search for: azua

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, 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, 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 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, 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

...ok of Statistical Analyses using R (CRC) - R in a Nutshell (Oreilly) - Statistical Computing with R (CRC) Thanks in advance, Best regards, Giovanni # ========================================================================================= # Computational Statistics # Series 4 # Author: Giovanni Azua # Date: 16 April 2010 # ========================================================================================= rm(list=ls()) # clear workspace # ========================================================================================= # EXERCISE 1.(c) #...

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, 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 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 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 three method types and 100 generalization errors for each, all in the range [0.65,0.81]. I would like to make a stacked histogram plot using ggplot2 with this data ... Therefore I need a data frame of the form e.g. Method GE ---------- ------ "Classic" 0.76 "Classic" 0.79 "Own

Hello, I upgraded my Mac R version to the newest 2.11.1, then I ran the option to update all packages but there was an error related to fetching one of those and the process stopped. I retried updating all packages but nothing happens. Although all my course project scripts work perfectly is there a way e.g. a command to manually fetch (most up to date version) and locally build all installed

Hello, I'd like to plot the logistic function for a specific model like this: > plot(formula=y~1/(1+exp(5.0993-0.1084*x)),data=data.frame(x=seq(0,100,length.out=1000))) Error in is.function(x) : 'x' is missing However, I get the 'x' is missing error above and don't know how to fix it ... Can anyone advice? Thanks in advance, Best regards, Giovanni

Hello, When I try to use TukeyHSD in the following way it shows the confidence interval corresponding to the last factor only. > throughput.aov <- aov(Throughput~No_databases+Partitioning+No_middlewares+Queue_size,data=throughput) plot(TukeyHSD(throughput.aov)) # I expected here to see the confidence intervals for all factors but see only the last. OTOH this one works but then it is

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, I have the following function that receives a "function pointer" formal parameter name "fnc": loocv <- function(data, fnc) { n <- length(data.x) score <- 0 for (i in 1:n) { x_i <- data.x[-i] y_i <- data.y[-i] yhat <- fnc(x=x_i,y=y_i) score <- score + (y_i - yhat)^2 } score <- score/n

Hello, I generate box plots from my data like this: qplot(x=xxx,y=column,data=data,geom="boxplot") + xlab("xxx") + ylab(ylabel) + theme_bw() + scale_y_log10() + geom_jitter(alpha=I(1/10)) The problem is that I see lot of points above the maximum at the same level as some outliers. It looks very weird as I expected the outliers to be "few" and specially not see any

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, 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,