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global.R
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global.R
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list.of.packages <- c("pbapply", "reshape2", "TTR", "dplyr", "ggtern", "ggplot2", "shiny", "rhandsontable", "random", "data.table", "DT", "shinythemes", "Cairo", "broom", "shinyjs", "gridExtra", "dtplyr", "formattable", "XML", "corrplot", "scales", "rmarkdown", "markdown", "peakPick", "shinyWidgets", "data.table", "baseline", "pls", "prospectr", "doSNOW", "parallel", "caret", "wavelets", "hexView", "GSIF", "gstat", "nnet", "neuralnet", "grid", "gridExtra", "compiler", "xgboost", "peakPick")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
if ("soil.spec" %in% installed.packages()[,"Package"]==FALSE){
install.packages("Packages/soil.spec_2.1.3.tgz", repos=NULL, type="binary")
}
if ("peakPick" %in% installed.packages()[,"Package"]==FALSE){
install.packages("Packages/peakPick_0.11.tar.gz", repos=NULL, type="source")
}
library(soil.spec)
#options(warn=-1)
#assign("last.warning", NULL, envir = baseenv())
library(shiny)
library(data.table)
library(compiler)
library(nnet)
library(neuralnet)
library(xgboost)
library(gridExtra)
library(grid)
library(shiny)
library(ggplot2)
library(pbapply)
library(reshape2)
library(dplyr)
library(plyr)
library(data.table)
library(DT)
library(gridExtra)
library(rhandsontable)
library(Cairo)
library(broom)
library(shinyjs)
library(formattable)
library(markdown)
library(rmarkdown)
library(XML)
library(corrplot)
library(scales)
library(TTR)
library(peakPick)
library(soil.spec)
library(parallel)
library(caret)
library(randomForest)
library(DescTools)
library(prospectr)
library(pls)
library(baseline)
library(doParallel)
pdf(NULL)
Sys.setenv(R_MAX_VSIZE = 16e9)
get_os <- function(){
sysinf <- Sys.info()
if (!is.null(sysinf)){
os <- sysinf['sysname']
if (os == 'Darwin')
os <- "osx"
} else { ## mystery machine
os <- .Platform$OS.type
if (grepl("^darwin", R.version$os))
os <- "osx"
if (grepl("linux-gnu", R.version$os))
os <- "linux"
}
tolower(os)
}
my.cores <- if(parallel::detectCores()>=3){
paste0(parallel::detectCores()-2)
} else if(parallel::detectCores()<=2){
"1"
}
my_combine <- function (...)
{
pad0 <- function(x, len) c(x, rep(0, len - length(x)))
padm0 <- function(x, len) rbind(x, matrix(0, nrow = len -
nrow(x), ncol = ncol(x)))
rflist <- list(...)
areForest <- sapply(rflist, function(x) inherits(x, "randomForest"))
if (any(!areForest))
stop("Argument must be a list of randomForest objects")
rf <- rflist[[1]]
classRF <- rf$type == "classification"
trees <- sapply(rflist, function(x) x$ntree)
ntree <- sum(trees)
rf$ntree <- ntree
nforest <- length(rflist)
haveTest <- !any(sapply(rflist, function(x) is.null(x$test)))
vlist <- lapply(rflist, function(x) rownames(importance(x)))
numvars <- sapply(vlist, length)
if (!all(numvars[1] == numvars[-1]))
stop("Unequal number of predictor variables in the randomForest objects.")
for (i in seq_along(vlist)) {
if (!all(vlist[[i]] == vlist[[1]]))
stop("Predictor variables are different in the randomForest objects.")
