# Adapted from R_fig14.txt in MW(2010)

# choose calibration period 1850-endyr (set in calling file)
# endyr=1980 or endyr = 1995  

## load lowpass versions of the data
lowpass.proxy <- read.table("proxylow/proxylow9",nrows=996)
years=1000:endyr
proxy=lowpass.proxy[1:(endyr-999),1:59]
rownames(proxy) <- years

#remove Tiljander proxies
proxy <- proxy[,-c(32:35)] 

# load target
lowpass.nh <- read.table("cru_nh_smooth",nrows=157)
temp=lowpass.nh[1:(endyr-1849),2]
names(temp)<-1850:endyr

# Run proxy pca:
proxy_pc <- prcomp(proxy, center=TRUE, scale=TRUE)

# Build insample dataframe:
my_years <- names(temp)
yy_in <- temp
names(yy_in) <- my_years
proxy_in <- data.frame( proxy[my_years,] )
ppc_in <- data.frame( proxy_pc$x[my_years,] )

# Build out of sample dataframe:
proxy_out <- data.frame( proxy )
ppc_out <- data.frame( proxy_pc$x )

# FIT VARIOUS MODELS:
library(MASS)
myfrac <- seq(0, 1, length=201)
yhat <- list(NULL)
  # Intercept:
  mylm <- lm(yy_in~1)
  yhat[[1]] <- predict(mylm, data.frame(ppc_out))

  # OLS_PC1:
  mylm <- lm(yy_in~., data=data.frame(x=ppc_in[,1]))
  yhat[[2]] <- predict(mylm, data.frame(x=ppc_out[,1]))

  # OLS_PC4:
  mylm <- lm(yy_in~., data=data.frame(ppc_in[,1:4]))
  yhat[[3]] <- predict(mylm, data.frame(ppc_out[,1:4]))

  # OLS_PC10:
  mylm <- lm(yy_in~., data=data.frame(ppc_in[,1:10]))
  yhat[[4]] <- predict(mylm, data.frame(ppc_out[,1:10]))

  # OLS_PC20:
  mylm <- lm(yy_in~., data=data.frame(ppc_in[,1:20]))
  yhat[[5]] <- predict(mylm, data.frame(ppc_out[,1:20]))