Package 'NADA2'

Description

Plots the permutation histogram and test statistic produced by an anosim (nonparametric multivariate) test of differences between groups.

Usage

anosimPlot(
  ano.out,
  hcol = "light blue",
  title = "Histogram of anosim permutations"
)

Arguments

Value

Plots a histogram of the permutation test statistics representing the null hypothesis along with the observed test statistic from the data. The p-value is the proportion of test statistics equal to or more extreme than the observed test statistic.

References

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Oksanen, J., Guillaume, F., 2018. Vegan: ecological diversity. CRAN R-Project. https://cran.r-project.org/package=vegan

See Also

vegan::anosim

Examples

data(PbHeron)

# ROS model for each group
PbHeron.high <- with(subset(PbHeron,DosageGroup=="High"),NADA::ros(Blood,BloodCen))
PbHeron.high <- data.frame(PbHeron.high)
PbHeron.high$DosageGroup <- "High"

PbHeron.low <- with(subset(PbHeron,DosageGroup=="Low"),NADA::ros(Blood,BloodCen))
PbHeron.low <- data.frame(PbHeron.low)
PbHeron.low$DosageGroup <- "Low"

PbHeron.ros=rbind(PbHeron.high,PbHeron.low)

# ANOSIM analysis
library(vegan)
PbHeron.anosim <- with(PbHeron.ros,anosim(modeled,DosageGroup))
summary(PbHeron.anosim)

# Plot
anosimPlot(PbHeron.anosim)

Description

From the NADA R-Package.

Atrazine concentrations in a series of Nebraska wells before (June) and after (September) the growing season.

Objective is to determine if concentrations increase from June to September. There is one detection limit, at 0.01 ug/L. Used in Chapters 4, 5, and 9 of the Helsel (2011) book.

Usage

data(atrazine)

Format

An object of class data.frame with 24 rows and 4 columns.

Source

Junk et al., 1980. Areal, vertical, and temporal differences in ground water chemistry: II. Journal of Environmental Quality. 9(3) 479 - 483.

References

Helsel, D.R., 2011. 2011. Statistics for Censored Environmental Data Using Minitab and R. 2nd ed. John Wiley and Sons, USA, N.J.

Description

Computes Kendall's tau and the Akritas-Theil-Sen (ATS) line for censored data, along with the test that the slope (and Kendall's tau) equal zero. For one x variable regression.

Usage

ATS(
  y.var,
  y.cen,
  x.var,
  x.cen = rep(0, times = length(x.var)),
  LOG = TRUE,
  retrans = FALSE,
  xlabel = NULL,
  ylabel = NULL,
  printstat = TRUE,
  drawplot = TRUE
)

Arguments

Value

Coefficients (intercept and slope) for the ATS line are printed, along with Kendall's tau correlation coefficient, test statistic S, and the (single) p-value for the test that tau and slope both equal zero. A scatterplot with the fitted trend-line superimposed is also drawn.

References

Akritas, M.G., Murphy, S.A., LaValley, M.P., 1995. The Theil-Sen Estimator With Doubly Censored Data and Applications to Astronomy. Journal of the American Statistical Association 90, 170–177. https://doi.org/10.2307/2291140

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Examples

# Both y and x are censored
data(PbHeron)
with(PbHeron, ATS(Blood, BloodCen, Kidney, KidneyCen))

# x is not censored
data(Brumbaugh)
with(Brumbaugh,ATS(Hg, HgCen, PctWetland))

Description

Computes Kendall's tau and the Akritas-Theil-Sen (ATS) line for censored data. Is called by censeaken because it is much faster than the ATS function. It is not expected to be of much use to users on its own. The x variable (time) may not be censored.

Usage

ATSmini(y.var, y.cen, x.var)

Arguments

Value

Returns the intercept and slope (ATS line), tau (Kendall's tau), p-value and S-value (test statistic).

References

Akritas, M.G., Murphy, S.A., LaValley, M.P., 1995. The Theil-Sen Estimator With Doubly Censored Data and Applications to Astronomy. Journal of the American Statistical Association 90, 170–177. https://doi.org/10.2307/2291140

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

See Also

NADA::cenken

Examples

# x may not be censored.  Use the ATS function when x is censored.
data(Brumbaugh)

with(Brumbaugh, ATSmini(Hg, HgCen, SedLOI))

Description

Computes (2^k-1) censored regression models and their AIC statistics. Prints out the lowest AIC models and the terms used.

Usage

bestaic(y.var, cen.var, x.vars, LOG = TRUE, n.models = 10)

Arguments

Details

x.vars: If 1 x variable only, enter its name. If multiple x variables, enter the name of a data frame of columns of the x variables. No extra columns unused in the regression allowed. Create this by x.frame <- data.frame (Temp, Flow, Time) for 3 variables (temperature, flow and time).

AIC of each model is printed from lowest to highest AIC to help evaluate the ‘best’ regression model. n.models determines how many lines of model info is printed.

LOG: The default is that the Y variable will be log transformed (LOG = TRUE).

Value

Prints number of x.vars, lists x.vars and AIC values.

References

Helsel, D.R., 2011. Statistics for censored environmental data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

See Also

survival::survreg

Examples

data(Brumbaugh)

# Multiple regression
bestaic(Brumbaugh$Hg, Brumbaugh$HgCen, Brumbaugh[, c("SedMeHg","PctWetland", "SedAVS")])

Description

Performs clustering of a matrix of 0s and 1s, ie. the censoring indicator columns for multiple variables. If there are multiple detection limits within a column first convert the 0/1 designated to be Below vs Above the highest detection limit in the column. Detection limits may differ among columns.

Usage

binaryClust(dat.frame, method = "ward.D2", group = NULL, ncluster = NULL)

Arguments

Value

Prints a cluster dendrogram based on clustering method

References

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Examples

data(PbHeron)

# without group specified
binaryClust(PbHeron[,4:15])

# With Group argument
binaryClust(PbHeron[,4:15],group=PbHeron$DosageGroup)

Description

Computes a simple matching dissimilarity coefficient

Usage

binaryDiss(dat.frame)

Arguments

Value

Returns a binary dissimilarity matrix.

References

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

See Also

vegan::designdist

Examples

data(PbHeron)

binaryDiss(PbHeron$LiverCen)

Description

Plots an NMDS of a matrix of 0s and 1s, the censoring indicator columns for multiple variables, to discern the pattern of data below vs. above the detection limit. With multiple detection limits within a column, re-censoring to the highest limit in the column must be done prior to running this function. May have different censoring levels in different columns.

Usage

binaryMDS(dat.frame, group = NULL, title = NULL, legend.pos = "bottomleft")

Arguments

Details

Binary data may not provide sufficient information to discern differences in location on the plot if sample size is small. Prior to running this analysis it is suggested to consult best analysis practice when performing NMDS. As a rule of thumb, an NMDS ordination with a stress value around or above 0.2 is deemed suspect and a stress value approaching 0.3 indicates that the ordination is arbitrary. Stress values equal to or below 0.1 are considered fair, while values equal to or below 0.05 indicate good fit.