}
haveForest <- sapply(rflist, function(x) !is.null(x$forest))
if (all(haveForest)) {
nrnodes <- max(sapply(rflist, function(x) x$forest$nrnodes))
rf$forest$nrnodes <- nrnodes
rf$forest$ndbigtree <- unlist(sapply(rflist, function(x) x$forest$ndbigtree))
rf$forest$nodestatus <- do.call("cbind", lapply(rflist,
function(x) padm0(x$forest$nodestatus, nrnodes)))
rf$forest$bestvar <- do.call("cbind", lapply(rflist,
function(x) padm0(x$forest$bestvar, nrnodes)))
rf$forest$xbestsplit <- do.call("cbind", lapply(rflist,
function(x) padm0(x$forest$xbestsplit, nrnodes)))
rf$forest$nodepred <- do.call("cbind", lapply(rflist,
function(x) padm0(x$forest$nodepred, nrnodes)))
tree.dim <- dim(rf$forest$treemap)
if (classRF) {
rf$forest$treemap <- array(unlist(lapply(rflist,
function(x) apply(x$forest$treemap, 2:3, pad0,
nrnodes))), c(nrnodes, 2, ntree))
}
else {
rf$forest$leftDaughter <- do.call("cbind", lapply(rflist,
function(x) padm0(x$forest$leftDaughter, nrnodes)))
rf$forest$rightDaughter <- do.call("cbind", lapply(rflist,
function(x) padm0(x$forest$rightDaughter, nrnodes)))
}
rf$forest$ntree <- ntree
if (classRF)
rf$forest$cutoff <- rflist[[1]]$forest$cutoff
}
else {
rf$forest <- NULL
}
#
#Tons of stuff removed here...
#
if (classRF) {
rf$confusion <- NULL
rf$err.rate <- NULL
if (haveTest) {
rf$test$confusion <- NULL
rf$err.rate <- NULL
}
}
else {
rf$mse <- rf$rsq <- NULL
if (haveTest)
rf$test$mse <- rf$test$rsq <- NULL
}
rf
}
#registerDoSNOW(makeCluster(as.numeric(my.cores), type="SOCK"))
options(digits=4)
options(warn=-1)
#assign("last.warning", NULL, envir = baseenv())
keep_singles <- function(v){
v[!(v %in% v[duplicated(v)])]
}
Hodder.v <- function(y)
{
n <-length(y)
for(i in 1:(n-1)) {
y[i] <- y[i+1] - y[i]
y[1:(n-1)]
y <- abs(y)
}
y <- c(0, y[1:(n-1)])
return(y)
}
Shepherd.v <- function(y) {
n <-length(y)
mean <- mean(y)
sd <- sd(y)
for(i in 1:n) {
y[i] <- (y[i]/mean)*sd
}
return(y)
}
cal.lmsummary <-function(lm.object){
res<-c(paste(as.character(summary(lm.object)$call),collapse=" "),
length(lm.object$model),
summary(lm.object)$r.squared,
summary(lm.object)$adj.r.squared,
summary(lm.object)$fstatistic,
pf(summary(lm.object)$fstatistic[1],summary(lm.object)$fstatistic[2],summary(lm.object)$fstatistic[3],lower.tail=FALSE))
names(res)<-c("Call","n", "R2","Adj. R2",
"F-statistic","numdf","dendf","p-value")
return(res)}
val.lmsummary <-function(lm.object){
res<-c(paste(as.character(summary(lm.object)$call),collapse=" "),
lm.object$coefficients[1],
lm.object$coefficients[2],
length(lm.object$model),
summary(lm.object)$coefficients[2,2],
summary(lm.object)$r.squared,
summary(lm.object)$adj.r.squared,
summary(lm.object)$fstatistic,
pf(summary(lm.object)$fstatistic[1],summary(lm.object)$fstatistic[2],summary(lm.object)$fstatistic[3],lower.tail=FALSE))
names(res)<-c("Call","Intercept","Slope","n","Slope SE","R2","Adj. R2",
"F-statistic","numdf","dendf","p-value")
return(res)}
read_csv_filename_x <- function(filename){
ret <- read.csv(file=filename, sep=",", header=FALSE)
return.res <- as.numeric(as.vector(ret$V2[18]))/1000
return.chan.counts <-as.numeric(as.vector(ret$V1[22:2069]))
return.Wavenumber <- return.chan.counts*return.res
return(return.Wavenumber)
}
read_csv_filename_y <- function(filename){
ret <- read.csv(file=filename, sep=",", header=FALSE)
return.live.time <- as.numeric(as.vector(ret$V2[10]))
return.counts <- as.numeric(as.vector(ret$V2[22:2069]))
return.cps <- return.counts/return.live.time
return(return.cps)
}
read_csv_net <- function(filepath) {
ret <- read.csv(file=filepath, sep=",", header=TRUE)
element <- ret$Element
line <- ret$Line
net <- ret$Net
background <- ret$Backgr.
