similar to: Understanding cenros Error

I have water chemistry data with censored values (i.e., those less than reporting levels) in a data frame with a narrow (i.e., database table) format. The structure is: $ site : Factor w/ 64 levels "D-1","D-2","D-3",..: 1 1 1 1 1 1 1 1 ... $ sampdate: Date, format: "2007-12-12" "2007-12-12" ... $ preeq0 : logi TRUE TRUE TRUE TRUE TRUE

I've successfully reformatted data frames from long to wide with reshape2, but this time I'm getting errors that I want to understand and resolve. Here's the data frame structure and the results of the melt() and dcast() functions: str(waterchem) 'data.frame': 128412 obs. of 8 variables: $ site : Factor w/ 64 levels "D-1","D-2","D-3",..: 1 1

The sample data sets that come with the NADA package are limited to one or two variables and a censored measurement indicator column. I try to mimic examples using my data but keep missing the target. My water chemistry data is available in two formats: long (as seen in a database table) and wide (as seen in a spreadsheet). The two structures are: str(chem) 'data.frame': 65349 obs. of

I need to understand how and why dcast() adds NAs to a data frame that contained no missing values. The database table of chemical concentrations has all missing values removed because they cannot contribute to data analyses. The structure of the R data frame of these data have no NA values, and neither does the data frame resulting from applying the reshape2 melt() function to it. However,

I've gone over the data and do not see my error; the dput() output of the data frame and the pdf output of cenboxplot() are attached. The command used: cenboxplot(sb.t$quant, sb.t$ceneq1, range=1.5, main='Total Recoverable Antimony', xlab='Pre-Mining Era', ylab='Concentration (log mg/L)') (on a single line in emacs). The RL on the plot is drawn at 0.01 rather

I've searched the Web with Google and do not find what might cause this particular error from an invocation of cenboxplot: cenboxplot(cu.t$quant, cu.t$ceneq1, cu.t$era, range=1.5, main='Total Recoverable Copper', ylab='Concentration (mg/L)', xlab='Time Period') Error in if (n > 0) (1L:n - a)/(n + 1 - 2 * a) else numeric() : argument is of length zero I do

I need a nudge in the right direction to get started using NADA. I bought Helsel's second addition and am currently reading it; NADA is installed in R. My data has been restructured with a couple of awk scripts. The data frame structure now has a flag if the quantity is censored (ceneq1 column) as well as a lower and upper limit for censored data. For present purposes, interval censoring

Rich, The documentation for cenboxplot states that the second argument must be logical and not integer. the function cenboxplot substitutes synthetic values for censored values using ros, hence the error message from the ros method. I also do not understand how you expect group = 'SO4' to work. It is not clear that the function will replicate 'SO4' to make a single group.

I have a data frame with 1 factor, one date, and 37 numeric values: str(waterchem) 'data.frame': 3525 obs. of 39 variables: site : Factor w/ 64 levels "D-1","D-2","D-3",..: 1 1 1 1 1 ... $ sampdate : Date, format: "2007-12-12" "2008-03-15" ... $ CO3 : num 1 1 6.7 1 1 1 1 1 1 1 ... $ HCO3 : num 231 228 118 246

One set of data has censored (less-than detection limits) water chemistry concentrations for 80-100% of all observations. My initial trial-and-error attempts to apply the cenboxplot() method suggests that it has an upper limit to the percentage of censored observations. I do not see this limit in Dennis Helsel's second edition. Has anyone experience plotting censored data and can provide

This is my first excursion into using reshape2 and I want to ensure that the melt() function call is syntactically correct. The unmodifed data frame is organized this way: head(tds.anal) site sampdate param quant 1 UDS-O 2006-12-06 TDS 10800 4 STC-FS 1996-06-14 Cond 280 7 UDS-O 2007-10-04 Mg 1620 9 UDS-O 2007-10-04 SO4 7580 19 JCM-10B 2007-06-21 Ca 79 20

Thanks to David's help I subset my large data set and produced a smaller one for a single stream and 7 factors of interest. The structure of this data frame is: str(burns.tds.anal) 'data.frame': 718 obs. of 4 variables: $ site : Factor w/ 143 levels "BC-0.5","BC-1",..: 1 1 4 6 4 4 4 5 5 5 $ sampdate: Date, format: "1996-06-02"

The data set (called 'chemdata') has 6 columns (4 factors, 1 date, 1 numeric) and I need to create subsets for each of one of the factors ('stream'). This has worked flawlessly for all but two streams which were created yesterday. The command I use to create the subsets is like this: > rnchH <- subset(chemdata, stream == 'RanchSpgsH', select = c(site, sampdate,

Hi, I am using the cenmle function from the NADA package and some of my data are negative values. As a result the cenmle function will not work and NaN's are produced. I try to change the distribution to Gaussian, but it still will not run. Could somebody please help me with this? Thanks -- Shane [[alternative HTML version deleted]]

I have read ?zoo but am not sure how to relate the parameters (x, order.by, frequency, and style) to my data.frame. The structure of the data.frame is 'data.frame': 11169 obs. of 4 variables: $ stream : Factor w/ 37 levels "Burns","CIL",..: 1 1 1 1 1 1 1 1 1 1 ... $ sampdate: Date, format: "1987-07-23" "1987-09-17" ... $ param : Factor w/

Data frame has this structure: 'data.frame': 11169 obs. of 4 variables: $ stream : Factor w/ 37 levels "Burns","CIL",..: 1 1 1 1 1 1 1 1 1 1 ... $ sampdate: Date, format: "1987-07-23" "1987-09-17" ... $ param : Factor w/ 8 levels "As","Ca","Cl",..: 1 1 1 1 1 1 1 1 1 1 ... $ quant : num 0.01 0.01 0.01 0.01

Working with 5 subset streams from my source data frame, three of them successfully call dcast(), but two fail: jerritt.cast <- dcast(jerritt.melt, site + sampdate ~ param) Aggregation function missing: defaulting to length and winters.cast <- dcast(winters.melt, site + sampdate ~ param) Aggregation function missing: defaulting to length Yet both data frames have the values in their

I believe that I have a basic understanding of zoo and how to use read.zoo on a text file, What I have not seen in the zoo help files and vignettes is how to convert a data frame to a zoo matrix for irregular time series analyses. An example data frame is structured like this: str(burns.cast) 'data.frame': 256 obs. of 47 variables: $ site : Factor w/ 143 levels

Dear authors, I?m an Italian PhD student and I?m dealing with linear mixed models. I?d like to use your lme library, but I have a problem. I?m able to estimate a null model. For example, using SAS data, I can estimate the model: lme(y ~ 1, data = Mississippi, random = ~ 1|influent, method="ML") As suggested in the literature I want to ?test? the significance of the second level

I have a data frame called chemdata with this structure: > str(chemdata) 'data.frame': 14886 obs. of 4 variables: $ site : Factor w/ 148 levels "BC-0.5","BC-1",..: 104 145 126 115 114 128 124 2 3 3 ... $ sampdate: Date, format: "1996-12-27" "1996-08-22" ... $ param : Factor w/ 8 levels "As","Ca","Cl",..: