R/DARLEQ.L.R
In aquaMetrics/aquaMetrics: Freshwater Biotic indices
######################################################################################
# #
# Version: 1.0 #
# Revision: 0 - 10/02/2015. Published version #
# #
######################################################################################
CalcDARLEQ.L <- function(data = NULL, version="latest") {
# Input columns:
#(Type), SampleID, siteID, Date (date object), Code, Taxa, Abundance, Alk
# Input handling
if (is.null(data))
stop("No dataframe has been specified as 'data'")
if (ncol(data) < 7)
stop("It seems the input data.frame does not have the required columns")
if(any(!version %in% c("latest","previous", "all")))
stop("Input version is not valid, please verify its value(s)")
# Define constants matching input parameters
METRICL <- "lakes"
VERSL <- "latest"
VERSP <- "previous"
EQR.CAP <- 1.00
# Matrices. EQR status class boundaries for lakes and rivers
dim.names <- list(c("Bad", "Poor", "Moderate", "Good", "High"),
"upperlimit")
LTDI1class.HA <- matrix(c(0.22, 0.44, 0.66, 0.90, 1.00), nrow=5,
dimnames = dim.names)
LTDI1class.MA <- matrix(c(0.22, 0.44, 0.66, 0.90, 1.00), nrow=5,
dimnames = dim.names)
LTDI1class.LA <- matrix(c(0.22, 0.44, 0.63, 0.90, 1.00), nrow=5,
dimnames = dim.names)
LTDI2class.HA <- matrix(c(0.23, 0.46, 0.70, 0.92, 1.00), nrow=5,
dimnames = dim.names)
LTDI2class.MA <- matrix(c(0.23, 0.46, 0.66, 0.93, 1.00), nrow=5,
dimnames = dim.names)
LTDI2class.LA <- matrix(c(0.23, 0.46, 0.70, 0.92, 1.00), nrow=5,
dimnames = dim.names)
if ("all" %in% version) {
versions <- c(VERSL, VERSP)
} else {
versions <- version
}
input.names <- names(data)
if (ncol(data) == 7) {
names(data) <- c("SampleID", "SiteID", "Date", "Code", "Taxa",
"Abundance", "Alk")
} else {
names(data) <- c("Type", "SampleID", "SiteID", "Date", "Code",
"Taxa", "Abundance", "Alk")
}
# Find out which taxa has no presence in the scoring list (LTDI2 or LTDI1)
not.in.list <- !(data$Code %in% LookUpDARLEQ2$TaxonId)
na.ltdi2 <- LookUpDARLEQ2[is.na(LookUpDARLEQ2$LTDI2) & LookUpDARLEQ2$TaxonId %in% unique(data$Code),"TaxonId"]
na.ltdi1 <- LookUpDARLEQ2[is.na(LookUpDARLEQ2$LTDI1) & LookUpDARLEQ2$TaxonId %in% unique(data$Code),"TaxonId"]
lacking.ltdi2 <- unique(data[not.in.list | data$Code %in% na.ltdi2, c("Code", "Taxa")])
lacking.ltdi2 <- lacking.ltdi2[order(lacking.ltdi2$Code), ]
rownames(lacking.ltdi2) <- NULL
lacking.ltdi1 <- unique(data[not.in.list | data$Code %in% na.ltdi1, c("Code", "Taxa")])
lacking.ltdi1 <- lacking.ltdi1[order(lacking.ltdi1$Code), ]
rownames(lacking.ltdi1) <- NULL
# Merge
data <- merge(data, LookUpDARLEQ2, by.x="Code", by.y="TaxonId")
# Initialisation
lake.samples <- NA
CoC.lakes <- NA
# Alkalinity
with (data, {
data$`Lake Alk class` <<- ifelse(Alk < 10, "LA", ifelse(Alk > 50, "HA", "MA"))
if (VERSL %in% versions) {
data$`eLTDI2` <<- ifelse(Alk < 10, 22, ifelse(Alk > 50, 42, 35))
}
if (VERSP %in% versions) {
data$`eLTDI1` <<- ifelse(Alk < 10, 20, 25)
}
})
agg.data <- unique(data[ , c("SampleID", "SiteID", "Date", "Alk")])
# Compute metrics
lake.samples <- as.data.frame(
do.call(rbind, by(data, data$SampleID, FUN=function(df) {
out <- data.frame(NA, stringsAsFactors=FALSE)
if (VERSL %in% versions) {
out$oLTDI2 <- (sum(df$Abundance * df$LTDI2, na.rm=TRUE) /
sum(df[!is.na(df$LTDI2), "Abundance"], na.rm=TRUE) * 25) - 25
out$`Lake Alk class` <- df$`Lake Alk class`[1]
out$eLTDI2 <- df$eLTDI2[1]
out$`EQR LTDI2` <- (100 - out$oLTDI2) / (100 - out$eLTDI2)
out[out$`EQR LTDI2` > EQR.CAP & !is.na(out$`EQR LTDI2`), "EQR LTDI2"] <- EQR.CAP
# LTDI2class.LA intervals
intervals <- cut(out[out$`Lake Alk class` == "LA", "EQR LTDI2"],
breaks=c(0, LTDI2class.LA[, "upperlimit"]), include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI2class.LA))
out[out$`Lake Alk class` == "LA", "Class LTDI2"] <-
key[match(intervals, key$range), 2]
# LTDI2class.MA intervals
intervals <- cut(out[out$`Lake Alk class` == "MA", "EQR LTDI2"],
breaks=c(0, LTDI2class.MA[, "upperlimit"]), include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI2class.MA))
out[out$`Lake Alk class` == "MA", "Class LTDI2"] <-
key[match(intervals, key$range), 2]
# LTDI2class.HA intervals
intervals <- cut(out[out$`Lake Alk class` == "HA", "EQR LTDI2"],
breaks=c(0, LTDI2class.HA[, "upperlimit"]), include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI2class.HA))
out[out$`Lake Alk class` == "HA", "Class LTDI2"] <-
key[match(intervals, key$range), 2]
}
if (VERSP %in% versions) { # older version
out$oLTDI1 <- (sum(df$Abundance * df$LTDI1, na.rm=TRUE) /
sum(df[!is.na(df$LTDI1), "Abundance"], na.rm=TRUE) * 25) - 25
out$`Lake Alk class` <- df$`Lake Alk class`[1]
out$eLTDI1 <- df$eLTDI1[1]
out$`EQR LTDI1` <- (100 - out$oLTDI1) / (100 - out$eLTDI1)
out[out$`EQR LTDI1` > EQR.CAP & !is.na(out$`EQR LTDI1`), "EQR LTDI1"] <- EQR.CAP
# LTDI1class.LA intervals
intervals <- cut(out[out$`Lake Alk class` == "LA", "EQR LTDI1"],
breaks=c(0, LTDI1class.LA[, "upperlimit"]), include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI1class.LA))
out[out$`Lake Alk class` == "LA", "Class LTDI1"] <-
key[match(intervals, key$range), 2]
# LTDI1class.MA intervals
intervals <- cut(out[out$`Lake Alk class` == "MA", "EQR LTDI1"],
breaks=c(0, LTDI1class.MA[, "upperlimit"]), include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI1class.MA))
out[out$`Lake Alk class` == "MA", "Class LTDI1"] <-
key[match(intervals, key$range), 2]
# LTDI1class.HA intervals
intervals <- cut(out[out$`Lake Alk class` == "HA" & !is.na(out$`Lake Alk class`),
"EQR LTDI1"], breaks=c(0, LTDI1class.HA[, "upperlimit"]),
include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI1class.HA))
out[out$`Lake Alk class` == "HA", "Class LTDI1"] <-
key[match(intervals, key$range), 2]
}
out$`% Planktic` <- sum(df$Abundance[df$Planktic == TRUE], na.rm=TRUE) /
sum(df$Abundance, na.rm=TRUE) * 100
out$`% Motile` <- sum(df$Abundance[df$Motile == TRUE], na.rm=TRUE) /
sum(df$Abundance, na.rm=TRUE) * 100