Value

Plots an NMDS of censored data represented as the binary Above vs Below a detection limit for each parameter.

References

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

See Also

vegan::metaMDS

Examples

data(PbHeron)

# without group specified
binaryMDS(PbHeron[,4:15])

# With Group argument
binaryMDS(PbHeron[,4:15],group=PbHeron$DosageGroup)

Description

Computes a simple matching similarity coefficient

Usage

binarySim(dat.frame)

Arguments

Value

Returns a binary similarity matrix.

References

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

See Also

vegan::designdist

Examples

data(PbHeron)

binarySim(PbHeron$LiverCen)

Description

From the NADA R-Package.

Mercury concentrations in fish across the United States.

Objective is to determine if mercury concentrations differ by watershed land use. Can concentrations be related to water and sediment characteristics of the streams?

There are three detection limits, at 0.03, 0.05, and 0.10 ug/g wet weight. Used in Chapters 10, 11 and 12 of the Helsel (2011) book.

Usage

data(Brumbaugh)

Format

An object of class data.frame with 133 rows and 14 columns.

Source

Brumbaugh et al., 2001, USGS Biological Science Report BSR-2001-0009.

References

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Description

Draws boxplots for left-censored data with one ore more detection limit(s). Portions below the maximum detection limit(s) are not shown by default, as their percentiles are not known.

Usage

cboxplot(
  x1,
  x2,
  xgroup = NULL,
  LOG = FALSE,
  show = FALSE,
  ordr = NULL,
  Ylab = yname,
  Xlab = gname,
  Title = NULL,
  dl.loc = "topright",
  dl.col = "red",
  dl.lwd = 2,
  dl.lty = "longdash",
  bxcol = "white",
  Ymax = NULL,
  minmax = FALSE,
  printstat = FALSE,
  Hlines = NULL
)

Arguments

Details

If maximum detection limits vary among groups, separate maxDL lines will be drawn for each group's boxplot. If one group has fewer than 3 detected observations its boxplot will not be drawn. Its detection limits will not count when computing the maximum limit. However, if only one boxplot is drawn for the entire dataset by not specifying a group variable, the detection limits from the portion that is the mostly ND group will be used when computing the maximum limit.

The reuired input to draw additional horizontal lines (Hlines option) is a data frame with 4 columns of input, one row per horizontal line. More than one line may be drawn. Column one is the Y axis value for the line. Column 2 is the line color, column 3 is the line type (lty) and column 4 is the text to be added just above the line. To add one line at a value of 40, for example, use Hlines = yline, after defining yline = data.frame(c(40, "purple", "dotted", "New Health Std")). To draw two lines, define yline as yline = data.frame(matrix(c(40, "purple", "dotted", "New Health Std", 70, "blue", "longdash", "Old Health Std"), ncol = 4, byrow=TRUE))) . If no text is wanted use " " for the column 4 entry for that line. See ?par under lty for standard line types.

Value

Prints a boxplot with detection limit identified and a concatenated list of the maximum detection limit for each group.

References

Helsel, D.R., 2011. Statistics for censored environmental data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Examples

data(PbHeron)
cboxplot(PbHeron$Liver,PbHeron$LiverCen,PbHeron$Group)

Description

Plots ecdfs of one or more groups of censored data. Illustrates the differences between groups for group tests such as those done using cen1way or cenanova.

Usage

cen_ecdf(
  x.var,
  cens.var,
  xgroup = NULL,
  xlim = c(0, max(y.var)),
  Ylab = varname
)

Arguments

Value

Plots an empirical cumulative distribution function. If group=NULL the ECDF of the entire dataset is produced. If group is identified then ECDFs are plotted for each group seperately and a legend added.

References

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J. Millard, S.P., 2013. EnvStats: An R Package for Environmental Statistics. Springer-Verlag, New York.

Examples

data(PbHeron)

# with groups
with(PbHeron, cen_ecdf(Liver, LiverCen, DosageGroup))

# all data
with(PbHeron, cen_ecdf(Liver, LiverCen))

Description

Performs a parametric test of whether the mean difference between two columns of paired censored data equals 0. Assumes that the paired differences follow a gaussian (normal) distribution.

Usage

cen_paired(xd, xc, yd, yc, alternative = "two.sided", printstat = TRUE)

Arguments

Details

You may also test for whether the mean of the xd data exceeds a standard by entering the single number for the standard as yd. In that case no yc is required.

Value

A list of statistics containing the following components:

• n Number of observations

• Z The value of the test statistic

• p.value the p-value of the test

• ⁠Mean difference⁠ the mean difference between xd and yd

References

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

See Also

survival::survreg

Examples

data(atrazine)

cen_paired(atrazine$June,atrazine$JuneCen,atrazine$Sept,atrazine$SeptCen)

# Comparing standard/guieline value
cen_paired(atrazine$June, atrazine$JuneCen, 0.01, alternative = "greater")

Description

Performs a nonparametric Wilcoxon signed-rank test of whether the median difference between two columns of paired censored data equals 0. Uses the Pratt adjustment for pairs of equal or indistinguishable values.

Usage

cen_signedranktest(xd, xc, yd, yc, alternative = "two.sided", printstat = TRUE)

Arguments

Value

Prints a list of Wilcoxon Signed-Rank test with Pratt correction for ties statistics containing the following components:

• n Number of samples

• Z The value of the test statistic

• p.value the p-value of the test

References

Helsel, D.R., 2011. Statistics for censored environmental data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Page, E.B., 1963. Ordered Hypotheses for Multiple Treatments: A Significance Test for Linear Ranks. Journal of the American Statistical Association 58, 216–230. doi:10.2307/2282965

Pratt, J.W., 1959. Remarks on Zeros and Ties in the Wilcoxon Signed Rank Procedures. Journal of the American Statistical Association 54, 655–667. doi:10.2307/2282543

Examples

data(atrazine)

cen_signedranktest(atrazine$June,atrazine$JuneCen,atrazine$Sept,atrazine$SeptCen)

Description

Performs a nonparametric sign test of whether the median difference between two columns of paired censored data equals 0. Uses the Fong adjustment for pairs of equal values.

Usage

cen_signtest(xd, xc, yd, yc, alternative = "two.sided", printstat = TRUE)

Arguments

Value

Returns the number of xd and yd values greater than, less than and tied. Corrected and uncorrected p-value for ties also displayed.

References

Fong, D.Y.T., Kwan, C.W., Lam, K.F., Lam, K.S.L., 2003. Use of the Sign Test for the Median in the Presence of Ties. The American Statistician 57, 237–240.

Helsel, D.R., 2011. Statistics for censored environmental data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J. #'

Examples

data(atrazine)

cen_signtest(atrazine$June,atrazine$JuneCen,atrazine$Sept,atrazine$SeptCen)

Description

Performs a Peto-Peto nonparametric test of differences in cdfs between groups. If more than two groups, the test is followed by a nonparametric multiple comparison test. Uses the BH method of adjusting p-values.

