Nothing
## ---- 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)
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