similar to: object gstat

Dear R Community - I hope you might be able to provide some guidance regarding the use of the rle function. I have a set of time-series data where a measured value is recorded every 30 seconds after the start of an experiment. Many of the measured values repeat and I am interested only in the values when there is a change. If I turn the measured values into a vector, the rle function works

Dear r-help, I would like to select a subset of levels from a factor variable in a data frame and return a data frame. The data set consists of 3 variables, 2 of which are factors (Site, Fish) and one numeric (Datavalue) as follows: Site Fish Datavalue AB 2-1 2.3 AB 2-1 2.4 AB 2-1 2.2 AB 2-2 2.6 AB 2-2 2.5 AB 2-2 2.7 AB 2-3

R Help - I'm trying to run a correlation matrix with a covariate of "age" and will at some point will also want to covary other variables concurrently. I'm using the "psych" package and have tried other methods such as writing a loop to extract semi-partial correlations, but it does not seem to be working. How can I accomplish this? library(psych) > set.cor(y =

How can ordinary polynomial coefficients be calculated from an orthogonal polynomial fit? I'm trying to do something like find a,b,c,d from lm(billions ~ a+b*decade+c*decade^2+d*decade^3) but that gives: "Error in eval(expr, envir, enclos) : Object "a" not found" > decade <- c(1950, 1960, 1970, 1980, 1990) > billions <- c(3.5, 5, 7.5, 13, 40) > #

First of all, I have GAMs noxd<-gam(newNOX~pressure+maxtemp+s(avetemp,bs="cr")+s(mintemp,bs="cr")+s(RH,bs="cr")+s(solar,bs="cr")+s(windspeed,bs="cr")+s(transport,bs="cr"),family=gaussian (link=log),groupD,methods=REML) Then I type " summary(noxd)". and show Family: gaussian Link function: log Formula: newNO2 ~ pressure

Dear all, Does any one knows why should I get the following error message, when trying to do a simple data.frame?? DataF<-data.frame(Subject,BiomR,Spp,Capas,Litter,Herbs,LitterD,MaxCanH,DDifS p,DSSp,Slope, CanDens,NearestSp) Erro em data.frame(Subject, BiomR, Spp, Capas, Litter, Herbs, LitterD, : arguments imply differing number of rows: 202, 0 The data I am using

I have a data set for different time intervals. The data has three comment lines before data for each time interval. For each time interval there are 500 data points. I want to change the dataset such that I have the following format: t1 t2 t3 ................ 0.00208 0.00417 0.00625 ................. a1 a2 a3 ...................

hello i'm trying the script of gstat course of munich "Spatial and spacetime classes in R " in http://geostat-course.org/Topic_Bivand_2012 the code is follow ... but a in timeIsInterval(dts) the script failed.... the message is "timeIsInterval function not found" i'm updated R and package spacetime but has not worked any suggestions? thanks setwd("C:/Documents

Hello all, I have a simple function which calculates summary statistics of a dataset in terms of a factor (say area). > x = data.frame(Area = c(rep("cleanup", 5), rep("ref", 5)), TcCB = c(rnorm(5)+2, rnorm(5)));x Area TcCB 1 cleanup 2.5829747 2 cleanup 2.6796868 3 cleanup 2.5437094 4 cleanup 2.8453616 5 cleanup 1.1789683 6 ref 1.0140391 7

Dear all, I am new to R and my question may be trivial to you... I am doing a GLM with binomial errors to compare proportions of species in different categories of seed sizes (4 categories) between 2 sites. In the model summary the residual deviance is much higher than the degree of freedom (Residual deviance: 153.74 on 4 degrees of freedom) and even after correcting for overdispersion by

Hello, I probably have a syntax error in trying to generate an expected survival curve from a weighted cox model, but I can't see it. I used the help sample code to generate a weighted model, with the addition of a "weights=albumin" argument (I only chose albumin because it had no missing values, not because of any real relevance). Below are my code with the resulting error

I have a file, each column of which is a separate year, and each row of each column is mean precipitation for that month. Looks like this (except it goes back to 1964). month X2000 X2001 X2002 X2003 X2004 X2005 X2006 X2007 X2008 X2009 1 1.600 1.010 4.320 2.110 0.925 3.275 3.460 0.675 1.315 2.920 2 2.960 3.905 3.230 2.380 2.720 1.880 2.430 1.380

Dear All- I have a dataset that is comprised of the following: doy yr mon day hr hgt1 hgt2 hgt3 co21 co22 co23 sig1 sig2 sig3 dif flag 244.02083 2005 09 01 00 2.6 9.5 17.8 375.665 373.737 373.227 3.698 1.107 0.963 -0.509 PRE 244.0625 2005 09 01 01 2.6 9.5 17.8 393.66 384.773 379.466 15.336 11.033 5.76 -5.307 PRE 244.10417 2005 09 01 02 2.6 9.5 17.8 411.162 397.866 387.755 6.835 5.61 6.728

I am trying to model the observed leaching of wood preservative chemicals from treated wood during an outdoor experiment where leaching is caused by rainfall events. For each rainfall event, the amount of rainfall was recorded as well as the amount of preservative chemical leached. A number of climatic variables were measured, but the most important is the amount of rainfall. I have tried a

Dear R list, I've been attempting to interpret the results from a three-way ANOVA. I think I understand contrasts and the R defaults for these (treatment contrasts). My question is: what is the intercept in this test? As far as I can tell, its NOT the expected value of a point that belongs to the first level of all three explanatory factors (because there is only one point that satisfies these

Colleagues I want to convert a 10x2 array: # create a 10x2 matrix. datavals <- matrix(nrow=10,ncol=2) datavals[,] <- rep(c(1,2),10)+c(rnorm(10),rnorm(10)) datavals into a 10x3 array, ThreeDArray, dim(10,2,10). The values storede in ThreeDArray's first dimensions will be the data stored in datavalues. ThreeDArray[i,,] <- datavals[i,] The values storede in ThreeDArray's second

Hi, I'm trying to match peaks between chromatographic runs. I'm able to match peaks when they are chromatographed with the same method, but not when there are different methods are used and spectra comes in to play. While searching I found the ALS package which should be usefull for my application, but I couldn't figure it out. I made some dummy chroms with R, which mimic my actual

I have an lm object called mro A summary gives > summary(mro) Call: lm(formula = REGRESSIONSTRING, data = wData) Residuals: Min 1Q Median 3Q Max -8.18077 -1.06867 -0.09387 1.03153 11.20201 Coefficients: (1 not defined because of singularities) Estimate Std. Error t value Pr(>|t|) (Intercept) 7.2096 1.0345 6.969 5.37e-11 ***

Hi all, I have run a regression and want to calculate the likelihood of obtaining the sample. Is there a way in which I can use R to get this likelihood value? Appreciate your help on this. The following are the details: raw_ols1=lm(data$LOSS~data$GDP+data$HPI+data$UE) summary(raw_ols1) Call: lm(formula = data$LOSS ~ data$GDP + data$HPI + data$UE) Residuals: Min 1Q

Hello R-help I don’t know how to interpret significance from the contr.poly() function . From the example below : how can I tell if data has a significant Linear/quadratic/cubic trend? > contr.poly(4, c(1,2,4,8))               .L         .Q          .C [1,] -0.51287764  0.5296271 -0.45436947 [2,] -0.32637668 -0.1059254  0.79514657 [3,]  0.04662524 -0.7679594 -0.39757328 [4,]  0.79262909