#  Prediction Problems

library(MASS)
zlrs = file.choose()
source(zlrs)

# Connectedness problem for group work
(1,1,1) (1,1,3) (1,2,1) (1,3,2) (1,3,5) (2,1,3)
(2,1,4) (2,2,1) (2,2,2) (2,2,5) (2,3,1) (3,1,4)
(3,1,5) (3,3,1) (3,3,3) (4,1,2) (4,2,2) (4,3,4)

(1,1,1) (1,1,3) (1,2,1) (2,1,3) (2,2,1) 

# 1)Determine the number of disconnected groups of the
# above filled subclasses.
# 2)Which subclasses (minimum number) are needed in 
# order to connect all of these groups?


# small prediction problem
y = matrix(data=c(128,150,90,110,120),ncol=1)
X = matrix(data=c(1,1,1,1,1),ncol=1)
herds = c(1,1,2,2,2)
sires = c(1,2,1,2,3)
Zh = desgn(herds,0)
Zs = desgn(sires,0)
Z = cbind(Zh,Zs)
Gh = id(2)*(1/6)
Gs = id(3)*(1/15)
G = block(Gh,Gs)
dd = c(5,4,5,20,2)
RI=diag(dd)

#  Pigs in litters
Litter
1         1,  2,  3,  4,  5
         13, 10,  9, 16,  8

2         6,  7,  8,  9
         15, 12,  7, 18

3        10, 11, 12, 13, 14, 15
         20, 11,  9, 17, 10, 14
----------------------------------
y = mu + litter + pig + e     e = 10 l = 3 p

lits = c(1,1,1,1,1,2,2,2,2,3,3,3,3,3,3)
pigs = c(1:15)
oval = rep(1,length(lits))
y = matrix(c(13,10,9,16,8,15,12,7,18,20,11,9,17,10,14),ncol=1)
Xm = desgn(oval,3)
Zl = desgn(lits,0)
Zp = desgn(pigs,0)
Gl = id(3)*10
G2 = id(15)*3
GI = block(Gl,G2)
Z= cbind(Zl,Zp)
RI=id(15)
att1=MME(Xm,Z,GI,RI,y)

litsol=att1$SOLNS[c(2:4), ]

gL1 = jd(5,5)*0.5 + id(5)*0.5
gL1
gL2 = jd(4,4)*0.5 + id(4)*0.5
gL3 = jd(6,6)*0.5 + id(6)*0.5
G2 = block(gL1,gL2,gL3)
dim(G2)
# FORGOT THIS PART !!!!!!
G2I = solve(G2)*3

GI=block(Gl,G2I)

att2=MME(Xm,Z,GI,RI,y)
p2nd = att2$SOLNS[c(5:19), ]
kb=order(-p2nd)
kb
l1st=att1$SOLNS[c(2:4), ]
l2nd=att2$SOLNS[c(2:4), ]
cbind(l1st,l2nd)
cbind(pall,p2nd)
sst = sum(y*y)
ssr = att2$SSR
vres = (sst - ssr)/(15-1)
vpe = att2$VPE
sep = sqrt(vpe)
sol=att2$SOLNS
cbind(sol,sep,rti)
rti = (1 - vpe*3)

PL = sol[c(2:19), ]
GEN= Z%*%PL
GEN
ka = order(-GEN)
ka

# Evaluation of pigs = lits + pigs solutions, SEP of these 
# pigs unrelated between litters
# put in full-sib relationships - compare models



#Homework Problem, plant heights
y = matrix(data=c(25.3,17.8,20.1,26.1,19.2,24.6,23.7,24.5,26.0,28.4,22.9,25.8),
ncol=1)
dd = c(40,39,37,35,40,40,33,38,40,30,28,34)
RI=diag(dd)
FB=c(1,1,1,2,2,2,3,3,3,4,4,4)
X =desgn(FB,0)
LL=c(1,2,3,1,2,3,1,2,3,1,2,3)
Z = desgn(LL,0)
qq = c(10,10,10)
GI=diag(qq)
dand=data.frame(y,dd,FB,LL)
dand