Usage

cen1way(x1, x2, group, mcomp.method = "BH", printstat = TRUE)

Arguments

Value

A list of summary statistics for each group evaluated containing the following components:

• N Number of samples

• PctND Percentage of non-detects

• KMmean Kaplan-Meier estimate of the mean

• KMsd Kaplan-Meier estimate of standard deviation

• KMmedian Kaplan-Meier estmate of the median

Peto-Peto test results including Chi-Squared value, degrees of freedom and p-value of the test.

If more than two groups, p-values of the pairwise multiple comparisons, adjusted using the BH false-discovery rate, are reported.

References

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Peto, R., Peto, J., 1972. Asymptotically Efficient Rank Invariant Test Procedures. Journal of the Royal Statistical Society. Series A (General) 135, 185. doi:10.2307/2344317

Benjamini, Y., Hochberg, Y., 1995. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological), 57, 289-300.

Examples

data(PbHeron)

# Two Groups
cen1way(PbHeron$Liver,PbHeron$LiverCen,PbHeron$DosageGroup)

# More than two groups
cen1way(PbHeron$Liver,PbHeron$LiverCen,PbHeron$Group)

Description

Performs a parametric test of differences in means between two groups of censored data, either in original or in log units (the latter becomes a test for difference in geometric means).

Usage

cen2means(x1, x2, group, LOG = TRUE, printstat = TRUE)

Arguments

Details

Because this is an MLE procedure, when a normal distribution model is used (LOG=FALSE) values may be modeled as below zero. When this happens the means may be too low and the p-values may be unreal (often lower than they should be). Because of this, testing in log units is preferable and is the default.

Value

Q-Q Plot with Shapiro-Francia test for normality W and p-values. Returns the Maximum Likelihood Estimation (MLE) test results including Chi-Squared value, degrees of freedom and p-value of the test.

References

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Shapiro, S.S., Francia, R.S., 1972. An approximate analysis of variance test for normality. Journal of the American Statistical Association 67, 215–216.

Examples

data(PbHeron)
cen2means(PbHeron$Liver,PbHeron$LiverCen,PbHeron$DosageGroup)

Description

Computes tests for each of the two factors and optionally for their interaction using likelihood ratio tests. p-values will not be identical to the usual method of moments ANOVA tests but will be similar.

Usage

cen2way(y1, y2, fac1, fac2, LOG = TRUE, interact = TRUE)

Arguments

Details

Tests are computed using Maximum Likelihood Estimation. When a gaussian distribution model is used (LOG=FALSE) modeled values may fall below zero, producing unreal p-values (often lower than they should be). Because of this, testing in log units is preferable and is the default unless you are transforming the y values prior to running the function (such as taking cube roots to approximate a gamma distribution). The 'delta.lr0x2' stat output is the -2loglikehood for the test of the model versus an intercept-only model.

Value

Q-Q plots of residuals. Likelihood ratio test statistics ("chisquare"), degrees of freedom ("df") and p-values (pval) for two factors and optionally the interaction. Data on the underlying models, including AIC and R2 are also provided.

References

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J. Millard, S.P., 2013. EnvStats: An R Package for Environmental Statistics. Springer-Verlag, New York.

See Also

survival::survreg

Examples

data(Gales_Creek)
Gales_Creek$Period <- c(rep("early", 35), rep("middle", 12), rep("late", 16))
with(Gales_Creek,cen2way(TCr, CrND, Season, Period))

Description

Performs a parametric test of differences in means between groups of censored data, followed by a parametric Tukey's multiple comparison test.

Usage

cenanova(x1, x2, group, LOG = TRUE, printstat = TRUE)

Arguments

Details

Test is computed using Maximum Likelihood Estimation. When a gaussian distribution model is used (LOG=FALSE) modeled values may fall below zero, producing unreal p-values (often lower than they should be). Because of this, testing in log units is preferable and is the default.

Value

Returns the Maximum Likelihood Estimation (MLE) comparison results including Chi-Squared value, degrees of freedom and p-value of the test. Test assumes log-normal(LOG=TRUE) or normal(LOG=FALSE) distribution of residuals from group means.

Tukey's multiple comparison p-values of pairwise differences in group means are also printed.

• Group Names of groups (NOTE: ⁠== 0⁠ indicates null hypothesis of "equals zero").

• Estimate Estimated difference between group means.

• ⁠Std. Error⁠ Standard error of estimate.

• ⁠z value⁠ Test statistic.

• ⁠Pr(>|z|)⁠ P-values for test that difference in means equals zero.

References

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

#' @examples data(PbHeron) cenanova(PbHeron$Liver,PbHeron$LiverCen,PbHeron$DosageGroup)

cenanova(PbHeron$Liver,PbHeron$LiverCen,PbHeron$DosageGroup,LOG=FALSE)

See Also

survival::survreg

Description

Plots the empirical cdf and cdfs of three theoretical distributions, fit by maximum likelihood estimation (MLE).

Usage

cenCompareCdfs(x.var, cens.var, dist3 = "norm", Yname = yname)

Arguments

Value

prints a plot of the empirical CDFs with BIC value for each distribution.

References

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Delignette-Muller, M., Dutang, C., 2015. fitdistrplus : An R Package for Fitting Distributions. Journal of Statistical Software, 64, 1-34. http://www.jstatsoft.org/v64/i04/.

Examples

data(Brumbaugh)
cenCompareCdfs(Brumbaugh$Hg,Brumbaugh$HgCen)

# With Weibull distribution
cenCompareCdfs(Brumbaugh$Hg,Brumbaugh$HgCen,dist3="weibull")

# Using an distribution not supported by this function (yet)
# you will get an error message
## Not run: cenCompareCdfs(Brumbaugh$Hg,Brumbaugh$HgCen,dist3="beta")

# With Yname specified
cenCompareCdfs(Brumbaugh$Hg,Brumbaugh$HgCen,Yname="TCE Conc (ug/L)\nLong Island, NY USA")

Description

Produces three quantile-quantile (Q-Q) plots, also called probability plots, based on three distributions (normal, lognormal and gamma distributions).

Usage

cenCompareQQ(x.var, cens.var, Yname = yname, printrslt = TRUE, ...)

Arguments

Details

Produces three Q-Q plots and reports which one has the highest Shapiro-Francia test statistic (W). The distribution with the highest W is the best fit of the three.

Value

Plots three Q-Q plots based on normal, lognormal and gamma distributions and prints the best-fit distribution.

References

Helsel, D.R., 2011. Statistics for censored environmental data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Millard, S.P., 2013. EnvStats: An R Package for Environmental Statistics. Springer-Verlag, New York.

Shapiro, S.S., Francia, R.S., 1972. An approximate analysis of variance test for normality. Journal of the American Statistical Association 67, 215–216.

Examples

data(Brumbaugh)
cenCompareQQ(Brumbaugh$Hg,Brumbaugh$HgCen)

Description

Computes three parametric correlation coefficients for one X variable and the corresponding R squared for multiple X variables, and a regression equation for censored data.