out$`% Organic tolerant` <- sum(df$Abundance[df$OrganicTolerant == TRUE],
na.rm=TRUE) / sum(df$Abundance, na.rm=TRUE) * 100
out$`% Saline` <- sum(df$Abundance[df$Saline == TRUE], na.rm=TRUE) /
sum(df$Abundance, na.rm=TRUE) * 100
out[, -1]
}))) # End of lake.samples
lake.samples$SampleID <- rownames(lake.samples)
rownames(lake.samples) <- NULL
lake.samples <- merge(agg.data, lake.samples)
if (VERSL %in% versions) {
lat.CoC.lakes <- as.data.frame(
do.call(rbind, by(lake.samples, lake.samples$SiteID, FUN=function(df) {
# Mean and number of samples
m <- mean(df$`EQR LTDI2`)
n <- length(df$`EQR LTDI2`)
# Class
if (df$`Lake Alk class`[1] == "LA") {
intervals <- cut(m, breaks=c(0, LTDI2class.LA[, "upperlimit"]),
include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI2class.LA))
b <- LTDI2class.LA
} else if (df$`Lake Alk class`[1] == "MA") {
intervals <- cut(m, breaks=c(0, LTDI2class.MA[, "upperlimit"]),
include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI2class.MA))
b <- LTDI2class.MA
} else {
intervals <- cut(m, breaks=c(0, LTDI2class.HA[, "upperlimit"]),
include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI2class.HA))
b <- LTDI2class.HA
}
class <- key[match(intervals, key$range), 2]
latest <- .DARLEQ.CoC(m, n, METRICL, class, b)
})))
lat.CoC.lakes$SiteID <- rownames(lat.CoC.lakes)
rownames(lat.CoC.lakes) <- NULL
lat.CoC.lakes <- lat.CoC.lakes[c(ncol(lat.CoC.lakes), 1:ncol(lat.CoC.lakes)-1)]
}
if (VERSP %in% versions) {
prev.CoC.lakes <- as.data.frame(
do.call(rbind, by(lake.samples, lake.samples$SiteID, FUN=function(df) {
# Mean and number of samples
m <- mean(df$`EQR LTDI1`)
n <- length(df$`EQR LTDI1`)
# Class
if (df$`Lake Alk class`[1] == "LA") {
intervals <- cut(m, breaks=c(0, LTDI1class.LA[, "upperlimit"]),
include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI1class.LA))
b <- LTDI1class.LA
} else if (df$`Lake Alk class`[1] == "MA") {
intervals <- cut(m, breaks=c(0, LTDI1class.MA[, "upperlimit"]),
include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI1class.MA))
b <- LTDI1class.MA
} else {
intervals <- cut(m, breaks=c(0, LTDI1class.HA[, "upperlimit"]),
include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI1class.HA))
b <- LTDI1class.HA
}
class <- key[match(intervals, key$range), 2]
previous <- .DARLEQ.CoC(m, n, METRICL, class, b)
})))
prev.CoC.lakes$SiteID <- rownames(prev.CoC.lakes)
rownames(prev.CoC.lakes) <- NULL
prev.CoC.lakes <- prev.CoC.lakes[c(ncol(prev.CoC.lakes), 1:ncol(prev.CoC.lakes)-1)]
}
DARLEQ.L <- switch(version,
all= { list(lakes=lake.samples,
ltdi2.CoC=lat.CoC.lakes,
ltdi1.CoC=prev.CoC.lakes,
taxa.lacking.ltdi2=lacking.ltdi2,
taxa.lacking.ltdi1=lacking.ltdi1)
},
latest= { list(lakes=lake.samples,
ltdi2.CoC=lat.CoC.lakes,
taxa.lacking.ltdi2=lacking.ltdi2)
},
previous= { list(lakes=lake.samples,
ltdi1.CoC=prev.CoC.lakes,
taxa.lacking.ltdi1=lacking.ltdi1)
}
)
} # end CalcDARLEQ.L
aquaMetrics/aquaMetrics documentation built on May 6, 2019, 7:26 a.m.
