similar to: How to get Quartiles when data contains both numeric variables and factors

Hi List, I want to read several parameters from data frame and load them as object into R session, Is there any package or function in R for this?? Here is example param <-c("clust_num", "minsamp_size", "maxsamp_size", "min_pct", "max_pct") value <-c(15, 20000, 200000, 0.001, .999) data <- data.frame ( cbind(param , value)) data

Hello, I'm currently using a simple plot to visualize some mean values. I'm having ~200 datapoints on the x-axis, each has 10 records. I'm currently plotting only the mean value of each of the datapoints. What I need is a way to visualize the quartiles/error/whatever of these points. I thought about boxplots, but I have to many points on the xaxis - it would be impossible to see

Why am I getting a wrong result for quartiles? here is my code: > cbiomass = c(910, 1058, 929, 1103, 1056, 1022, 1255, 1121, 1111, 1192, > 1074, 1415) > summary(cbiomass) > IQR(cbiomass) The result R gives me is: For the summary > Min. 1st Qu. Median Mean 3rd Qu. Max. 910 1048 1088 1104 1139 1415 For IQR > 91.25 ********* The true Q1 is 1039

For boxplot(), is it possible to pass in a parameter to change the default way that the 1st and 3rd quartiles are computed? (specifically, I'd like to use type 6 described in the quantile function). Also, what are the options for how outliers are computed, and how can one change them? Thank you [[alternative HTML version deleted]]

Dear All, I am new to R, I have one question which might be easy. I have a large data with more than 250 variable, i am reducing number of variables by redun function as in the example below, n <- 100 x1 <- runif(n) x2 <- runif(n) x3 <- x1 + x2 + runif(n)/10 x4 <- x1 + x2 + x3 + runif(n)/10 x5 <- factor(sample(c('a','b','c'),n,replace=TRUE)) x6 <-

Hi, all. I have a list: process <- c( 5 , 7 , 4 , 1 , 4 , 1) and I'd like to get each half (or each third or each quartile) of the list: process.firsthalf would be (5, 7, 4) and process.secondhalf would be (1, 4, 1). note that I'm not interested in the numeric quartiles (then I could use quantile or several other functions). what is the best way to get this kind of thing? for

Hi, I am new to R for solving optimization problems, I have set of communication channels with limited capacity with two types of costs, fixed and variable cost. Each channel has expected gain for a single communication. I want to determine optimal number of communications for each channel maximizing ROI)return on investment) with overall budget as constraint.60000 is the budget allocated.

Dear All, I have got the limits for removing extreme values for each variables using following function . f=function(x){quantile(x, c(0.25, 0.75),na.rm = TRUE) - matrix(IQR(x,na.rm = TRUE) * c(1.5), nrow = 1) %*% c(-1, 1)} #Example: n <- 100 x1 <- runif(n) x2 <- runif(n) x3 <- x1 + x2 + runif(n)/10 x4 <- x1 + x2 + x3 + runif(n)/10 x5 <-

Hi List, I am new to R, this may be simple. I want to store directory path as parameter which in turn to be used while reading and writing data from csv files. How I can use dir defined in the below mentioned example while reading the csv file. Example: dir <- "C:/Users/Desktop" #location of file temp_data <- read.csv("dir/bs_dev_segment_file.csv") If I run this

I am using the describe function in prettyR. I would like to add the 25% 75% quartiles to the summary table how do I do this I have tried describe(x.f, num.desc=c("mean", "median", "sd", "min", "max", "skewness", "quantile(x.f, na.rm=T, probs=seq(0.25, 0.75))", "valid.n")) help -- Let's not spend our time

R users: I am trying to replicate the boxplot output I achieve with Minitab in R. I realize that R gives the user many more options on the algorithm used to calculate the IQR than Minitab, so I concentrated on type=6 when using the quantile() function in R. The problem I am having is setting the upper and lower limit of the whisker based on the nearest actual data that should be included. If

Dear List, Java Exception error while reading large data in R from DB using RJDBC. I am trying to read large data from DB table(Vectorwise), using RJDBC connection. I have tested the connection with small size data and was able to fetch DB tables using same connection(conn as in my code). Please suggest where am i going wrong or alternate option to solve such issues while reading large DB

Hi, I'm trying to create a qqplot with reference to a Weibull distribution including a reference line. This is my current code: lights.data <- scan("lights.dat") #Generate Weibull quantiles prob.grid <- ppoints(length(lights.data)) prob.quant <- qweibull(prob.grid , 1.5,4) #Draw QQ plot qqplot(prob.quant,lights.data) #add red reference line qqline(lights.data,col = 2)

Hi, I have a simple question about quartiles in R, especially how they are calculated using the boxplot. Quartiles (.25 and .75) in boxplot are different from the summary function and also don't match with the 9 types in the quantile function. See attachment for details. Can you give me the details on how the boxplot function does calculate these values? Cheers, Rene Brinkhuis

Hi All, I am working on the dataset in which some of the variables have more than one observations with outliers . I am using below mentioned sample script library(outliers) x1 <- c(10, 10, 11, 12, 13, 14, 14, 10, 11, 13, 12, 13, 10, 19, 18, 17, 10099, 10099, 10098) outlier_tf1 = outlier(x1,logical=TRUE) find_outlier1 = which(outlier_tf1==TRUE, arr.ind=TRUE) beh_input_ro1 =

I think qqline does not do exactly what it is advertised to do ("`qqline' adds a line to a normal quantile-quantile plot which passes through the first and third quartiles."). Consider the graph: tmp <- qnorm(ppoints(10)) qqnorm(tmp) qqline(tmp) The line (which I expected go through all the points), has a slightly shallower slope than does the points plotted by qqnorm. I think

Hi Experts, I am new to R, using decision tree model for getting segmentation rules. A) Using behavioural data (attributes defining customer behaviour, ( example balances, number of accounts etc.) 1. Clustering: Cluster behavioural data to suitable number of clusters 2. Decision Tree: Using rpart classification tree for generating rules for segmentation using cluster number(cluster id) as target

I really like the ease of use with the boxplot command in R. I would rather have a boxplot that shows the average value and the standard deviation then the median value and the quartiles. Is there a way to do this? Chad Junkermeier, Graduate Student Dept. of Physics West Virginia University PO Box 6315 210 Hodges Hall Morgantown WV 26506-6315 phone: (304) 293-3442 ext. 1430 fax: (304)

I'm trying to devide x into tertiles, but ends up with integer limits even x holds one decimal. The analysis is extremely sensitive to the limits and I like to keep them right. How can that be done? quartiles <- quantcut( x[x >= 0], q=seq(0,1, by=(1/3)) > table(quartiles) quartiles [180,344] (344,448] (448,644] 16467 16476 16452 [[alternative HTML version deleted]]

Dear all, The incredibly useful Hmisc package provides a method to generate summary tables that can be typeset in latex. The Alzola and Harrell book "An introduction to S and the Hmisc and Design libraries" provides an example that generates mean and quartiles for continuous variables, and numbers and percentages for count variables: summary() with method = 'reverse'. I