similar to: data frame and cumulative sum

Hello, How can I fix this? I have the latest version of R 2.13.2 and I use Mac OS X 10.7.2 > library(doBy) Loading required package: lme4 Error in dyn.load(file, DLLpath = DLLpath, ...) : function 'cholmod_l_start' not provided by package 'Matrix' Error: package 'lme4' could not be loaded > library(lme4) Error in dyn.load(file, DLLpath = DLLpath, ...) : function

Hello, Can anyone point me to an online tutorial or book containing the easiest way to do ANOVA over the result data from a 2^k*r experiment. It is not clear to me if I can pass the raw data corresponding to each experiment or just the summarized data i.e. mean, sse, std, etc. I would like to get the: - box plot showing the effect for the different factors and levels - plot showing whether there

Hello all. I am trying to do the equivalent of: @componentlogs = Componentlog.find(:all, :conditions => [ "cl_compname like ?", @criteria ], :offset => offset, :limit => items_per_page, :order => "cl_spr DESC" ) in a find_by_sql statement. I cannot use the build in because the adaptor isn''t quite right (OCI8) When I use it I get the following error

>I have a series of write statements because >i am writing to a file >where the characters strings are the column names of a dataframe >and the numbers are the elements in a particular row. >So, a file might look like > >AAA 2.1 >BB 3.1 >AHLZ 0.2 > >and it would be named "rowname".mls. > >so, each time i get to a new row, i create a new file and

Hi all, This is a very basic question, but I just can't figure out why R is handling a loop I'm writing the way it is. Here is the script I have written: grid_2_series<-function(gage_handle,data_type,filename) series_name<-paste(gage_handle,data_type,sep="_") data_grid<-read.table(file=paste(filename,".txt",sep=""))

Hello, This is one problem at the time :) I have a data frame df that looks like this: time partitioning_mode workload runtime 1 1 sharding query 607 2 1 sharding query 85 3 1 sharding query 52 4 1 sharding query 79 5 1 sharding query 77 6 1 sharding query 67 7 1

Hello all. I am using the Paginate_with_ajax code as described on the wiki but I am running into the following problem. The following code will work fine and retrieved unsorted records in a hunky dory fashion: @componentlogs = Componentlog.find(:all, :conditions => [ "cl_compname like ?", @criteria ], :offset => offset, :limit => items_per_page ) However if I try and add an

Hello, I have tried reading the documentation and googling for the answer but reviewing the online matches I end up more confused than before. My problem is apparently simple. I fit a glm model (2^k experiment), and then I would like to predict the response variable (Throughput) for unseen factor levels. When I try to predict I get the following error: > throughput.pred <-

Dear R-help readers, I am writing to you in order to ask you a few questions about distribution fitting in R. I am trying to find out whether the set of event interarrival times that I am currently analyzing is distributed with a Gamma or General Pareto distribution. The event arrival granularity is in minutes and interarrival times are in seconds, so the values I have are 0, 60, 120, 180, and

Hello, I have a loop where I iterate performance data files within a folder, parse and plot them in one shot (see below). However, when executing plot_raw which invokes dev.new(..) all windows come out blank whereas if I execute each file outside of a loop then I can see the plots properly. What's wrong here? Thanks in advance, Best regards, Giovanni # given a directory name, it will

Hello Lets say as an example I have a dataframe with the following attributes: rownum(1:405), colnum(1:287), year(2000:2009), daily(rownum x colnum x year) and foragePotential (0:1, by 0.01). The data is actually stored in a netcdf file and I'm trying to provide a conceptual version of the data. Ok. I need to calculate a moving mean and a moving variance for each cell on the following

Hello, I am attending a course in Computational Statistics at ETH and in one of the assignments I am asked to prove that a time series is not autocorrelated using the R function "acf". I tried out the acf function with the given data, according to what I found here: http://landshape.org/enm/options-for-acf-in-r/ this test data does not look IID but rather shows some trends so how can I

Further pursuing my curiosity to measure the efficiency of R/C++ interface, I conducted a simple matrix-vector multiplication test using .C and .Call functions in R. In each case, I measured the execution time in R, as well as inside the C++ function. Subtracting the two, I came up with a measure of overhead associated with each call. I assume that this overhead would be non-existent of the entire

Hello, I create a simple ggplot that only shows a straight line. I then add three datasets of CI using the geom_errorbar function. The problem is that I can't find any way to have the legend showing up ... I need to show what each color of the CIs corresponds to i.e. which method. Can anyone advice please? TIA, Best regards, Giovanni

Hi All, We have a raw dataset that, on the sampling time column, for example: t <- c(0,1,2,5,0,5,0,2,5) And we need to create a second column that indicates the day of the sampling, so the end result of what we want is a vector: 1,1,1,1,2,2,3,3,3 Additional information that might simplify the matter is that, for each day, the time starts with zero and ends with 5. I am a beginner with R.? So

Hello, Can anyone provide or point me to a good setup for the listings latex package that would produce nice R-syntax highlighting? I am using an example I found in internet for setting up listings like this: \lstset{ language=R, basicstyle=\scriptsize\ttfamily, commentstyle=\ttfamily\color{gray}, numbers=left, numberstyle=\ttfamily\color{red}\footnotesize, stepnumber=1, numbersep=5pt,

Hello, I bumped into the following funny use-case. I have too much data for a given plot. I have the following data frame df: > str(df) 'data.frame': 5015 obs. of 5 variables: $ n : Factor w/ 5 levels "1000","2000",..: 1 1 1 1 1 1 1 1 1 1 ... $ iter : int 10 20 30 40 50 60 70 80 90 100 ... $ Error : num 1.05e-02 1.24e-03 3.67e-04 1.08e-04

Hello, Suppose I have the dataset shown below. The amount of observations is too massive to get a nice geom_point and smoother on top. What I would like to do is to bin the data first. The data is indexed by Time (minutes from 1 to 120 i.e. two hours of System benchmarking). Option 1) group the data by Time i.e. minute 1, minute 2, etc and within each group create bins of N consecutive

Hello, What would be the best set of R functions to parse and transform a file? My file looks as shown below. I would like to plot this data and I need to parse it into a single data frame that sorts of "transposes the data" with the following structure: > df <- data.frame(n=c(1,1,2,2),iter=c(1,2,1,2),step=as.factor(c('Step 1', 'Step2', 'Step 1',

Dear R gurus, I have the following code, but I still not know how to estimate and extract confidence intervals (95%CI) from resampling. Thanks! ~Adriana #data penta<-c(770,729,640,486,450,410,400,340,306,283,278,260,253,242,240,229,201,198,190,186,180,170,168,151,150,148,147,125,117,110,107,104,85,83,80,74,70,66,54,46,45,43,40,38,10) x<-log(penta+1) plot(ecdf(x),