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######################################################################################
# #
# Version: 1.0 #
# Revision: 0 - 10/02/2015. Published version #
# #
######################################################################################
CalcDARLEQ.L <- function(data = NULL, version="latest") {
# Input columns:
#(Type), SampleID, siteID, Date (date object), Code, Taxa, Abundance, Alk
# Input handling
if (is.null(data))
stop("No dataframe has been specified as 'data'")
if (ncol(data) < 7)
stop("It seems the input data.frame does not have the required columns")
if(any(!version %in% c("latest","previous", "all")))
stop("Input version is not valid, please verify its value(s)")
# Define constants matching input parameters
METRICL <- "lakes"
VERSL <- "latest"
VERSP <- "previous"
EQR.CAP <- 1.00
# Matrices. EQR status class boundaries for lakes and rivers
dim.names <- list(c("Bad", "Poor", "Moderate", "Good", "High"),
"upperlimit")
LTDI1class.HA <- matrix(c(0.22, 0.44, 0.66, 0.90, 1.00), nrow=5,
dimnames = dim.names)
LTDI1class.MA <- matrix(c(0.22, 0.44, 0.66, 0.90, 1.00), nrow=5,
dimnames = dim.names)
LTDI1class.LA <- matrix(c(0.22, 0.44, 0.63, 0.90, 1.00), nrow=5,
dimnames = dim.names)
LTDI2class.HA <- matrix(c(0.23, 0.46, 0.70, 0.92, 1.00), nrow=5,
dimnames = dim.names)
LTDI2class.MA <- matrix(c(0.23, 0.46, 0.66, 0.93, 1.00), nrow=5,
dimnames = dim.names)
LTDI2class.LA <- matrix(c(0.23, 0.46, 0.70, 0.92, 1.00), nrow=5,
dimnames = dim.names)
if ("all" %in% version) {
versions <- c(VERSL, VERSP)
} else {
versions <- version
}
input.names <- names(data)
if (ncol(data) == 7) {
names(data) <- c("SampleID", "SiteID", "Date", "Code", "Taxa",
"Abundance", "Alk")
} else {
names(data) <- c("Type", "SampleID", "SiteID", "Date", "Code",
"Taxa", "Abundance", "Alk")
}
# Find out which taxa has no presence in the scoring list (LTDI2 or LTDI1)
not.in.list <- !(data$Code %in% LookUpDARLEQ2$TaxonId)
na.ltdi2 <- LookUpDARLEQ2[is.na(LookUpDARLEQ2$LTDI2) & LookUpDARLEQ2$TaxonId %in% unique(data$Code),"TaxonId"]
na.ltdi1 <- LookUpDARLEQ2[is.na(LookUpDARLEQ2$LTDI1) & LookUpDARLEQ2$TaxonId %in% unique(data$Code),"TaxonId"]
lacking.ltdi2 <- unique(data[not.in.list | data$Code %in% na.ltdi2, c("Code", "Taxa")])
lacking.ltdi2 <- lacking.ltdi2[order(lacking.ltdi2$Code), ]
rownames(lacking.ltdi2) <- NULL
lacking.ltdi1 <- unique(data[not.in.list | data$Code %in% na.ltdi1, c("Code", "Taxa")])
lacking.ltdi1 <- lacking.ltdi1[order(lacking.ltdi1$Code), ]
rownames(lacking.ltdi1) <- NULL
# Merge
data <- merge(data, LookUpDARLEQ2, by.x="Code", by.y="TaxonId")
# Initialisation
lake.samples <- NA
CoC.lakes <- NA
# Alkalinity
with (data, {
data$`Lake Alk class` <<- ifelse(Alk < 10, "LA", ifelse(Alk > 50, "HA", "MA"))
if (VERSL %in% versions) {
data$`eLTDI2` <<- ifelse(Alk < 10, 22, ifelse(Alk > 50, 42, 35))
}
if (VERSP %in% versions) {
data$`eLTDI1` <<- ifelse(Alk < 10, 20, 25)
}
})
