
t1[which(t1[,246]==50),246] <- NA
t1[which(t1[,247]==100),247] <- NA
t1[which(t1[,248]==50),248] <- NA
t1[which(t1[,248]==55),248] <- NA
t1[which(t1[,250]==50),250] <- NA
t1[which(t1[,250]==83),250] <- NA
t1[which(t1[,250]==92),250] <- NA
t1[which(t1[,250]==93),250] <- NA

qs <- as.matrix(t1[,116:211])
colnames(qs) <- c(paste("q01",1:24,sep="."),paste("q02",1:24,sep="."),
                  paste("q03",1:24,sep="."),paste("q04",1:24,sep="."))
times <- qs[,c(0:3*24+3,0:3*24+24)]
tmean <- apply(times,1,mean,trim=.15)

#tmean is time mean for each Ss and trims off top and bottom 15%
plot(sort(tmean))

#each Ss has 6 times
#throw out the longest and shortest 15% times for each Ss and then compute time means for each Ss
#look at Ss mean for times (plot(sort(tmean)) and eliminate fast outliers. 

act <- as.matrix(t1[,c(1:20,31:40,73:82)]) # 1=y 2=n
colnames(act) <-
c("B.actl.100","B.actl.20","B.actl.40","B.actl.60","B.actl.80","Bfr.actl.100",
"Bfr.actl.20","Bfr.actl.40","Bfr.actl.60","Bfr.actl.80",
"C.actl.100","C.actl.20","C.actl.40","C.actl.60","C.actl.80","Cfr.actl.100",
"Cfr.actl.20","Cfr.actl.40","Cfr.actl.60","Cfr.actl.80",
"L.actl.100","L.actl.20","L.actl.40","L.actl.60","L.actl.80","Lfr.actl.100",
"Lfr.actl.20","Lfr.actl.40","Lfr.actl.60","Lfr.actl.80",
"P.actl.100","P.actl.20","P.actl.40","P.actl.60","P.actl.80","Pfr.actl.100",
"Pfr.actl.20","Pfr.actl.40","Pfr.actl.60","Pfr.actl.80")
act[act=="y"] <- 1 # clean up responses
act[act=="n"] <- 2
act[act=="0"] <- NA # don't know what else to do with these (4 of them)
act[act=="11"] <- 1
act <- apply(act,2,as.numeric) # convert to numeric

lot <- as.matrix(t1[,21:30]) # 5=strongly agree, 1=strongly disagree
colnames(lot) <-
  c("LOTa","LOTb","LOTc","LOTd","LOTe","LOTf","LOTg","LOTh","LOTi","LOTj")

lot[,c(3,7,9)] <- 6-lot[,c(3,7,9)]
lot.score <- apply(lot[,c(1,3,4,7,9,10)],1,mean,na.rm=T)
#columns correspond to LOTa, LOTc, LOTd, LOTg, LOTi, LOTj

mss <- as.matrix(t1[,41:72]) # 1=do 2=not do
colnames(mss) <- c("MBSSa1","MBSSa2","MBSSa3","MBSSa4",
                   "MBSSa5","MBSSa6","MBSSa7","MBSSa8",
                   "MBSSb1","MBSSb2","MBSSb3","MBSSb4",
                   "MBSSb5","MBSSb6","MBSSb7","MBSSb8",
                   "MBSSc1","MBSSc2","MBSSc3","MBSSc4",
                   "MBSSc5","MBSSc6","MBSSc7","MBSSc8",
                   "MBSSd1","MBSSd2","MBSSd3","MBSSd4",
                   "MBSSd5","MBSSd6","MBSSd7","MBSSd8")
mss[mss==11] <- 3 # mss is numeric
mss[mss==2] <- 0

mss.m.score <- (mss[,1] + mss[,4] + mss[,6] + mss[,7] + mss[,10] + mss[,12] + mss[,13] + mss[,16] + mss[,17] + mss[,18] 
 + mss[,20] + mss[,23] + mss[,25] + mss[,28] + mss[,30] + mss[,31])
 
