similar to: NADA and cenmle

The cenmle function is used to fit two sets of censored data and test if they are significantly different. I can print out the results of the analysis on the screen but can't seem to figure out how to access these results in R and assign them to new variables, e.g., assign the slope calculated with cenmle to the variable m. Any suggestions? Tom -- View this message in context:

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 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

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

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

On Mon, 22 Jan 2024, Rich Shepard wrote: > As an aquatic ecologist I see regulators apply the geometric mean to > geochemical concentrations rather than using the arithmetic mean. I want to > know whether the geometric mean of a set of chemical concentrations (e.g., > in mg/L) is an appropriate representation of the expected value. If not, I > want to explain this to non-technical

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 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

Before reading water chemistry into a data frame I removed all missing data. Yet when I try to run cenros() to summarize a specific chemical I get an error that I do not understand: with( subset(chem, param=='Ag'), cenros(quant,ceneq1) ) Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : NA/NaN/Inf in 'y' I would like to learn what I did incorrectly

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

A statistical question, not specific to R. I'm asking for a pointer for a source of definitive descriptions of what types of data are best summarized by the arithmetic, geometric, and harmonic means. As an aquatic ecologist I see regulators apply the geometric mean to geochemical concentrations rather than using the arithmetic mean. I want to know whether the geometric mean of a set of

Super, Thanks On Fri, Sep 22, 2017 at 4:57 PM, Boris Steipe <boris.steipe at utoronto.ca> wrote: > > a <- c("<0.1", NA, 0.3, 5, "Nil") > > a > [1] "<0.1" NA "0.3" "5" "Nil" > > > b <- as.numeric(a) > Warning message: > NAs introduced by coercion > > b > [1] NA NA 0.3

myDF <- data.frame(a = c("<0.1", NA, 0.3, 5, "Nil"), b = c("<0.1", 1, 0.3, 5, "Nil"), stringsAsFactors = FALSE) # you can subset the b-column in several ways myDF[ , 2] myDF[ , "b"] myDF$b # using the column, you make a logical vector ! is.na(as.numeric(myDF$b)) # This can be used to select the

Hi, Lets say this was a dataframe where I had two columns a <- c("<0.1", NA, 0.3, 5, "Nil") b <- c("<0.1", 1, 0.3, 5, "Nil") And I just want to remove the rows from the dataframe where there were NAs in the b column, what is the syntax for doing that? Thanks in advance On Fri, Sep 22, 2017 at 5:04 PM, Shane Carey <careyshan at

Hi , I am trying to get some estimator based on lognormal distribution when we have left,interval, and right censored data. Since, there is now avalible pakage in R can help me in this, I had to write my own code using Newton Raphson method which requires first and second derivative of log likelihood but my problem after runing the code is the estimators were too high. with this email ,I provide

Always via logical expressions. In this case you can use the logical expression myDF$b != "0" to give you a vector of TRUE/FALSE B. > On Sep 25, 2017, at 8:00 AM, Shane Carey <careyshan at gmail.com> wrote: > > This is super, really helpfull. Sorry, one final question, lets say I wanted to remove 0's rather than NAs , what would it be? > > Thanks >

This is super, really helpfull. Sorry, one final question, lets say I wanted to remove 0's rather than NAs , what would it be? Thanks On Mon, Sep 25, 2017 at 12:41 PM, Boris Steipe <boris.steipe at utoronto.ca> wrote: > myDF <- data.frame(a = c("<0.1", NA, 0.3, 5, "Nil"), > b = c("<0.1", 1, 0.3, 5, "Nil"), >

I'm trying to use either mice or norm to perform multiple imputation to fill in some missing values in my data. The data has some missing values because of a chemical detection limit (so they are left censored). I'd like to use MI because I have several variables that are highly correlated. In SAS's proc MI, there is an option with which you can limit the imputed values that are

Hi, I have a set of x, y points where x represents dates and y actual values. I am trying to plot a line graph of the data with points on top, but R is connecting the wrong points with lines. Does anyone know how I can rectify this. Please see sample below: x= 24/09/2009 09:13 16/10/2009 11:17 24/10/2009 21:43 11/09/2009 18:34 22/08/2009 15:45 10/08/2009 00:30 14/08/2009 14:52 24/09/2009

What does these operators do: %*% Thanks -- Shane [[alternative HTML version deleted]]