similar to: standard error of difference for mixed effects

I have a dataframe that contains time values in the form of yyyy-mm-dd hh:mm i.e. 2010-07-14 13:00. When I convert this to numeric via tvec <- as.numeric(Time) I get a number that is in seconds. So far so good. When I then divide the numeric value by the number of seconds in a day eg tvec/(60*60*24) I only get integer values and not fraction of a day which is what I want. What do I need to

I have a data frame (morgan) of hourly river flow, river levels and wind direction and speed thus: Time hour lev.morgan lev.lock2 lev.lock1 flow direction velocity 1 2009-07-06 15:00:00 15 3.266 3.274 3.240 1710.6 180.282 4.352 2 2009-07-06 16:00:00 16 3.268 3.272 3.240 1441.8 192.338 5.496 3 2009-07-06 17:00:00 17 3.268

Is there an equivalent package for mixed linear effects models developed using the package "nlme" as there is for linear models? Tschüß Tony Meissner Principal Scientist (Monitoring/Statistics) Resource Monitoring Science, Monitoring and Information Division Department for Water "Imagine" © *(ph) (08) 8595 2209 *(mob) 0401 124 971 *(fax) (08) 8595 2232 * 28 Vaughan Terrace,

I have the following function defined as below match.trace <- function(dfobj, distance, day1, day2) { day1 <- substitute(dfobj$day1); day1 day2 <- substitute(dfobj$day2) distance <- substitute(dfobj$distance) xx <- NULL for (i in 0:10) xx[i+1] <- with(dfobj, cor(Lag((day1-day1[1]),i), (day2-day2[1]), use='pair')) i <- match(max(xx), xx) with(dfobj, {

Hello, I'm doing the following: library(ncdf) library(fields) library(animation) saline <- open.ncdf("salinity_1990.nc") salt = get.var.ncdf(nc=saline, varid="Salinity") # create an animation of the complete temporal domain in the ncdf file. saveHTML({ for (i in 1:364) { image.plot(salt[, , i]) } }, img.name = "salinity.img",

My x axis is a series of daily dates (e.g., 01/01/2000, 01/02/2000, etc.) from 2000 to end of 2008. The default only gives me 4 ticks. I want more. Why doesn't this work? sdate<-as.POSIXct(strptime(date,format="%m/%d/%Y")) plot(ppt~sdate,type="l",ylim=c(0,47),col=1,lwd=1,pch=16,ylab="Salinity, psu",xlab="Year",las=1,main="Duck Key

Hi list. I'm plotting pluviometric (Rain) data as a barplot, and then adding the salinity variable to this plot as lines. Obviously as these Y scales are completely different the salinity appears at the lower part of the graph extremely compacted. I need to plot the line at the exactly same area of the barplot but with its own Y axis (at the right), so the salinity can use the plot area

Hi, have anybody a hint how i could avoid the cumbersome way (..especially the value part) change the data representation from n column data.frame to an stacked representation. many thanks Christian #example data dfw <- as.data.frame(matrix(runif(10*10),ncol=10)) dfw$group <- sample(c("X","Z"),nrow(dfw),replace=T,prob=c(0.75,0.25)) # change var <-

Colleagues, I am analyzing data collected during oceanographic cruises. We have conducted many cruises over the last decade. On each cruise we visit ~50 stations. At each station (termed EventNum)we lower an instrument that measures depth, temperature, salinity and oxygen every few seconds as it is lowered through the water. Data from all EventNums on all cruises are stacked, generating a data

Hi, I have used R for some time, but managed to avoid writing loops. But this time I am afraid there is no way around it. I have a dataframe with time and salinity (see below). I would like to extract the time intervals where salinity changes by less than 0.05. So using the values below this would mean that a subset was made from 2003-07-19 to 2003-07-24, where the change in salinity exceeded

Dear R Wizards, After much frustration and days of confusion I have finally broken down and am asking for help, which I don’t like doing, but I just can’t figure this one out on my own. I’ve conducted a laboratory experiment testing the effects of temperature and salinity on whether or not a biological event will occur (Go or NoGo). I’ve coded the factors temperature and salinity as factors for

Hi, I am trying to test the proportional-odds model using the "polr" function in the MASS library with the dataset of "housing" contained in the MASS book ("Sat" (factor: low, medium, high) is the dependent variable, "Infl" (low, medium, high), "Type" (tower, apartment, atrium, terrace) and "Cont" (low, high) are the predictor variables

Dear Mr. and Mrs. I'm very grateful for these software and this list. My question is: when a use linear multiple regression (lm()) for my data, abundance ichthyoplankton ~ salinity + temperature + month of the year(f1 is a factor: 1 for january, 2 for february, ..., 12 for december), the summary() of results is ... Coefficients: Estimate Std. Error t value Pr(>|t|)

I have a data assimilation problem that might be amenable to the use of GAMS, but I am not sure how feasible it is to implement. I was told the R mailing list was a great resource. My observations are spatiotemporal salinity in the San Francisco Bay at a number of instruments over a few days. The thing that I want to fit is the initial condition for a salt transport model at the beginning of this

I am very new to R and programming and thank you in advance for your patience and help with a complete novice! I am working with a large multivariate data set that has 10 explanatory environmental variables (e.g. temp, depth) and over 60 response variables (each is a separate species). My data frame is set up like the simplified version below: JulianDay Temperature Salinity Depth Copepod

Oh sorry. An example say lots more than words. The data below, when submitted to twoord.plot return the mentioned error. Rain are bars and Salt lines, the bars appear and the error occurs with the salt data. Point Rain Salt Fev/03 365.6 13 Mar/03 235 18 Abr/03 115.1 18 Mai/03 47.4 18.75 Jun/03 112 15 Jul/03 156.8 17 Ago/03 66.1 15 Set/03 149.8 14 Out/03 167,1 11.5 Nov/03 269.3 17.5 Dez/03 283.7

Hello all, I have a huge data set that I'm cleaning up a bit. I am extracted the means per condition, but also need to get the n. Strangely enough I am unable to find a function that could actually pull this off. I am unable to use replications or count the rows using nrow. What I am trying to obtain is the number of observations fitting something along the lines of

Dear R listers, When using smooth.spline to interpolate data, results are generally good. However, some cases produce totally unreasonable results. The data are values of pressure, temperature, and salinity from a probe that is lowered into the ocean, and the objective is to interpolate temperature and salinity to specified pressures. While smooth.spline provides excellent values at the

Background: OS: Linux Mandrake 10.1 release: R 2.0.0 editor: GNU Emacs 21.3.2 front-end: ESS 5.2.3 --------------------------------- Colleagues I am having some trouble extracting results from the function by, used to average variables in a data.frame first by one factor (depth) and then by a second factor (station). The real data.frame is quite large > dim(data.2001) [1] 32049 11 Here is a

Hi Folks, I have data that looks like this: freq gender xBar 1000 m 2.32 1000 f 3.22 2000 m 4.32 2000 f 4.53 3000 m 3.21 3000 f 3.44 4000 m 4.11 4000 f 3.99 I want to plot two lines (with symbols) for the two groups "m" and "f". I have tried the following: plot(xBar[gender=="m"]~freq[gender=="f"]) followed by