mss.b.score <- (mss[,2] + mss[,3] + mss[,5] + mss[,8] + mss[,9] + mss[,11] + mss[,14] + mss[,15] + mss[,19] + mss[,21] 
 + mss[,22] + mss[,24] + mss[,26] + mss[,27] + mss[,29] + mss[,32])

demos <- as.matrix(t1[,83:115])
denial <- apply(demos[,11:14],c(1,2),as.numeric) # convert to numeric
denial.score <- rowMeans(denial) # subject means (no missing data)

hb <- apply(demos[,15:22],c(1,2),as.numeric) # convert to numeric
num <- demos[,26:30]
num <- demos[,c(26:30,7,10,25,33)] # include bctrans,cc..,lc..,pc..
num[num=="a"] <- 1 # recode a-c to numbers to make scoring easier
num[num=="b"] <- 2
num[num=="c"] <- 3
num[num=="1/4"] <- .25 # happened 2x in pctrans - really a wrong answer
num <- apply(num,c(1,2),as.numeric)
num.correct <- t(num)==c(500,3,10,100,2,60,.03,10,25)
num.score <- colMeans(num.correct)
demos <- demos[,c(1:6,8,9,23,24,31,32)]
demos[demos=="y"] <- 1
demos[demos=="n"] <- 2
demos[demos=="m"] <- 1
demos[demos=="f"] <- 2
demos[demos=="0"] <- 2 # these were responses to "die" questions, counted as "no"
demos[demos=="mi"] <- NA # demos is still character, not numeric
demos <- apply(demos,2,as.numeric)
educ <- as.vector(demos[,1])
age <- as.vector(demos[,2])
# health <- as.vector(demos[,3]) # missing
sex <- as.vector(demos[,4])
bc.die <- as.vector(demos[,5])
bc.had <- as.vector(demos[,6])
cc.die <- as.vector(demos[,7])
cc.had <- as.vector(demos[,8])
lc.die <- as.vector(demos[,9])
lc.had <- as.vector(demos[,10])
pc.die <- as.vector(demos[,11])
pc.had <- as.vector(demos[,12])
rm(demos)



sf12 <- as.matrix(t1[,212:222])
colnames(sf12) <- c("sf12.1","sf12.2a","sf12.2b","sf12.3","sf12.4a","sf12.4b",
"sf12.5","sf12.6a","sf12.6b","sf12.6c","sf12.7")
sf12[sf12=="y"] <- 1
sf12[sf12=="11"] <- 1
sf12[sf12=="n"] <- 2
sf12 <- apply(sf12,2,as.numeric)

stai <- as.matrix(t1[,223:242])
colnames(stai) <- c("stai.a","stai.b","stai.c","stai.d","stai.e","stai.f",
"stai.g","stai.h","stai.i","stai.j","stai.k","stai.l","stai.m","stai.n",
"stai.o","stai.p","stai.q","stai.r","stai.s","stai.t")
stai[stai==12] <- NA # assume this was an effort to change 1 to 2 (one response)
stai[stai==5] <- 4
stai.score <- 50+((stai-2.5) %*%
                   c(-1,1,-1,1,1,-1,-1,1,1,-1,1,1,-1,-1,1,-1,1,1,-1,1))


truste <- as.matrix(t1[,243:250])
colnames(truste) <-  c("trust1a","trust1b","trust1c","trust1d","trust1e",
"trust1f","trust1g","trust2")

truste[,1] <- 6 - truste[,1]
truste[,3] <- 6 - truste[,3]
truste[,5] <- 6 - truste[,5]
truste[,6] <- 6 - truste[,6]
truste[,8] <- ceiling(truste[,8]/2 + (0+truste[,8]==0))

truste.score <- 100*(apply(truste,1,mean,na.rm=T)-1)/4


# write csv file of all data
#write.table(data.frame(act,lot,mss,denial,hb,num,age,truste,
#sex, bc.die, bc.had, cc.die, cc.had, lc.die, lc.had, pc.die,
#pc.had,denial,qs,sf12,stai), file="hrp2.csv",sep=",", row.names=F)

qs <- array(qs,c(ns,24,4)) # subjects, items, cancers
cancer <- as.vector(qs[,1,])
 cancer[cancer>4] <- 4 # correct some js error
 cancer <- as.factor(cancer) # like "cat" in Systat
probtype <- as.factor(as.vector(floor(qs[,2,]/4)))
subj <- as.factor(rep(1:ns,4))