eline <- paste(element, line, sep="-")
simple.table <- data.frame(net)
colnames(simple.table) <- NULL
simple.transpose <- as.data.frame(t(simple.table))
colnames(simple.transpose) <- eline
simple.transpose
}
readDPTData <- function(filepath, filename){
filename <- gsub(".dpt", "", filename)
file <- read.csv(filepath)
file.frame <- data.frame(
Spectrum=rep(filename, length(file[,1])),
Wavenumber=file[,1],
Amplitude=file[,2]
)
return(file.frame)
}
readCSVData <- function(filepath, filename){
filename <- gsub(".csv", "", filename)
file <- read.csv(filepath)
file.frame <- data.frame(
Spectrum=rep(filename, length(file[,1])),
Wavenumber=file[,1],
Amplitude=file[,2]
)
return(file.frame)
}
readOpusData <- function(filepath, filename){
filename <- make.names(filename)
alldata <- soil.spec::read.opus(file=filepath)
thedata <- t(slot(slot(alldata, "data"), "ab"))
file.frame <- data.frame(
Spectrum=rep(filename, length(thedata[,1])-1),
Wavenumber=as.numeric(as.vector(substring(rownames(thedata), 2)[2:length(thedata[,1])])),
Amplitude=as.numeric(as.vector(thedata[,1][2:length(thedata[,1])]))
)
return(file.frame)
}
readJascoData <- function(filepath, filename=NULL){
if(is.null(filename)){filename <- basename(filepath)}
ir_file <- read.delim2(filepath)[19:22519,]
ir_file <- data.frame(Spectrum=gsub(".txt", "", filename), Wavenumber=as.numeric(ir_file[,1]), Amplitude=as.numeric(ir_file[,2]))
return(ir_file)
}
readJascoData2 <- function(filepath, filename=NULL){
if(is.null(filename)){filename <- basename(filepath)}
ir_file <- read.delim2(filepath)[19:1694,]
ir_file <- data.frame(Spectrum=gsub(".txt", "", filename), Wavenumber=as.numeric(ir_file[,1]), Amplitude=as.numeric(ir_file[,2]))
return(ir_file)
}
range_subset_ftir <- function(range.frame, data){
new.data <- subset(data, Wavenumber >= range.frame$WaveMin & Wavenumber <= range.frame$WaveMax, drop=TRUE)
newer.data <- aggregate(new.data, by=list(new.data$Spectrum), FUN=mean, na.rm=TRUE)[,c("Group.1", "Amplitude")]
colnames(newer.data) <- c("Spectrum", as.character(range.frame$Name))
newer.data
}
ftir_parse <- function(range.table, data){
choice.lines <- range.table[complete.cases(range.table),]
choice.list <- split(choice.lines, f=choice.lines$Name)
names(choice.list) <- choice.lines[,"Name"]
index <- choice.lines[,"Name"]
selected.list <- lapply(index, function(x) range_subset_ftir(range.frame=choice.list[[x]], data=data))
Reduce(function(...) merge(..., all=T), selected.list)
}
spectra_frame <- function(spectra){
data <- reshape2::dcast(spectra, Spectrum~Wavenumber)
#test <- apply(test, 2, as.numeric)
colnames(data) <- make.names(colnames(data))
data[complete.cases(data),]
}
spectra_table <- function(spectra, concentration){
data <- reshape2::dcast(spectra, Spectrum~Wavenumber)
data$Concentration <- concentration
#test <- apply(test, 2, as.numeric)
colnames(data) <- make.names(colnames(data))
data[complete.cases(data),]
}
###Train Functions
pull_test <- function(a.vector, a.value.position){
scaled <- scale(a.vector)[,1]
value <- scaled[a.value.position]
scale.vector <- scaled[-a.value.position]
ZScore <- (value-mean(scale.vector))/sd(scale.vector)
pvalue <- pnorm(-abs(ZScore))
is.sig <- pvalue < 0.05
data.frame(Value=a.vector[a.value.position], ZScore=ZScore, pvalue=pvalue, Sig=is.sig)
}
Z_frame <- function(a.vector){
do.call("rbind", lapply(seq(1, length(a.vector), 1), function(x) pull_test(a.vector, x)))
}
Z_choose <- function(a.vector){
full <- Z_frame(a.vector)
full[full$Sig,]
}
variable_select <- function(amplitudes, values, analyte){
control <- trainControl(method="cv", number=5)
seed <- 7
metric <- "RMSE"
set.seed(seed)
cal.table <- data.frame(amplitudes, Concentration=values[,analyte])
fit.lm <- train(Concentration~., data=cal.table, method="lm", metric=metric, preProc=c("center", "scale"), trControl=control)
importance <- varImp(fit.lm, scale=FALSE)
importance.frame <- as.data.frame(importance$importance)
elements <- rownames(importance$importance)
elements[as.numeric(rownames(Z_choose(importance.frame$Overall)))]
}
variable_select_short <- function(importance){
importance.frame <- as.data.frame(importance$importance)
lines <- rownames(importance$importance)
lines[as.numeric(rownames(Z_choose(importance.frame$Overall)))]
}
file.0 <- function(file) {
if (length(file) > 0)
{
return(file)
}else{
return(levels(file))
}
}
is.0 <- function(amplitude, file) {
file.0 <- function(file) {
if (length(file) > 0)
{
return(file)
}else{
return(levels(file))
}
}
if (length(amplitude) > 0)
{
hope <-data.frame(amplitude, file.0(file))
return(hope)
} else {
empty <- rep(0, length(file.0(file)))
framed <- data.frame(empty, file.0(file))
return(framed)
}
}
dt_options <- reactive({
# dynamically create options for `aoColumns` depending on how many columns are selected.
toggles <- lapply(1:length(input$show_vars), function(x) list(bSearchable = F))
# for `species` columns
toggles[[length(toggles) + 1]] <- list(bSearchable = T)
list(
aoColumns = toggles,
bFilter = 1, bSortClasses = 1,
aLengthMenu = list(c(10,25,50, -1), list('10','25', '50', 'Todas')),
iDisplayLength = 10
)
})
ifrm <- function(obj, env = globalenv()) {
obj <- deparse(substitute(obj))
if(exists(obj, envir = env)) {
rm(list = obj, envir = env)
}
}
lmp <- function (modelobject) {
if (class(modelobject) != "lm") stop("Not an object of class 'lm' ")
f <- summary(modelobject)$fstatistic
p <- pf(f[1],f[2],f[3],lower.tail=F)
attributes(p) <- NULL
return(p)
}
lm_eqn.old <- function(df){
m <- lm(y ~ x, df);
eq <- substitute(italic(y) == a + b %.% italic(x)*","~~italic(r)^2~"="~r2,
list(a = format(coef(m)[1], digits = 2),
b = format(coef(m)[2], digits = 2),
r2 = format(summary(m)$r.squared, digits = 3)))
as.character(as.expression(eq));
}
lm_eqn = function(m) {
l <- list(a = as.numeric(format(coef(m)[1], digits = 2)),
b = as.numeric(format(abs(coef(m)[2]), digits = 2)),
r2 = format(summary(m)$r.squared, digits = 3));
eq <- substitute(italic(C)[i] == a + b %.% italic(I)[i]*","~~italic(r)^2~"="~r2,l)
as.character(as.expression(eq));
}
lm_eqn_poly = function(m) {
l <- list(a = as.numeric(format(coef(m)[1], digits = 2)),
b = as.numeric(format(abs(coef(m)[2]), digits = 2)),
c = as.numeric(format(abs(coef(m)[3]), digits = 2)),
r2 = format(summary(m)$r.squared, digits = 3));
eq <- substitute(italic(C)[i] == a + c %.% italic(I)[i]^2 + b %.% italic(I)[i]*","~~italic(r)^2~"="~r2,l)
as.character(as.expression(eq));
}
lm_eqn_val = function(m) {
l <- list(a = as.numeric(format(coef(m)[1], digits = 2)),
b = as.numeric(format(abs(coef(m)[2]), digits = 2)),
r2 = format(summary(m)$r.squared, digits = 3));
eq <- substitute(italic(y) == a + b %.% italic(x)*","~~italic(r)^2~"="~r2,l)
as.character(as.expression(eq));
}
numericInput2<-function (inputId, label, value = "",...)
{
div(style="display:inline-block",
tags$label(label, `for` = inputId),
tags$input(id = inputId, type = "text", value = value,...))
}
numericInputRow<-function (inputId, label, min, max, value = "")
{
div(style="display:inline-block",
tags$label(label, `for` = inputId),
tags$input(id = inputId, type = "text", value = value, class="input-mini", width='20%'))
}
diagPlot<-function(model){
p1<-ggplot(model, aes(as.vector(.fitted), as.vector(.resid)))+geom_point()
p1<-p1+stat_smooth(method="loess")+geom_hline(yintercept=0, col="red", linetype="dashed")
p1<-p1+xlab("Fitted values")+ylab("Residuals")
p1<-p1+ggtitle("Residual vs Fitted Plot")+theme_light()
p2 <- ggplot(model, aes(qqnorm(.stdresid)[[1]], .stdresid))+geom_point(na.rm = TRUE)
p2 <- p2+geom_abline()+xlab("Theoretical Quantiles")+ylab("Standardized Residuals")
p2 <- p2+ggtitle("Normal Q-Q")+theme_bw()
p2
p3<-ggplot(model, aes(as.vector(.fitted), sqrt(abs(as.vector(.stdresid)))))+geom_point(na.rm=TRUE)
p3<-p3+stat_smooth(method="loess", na.rm = TRUE)+xlab("Fitted Value")
p3<-p3+ylab(expression(sqrt("|Standardized residuals|")))
p3<-p3+ggtitle("Scale-Location")+theme_light()
p4<-ggplot(model, aes(seq_along(as.vector(.cooksd)), as.vector(.cooksd)))+geom_bar(stat="identity", position="identity")
p4<-p4+xlab("Obs. Number")+ylab("Cook's distance")
p4<-p4+ggtitle("Cook's distance")+theme_light()
p5<-ggplot(model, aes(as.vector(.hat), as.vector(.stdresid)))+geom_point(aes(size=as.vector(.cooksd)), na.rm=TRUE)
p5<-p5+stat_smooth(method="loess", na.rm=TRUE)
p5<-p5+xlab("Leverage")+ylab("Standardized Residuals")
p5<-p5+ggtitle("Residual vs Leverage Plot")
p5<-p5+scale_size_continuous("Cook's Distance", range=c(1,5))
p5<-p5+theme_light()+theme(legend.position="bottom")
p6<-ggplot(model, aes(as.vector(.hat), as.vector(.cooksd)))+geom_point(na.rm=TRUE)+stat_smooth(method="loess", na.rm=TRUE)
p6<-p6+xlab("Leverage hii")+ylab("Cook's Distance")
p6<-p6+ggtitle("Cook's dist vs Leverage hii/(1-hii)")
p6<-p6+geom_abline(slope=seq(0,3,0.5), color="gray", linetype="dashed")
p6<-p6+theme_light()
return(list(rvfPlot=p1, qqPlot=p2, sclLocPlot=p3, cdPlot=p4, rvlevPlot=p5, cvlPlot=p6))
}
rbind.match.columns <- function(input1, input2) {
n.input1 <- ncol(input1)
n.input2 <- ncol(input2)
if (n.input2 < n.input1) {
TF.names <- which(names(input2) %in% names(input1))
column.names <- names(input2[, TF.names])
} else {
TF.names <- which(names(input1) %in% names(input2))
column.names <- names(input1[, TF.names])
}
return(rbind(input1[, column.names], input2[, column.names]))
}
strip_glm <- function(cm) {
cm$y = c()
cm$model = c()
cm$residuals = c()
cm$fitted.values = c()
cm$effects = c()
cm$qr$qr = c()
cm$linear.predictors = c()
cm$weights = c()
cm$prior.weights = c()
cm$data = c()
cm$family$variance = c()
cm$family$dev.resids = c()
cm$family$aic = c()
cm$family$validmu = c()
cm$family$simulate = c()
attr(cm$terms,".Environment") = c()
attr(cm$formula,".Environment") = c()
cm
}
merge_Sum <- function(.df1, .df2, .id_Columns, .match_Columns){
merged_Columns <- unique(c(names(.df1),names(.df2)))
merged_df1 <- data.frame(matrix(nrow=nrow(.df1), ncol=length(merged_Columns)))
names(merged_df1) <- merged_Columns
for (column in merged_Columns){
if(column %in% .id_Columns | !column %in% names(.df2)){
merged_df1[, column] <- .df1[, column]
} else if (!column %in% names(.df1)){
merged_df1[, column] <- .df2[match(.df1[, .match_Columns],.df2[, .match_Columns]), column]
} else {
df1_Values=.df1[, column]
df2_Values=.df2[match(.df1[, .match_Columns],.df2[, .match_Columns]), column]
df2_Values[is.na(df2_Values)] <- 0
merged_df1[, column] <- df1_Values + df2_Values
}
}
return(merged_df1)
}
GG_save_pdf = function(list, filename) {
#start pdf
pdf(filename)
#loop
for (p in list) {
print(p)
}
#end pdf
dev.off()
invisible(NULL)
}
####Cal Models
linear_simp_ftir <- function(concentration.table, spectra.line.table, element.line) {
concentration <- na.omit(as.vector(as.numeric(unlist(concentration.table[element.line]))))
Amplitude <- na.omit(as.vector(as.numeric(unlist(spectra.line.table[element.line]))))
predict.frame <- data.frame(concentration, Amplitude)
colnames(predict.frame) <- c("Concentration", "Amplitude")
predict.amplitude <- data.frame(predict.frame$Amplitude)
colnames(predict.amplitude) <- c("Amplitude")
cal.lm <- lm(predict.frame$Concentration~predict.frame$Amplitude)
cal.lm
}
poly_simp_ftir <- function(concentration.table, spectra.line.table, element.line) {
concentration <- na.omit(as.vector(as.numeric(unlist(concentration.table[element.line]))))
Amplitude <- na.omit(as.vector(as.numeric(unlist(spectra.line.table[element.line]))))
predict.frame <- data.frame(concentration, Amplitude)
colnames(predict.frame) <- c("Concentration", "Amplitude")
predict.amplitude <- data.frame(predict.frame$Amplitude)
colnames(predict.amplitude) <- c("Amplitude")
cal.lm.poly <- lm(predict.frame$Concentration~poly(predict.frame$Amplitude, 2))
cal.lm.poly
}
lucas_simp_ftir <- function(concentration.table, spectra.line.table, element.line, slope.element.lines, intercept.element.lines) {
concentration <- na.omit(as.vector(as.numeric(unlist(concentration.table[element.line]))))
Amplitude <- na.omit(as.vector(as.numeric(unlist(spectra.line.table[element.line]))))
lucas.intercept.table <- data.frame(rowSums(lucas.intercept.table.x[intercept.element.lines]))
colnames(lucas.intercept.table) <- c("first")
lucas.intercept <- lucas.intercept.table$first
lucas.slope <- data.frame(lucas.slope.table[slope.element.lines])
predict.frame.luk <- data.frame(concentration, ((1+Amplitude/(Amplitude+lucas.intercept))-lucas.intercept/(Amplitude+lucas.intercept)),lucas.slope)
colnames(predict.frame.luk) <- c("Concentration", "Amplitude", names(lucas.slope))
predict.amplitude.luk <- data.frame(predict.frame.luk$Amplitude, lucas.slope)
colnames(predict.amplitude.luk) <- c("Amplitude", names(lucas.slope))
lucas.lm <- lm(Concentration~., data=predict.frame.luk)
lucas.lm
}
linear_tc_ftir <- function(concentration.table, spectra.line.table, element.line) {
concentration <- na.omit(as.vector(as.numeric(unlist(concentration.table[element.line]))))
Amplitude <- na.omit(as.vector(as.numeric(unlist(spectra.line.table[element.line]))))
total.counts <- aggregate(Amplitude~Spectrum, data=data, sum)
colnames(total.counts) <- c("Spectrum", "Amplitude")
predict.frame.tc <- data.frame(concentration, Amplitude/total.counts$Amplitude)
colnames(predict.frame.tc) <- c("Concentration", "Amplitude")
predict.amplitude.tc <- data.frame(predict.frame.tc$Amplitude)
colnames(predict.amplitude.tc) <- c("Amplitude")
cal.lm.tc <- lm(predict.frame.tc$Concentration~predict.frame.tc$Amplitude)
