similar to: Quartiles and Inter-Quartile Range

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

I have data that looks like lake,loglength,logweight 1,2.369215857,1.929418926 1,2.426511261,2.230448921 1,2.434568904,2.298853076 1,2.437750563,2.298853076 1,2.442479769,2.230448921 1,2.445604203,2.356025857 ... 102,2.722633923,3.310268367 102,2.781755375,3.502153893 102,2.836324116,3.683407299 102,2.802773725,3.583312152 102,2.790285164,3.546419267 102,2.806179974,3.599118565

Hello, I have a doubt, but it is more statistic than just about R: How the people deal usually with negative percentile and quartile? In my concrete case, I want to know the distribution of an error, so the nearest it is to 0 the better (I think it would be optimun to have the nearest values to 0 in the lower percentiles). The best result would be making the percentile of the absolute value

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

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

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

Greetings R users, My goal is to generate quartile groups of each variable in my data set. I would like each experiment to have its designated group added as a subsequent column. I can accomplish this individually with the following code: brks <- with(data_variables, cut2(var2, g=4)) #I don't want the actual numbers, I need a numbered group data$test1=factor(brks,

When data contains both factor and numeric variables, how to get quartiles for all numeric variables? 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 <- factor(1*(x5=='a' | x5=='c')) data1 <- cbind(x1,x2,x3,x4,x5,x6) data

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]]