# ---- this makes one array where the third argument is the condition
# conditions are 1-4=verbal, 5-8=percent, 9-12 fraction
# 1-4 are the four cancers, also 5-8 and 9-12

qsall <- array(NA,c(ns,24,12))
for (i in 1:ns) {for (j in 1:4) {condition <- qs[i,2,j]+1
                                 qsall[i,,condition] <- qs[i,,j]}}
qsall[,1,] <- qsall[,2,] %% 4 + 1
qsall[,2,] <- floor(qsall[,2,]/4)

vnames <- c("Cancer","Probtype","time1","nprob","conf","vprob","worry",
  "QOL","trust","comfort","getit.100","getit.80","getit.60","getit.40",
  "getit.20","docsaid","notnotice","minrisk","exagg","docwrong","notapply",
  "trick","misunderst","time2")
cnames <- c("prostate.v","colon.v","lung.v","breast.v",
            "prostate.p","colon.p","lung.p","breast.p",
            "prostate.f","colon.f","lung.f","breast.f")
dimnames(qsall) <- list(NULL,vnames,cnames)
prostate <- c(1,5,9)
colon <- c(2,6,10)
lung <- c(3,7,11)
breast <- c(4,8,12)
words <- 1:4
percent <- 5:8
fraction <- 9:12
numbers <- 5:12

#Now if you want just the nprob data for numbers (fractions or
#percent), you can refer to nprob.m[,numbers].  You need the comma
#because "numbers" refers to the columns in the matrix.

# use .m for these to distinguish them from later ones
cancer.m <- qsall[,1,]
probtype.m <- qsall[,2,] # skipped time
verbal.m <- (probtype.m==0) + 0 # now coded 1,0
nprob.m <- qsall[,4,]
conf.m <- qsall[,5,]
vprob.m <- qsall[,6,]
worry.m <- qsall[,7,] 
QOL.m <- qsall[,8,]
trust.m <- qsall[,9,]
comfort.m <- qsall[,10,]
getit.100.m <- qsall[,11,]
getit.80.m <- qsall[,12,]
getit.60.m <- qsall[,13,]
getit.40.m <- qsall[,14,] 
getit.20.m <- qsall[,15,]
docsaid.m <- qsall[,16,]
notnotice.m <- qsall[,17,]
minrisk.m <- qsall[,18,]
exagg.m <- qsall[,19,]
docwrong.m <- qsall[,20,]
notapply.m <- qsall[,21,] 
trick.m <- qsall[,22,]
misunderst.m <- qsall[,23,]

# ----------- this part creates a whole bunch of variables from qs for anova

nprob <- as.vector(qs[,4,])
conf <- as.vector(qs[,5,])
vprob <- as.vector(qs[,6,])
worry <- as.vector(qs[,7,])
QOL <- as.vector(qs[,8,])
trust <- as.vector(qs[,9,])
comfort <- as.vector(qs[,10,])
getit.100 <- as.vector(qs[,11,])
getit.80 <- as.vector(qs[,12,])
getit.60 <- as.vector(qs[,13,])
getit.40 <- as.vector(qs[,14,])
getit.20 <- as.vector(qs[,15,])
docsaid <- as.vector(qs[,16,])
docsaid[docsaid==9] <- NA
notnotice <- as.vector(qs[,17,])
notnotice[notnotice==9] <- NA
minrisk <- as.vector(qs[,18,])
exagg <- as.vector(qs[,19,])
docwrong <- as.vector(qs[,20,])
notapply <- as.vector(qs[,21,])
trick <- as.vector(qs[,22,])
misunderst <- as.vector(qs[,23,])
rm(qs)

group <- as.factor((1-is.na(nprob.m[,1:3])) %*% c(1,2,3))