Usage

cencorreg(
  y.var,
  cen.var,
  x.vars,
  LOG = TRUE,
  verbose = 2,
  pred.plot = FALSE,
  pred.col = "purple"
)

Arguments

Details

x.vars: If one x variable only, enter its name. If multiple x variables, enter the name of a data frame of columns of the x variables. Only columns used as X variables in the regression are allowed. Create this by x.frame <- data.frame (Temp, Flow, Time) for 3 variables (temperature, flow and time) used as the X variables in the regression. To produce a pred.plot plot of predicted values the first variable in the array must be a continuous (not a factor) variable.

AIC and BIC are printed to help evaluate the ‘best’ regression model. Lower values are better when comparing models with the same Y units and same data.Cannot be used to compare models with differing Y units (such as Y~X versus logY~X). Can be used to compare models with differing X units such as Y~X vs Y~logX.

The default Y units are that the Y variable will be log transformed. Change this with the LOG = option, setting LOG = FALSE.

verbose option. Default is 2 which provides full output in the console and qqplots in a graphics window. A value of 1 only provides partial results in the console and no plots. A value of 0 provides no output; the returning computations will be stored in the specified object.

The Y parameter in the model output (modelname$y) is -1 times the data that were input. This is due to the shift from left censored data to the required right censored data of the survreg function. The original data are not changed and are used to draw the pred.plot.

Value

When x.vars is one variable, likelihood, rescaled likelihood and McFaddens correlation coefficient (R) are printed. When x.vars is a data.frame of more than one variable, likelihood, rescaled likelihood and McFaddens coefficent of determination (R2) are printed.

Model coefficients (intercept and slopes), Chi-Squared statistic and p-value for the test that all slope coefficients equal zero (overall test), and model AIC and BIC are provided.

A Q-Q plot of model residuals with corresponding Shapiro-Francia W and p-value are plotted for evaluation of model distributional assumptions when verbose=2 (the default).

References

Helsel, D.R., 2011. Statistics for censored environmental data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Helsel, D.R., 2005. Nondetects and Data Analysis: Statistics for Censored Environmental Data, 1st ed. John Wiley and Sons, USA, N.J.

See Also

survival::survreg

Examples

data(Brumbaugh)

# One variable
cencorreg(Brumbaugh$Hg,Brumbaugh$HgCen,Brumbaugh$SedMeHg)

# One variable with pred.plot=T
cencorreg(Brumbaugh$Hg,Brumbaugh$HgCen,Brumbaugh$SedMeHg,pred.plot=TRUE)

# More than one variable for demostration purposes
cencorreg(Brumbaugh$Hg,Brumbaugh$HgCen,Brumbaugh[,c("SedMeHg","PctWetland")])

Description

Performs a permutation test of differences in means between two groups of censored data.

Usage

cenperm2(y1, y2, grp, R = 9999, alternative = "two.sided", printstat = TRUE)

Arguments

Details

Because this is a permutation test it avoids the problem with MLE tests (cen2means) that assume a normal distribution. No values are modeled as below zero and p-values are trustworthy. Ranges in means and p-values are due to interval-censoring of censored data means.

Value

Permutation test results with the number of permutations, range in group means and their difference, and range in p-value.

References

Good, P., 2000. Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses, 2nd ed, Springer Series in Statistics. Springer-Verlag, New York, NY. doi:10.1007/978-1-4757-3235-1

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Shapiro, S.S., Francia, R.S., 1972. An approximate analysis of variance test for normality. Journal of the American Statistical Association 67, 215–216.

Examples

data(PbHeron)
cenperm2(PbHeron$Liver,PbHeron$LiverCen,PbHeron$DosageGroup,alternative="t")

Description

Performs a permutation test of differences in means between groups of censored data.

Usage

cenpermanova(y1, y2, grp, R = 9999, printstat = TRUE)

Arguments

Details

Because this is a permutation test it avoids the problem with MLE tests (see cenanova) that assume a normal distribution. No values are modeled as below zero and group means and p-values are trustworthy.

Value

Permutation test results with the number of permutations, range in test statistics and p-value values through the various permutations. Group means are also listed.

References

Good, P., 2000. Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses, 2nd ed, Springer Series in Statistics. Springer-Verlag, New York, NY. doi:10.1007/978-1-4757-3235-1

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Examples

data(PbHeron)
cenpermanova(PbHeron$Liver,PbHeron$LiverCen,PbHeron$DosageGroup)

Description

Computes prediction intervals for censored data assuming lognormal, gamma and normal distributions.

Usage

cenPredInt(
  x.var,
  cens.var,
  pi.type = "two-sided",
  conf = 0.95,
  newobs = 1,
  method = "mle",
  printstat = TRUE
)

Arguments

Details

Computes prediction intervals for three distributions. This is a front-end to the individual functions from the EnvStats package. By default all three are computed using maximum likelihood estimation (mle). The gamma distribution for censored data uses the Wilson-Hilferty approximation (normal distribution on cube roots of data). Other methods are available in EnvStats, but few methods are available for all three distributions. For info on other methods, see help for elnormCensored and enormCensored commands in EnvStats.

Value

A table of prediction limits based on user provided confidence coefficient (conf) and prediction invterval type (pi.type)

References

Helsel, D.R., 2011. Statistics for censored environmental data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Millard, S.P., 2013. EnvStats: An R Package for Environmental Statistics. Springer-Verlag, New York.

Krishnamoorthy, K., Mathew, T., Mukherjee, S., 2008. Normal-Based Methods for a Gamma Distribution, Technometrics, 50, 69-78.

See Also

EnvStats::enormCensored

Examples

data(PbHeron)

# Default
cenPredInt(PbHeron$Liver,PbHeron$LiverCen)

# User defined confidence coefficient
cenPredInt(PbHeron$Liver,PbHeron$LiverCen, conf=0.5)

# User defined confidence coefficient outside of acceptable range
# the procedure will stop and give an error.
# cenPredInt(PbHeron$Liver,PbHeron$LiverCen, conf=1.1)

# User defined prediction interval type
cenPredInt(PbHeron$Liver,PbHeron$LiverCen,pi.type="lower")
cenPredInt(PbHeron$Liver,PbHeron$LiverCen,pi.type="upper")

Description

Plots a quantile-quantile (Q-Q) plot of censored data versus a fitted data distribution

Usage

cenQQ(x.var, cens.var, dist = "lnorm", Yname = yname)

Arguments

Value

A single Q-Q plot of data fitted by normal, lognormal or gamma distributions with Shapiro-Francia W value printed on plot.

References

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Shapiro, S.S., Francia, R.S., 1972. An approximate analysis of variance test for normality. Journal of the American Statistical Association 67, 215–216.

Examples

data(Brumbaugh)
cenQQ(Brumbaugh$Hg,Brumbaugh$HgCen)

# User defined distribution
cenQQ(Brumbaugh$Hg,Brumbaugh$HgCen,dist="gamma")

Description

Plots a quantile-quantile (Q-Q) plot of censored regression residuals for simple or multiple regression.

Usage

cenregQQ(y.var, cen.var, x.vars, LOG = TRUE, intcens = FALSE, main = NULL)

Arguments

Value

Q-Q Plot of model residuals and Shapiro-Francia test results.

