1 files changed, 0 insertions, 95 deletions
diff --git a/R/chemCal.R b/R/chemCal.R
deleted file mode 100644
index fab7db4..0000000
--- a/R/chemCal.R
+++ /dev/null
@@ -1,95 +0,0 @@
-calm <- function(data)
-{
-    y <- data[[2]]
-    x <- data[[1]]
-    m <- lm(y ~ x)
-    s <- summary(m)
-    if (s$coefficients[1,4] > 0.05) 
-    {
-        m <- lm(y ~ x - 1)
-        s <- summary(m)
-        m$intercept <- FALSE
-    } else {
-        m$intercept <- TRUE
-    }
-    class(m) <- "calm"
-    m$yname <- names(data)[[1]]
-    m$xname <- names(data)[[2]]
-    return(m)    
-}
-predict.calm <- predict.lm
-print.calm <- print.lm
-summary.calm <- summary.lm
-plot.calm <- function(x,...,
-    xunit="",yunit="",measurand="",
-    level=0.95)
-{
-    m <- x
-    x <- m$model$x
-    y <- m$model$y
-    newdata <- data.frame(x = seq(0,max(x),length=250))
-    pred.lim <- predict(m, newdata, interval = "prediction",level=level)
-    conf.lim <- predict(m, newdata, interval = "confidence",level=level)
-    if (xunit!="") {
-        xlab <- paste("Concentration in ",xunit)
-    } else xlab <- m$xname
-    if (yunit=="") yunit <- m$yname
-    if (measurand!="") {
-        main <- paste("Calibration for",measurand)
-    } else main <- "Calibration"
-    plot(1,
-        xlab = xlab,
-        ylab = yunit,
-        type = "n", 
-        main = main,
-        xlim = c(0,max(x)), 
-        ylim = range(pred.lim)
-    )
-    points(x,y, pch = 21, bg = "yellow")
-    matlines(newdata$x, pred.lim, lty = c(1, 4, 4), 
-        col = c("black", "red", "red"))
-    matlines(newdata$x, conf.lim, lty = c(1, 3, 3), 
-        col = c("black", "green4", "green4"))
-
-    legend(min(x), 
-        max(pred.lim, na.rm = TRUE), 
-        legend = c("Fitted Line", "Confidence Bands", 
-            "Prediction Bands"), 
-        lty = c(1, 3, 4), 
-        lwd = 2, 
-        col = c("black", "green4", "red"), 
-        horiz = FALSE, cex = 0.9, bg = "gray95")
-}
-predictx <- function(m,yobs,level=0.95)
-{
-    s <- summary(m)    
-    xi <- m$model$x
-    yi <- m$model$y
-    n <- length(yi)
-    p <- length(yobs)
-    if (p > 1) {
-        varyobs <- var(yobs)
-    } else {
-        varyobs <- 0
-    }
-    if (!m$intercept) {
-        b1 <- summary(m)$coef["x","Estimate"]
-        varb1 <- summary(m)$coef["x","Std. Error"]^2
-        xpred <- mean(yobs)/b1
-        varxpred <- (varyobs + xpred^2 * varb1) / b1^2
-        sdxpred <- sqrt(varxpred) 
-    } else 
-    {
-        b0 <- summary(m)$coef["(Intercept)","Estimate"]
-        b1 <- summary(m)$coef["x","Estimate"]
-        S <- summary(m)$sigma
-        xpred <- (mean(yobs) - b0)/b1
-        sumxxbar <- sum((xi - mean(xi))^2) 
-        yybar <- (mean(yobs) - mean(yi))^2
-        sdxpred <- (S/b1) * (1/p + 1/n + yybar/(b1^2 * sumxxbar))^0.5
-    }
-    t <- qt((1 + level)/2,n - 2)
-    confxpred <- t * sdxpred
-
-    result <- c(estimate=xpred,sdxpred=sdxpred,confxpred=confxpred)
-}