#--------------------
# NEW SECTION 8/6/02
# We need both matrix and vector forms of each deviation score
# New names: deviat
#log odds transformation of nprob to stretch out differences near zero.
rightans.m <- matrix(c(25,.03,10,60),ns,12,byrow=T)
rightans <- rep(c(25,.03,10,60),rep(ns,4))
logo <- function(x) log(x/(100-x))
trim <- function(x) x+(.0001)*(x<.0001)-(.0001)*(x==100)
deviat.logo.m <- logo(trim(nprob.m))-logo(rightans.m)
deviat.logo <- logo(trim(nprob))-logo(rightans)
# apply this to nprob.dev.logo
ctrim <- function(x) {x*(x>0-10 & x<10)-10*(x<0-10)+10*(x>10)}

deviatt.logo.m <- ctrim(deviat.logo.m)
deviatt.logo <- ctrim(deviat.logo)

plot(deviat.logo,ctrim(deviat.logo))

#within-Ss mean of deviation score
deviat.logo.sm <- apply(deviat.logo.m[,5:12],1,mean,na.rm=T)

mean(deviat.logo.sm,na.rm=T)
              
# Looking at this you can see right away what is going on.
# Estimates are too high, especially for cancer #2 (the low
# one).  But the outliers are also apparent.  I thought that
# cutting them off at + or - 10 seemed reasonable.  So:
                       
#linear transformation of nprob
deviat.lino.m <- (nprob.m>rightans.m)*(nprob.m-rightans.m)/(100-rightans.m) +
                 (nprob.m<rightans.m)*(nprob.m-rightans.m)/rightans.m
deviat.lino <- (nprob>rightans)*(nprob-rightans)/(100-rightans) +
               (nprob<rightans)*(nprob-rightans)/rightans
deviat.raw.m <- nprob.m-rightans.m

par(las=1)
stripchart(c(t(deviat.lino.m)) ~ rep(cnames,ns))
stripchart(c(t(deviat.logo.m)) ~ rep(cnames,ns))
stripchart(c(t(ctrim(deviat.logo.m))) ~ rep(cnames,ns))
stripchart(c(t(deviat.raw.m)) ~ rep(cnames,ns))

# end of new section (but nprob.dev changed to deviat throughout

# ----- everything after this is commented out and is deletable


nprob.p0 <- nprob[probtype==0]
nprob.p12 <- nprob[probtype!=0]

round(nprob[cancer==0],2)
round(nprob[cancer==1],2)
round(nprob[cancer==2],2)

table(sex)/sum(table(sex))

#na.rm=T  removes NAs so you can take the mean of it.

mean(stai.score,na.rm=T)
mean(denial,na.rm=T)
mean(age,na.rm=T)
sd(age,na.rm=T)
mean(educ,na.rm=T)
sd(educ,na.rm=T)
mean(sf12[,1],na.rm=T)


verbal <- probtype==0

deviat.p.fr <- (25 - (nprob[cancer==1 & probtype==2]))
deviat.p.pt <- (25 - (nprob[cancer==1 & probtype==1]))
deviat.c.fr <- (.03 - (nprob[cancer==2 & probtype==2]))
deviat.c.pt <- (.03 - (nprob[cancer==2 & probtype==1]))
deviat.l.fr <- (10 - (nprob[cancer==3 & probtype==2]))
deviat.l.pt <- (10 - (nprob[cancer==3 & probtype==1]))
deviat.b.fr <- (60 - (nprob[cancer==4 & probtype==2]))
deviat.b.pt <- (60 - (nprob[cancer==4 & probtype==1]))


mean(deviat.p.fr,na.rm=T)
mean(deviat.p.pt,na.rm=T)
mean(deviat.c.fr,na.rm=T)
mean(deviat.c.pt,na.rm=T)
mean(deviat.l.fr,na.rm=T)
mean(deviat.l.pt,na.rm=T)
mean(deviat.b.fr,na.rm=T)
mean(deviat.b.pt,na.rm=T)

nprob.p.fr.acc <- sign(deviat.p.fr)
nprob.p.pt.acc <- sign(deviat.p.fr)
nprob.c.fr.acc <- sign(deviat.c.fr)
nprob.c.pt.acc <- sign(deviat.c.fr)
nprob.l.fr.acc <- sign(deviat.l.fr)
nprob.l.pt.acc <- sign(deviat.l.fr)
nprob.b.fr.acc <- sign(deviat.b.fr)
nprob.b.pt.acc <- sign(deviat.b.fr)

table(nprob.p.fr.acc)/sum(table(nprob.p.fr.acc))
table(nprob.p.pt.acc)/sum(table(nprob.p.pt.acc))
table(nprob.c.fr.acc)/sum(table(nprob.c.fr.acc))
table(nprob.c.pt.acc)/sum(table(nprob.c.pt.acc))
table(nprob.l.fr.acc)/sum(table(nprob.l.fr.acc))
table(nprob.l.pt.acc)/sum(table(nprob.l.pt.acc))
table(nprob.b.fr.acc)/sum(table(nprob.b.fr.acc))
table(nprob.b.pt.acc)/sum(table(nprob.b.pt.acc))

 #------------ notes:
 #P.actl.100 not included in comput, so missing
# recoding work needed on hb (high numbers), trust (same), stai (y/n)

# --------------- do basic analyses (examples)

#readline("examples of data summary follow - press any key to continue")
#summary(num)
#readline("ugly summary of nprob by cancer and probtype, and plot (look at it!)")
#by(nprob,list(cancer,probtype),summary)
#stripchart(nprob~cancer,method="stack",offset=1/6,main="by cancer")
#readline("prostate (look at the plot!)")

stripchart(nprob[cancer==1] ~ probtype[cancer==1],method="stack", main="Prostate, 0=words 1=pct 2=frac")

#readline("colon")
stripchart(nprob[cancer==2] ~ probtype[cancer==2],method="stack",main="Colon, 0=words 1=pct 2=frac")
#window()
#readline("lung")

stripchart(nprob[cancer==3] ~ probtype[cancer==3],method="stack", main="Lung, 0=words 1=pct 2=frac")
#window()

#readline("breast")

#commented this below out b/c file is crashing on it.
#stripchart(nprob[cancer==4] ~ probtype[cancer==4],method="stack", main="Breast, 0=words 1=pct 2=frac")

#readline("analysis of variance - very crude")
#summary(aov(nprob ~ subj+cancer*probtype))

table(sex)/sum(table(sex))

#na.rm=T  removes NAs so you can take the mean of it.

mean(stai.score,na.rm=T)
mean(denial,na.rm=T)
mean(age,na.rm=T)
mean(educ,na.rm=T)
mean(sf12[,1],na.rm=T)

by(nprob, list(cancer,probtype), range)

# calculates the % of times that each response was endorsed across cancer and probtypes.
# this has double-counting problem
table(docsaid)/sum(table(docsaid))
table(notnotice)/sum(table(notnotice))
table(minrisk)/sum(table(minrisk))
table(exagg)/sum(table(exagg))
table(docwrong)/sum(table(docwrong))
table(notapply)/sum(table(notapply))
table(trick)/sum(table(trick))
table(misunderst)/sum(table(misunderst))

#hist(nprob[cancer==1 & probtype==0], main="Prostate, words only", xlab="Probability", ylim=range(12,0), br=c(0, 10, 20, 
#30, 40, 50, 60, 70, 80, 90, 100))
#hist(nprob[cancer==1 & probtype==1], main="Prostate, words & percent", xlab="Probability", ylim=range(12,0), br=c(0, 
#10, 20, 30, 40, 50, 60, 70, 80, 90, 100))
#hist(nprob[cancer==1 & probtype==2], main="Prostate, words & fraction", xlab="Probability", ylim=range(12,0), br=c(0, 
#10, 20, 30, 40, 50, 60, 70, 80, 90, 100))