agg.data <- unique(data[ , c("SampleID", "SiteID", "Date", "Alk")])
# Compute metrics
lake.samples <- as.data.frame(
do.call(rbind, by(data, data$SampleID, FUN=function(df) {
out <- data.frame(NA, stringsAsFactors=FALSE)
if (VERSL %in% versions) {
out$oLTDI2 <- (sum(df$Abundance * df$LTDI2, na.rm=TRUE) /
sum(df[!is.na(df$LTDI2), "Abundance"], na.rm=TRUE) * 25) - 25
out$`Lake Alk class` <- df$`Lake Alk class`[1]
out$eLTDI2 <- df$eLTDI2[1]
out$`EQR LTDI2` <- (100 - out$oLTDI2) / (100 - out$eLTDI2)
out[out$`EQR LTDI2` > EQR.CAP & !is.na(out$`EQR LTDI2`), "EQR LTDI2"] <- EQR.CAP
# LTDI2class.LA intervals
intervals <- cut(out[out$`Lake Alk class` == "LA", "EQR LTDI2"],
breaks=c(0, LTDI2class.LA[, "upperlimit"]), include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI2class.LA))
out[out$`Lake Alk class` == "LA", "Class LTDI2"] <-
key[match(intervals, key$range), 2]
# LTDI2class.MA intervals
intervals <- cut(out[out$`Lake Alk class` == "MA", "EQR LTDI2"],
breaks=c(0, LTDI2class.MA[, "upperlimit"]), include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI2class.MA))
out[out$`Lake Alk class` == "MA", "Class LTDI2"] <-
key[match(intervals, key$range), 2]
# LTDI2class.HA intervals
intervals <- cut(out[out$`Lake Alk class` == "HA", "EQR LTDI2"],
breaks=c(0, LTDI2class.HA[, "upperlimit"]), include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI2class.HA))
out[out$`Lake Alk class` == "HA", "Class LTDI2"] <-
key[match(intervals, key$range), 2]
}
if (VERSP %in% versions) { # older version
out$oLTDI1 <- (sum(df$Abundance * df$LTDI1, na.rm=TRUE) /
sum(df[!is.na(df$LTDI1), "Abundance"], na.rm=TRUE) * 25) - 25
out$`Lake Alk class` <- df$`Lake Alk class`[1]
out$eLTDI1 <- df$eLTDI1[1]
out$`EQR LTDI1` <- (100 - out$oLTDI1) / (100 - out$eLTDI1)
out[out$`EQR LTDI1` > EQR.CAP & !is.na(out$`EQR LTDI1`), "EQR LTDI1"] <- EQR.CAP
# LTDI1class.LA intervals
intervals <- cut(out[out$`Lake Alk class` == "LA", "EQR LTDI1"],
breaks=c(0, LTDI1class.LA[, "upperlimit"]), include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI1class.LA))
out[out$`Lake Alk class` == "LA", "Class LTDI1"] <-
key[match(intervals, key$range), 2]
# LTDI1class.MA intervals
intervals <- cut(out[out$`Lake Alk class` == "MA", "EQR LTDI1"],
breaks=c(0, LTDI1class.MA[, "upperlimit"]), include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI1class.MA))
out[out$`Lake Alk class` == "MA", "Class LTDI1"] <-
key[match(intervals, key$range), 2]
# LTDI1class.HA intervals
intervals <- cut(out[out$`Lake Alk class` == "HA" & !is.na(out$`Lake Alk class`),
"EQR LTDI1"], breaks=c(0, LTDI1class.HA[, "upperlimit"]),
include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI1class.HA))
out[out$`Lake Alk class` == "HA", "Class LTDI1"] <-
key[match(intervals, key$range), 2]
}
out$`% Planktic` <- sum(df$Abundance[df$Planktic == TRUE], na.rm=TRUE) /
sum(df$Abundance, na.rm=TRUE) * 100
out$`% Motile` <- sum(df$Abundance[df$Motile == TRUE], na.rm=TRUE) /
sum(df$Abundance, na.rm=TRUE) * 100