References

Helsel, D.R., 2011. Statistics for censored environmental data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Millard, S.P., 2013. EnvStats: An R Package for Environmental Statistics. Springer-Verlag, New York.

Shapiro, S.S., Francia, R.S., 1972. An approximate analysis of variance test for normality. Journal of the American Statistical Association 67, 215–216.

Examples

data(Brumbaugh)

# One variable
cenregQQ(Brumbaugh$Hg,Brumbaugh$HgCen,Brumbaugh$PctWetland)

# More than one variable for demostration purposes
cenregQQ(Brumbaugh$Hg,Brumbaugh$HgCen,Brumbaugh[,c("PctWetland","SedLOI","Weight")])

Description

Seasonal Kendall permutation test on censored data

Usage

censeaken(
  time,
  y,
  y.cen,
  group,
  LOG = FALSE,
  R = 4999,
  nmin = 4,
  seaplots = FALSE,
  printstat = TRUE,
  ...
)

Arguments

Details

For each season the ATS function is used to compute the season's Kendall-S statistic and p-value for the trend test. The test is the usual ATS procedure, not a permutation test. For the overall test there are R (default 4999) random rearrangements of data that are generated with no mixing of data from one season to another season within a permutation – data over time within a season are randomized. This retains any between-seasons differences while removing any trend from year to year to use as the permuted "representation of the null hypothesis" of no trend in the R Seasonal Kendall tests. For a 2-sided trend test the p-value is the number of the absolute value of the permutation S statistics that are equal to or greater than the absolute value of the observed S from the original data, plus 1 (for the observed S of the original data), divided by the total (R+1) S values.

The censeaken function differs from other R packages that do not incorporate censored data in their trend tests (EnvStats, Kendall, rkt and others) in that censeaken uses all of the data across the years without an option to equalize each year's or season's influence by using the overall mean or median of the period's data rather than the original observations. Seasons with more data will have more influence on the outcome, just as years with more data will have more influence. If the numbers of observations differ enough between seasons or years that this is of concern, it is up to the user to perhaps eliminate some data in data-rich periods that are most unlike the conditions or times of data collected in sparse periods. Or to compute summary statistics such as the median of each season/year combination and run the test on the medians, though this will result in a loss of power to detect trends and will require the user to use methods such as those in NADA2 to compute medians when there are censored data.

If seaplots=TRUE each season's trend line will be plotted seperately. A plot of the overall Seasonal Kendall (Akritas-Theil-Sen) line is always plotted. If seaplots=FALSE only the overall Seasonal Kendall (Akritas-Theil-Sen) line will be plotted on a data scatterplot.

Value

Prints the Kendall trend test results for each season individually. The overall Seasonal Kendall test and Theil-Sen line results are both printed and returned.

References

Helsel, D.R., 2011. Statistics for censored environmental data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Hirsch, R.M., Slack, J.R., Smith, R.A., 1982. Techniques of Trend Analysis for Monthly Water Quality Data, Water Res. Reseach 18, 107-121.

See Also

NADA::cenken

Examples

data(Brumbaugh)

# Artificial time and season variables for demonstration purposes
Brumbaugh$time=1:nrow(Brumbaugh)
Brumbaugh$sea=as.factor(round(runif(nrow(Brumbaugh),1,4),0))

with(Brumbaugh,censeaken(time,Hg,HgCen,sea,seaplots = TRUE))

Description

Computes a one-sided upper tolerance interval for censored data assuming log-normal, gamma and normal distributions.

Usage

cenTolInt(
  x.var,
  cens.var,
  conf = 0.95,
  cover = 0.9,
  method.fit = "mle",
  printstat = TRUE
)

Arguments

Details

Computes upper one-sided tolerance intervals for three distributions. This is a front-end to the individual functions from the EnvStats package. By default all three are computed using maximum likelihood estimation (mle); robust ROS is available as an alternate method for all three distributions. The gamma distribution for censored data uses the Wilson-Hilferty approximation (normal distribution on cube roots of data). For more info on the relative merits of robust ROS versus mle, see Helsel (2011) and Millard (2013).

Value

Prints and returns the percentile (cover), upper tolerance limit (conf) and BIC of fit for lognormal, normal and approximated gamma distributions. Plots empirical and theoretical CDFs with BIC values in the legend.

References

Helsel, D.R., 2011. Statistics for censored environmental data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Millard, S.P., 2013. EnvStats: An R Package for Environmental Statistics. Springer-Verlag, New York.

Krishnamoorthy, K., Mathew, T., Mukherjee, S., 2008. Normal-Based Methods for a Gamma Distribution, Technometrics, 50, 69-78.

Examples

data(PbHeron)

# Default
cenTolInt(PbHeron$Liver,PbHeron$LiverCen)

# User defined conficence interval
cenTolInt(PbHeron$Liver,PbHeron$LiverCen,conf=0.75)

# User defined percentile
cenTolInt(PbHeron$Liver,PbHeron$LiverCen,cover=0.5)

# inputs outside acceptable ranges
# Will result in errors/warnings
# cenTolInt(PbHeron$Liver,PbHeron$LiverCen,cover=1.25)
# cenTolInt(PbHeron$Liver,PbHeron$LiverCen,conf=1.1)
# cenTolInt(PbHeron$Liver,PbHeron$LiverCen,method.fit="ROS")

Description

Computes the ATS (Mann-Kendall trend test for censored data) after adjustment of censored data for a covariate.

Usage

centrend(
  y.var,
  y.cens,
  x.var,
  time.var,
  link = "identity",
  Smooth = "cs",
  printstat = TRUE,
  stackplots = FALSE,
  drawplot = TRUE
)

Arguments

Details

Default link = identity. The y variables are then used in their original units. Other options are available see cenGAM::tobit1 for more options.

Default Smooth is "cs" for shrinkage cubic regression splines. See mgcv::smooth.terms for other types of smoothing algorithms. '"ts"' is a thin-plate regression spline and is also commonly used.

Value

Prints three plots: Y data vs time with GAM Smooth, Residuals from GAM Smooth vs time, and ATS trend line of residuals vs time.

Returns GAM residuals and ATS results on trend test of residuals (intercept, slope, Kendall's tau, p-value for trend)

References

Helsel, D.R., 2011. Statistics for censored environmental data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

See Also

mgcv::gam

Examples

data(Brumbaugh)

Brumbaugh$time=1:nrow(Brumbaugh)

with(Brumbaugh,centrend(Hg,HgCen,SedTotHg,time.var=time,drawplot=TRUE))

Description

Computes the Seasonal Kendall trend test for censored data after adjustment of censored data for a covariate. The adjustment is by subtracting off a censored GAM smooth, removing the effect of the covariate. Trend analysis is performed on the residuals from the GAM smooth.

Usage

centrendsea(
  y.var,
  y.cens,
  x.var,
  time.var,
  season,
  R = 4999,
  nmin = 4,
  link = "identity",
  Smooth = "cs",
  printstat = TRUE,
  seaplots = TRUE
)

Arguments

Details

Default link = identity. The y variables are then used in their original units. Other options are available see cenGAM::tobit1 for more options.