#hist(nprob[cancer==2 & probtype==0], main="Colon, words only", xlab="Probability", ylim=range(12,0), br=c(0, 10, 20, 
#30, 40, 50, 60, 70, 80, 90, 100))
#hist(nprob[cancer==2 & probtype==1], main="Colon, words & percent", xlab="Probability", ylim=range(12,0), br=c(0, 10, 
#20, 30, 40, 50, 60, 70, 80, 90, 100))
#hist(nprob[cancer==2 & probtype==2], main="Colon, words & fraction", xlab="Probability", ylim=range(12,0), br=c(0, 10, 
#20, 30, 40, 50, 60, 70, 80, 90, 100))

#hist(nprob[cancer==3 & probtype==0], main="Lung, words only", xlab="Probability", ylim=range(12,0), br=c(0, 10, 20, 30, 
#40, 50, 60, 70, 80, 90, 100))
#hist(nprob[cancer==3 & probtype==1], main="Lung, words & percent", xlab="Probability", ylim=range(12,0), br=c(0, 10, 
#20, 30, 40, 50, 60, 70, 80, 90, 100))
#hist(nprob[cancer==3 & probtype==2], main="Lung, words & fraction", xlab="Probability", ylim=range(12,0), br=c(0, 10, 
#20, 30, 40, 50, 60, 70, 80, 90, 100))

#hist(nprob[cancer==4 & probtype==0], main="Breast, words only", xlab="Probability", ylim=range(12,0), br=c(0, 10, 20, 
#30, 40, 50, 60, 70, 80, 90, 100))
#hist(nprob[cancer==4 & probtype==1], main="Breast, words & percent", xlab="Probability", ylim=range(12,0), br=c(0, 10, 
#20, 30, 40, 50, 60, 70, 80, 90, 100))
#hist(nprob[cancer==4 & probtype==2], main="Breast, words & fraction", xlab="Probability", ylim=range(12,0), br=c(0, 10, 
#20, 30, 40, 50, 60, 70, 80, 90, 100))

#could do for prostate in words only b/c in cnames it is column 1
#hist(nprob.m[,1], main="Prostate, words only", xlab="Probability", ylim=range(12,0), br=c(0, 10, 20, 30, 40, 50, 60, 
#70, 80, 90, 100))

#could also do means this way, converting to a dataframe:

#nprob.d <- as.data.frame(nprob.m)
#mean(nprob.d$prostate.v, na.rm=T)

table(pc.die)/sum(table(pc.die))
table(cc.die)/sum(table(cc.die))
table(lc.die)/sum(table(lc.die))
table(bc.die)/sum(table(bc.die))

table(pc.had)/sum(table(pc.had))
table(cc.had)/sum(table(cc.had))
table(lc.had)/sum(table(lc.had))
table(bc.had)/sum(table(bc.had))

table(num.score)/sum(table(num.score))

cor.test(QOL, nprob)
# want to do the same test, but with deviations.

cor.test(worry, nprob)

#So the within subject mean trust for numbers would be
apply(trust.m[,numbers],1,mean,na.rm=T)
apply(comfort.m[,numbers],1,mean,na.rm=T)

#The 1 is for rows, and you need na.rm=T because most data are
#missing.  Of course, most of the time, this would just be the
#value for the one trial that the subject got in that condition.
#The whole t test would be

t.test(apply(trust.m[,5:12],1,mean,na.rm=T)-apply(trust.m[,1:4],1,mean,na.rm=T))
t.test(apply(comfort.m[,5:12],1,mean,na.rm=T)-apply(comfort.m[,1:4],1,mean,na.rm=T))
#These are within-Ss ttests of trust in numbers versions compared to trust in words versions.