cal.lm.tc
}
poly_tc_ftir <- function(concentration.table, spectra.line.table, element.line) {
concentration <- na.omit(as.vector(as.numeric(unlist(concentration.table[element.line]))))
Amplitude <- na.omit(as.vector(as.numeric(unlist(spectra.line.table[element.line]))))
total.counts <- aggregate(Amplitude~Spectrum, data=data, sum)
colnames(total.counts) <- c("Spectrum", "Amplitude")
predict.frame.tc <- data.frame(concentration, Amplitude/total.counts$Amplitude)
colnames(predict.frame.tc) <- c("Concentration", "Amplitude")
predict.amplitude.tc <- data.frame(predict.frame.tc$Amplitude)
colnames(predict.amplitude.tc) <- c("Amplitude")
cal.lm.poly.tc <- lm(predict.frame.tc$Concentration~poly(predict.frame.tc$Amplitude, 2))
cal.lm.poly.tc
}
lucas_tc_ftir <- function(concentration.table, spectra.line.table, element.line, slope.element.lines, intercept.element.lines) {
concentration <- na.omit(as.vector(as.numeric(unlist(concentration.table[element.line]))))
Amplitude <- na.omit(as.vector(as.numeric(unlist(spectra.line.table[element.line]))))
lucas.intercept.table.tc <- data.frame(rowSums(lucas.intercept.table.x[intercept.element.lines]))/total.counts$Amplitude
colnames(lucas.intercept.table.tc) <- c("first")
lucas.intercept.tc <- lucas.intercept.table.tc$first
lucas.slope.tc <- data.frame(lucas.slope.table[slope.element.lines])/total.counts$Amplitude
predict.frame.luc.tc <- data.frame(concentration, ((Amplitude/total.counts$Amplitude-lucas.intercept.tc)/(Amplitude/total.counts$Amplitude+lucas.intercept.tc)),lucas.slope.tc)
colnames(predict.frame.luc.tc) <- c("Concentration", "Amplitude", names(lucas.slope.tc))
predict.amplitude.luc.tc <- data.frame(predict.frame.luc.tc$Amplitude, lucas.slope.tc)
colnames(predict.amplitude.luc.tc) <- c("Amplitude", names(lucas.slope.tc))
lucas.lm.tc <- lm(Concentration~., data=predict.frame.luc.tc)
lucas.lm.tc
}
linear_comp_ftir <- function(data, concentration.table, spectra.line.table, element.line) {
concentration <- na.omit(as.vector(as.numeric(unlist(concentration.table[element.line]))))
Amplitude <- na.omit(as.vector(as.numeric(unlist(spectra.line.table[element.line]))))
compton.norm <- subset(data$Amplitude, !(data$Wavenumber < input$comptonmin | data$Wavenumber > input$comptonmax))
compton.file <- subset(data$Spectrum, !(data$Wavenumber < input$comptonmin | data$Wavenumber > input$comptonmax))
compton.frame <- data.frame(is.0(compton.norm, compton.file))
colnames(compton.frame) <- c("Compton", "Spectrum")
compton.frame.ag <- aggregate(list(compton.frame$Compton), by=list(compton.frame$Spectrum), FUN="sum")
colnames(compton.frame.ag) <- c("Spectrum", "Compton")
predict.frame.comp <- data.frame(concentration, Amplitude/compton.frame.ag$Compton)
colnames(predict.frame.comp) <- c("Concentration", "Amplitude")
predict.amplitude.comp <- data.frame(predict.frame.comp$Amplitude)
colnames(predict.amplitude.comp) <- c("Amplitude")
cal.lm.comp <- lm(predict.frame.comp$Concentration~predict.frame.comp$Amplitude)
cal.lm.comp
}
poly_comp_ftir <- function(data, concentration.table, spectra.line.table, element.line) {
concentration <- na.omit(as.vector(as.numeric(unlist(concentration.table[element.line]))))
Amplitude <- na.omit(as.vector(as.numeric(unlist(spectra.line.table[element.line]))))