Default Smooth is "cs" for shrinkage cubic regression splines. See mgcv::smooth.terms for other types of smoothing algorithms. '"ts"' is a thin-plate regression spline and is also commonly used.

As with the censeaken function, observations are not edited when there are more data in some seasons than others. Seasons with more data will have more influence on the overall SK test than seasons with fewer data. To avoid this (as done with some Seasonal Kendall software) data in the seasons with more can be selectively deleted to better match the data frequency of the seasons with fewer data.

Value

Prints four plots: 1. Y data vs X covariate with GAM Smooth, 2. Residuals from GAM Smooth vs X covariate, 3. histogram of the SK test results illlustrating the p-value, and 4. Seasonal Kendall trend line of residuals vs time. Plots of data and SK trend line for each season are produced when the seaplots option is TRUE.

Returns the seasonal Kendall trend test results on residuals (intercept, slope, Kendall's tau, p-value for trend)

References

Helsel, D.R., 2011. Statistics for censored environmental data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

See Also

mgcv::gam

Examples

data(Gales_Creek)
with(Gales_Creek,centrendsea(TCr,CrND,discharge,dectime,Season))

Description

Computes the empirical cumulative distribution function (ECDF) for censored data. Estimates parameters of the distribution, including the mean and quantiles.

Usage

cfit(
  y,
  cens,
  conf = 0.95,
  qtls = c(0.1, 0.25, 0.5, 0.75, 0.9),
  q.type = 6,
  Cdf = TRUE,
  ecdf.col = 1,
  km.orig = TRUE,
  printstat = TRUE,
  Ylab = NULL
)

Arguments

Details

Moment statistics are estimated using the enparCensored function of the EnvStats package. This avoids a small bias in the mean produced by the NADA package's cenfit function, which uses the reverse Kaplan-Meier procedure, converting left-censored to right-censored data prior to computing the ecdf and mean. See Gillespie et al.(2010) for more discussion on the bias of the estimate of the mean.

Quantiles and their two-sided confidence limits are estimated using the quantile function of the survfit command. See ?quantiles or Helsel et al. (2020) for choosing the q.type; default q.type = 4 (Kaplan-Meier; prob = i/n) when km.orig = TRUE. This is standard procedure in the survival analysis discipline and in the survival package of R, and is also used by the cenfit function in the NADA package. While this is 'industry standard' in medical applications it is a poor choice for observed sample (rather than census) data because it means that the largest observation is assigned a probability equal to 1, the 100th percentile. This implies that this value is never expected to be exceeded when more sample data are collected. It also means the largest observation is not plotted on the ecdf in most software because it is "off the chart". For small datasets in particular it is unlikely that the current largest observation is the largest value in the population and so the resulting ecdf quantiles are likely not opotimal. The default q.type = 6 (Weibull; prob = i/(n+1)) when km.orig = FALSE, though that may be changed by the user. the largest observation plots at a probability less than 1 on the ecdf. Differences in results when differing q.types are used will decrease as sample size increases.

All printed values will also be output to an object if saved. Confidence intervals on the quantiles are also output when data include nondetects. Values are character because of the possibility of a ⁠<1⁠, but if no < symbol can be converted to numeric using the as.numeric(...) command. For data without censoring cfit will return numeric values. In that case it returns standard arithmetic mean, standard deviation and quantiles instead of K-M versions.

Value

If printstat=TRUE: Based on the provided conf value, Kaplan-Meier summary statistics (mean,sd,median), lower and upper confidence intervals around the mean and median value, sample size and percent of censored samples are returned. The specified quantile values are also printed and returned.

If Cdf=TRUE: The ecdf of censored data is plotted.

References

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Gillespie, B.W., et al., 2010. Estimating Population Distributions When Some Data Are Below a Limit of Detection by Using a Reverse Kaplan-Meier Estimator. Epidemiology 21, 564-570.

Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., and Gilroy, E.J., 2020, Statistical Methods in Water Resources: U.S. Geological Survey Techniques and Methods, book 4, chapter A3, 458 p., https://doi.org/10.3133/tm4a3.

Millard, S.P, 2013. EnvStats: An R Package for Environmental Statistics, 2nd ed. Springer Science+Business Media, USA, N.Y. DOI 10.1007/978-1-4614-8456-1© Springer Science+Business Media New York 2013”

Excerpt From: Steven P. Millard. “EnvStats.” Apple Books.

See Also

survival::survfit

Examples

data(Brumbaugh)

cfit(Brumbaugh$Hg,Brumbaugh$HgCen)

Description

Computes a Kendall rank correlation S-statistic for permutations of censored data. Collectively these represent the variation in S expected when the null hypothesis is true. Called by censeaken. computeS is not expected to be of much use to users on its own.

Usage

computeS(x, y, ycen, seas = NULL, R = R)

Arguments

Value

An Rx1 matrix containing an S-value for each of the R data permutations.

References

Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., Gilroy, E.J., 2020. Statistical Methods in Water Resources. U.S. Geological Survey Techniques and Methods, book 4, chapter A3, 458p., https://doi.org/10.3133/tm4a3.

See Also

Kendall::Kendall

Examples

data(Brumbaugh)

#Artifical time and season variables for demonstration purposes
Brumbaugh$time=1:nrow(Brumbaugh)
Brumbaugh$sea=as.factor(round(runif(nrow(Brumbaugh),1,4),0))


with(Brumbaugh,computeS(time,Hg,HgCen,sea,R=100))

Description

Computes the equivalent sample size of censored data. Observations at lower detection limits have a greater percent of the equivalent information of a detected value than observations at higher detection limits.

Usage

equivalent_n(y.var, y.cen, printstat = TRUE)

Arguments

Details

Based on "Method 2" of Dr. Brenda Gillespie's talk at ASA National Meeting 2019. This method differs from hers in how the percentile probabilities for the detection limits are computed. Probabilities here are computed using Regression on Order Statistics (ROS).

Computes the equivalent n, the number of observations including censored values, as a measure of information content for data with nondetects.

Value

Prints summary statistics including

• n sample size

• n.cen number of censored data

• pct.cen percent of data censored

• min minimum reported value

• max maximum reported value

Summary of censored data including

• limit detection limit

• n number of censored values per limit

• uncen number of detected values at or above the limit

• pexceed proportion of data that exceeds the limit

Summary of the equivalent sample size for detected and censored values.

• n.equiv the equivalent number of observations

• n.cen.equiv equivalent number of detected obs in the censored data

• n.detected number of uncensored values

References

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Gillespie, B.W., Dominguez, A., Li, Y., 2019. Quantifying the information in values below the detection limit (left-censored data). Presented at the 2019 Joint Statistical Meetings of the Amer. Stat. Assoc., Denver, CO., July 31, 2019.

See Also

NADA::censummary

Examples

data(Brumbaugh)

equivalent_n(Brumbaugh$Hg,Brumbaugh$HgCen)

Description

Arsenic concentrations (possibly artificial) in groundwater.

Objective – To test whether the mean concentration can be show to have exceeded the 10 microgram per liter health standard.

Usage

data(Example1)

Format

An object of class data.frame with 21 rows and 3 columns.