#Notice that this simply tests the difference.  Of course, this
#totally ignores cancer type.  This works, at p<.05.  But that is
#still important because it rejects the view that the effect goes
#the other way.

verbal <- probtype==0
lm0 <- lm(trust ~ subj)
lm1 <- lm(trust ~ subj+cancer)
lm2 <- lm(trust ~ subj+cancer+verbal)

#Then I compared successive models:
anova(lm0,lm1)
anova(lm1,lm2)
#The first comparison shows that cancer matters.  The second shows
#that verbal is still significant.  The result is slightly
#different from the t test, and slightly not as good, but I
#suspect that this is just random.  


lm13 <- lm(comfort ~ subj)
lm14 <- lm(comfort ~ subj+cancer)
lm15 <- lm(comfort ~ subj+cancer+verbal)

anova(lm1)
anova(lm2)
anova(lm14)
anova(lm15)


#to get one probtype at a time, "sort" removes missing data
#stripchart(sort(nprob.m[,c(1)]), method="stack", main="Prostate, words only")
#window()
#stripchart(sort(nprob.m[,c(5)]), method="stack", main="Prostate, percent")
#window()
#stripchart(sort(nprob.m[,c(9)]), method="stack", main="Prostate, fraction")
#window()

#t.test(nprob.m[pc.had==1,c(1,5,9)],nprob.m[pc.had==2,c(1,5,9)])
#t.test(nprob.m[cc.had==1,c(2,6,10)],nprob.m[cc.had==2,c(2,6,10)])
#t.test(nprob.m[lc.had==1,c(3,7,11)],nprob.m[lc.had==2,c(3,7,11)])
#t.test(nprob.m[bc.had==1,c(4,8,12)],nprob.m[bc.had==2,c(4,8,12)])

c.had <- c(pc.had,cc.had,lc.had,bc.had)
# to use for an anova to see if having known someone w/ a certain cancer
#  makes your nprob deviate more in the pos
#direction
#do this w/ the transformed nprob!

summary(aov(deviat.logo~subj+cancer+verbal+c.had))

#to test within-Ss to see if Ss were more likely to say yes to minrisk (etc) in words than numbers versions
minrisk.words.sm <- apply(minrisk.m[,1:4],1,mean,na.rm=T)
minrisk.numbers.sm <- apply(minrisk.m[,5:12],1,mean,na.rm=T)

exagg.words.sm <- apply(exagg.m[,1:4],1,mean,na.rm=T)
exagg.numbers.sm <- apply(exagg.m[,5:12],1,mean,na.rm=T)

#or just go directly to:
wilcox.test(apply(minrisk.m[,1:4],1,mean,na.rm=T)-
       apply(minrisk.m[,5:12],1,mean,na.rm=T))
wilcox.test(apply(exagg.m[,1:4],1,mean,na.rm=T)-
       apply(exagg.m[,5:12],1,mean,na.rm=T))

minrisk.sm <- (apply(minrisk.m[,1:4],1,mean,na.rm=T)+
               apply(minrisk.m[,5:12],1,mean,na.rm=T))/2
   
exagg.sm <- (apply(exagg.m[,1:4],1,mean,na.rm=T)+ apply(exagg.m[,5:12],1,mean,na.rm=T))/2  
       
sf12.1 <- rep(sf12[,1],4)
   
QOL.numbers.sm <-apply(QOL.m[,5:12],1,mean,na.rm=T)
worry.numbers.sm <-apply(worry.m[,5:12],1,mean,na.rm=T)
trust.numbers.sm <-apply(trust.m[,5:12],1,mean,na.rm=T)
comfort.numbers.sm <-apply(comfort.m[,5:12],1,mean,na.rm=T)


lm4 <- lm(deviat.logo.sm ~ 
stai.score+lot.score+educ+denial.score+sex+truste.score+minrisk.numbers.sm+exagg.numbers.sm+QOL.numbers.sm
+worry.numbers.sm+trust.numbers.sm+comfort.numbers.sm+mss.m.score+mss.b.score)
anova(lm4)

