similar to: apply question

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, 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

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, I need to plot multiple confidence intervals for the same model parameter e.g. so for the same value of the parameter in point x_1 I would like to see four different confidence intervals so that I can compare the accuracy e.g. boot basic vs normal vs my own vs classic lm CI etc. I like very very much the plotCI implemented here: http://cran.r-project.org/web/packages/plotrix/index.html

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 <-

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, 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

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, I managed to "linearize" my LDA decision boundaries now I would like to call abline three times but be able to specify the exact x range. I was reading the doc but it doesn't seem to support this use-case? are there alternatives. The reason why I use abline is because I first call plot to plot all the three datasets and then call abline to "append" these decision

Hello, I have a data frame that looks like this (containing interarrival times): > str(df) 'data.frame': 18233 obs. of 1 variable: $ Interarrival: int 135 806 117 4 14 1 9 104 169 0 ... > head(df) Interarrival 1 135 2 806 3 117 4 4 5 14 6 1 > This corresponds to the time differences (in ms) of a poisson arrival

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 could not find an easy way to have the plot function not display the default x and y-axis labels, I would like to customize it to show only points of interest ... I would like to: 1- call plot that show no x-axis and y-axis labels 2- call axis specifying the exact points of interest for the x and y-axis Maybe they can both be achieved in the plot function call but I can't find the

Hello, While looking for info on 2^k experimental design and anova I remember I saw somewhere there was a function to generate all the experiments. I can't find the function anymore can anyone suggest? The function takes as input the factors and levels and generates all the experiments. I know I can do it myself using recursion but I want to avoid 1) reinventing the wheel and 2) making

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',

Hello, What would be in R the closest match to a c-struct? e.g. data.frame requires all elements to be of the same length ... or is there a way to circumvent this? TIA, Best regards, Giovanni

Hello, I have one replication (r=1 of the 2^k*r) of a 2^k experimental design in the context of performance analysis i.e. my response variables are Throughput and Response Time. I use the "aov" function and the results look ok: > str(throughput) 'data.frame': 286 obs. of 7 variables: $ Time : int 6 7 8 9 10 11 12 13 14 15 ... $ Throughput : int 42 44 33 41

Hello, I use the following function "bootstrapge" to calculate (and compare) the generalization error of several bootstrap implementations: ## ## Calculates and returns a coefficient corresponding to the generalization ## error. The formula for the bootstrap generalization error is: ## $N^{-1}\sum_{i=1}^n B^{-1}\sum_{j=1}^B |y_i - (\beta_n^{*j})^T x|$ ## ## x - mxn matrix where m is

Hello, I used R a lot one year ago and now I am a bit rusty :) I have my raw data which correspond to the list of runtimes per minute (minute "1" "2" "3" in two database modes "sharding" and "query" and two workload types "query" and "refresh") and as a list of char arrays that looks like this: > str(data) List of 122 $ :