Source

unknown

References

unknown

Description

Methyl Isobutyl Ketone (MIBK) was measured in air above a medium-sized US city. Data were reported only as "ND" or as a detected concentration (no information on reporting limits).

Objective – To compute an upper confidence limit on the mean without having the value(s) of the reporting limit(s).

Usage

data(Example2)

Format

An object of class data.frame with 31 rows and 5 columns.

Source

unknown

References

unknown

Description

Arsenic concentrations in ground water from a private supply well. All 14 observations are below one of several reporting limits (1005 non-detects).

Objective – To show what type of comparison to a health standard can be made using a dataset without detected observations.

Usage

data(Example3)

Format

An object of class data.frame with 14 rows and 4 columns.

Source

unknown

References

unknown

Description

Total recoverable chromium concentrations in streamflows of Gales Creek in Oregon, USA.

Objective is to relate chromium concentrations including censored values to mean daily flows over time and by season (wet versus dry seasons). Two detection limits decreasing over time.

Usage

data(Gales_Creek)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 63 rows and 11 columns.

Source

US Geological Survey. https://waterdata.usgs.gov/monitoring-location/453026123063401/

References

Publicly available data.

Description

Function used by other functions to plot the Akritas-Theil-Sen (ATS) line for censored data. Only one x variable allowed. Both Y and X variables may be censored.

Usage

kenplot(
  y1,
  ycen,
  x1,
  xcen,
  atsline = FALSE,
  xnam = NULL,
  ynam = NULL,
  Title = "Akritas - Theil - Sen line",
  ylim = NULL,
  xlim = NULL,
  pch = NULL,
  cex = NULL,
  xaxs = "r",
  yaxs = "r",
  ...
)

Arguments

Value

Scatterplot of data plus ATS line. Censored values are drawn for both X and Y variables as dashed lines up to the detection limits.

References

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

See Also

NADA::cenken

Examples

# Both y and x are censored
data(PbHeron)
with(PbHeron, kenplot(Blood, BloodCen, Kidney, KidneyCen))

# x is not censored
data(Brumbaugh)
with(Brumbaugh, kenplot(Hg, HgCen, PctWetland,rep(0, times=length(PctWetland))))

Description

Three MST markers of human-origin contamination, and three of animal contamination, were evaluated along with a general fecal indicator to determine if the pattern of their levels differed among five coastal sites off the coast of Florida. Data are interval-censored.

Objective – Test whether the pattern of six microbial source tracking (MST) markers is related to the Entero1A indicator of general fecal pollution. Also determine if the pattern of markers plus Entero1A indicator differs among five coastal water sites using ANOSIM.

Usage

data(Markers)

Format

An object of class data.frame with 30 rows and 15 columns.

Source

Symonds et al. (2016) Journ Applied Microbio 121, p. 1469-1481.

References

Symonds et al. (2016) Journ Applied Microbio 121, p. 1469-1481.

Description

Computes the within-column ranks of data having one or more detection limits. If multiple limits are present in a column, data are first re-censored at the highest detection limit.

Usage

ordranks(dat.frame, paired = TRUE)

Arguments

Value

Returns columns of ranks of censored data in the same order as the paired columns of input data. For 3 chemical parameters, the data frame returned will be R1 R2 R3 where R represents the ranks of the C1 C2 C3 input data accounting for the censoring indicated by columns I1 I2 I3.

References

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Examples

library(NADA) #For example data
data(PbHeron)

ordranks(PbHeron[,4:15])

Description

Draws a partial plot for each X variable in regression of a censored Y variable against multiple X variables.

Usage

partplots(
  y.var,
  cen.var,
  x.vars,
  LOG = TRUE,
  smooth.method = "gam",
  gam.method = "tp",
  multiplot = TRUE,
  printstat = TRUE
)

Arguments

Details

Partial plots for uncensored data often are drawn with superimposed smooths. At times looking only at the data values without a smooth can better enable the human eye to determine whether the overall pattern is linear or not. If this is the best method for you, use the smooth.method = "none" option to not draw a smooth. The most common smooth used for uncensored data is loess, which does not recognize censored data and so uses the detection limit (DL) value itself. This results in biased-high smooths that incorrectly treat values at the DLs equal to uncensored (detected) data. The partplots function in NADA2 was written to provide a better alternative, smoothing the partial residual pattern with a censored generalized additive model (gam). The censored gam recognizes the nondetects as left-censored data with a maximum at the DL when computing the smooth. DLs may vary with each observation – multiple DLs in a dataset are not a problem in routines of the NADA2 package.

'y.var': The default is that the Y variable will be log transformed.

x.vars: Enter the name of a data frame of columns of the x variables. No extra columns unused in the regression allowed. Create this by x.frame <- data.frame (Temp, Flow, Time) for 3 variables (temperature, flow and time).

Gray open circles represent censored data and are the residual between the detection limit and the predicted value from the censored regression. The GAM recognizes that the detection limit is an upper limit, predicted values on the regression line are most often below the detection limit, leading to positive residuals. Note that the true residual for censored data could be anywhere below the plotted value. That fact is recognized by the censored GAM but is difficult to represent on a plot.

AIC for regression models with un-transformed X, log and cube-root transforms of X are printed to evaluate which of the three transformations results in the ‘best’ model.

Value

When x.vars is one variable, a message is printed that partial plots are not possible with only one X variable and execution stops. When x.vars is a data frame of more than one variable, partial plots are drawn for each X variable and text is printed comparing AICs for regression using the untransformed X variable with log and cube-root transforms of the X variable, as a supplement to evaluating linearity on the partial plots alone.

References

Helsel, D.R., 2011. Statistics for censored environmental data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Cook, R.D., 1993. Exploring Partial Residual Plots, Technometrics 35, 351-362.

See Also

survival::survreg

Examples

data(Brumbaugh)

# For demostration purposes
partplots (Brumbaugh$Hg,Brumbaugh$HgCen,Brumbaugh[,c("SedMeHg","PctWetland")])

Description

From the NADA R-Package.

Lead concentrations in the blood and several organs of herons in Virginia.

Objective is to determine the relationships between lead concentrations in the blood and various organs. Do concentrations reflect environmental lead concentrations, as represented by dosing groups? There is one detection limit, at 0.02 ug/g. Used in Chapters 10 and 11 of the Helsel (2011).

Usage

data(PbHeron)

Format

An object of class data.frame with 27 rows and 15 columns.

Source

Golden et al., 2003, Environmental Toxicology and Chemistry 22, pp. 1517-1524.

References

Helsel, D.R., 2011. Statistics for censored environmental data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Description

Thiamethoxam concentrations in pollen from the Ontario Pollen Monitoring Network study https://data.ontario.ca/en/dataset/pollen-monitoring-network-study

Variables are:

Thiamethoxam:

Thiamethoxam concentration in Concentrations in microgram per gram.

ThiaCens:

Censoring indicator. 1 denotes that the value in column 1 is a reporting limit not a specific concentration.

SamplingEvent:

A grouping variable from the sample design. A concentration is from 1 of 4 events in time.