out$`% Organic tolerant` <- sum(df$Abundance[df$OrganicTolerant == TRUE],
na.rm=TRUE) / sum(df$Abundance, na.rm=TRUE) * 100
out$`% Saline` <- sum(df$Abundance[df$Saline == TRUE], na.rm=TRUE) /
sum(df$Abundance, na.rm=TRUE) * 100
out[, -1]
}))) # End of lake.samples
lake.samples$SampleID <- rownames(lake.samples)
rownames(lake.samples) <- NULL
lake.samples <- merge(agg.data, lake.samples)
if (VERSL %in% versions) {
lat.CoC.lakes <- as.data.frame(
do.call(rbind, by(lake.samples, lake.samples$SiteID, FUN=function(df) {
# Mean and number of samples
m <- mean(df$`EQR LTDI2`)
n <- length(df$`EQR LTDI2`)
# Class
if (df$`Lake Alk class`[1] == "LA") {
intervals <- cut(m, breaks=c(0, LTDI2class.LA[, "upperlimit"]),
include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI2class.LA))
b <- LTDI2class.LA
} else if (df$`Lake Alk class`[1] == "MA") {
intervals <- cut(m, breaks=c(0, LTDI2class.MA[, "upperlimit"]),
include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI2class.MA))
b <- LTDI2class.MA
} else {
intervals <- cut(m, breaks=c(0, LTDI2class.HA[, "upperlimit"]),
include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI2class.HA))
b <- LTDI2class.HA
}
class <- key[match(intervals, key$range), 2]
latest <- .DARLEQ.CoC(m, n, METRICL, class, b)
})))
lat.CoC.lakes$SiteID <- rownames(lat.CoC.lakes)
rownames(lat.CoC.lakes) <- NULL
lat.CoC.lakes <- lat.CoC.lakes[c(ncol(lat.CoC.lakes), 1:ncol(lat.CoC.lakes)-1)]
}
if (VERSP %in% versions) {
prev.CoC.lakes <- as.data.frame(
do.call(rbind, by(lake.samples, lake.samples$SiteID, FUN=function(df) {
# Mean and number of samples
m <- mean(df$`EQR LTDI1`)
n <- length(df$`EQR LTDI1`)
# Class
if (df$`Lake Alk class`[1] == "LA") {
intervals <- cut(m, breaks=c(0, LTDI1class.LA[, "upperlimit"]),
include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI1class.LA))
b <- LTDI1class.LA
} else if (df$`Lake Alk class`[1] == "MA") {
intervals <- cut(m, breaks=c(0, LTDI1class.MA[, "upperlimit"]),
include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI1class.MA))
b <- LTDI1class.MA
} else {
intervals <- cut(m, breaks=c(0, LTDI1class.HA[, "upperlimit"]),
include.lowest=TRUE)
key <- data.frame(range=levels(intervals), rc=rownames(LTDI1class.HA))
b <- LTDI1class.HA
}
class <- key[match(intervals, key$range), 2]
previous <- .DARLEQ.CoC(m, n, METRICL, class, b)
})))
prev.CoC.lakes$SiteID <- rownames(prev.CoC.lakes)
rownames(prev.CoC.lakes) <- NULL
prev.CoC.lakes <- prev.CoC.lakes[c(ncol(prev.CoC.lakes), 1:ncol(prev.CoC.lakes)-1)]
}
DARLEQ.L <- switch(version,
all= { list(lakes=lake.samples,
ltdi2.CoC=lat.CoC.lakes,
ltdi1.CoC=prev.CoC.lakes,
taxa.lacking.ltdi2=lacking.ltdi2,
taxa.lacking.ltdi1=lacking.ltdi1)
},
latest= { list(lakes=lake.samples,
ltdi2.CoC=lat.CoC.lakes,
taxa.lacking.ltdi2=lacking.ltdi2)
},
previous= { list(lakes=lake.samples,
ltdi1.CoC=prev.CoC.lakes,
taxa.lacking.ltdi1=lacking.ltdi1)
}
)
} # end CalcDARLEQ.L
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