compton.norm <- subset(data$Amplitude, !(data$Wavenumber < input$comptonmin | data$Wavenumber > input$comptonmax))
compton.file <- subset(data$Spectrum, !(data$Wavenumber < input$comptonmin | data$Wavenumber > input$comptonmax))
compton.frame <- data.frame(is.0(compton.norm, compton.file))
colnames(compton.frame) <- c("Compton", "Spectrum")
compton.frame.ag <- aggregate(list(compton.frame$Compton), by=list(compton.frame$Spectrum), FUN="sum")
colnames(compton.frame.ag) <- c("Spectrum", "Compton")
predict.frame.comp <- data.frame(concentration, Amplitude/compton.frame.ag$Compton)
colnames(predict.frame.comp) <- c("Concentration", "Amplitude")
predict.amplitude.comp <- data.frame(predict.frame.comp$Amplitude)
colnames(predict.amplitude.comp) <- c("Amplitude")
cal.lm.poly.comp <- lm(predict.frame.comp$Concentration~poly(predict.frame.comp$Amplitude, 2))
cal.lm.poly.comp
}
lucas_comp_ftir <- function(data, concentration.table, spectra.line.table, element.line, slope.element.lines, intercept.element.lines) {
concentration <- na.omit(as.vector(as.numeric(unlist(concentration.table[element.line]))))
Amplitude <- na.omit(as.vector(as.numeric(unlist(spectra.line.table[element.line]))))
compton.norm <- subset(data$Amplitude, !(data$Wavenumber < input$comptonmin | data$Wavenumber > input$comptonmax))
compton.file <- subset(data$Spectrum, !(data$Wavenumber < input$comptonmin | data$Wavenumber > input$comptonmax))
compton.frame <- data.frame(is.0(compton.norm, compton.file))
colnames(compton.frame) <- c("Compton", "Spectrum")
compton.frame.ag <- aggregate(list(compton.frame$Compton), by=list(compton.frame$Spectrum), FUN="sum")
colnames(compton.frame.ag) <- c("Spectrum", "Compton")
lucas.intercept.table.comp <- data.frame(rowSums(lucas.intercept.table.x[intercept.element.lines]))/compton.frame.ag$Compton
colnames(lucas.intercept.table.comp) <- c("first")
lucas.intercept.comp <- lucas.intercept.table.comp$first
lucas.slope.comp <- data.frame(lucas.slope.table[slope.element.lines])/compton.frame.ag$Compton
predict.frame.luc.comp <- data.frame(concentration, ((1+Amplitude/compton.frame.ag$Compton)/(Amplitude/compton.frame.ag$Compton+lucas.intercept.comp)-lucas.intercept.comp/(Amplitude/compton.frame.ag$Compton+lucas.intercept.comp)),lucas.slope.comp)
colnames(predict.frame.luc.comp) <- c("Concentration", "Amplitude", names(lucas.slope.comp))
predict.amplitude.luc.comp <- data.frame(predict.frame.luc.comp$Amplitude, lucas.slope.comp)
colnames(predict.amplitude.luc.comp) <- c("Amplitude", names(lucas.slope.comp))
lucas.lm.comp <- lm(Concentration~., data=predict.frame.luc.comp)
lucas.lm.comp
}
###############
###Prep Data###
###############
###############
###Full Spectra##
###############
spectra_frame_ftir <- function(spectra){
data <- reshape2::dcast(spectra, Spectrum~Wavenumber)
#test <- apply(test, 2, as.numeric)
colnames(data) <- make.names(colnames(data))
data[complete.cases(data),]
}
spectra_table_ftir <- function(spectra, concentration){
data <- reshape2::dcast(spectra, Spectrum~Wavenumber)
data$Concentration <- concentration
#test <- apply(test, 2, as.numeric)
colnames(data) <- make.names(colnames(data))
data[complete.cases(data),]
}
spectra_simp_prep_ftir <- function(spectra, compression=0){
spectra$Wavenumber <- if(compression=="100 cm"){ round(spectra$Wavenumber/0.1)*0.1
} else if(compression=="50 cm"){