ThiaAbvBelow:

A binary variable denoting whether the Thiamethoxam concentration is above or below 0.05 ug/g.

Usage

data(Pollen_Thia)

Format

An object of class data.frame with 204 rows and 4 columns.

Source

Ontario Ministry of Agriculture, Food and Rural Affairs (Pollen Monitoring Network)

Description

Performs a nonparametric Paired Prentice-Wilcoxon test of whether the median difference between two columns of paired censored data equals 0 (O'Brien and Fleming, 1987)

Usage

ppw.test(xd, xc, yd, yc, alternative = "two.sided", printstat = TRUE)

Arguments

Value

Paired Prentice-Wilcoxon test results including Z-statistic, n (sample size), p-value and median difference

References

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

O’Brien, P.C., Fleming, T.R., 1987. A Paired Prentice-Wilcoxon Test for Censored Paired Data. Biometrics 43, 169–180. https://doi.org/10.2307/2531957

See Also

survival::survfit survival::Surv

Examples

data(PbHeron)
ppw.test(PbHeron$Liver,PbHeron$LiverCen,PbHeron$Bone,PbHeron$BoneCen)

Description

Atrazine exceedances (Y/N) above 1 ug/L in streams throughout the Midwestern United States. Original concentration data were censored to show either greater than (1) or less than (0) 1 ug/L.

Objective – determine if the pattern of atrazine concentrations above versus below 1 ug/L were related to seven environmental variables.

Usage

data(ReconLogistic)

Format

An object of class data.frame with 423 rows and 8 columns.

Source

Mueller et al., 1997, Journal of Environmental Quality 26, pp. 1223-1230.

References

Chapter 12 of Helsel, Dennis R. (2012). Statistics for Censored Environmental Data using Minitab and R. John Wiley and Sons, USA, NJ..

Description

Uses ROS model output from the NADA package and computes the Zhou and Gao 1997 modified Cox’s method two-sided confidence interval around the mean for a lognormal distribution. Computes a t-interval for a gaussian ROS model output.

Usage

ROSci(cenros.out, conf = 0.95, printstat = TRUE)

Arguments

Details

This function uses an ROS model output based on the ros function in the NADA package. The lognormal distribution is the default for the NADA package but a gaussian distribution is optional here. For more detail on ROS modeling see the ros help file (?NADA::ros).

For implementation of ROSci(...) see the examples below.

Value

Prints a lower (LCL) and upper (UCL) confidence interval based on the conf provided (Default is 95%)

References

Helsel, D.R., 2011. Statistics for censored environmental data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Lee, L., Helsel, D., 2005. Statistical analysis of water-quality data containing multiple detection limits: S-language software for regression on order statistics. Computers & Geosciences 31, 1241–1248. doi:10.1016/j.cageo.2005.03.012

Zhou, X.-H., Gao, S., 1997. Confidence Intervals for the Log-Normal Mean. Statistics in Medicine 16, 783–790. doi:10.1002/(SICI)1097-0258(19970415)16:7<783::AID-SIM488>3.0.CO;2-2

See Also

NADA::ros

Examples

data(Brumbaugh)
myros <- NADA::ros(Brumbaugh$Hg,Brumbaugh$HgCen)

summary(myros)

# ROS Mean
mean(myros$modeled)

# 95% CI around the ROS mean
ROSci(myros)

Description

TCE concentrations (ug/L) in ground waters of Long Island, New York. Categorized by two of the three land use types (medium and high density residential) surrounding the wells and measured in the study.

Objective – determine if TCE concentrations differ in ground water under two land use types. There are four detection limits, at 1,2,4 and 5 ug/L.

Usage

data(TCE2)

Format

An object of class data.frame with 222 rows and 6 columns.

Source

Eckhardt et al., 1989, USGS Water Resources Investigations Report 86-4142.

References

The full dataset with three land use types is used in Chapter 10 of Statistics for Censored Environmental Data using Minitab and R, by D.R.Helsel; Wiley (2012).

Description

Plots an NMDS of uscores output from the uscores or uscoresi functions.

Usage

uMDS(uscor, group = NULL, title = NULL, legend.pos = "bottomleft")

Arguments

Value

Prints an NMDS plot of censored data groupings based on U-scores #' @references Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Examples

data(PbHeron)

PbHeron.u <- uscores(PbHeron[,4:15])
uMDS(PbHeron.u)

# With group specific
uMDS(PbHeron.u,group=PbHeron$DosageGroup)

Description

Computes the column of uscores from 2 columns of data in the indicator value format. Multiple detection limits allowed. Called by the uscores function, Usc (this function) is not expected to be of much use to users on its own.

Usage

Usc(y, ind, rnk = TRUE)

Arguments

Value

Returns a single column of uscores or the ranks of uscores for a single pair of (concentration, indicator) censored data columns.

Examples

data(Brumbaugh)
uscore(Brumbaugh$Hg,Brumbaugh$HgCen)

Description

Interval-censored computation of uscores and their ranks for 1 parameter. Called by uscoresi. Usci is not expected to be of much use to users on its own.

Usage

Usci(ylo, yhi, rnk = TRUE)

Arguments

Value

Returns a single column of uscores or the ranks of uscores for a single pair of (low, high) interval-censored data columns.

Examples

data(Brumbaugh)

# for demonstration purposes create a lower end concentration interval
Brumbaugh$lowHg<-Brumbaugh$Hg*(1-Brumbaugh$HgCen)

with(Brumbaugh,Usci(lowHg,Hg))

Description

Computes the uscore of data (required 2-columns) with one or more detection limits.

Usage

uscore(y, ind, rnk = TRUE)

Arguments

Value

prints the uscore number of observations known to be lower - number of observations known to be higher, for each observation.

Examples

data(Brumbaugh)
uscore(Brumbaugh$Hg,Brumbaugh$HgCen)

Description

Computes uscores or the ranks of uscores of censored data in the indicator format. Multiple DLs allowed.

Usage

uscores(dat.frame, paired = TRUE, rnk = TRUE)

Arguments

Value

A matrix of uscores or ranks of uscores, one column for each chemical parameter. If there is only one chemical parameter a vector of uscores or ranks of uscores is returned.

Examples

data(PbHeron)

uscores(PbHeron[,4:15])

Description

Computes uscores or the ranks off uscores within columns of interval-censored data (the "i"). Data may have one or more detection limits.

Usage

uscoresi(dat.frame, paired = TRUE, rnk = TRUE, Cnames = 1)

Arguments

Details

Input is a data.frame of paired low and high possible range of values, in an interval-censored format. ylo = the lower end of the interval is the first (left) column in the pair. yhi is the upper end of the interval, at the second (right) column in the pair. For a detected value, ylo=yhi. For a ND, ylo != yhi. The uscore is the number of observations known to be lower minus the number of observations known to be higher. Ties, including those such as <1 vs <3 or 4 vs 4 or <3 vs 2, are given a 0 value in the uscore computation. The ranks of uscores provides a scale that is often more manageable than the uscores themselves.

Value

prints the uscore number of observations known to be lower - number of observations known to be higher, for each observation.

References

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.