#lm5 <- lm(deviat.logo.sm ~ stai.score+lot.score+educ+sex+minrisk.numbers.sm+QOL.numbers.sm
#+worry.numbers.sm)
#anova(lm5)

#lm6 <- lm(deviat.logo.sm ~ educ+sex+age)
#anova(lm6)

#lm7 <- lm(deviat.logo.sm ~ stai.score)
#anova(lm7)

#lm8 <- lm(deviat.logo.sm ~ worry.numbers.sm)
#anova(lm8)
 
#lm9 <- lm(minrisk.numbers.sm ~ truste.score+mss.m.score+worry.numbers.sm+stai.score)
#anova(lm9)

lm10 <- lm(minrisk.numbers.sm ~ truste.score+mss.m.score+worry.numbers.sm+sex+educ+age)
anova(lm10)

lm11 <- lm(minrisk.numbers.sm ~ truste.score+worry.numbers.sm)
anova(lm11)


#lm12 <- lm(nprob.m[,1,5,9] ~ worry.numbers.sm+pc.had+stai.score)
#anova(lm12)

#lm 13-15 used above

# tells you which Ss said both
#apply((minrisk.m==1 & exagg.m==1)+0,1,sum,na.rm=T)

toohigh.sm <- apply(nprob.m>rightans.m,1,mean,na.rm=T)

toohigh.numbers.sm <- apply(nprob.m[,5:12]>rightans.m[,5:12],1,mean,na.rm=T)

#If in a given scenario, your nprob is greater than the right answer, you 
#get a 1 and if not, you get a 0. So if we take the avg of these across the 
#2-3 numbers versions that a given Ss got, we get a mean for "how often was 
#nprob > right ans".  

summary(lm(toohigh.sm ~ minrisk.numbers.sm+worry.numbers.sm+sex+educ))
rawdev.sm <- apply(nprob.m-rightans.m,1,mean,na.rm=T)

lm16 <- lm(toohigh.sm ~ 
stai.score+lot.score+educ+denial.score+sex+truste.score+minrisk.numbers.sm+exagg.numbers.sm+QOL.numbers.sm
+worry.numbers.sm+trust.numbers.sm+comfort.numbers.sm+mss.m.score+mss.b.score)
anova(lm16)

lm17 <- lm(toohigh.sm ~ stai.score+lot.score+educ+denial.score+sex+truste.score+minrisk.numbers.sm+QOL.numbers.sm
+worry.numbers.sm+trust.numbers.sm+comfort.numbers.sm+mss.m.score)
anova(lm17)

#lm18 <- lm(toohigh.sm ~ stai.score+lot.score+educ+sex+truste.score+minrisk.numbers.sm+QOL.numbers.sm
#+worry.numbers.sm+trust.numbers.sm+mss.m.score)
#anova(lm18)

#lm19 <- lm(toohigh.sm ~ stai.score+educ+sex+truste.score+minrisk.numbers.sm+QOL.numbers.sm
#+worry.numbers.sm+comfort.numbers.sm)
#anova(lm19) 

lm20 <- lm(toohigh.sm ~ stai.score)
anova(lm20)

lm21 <- lm(toohigh.sm ~ educ)
anova(lm21)

t.test(toohigh.sm[sex==1], toohigh.sm[sex==2])

lm22 <- lm(toohigh.sm ~ educ)
anova(lm22)

lm23 <- lm(toohigh.sm ~ lot.score)
anova(lm23)

lm24 <- lm(toohigh.sm ~ denial.score)
anova(lm24)

lm25 <- lm(toohigh.sm ~ truste.score)
anova(lm25)
lm26 <- lm(toohigh.sm ~ QOL.numbers.sm)
anova(lm26)
lm27 <- lm(toohigh.sm ~ worry.numbers.sm)
anova(lm27)
lm28 <- lm(toohigh.sm ~ mss.m.score)
anova(lm28)
lm29 <- lm(toohigh.sm ~ trust.numbers.sm)
anova(lm29)

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