advanced_climwin.R
In climwin: Climate Window Analysis

## ----eval = F-----------------------------------------------------------------
# 
# Mass$climate <- 1
# 

## ----eval = F-----------------------------------------------------------------
# 
# Interaction <- slidingwin(xvar = list(Temp = MassClimate$Temp),
#                           cdate = MassClimate$Date,
#                           bdate = Mass$Date,
#                           baseline = lm(Mass ~ climate*Age, data = Mass),
#                           cinterval = "day",
#                           range = c(150, 0),
#                           type = "absolute", refday = c(20, 05),
#                           stat = "mean",
#                           func = "lin")
# 

## ----eval = F-----------------------------------------------------------------
# 
# summary(Interaction[[1]]$BestModel)
# 

## ----eval = F-----------------------------------------------------------------
# 
# Call:
# lm(formula = yvar ~ climate + Age + climate:Age, data = modeldat)
# 
# Residuals:
#     Min      1Q  Median      3Q     Max
# -5.6266 -1.5716  0.2878  1.6086  4.7510
# 
# Coefficients:
#             Estimate Std. Error t value Pr(>|t|)
# (Intercept) 170.2628     7.1678  23.754  < 2e-16 ***
# climate      -5.5466     0.9200  -6.029 3.32e-07 ***
# Age          -2.6046     2.6603  -0.979    0.333
# climate:Age   0.4024     0.3395   1.185    0.242
# ---
# Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# 
# Residual standard error: 2.449 on 43 degrees of freedom
# Multiple R-squared:  0.7778,	Adjusted R-squared:  0.7623
# F-statistic: 50.17 on 3 and 43 DF,  p-value: 4.267e-14
# 

## ----message = FALSE----------------------------------------------------------

library(climwin)


## ----eval = FALSE-------------------------------------------------------------
# 
# MassWin <- slidingwin(xvar = list(Temp = MassClimate$Temp),
#                       cdate = MassClimate$Date,
#                       bdate = Mass$Date,
#                       baseline = lm(Mass ~ 1, data = Mass),
#                       cinterval = "day",
#                       range = c(150, 0),
#                       upper = 0, binary = TRUE,
#                       type = "absolute", refday = c(20, 05),
#                       stat = "sum",
#                       func = "lin")
# 

## ----eval = FALSE-------------------------------------------------------------
# 
# head(MassWin[[1]]$BestModelData)
# 

## ----eval = FALSE-------------------------------------------------------------
# 
# SizeWin <- slidingwin(xvar = list(Temp = SizeClimate$Temperature),
#                       cdate = SizeClimate$Date,
#                       bdate = Size$Date,
#                       baseline = lm(Size ~ 1, data = Size),
#                       cohort = Size$Cohort,
#                       cinterval = "day",
#                       range = c(150, 0),
#                       type = "absolute", refday = c(01, 10),
#                       stat = "mean",
#                       func = "lin")
# 

## ----eval = FALSE-------------------------------------------------------------
# 
# MassWin <- slidingwin(xvar = list(Temp = Climate$Temp),
#                       cdate = Climate$Date,
#                       bdate = Biol$Date,
#                       baseline = lm(Mass ~ 1, data = Biol),
#                       cinterval = "day",
#                       range = c(150, 0),
#                       type = "absolute", refday = c(20, 05),
#                       stat = "mean",
#                       func = "lin", spatial = list(Biol$SiteID, Climate$SiteID))
# 

## ----echo = FALSE, fig.width = 5, fig.height = 5------------------------------

Unweight <- data.frame(Time = seq(0, 100, 1), Weight = c(rep(0, times = 25), rep(1, times = 50), rep(0, 26)))
Unweight$Weight <- Unweight$Weight/sum(Unweight$Weight)

par(mar = c(5, 4.25, 4, 2) + 0.1)
plot(x = Unweight$Time, y = Unweight$Weight, type = "l", ylab = "Weight", xlab = "Time", ylim = c(0, 0.05),
     yaxt = "n", xaxt = "n",
     lwd = 2, 
     cex.lab = 1.25, cex.axis = 1.25, cex = 1.5)
axis(2, cex.axis = 1.25, cex.lab = 1.25, yaxp = c(0, 0.05, 2))
axis(1, cex.axis = 1.5, cex.lab = 1.25, xaxp = c(0, 100, 2),
     mgp = c(2, 1.5, 0))


## ----echo = FALSE, fig.width = 8, fig.height = 4------------------------------

par(mfrow = c(1, 2))
duration <- 365
j        <- seq(1:duration) / duration
k        <- seq(-10, 10, by = (2 * 10 / duration))
weight   <- 3 / 0.2 * ((j[1:duration] - 0) / 0.2) ^ (3 - 1) * exp( - ((j[1:duration] - 0) / 0.2) ^ 3)
plot((weight / sum(weight)), type = "l", ylab = "Weight", xlab = "Day", cex.lab = 1.5, cex.axis = 1.5, main = "Weibull distribution")

weight <- evd::dgev(k[1:duration], loc = 1, scale = 2, shape = -1, log = FALSE)
plot((weight / sum(weight)), type = "l", ylab = "Weight", xlab = "Day", cex.lab = 1.5, cex.axis = 1.5, main = "GEV distribution")


## ----eval = FALSE-------------------------------------------------------------
# 
# set.seed(100)
# 
# weight <- weightwin(n = 5, xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date,
#                     bdate = Mass$Date,
#                     baseline = lm(Mass ~ 1, data = Mass),
#                     range = c(150, 0),
#                     func = "lin", type = "absolute",
#                     refday = c(20, 5),
#                     weightfunc = "W", cinterval = "day",
#                     par = c(3, 0.2, 0))
# 

## ----eval = F-----------------------------------------------------------------
# 
# weight$iterations
# 

## ----eval = F-----------------------------------------------------------------
# 
# weight[[1]]$WeightedOutput
# 

## ----echo = FALSE, fig.width = 5, fig.height = 5------------------------------

explore(weightfunc = "W", shape = 2.17, scale = 0.35, loc = 0)