similar to: warning with cut2 function

Hello, I'm using the Hmisc cut2 function to bin a set of data. It produces bins that I like with results like this: [96,270]:171 [69, 96): 54 [49, 69): 40 [35, 49): 28 [28, 35): 14 [24, 28): 8 (Other) : 48 I would like to take a second set of data, and assign it to bins based on factors defined by my call to cut 2. Does anyone know how I can do this? Thank you, -S -- View this message

To R users, I am trying to use cut2 function from the 'Hmisc' library. However, when I try and run the function on the following variable, I get an error message (displayed below). I suspect it is because of the NA but I have no idea how to address the error. Many thanks to any insights. structure(list(var1 = c(97, 97, 98, 98, 97, 99, 97, 98, 99, 98, 99, 98, 98, 97, 97, 98, 99, 98,

Hello, I have a set of data with two columns: "Target" and "Actual". A http://n4.nabble.com/file/n1584647/Sample_table.txt Sample_table.txt is attached but the data looks like this: Actual Target -0.125 0.016124906 0.135 0.120799865 ... ... ... ... I want to be able to break the data into tables based on quantiles in the "Target" column. I can see (using

Hi R-users, This is a generic question, is there a way to plot a line graph for the output from crosstable function? one of the inputs to the crosstab function is categorical. Taby -- [[alternative HTML version deleted]]

Hello Everyone, My name is Thomas and I have been using R for one week. I recently found your site and have been able to search the archives of posts. This has given me some great information that has allowed me to craft an initial design to an inquiry I would like to make into the breakdown of McNemar's test. I have read an intro to R manual and the posting guides and hope I am not violating

I am attempting to produce crosstabulations between two variables for subgroups defined by a third factor variable. I'm using by() and CrossTable() in package gmodels. I get the printing of the tables first and then a printing of each level of the INDICES. For example: library(gmodels) by(warpbreaks, warpbreaks$tension, function(x){CrossTable(x$wool, x$breaks > 30,

I am trying to run a confirmatory factor analysis using the SEM package. My data are ordinal. I have read http://socserv.mcmaster.ca/jfox/Misc/sem/SEM-paper.pdf. When I apply the hetcor function, I receive the following error: Error in checkmvArgs(lower = lower, upper = upper, mean = mean, corr = corr, : at least one element of 'lower' is larger than 'upper' Example:

Good evening R-users! I have the following problem: i want to get a weighted crosstable along with the adjusted standardized residuals test Example: a<-c(1,1,1,1,1,2,2,2,3,3) b<-c(10,10,23,24,33,45,46,70,21,66) c<-c(3,3,2,3,4,1,1,1,3,3) d<-c("a", "b","b","c","a","a","a", "b",

Hi, I've been trying to prepare some crosstables for some survey questions for a client. I have been using the CrossTable function in the gmodels package. However, this command only seems to be able to create CrossTables in text documents. I've been trying to use odfTable in odfweave to create tables that are standalone objects in the document that I can then convert to other

Hi, I've been running some simulations for a while and the performance of R has been great. However, I've recently changed the code to perform a sort of chi-square goodness-of-fit test. To get the observed values for each cell I've been using table() - specifically I've been using cut2 from Hmisc to divide up the range into a specified number of cells and then using

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,

Full_Name: John Hendrickx Version: 1.6.0 OS: Windows 98 Submission from: (NULL) (137.224.174.216) CrossTable in the "gregmisc" package fails when the fisher.exact test produces an error (I suspect this is because the number of cases is too large). This can be fixed using "FTt <- try(fisher.test(t, alternative = "two.sided"))" or by making the test optional.

Hi R-helpers, I have two queries relating to generalising to n dimensions: What I want to do in the first one is generalise the following statement: expand<-expand.grid(1:x[1],1:x[2],...1:x[n]) where x is a vector of integers and expand.grid gives every combination of the set of numbers, so for example, expand.grid(1:2, 1:3) takes 1,2 and 1,2,3 and gives 1,1 2,1 1,2 2,2 1,3 2,3 My x

Is the code below showing a bug in Crosstable()? My expectation was that the values produced by xtabs were rounded instead of truncated: library(gmodels) abc <- c("a", "a", "b", "b", "c", "c") def <- c("d", "e", "f", "f", "d", "e") wgt <- c(0.8, 0.6, 0.4, 0.5, 1.4, 1.3)

Hi R-users, I have the following problem with CrossTable function within ?gmodels? package: the output of the function (format ?spss? and asresid=T) can not be stored within another object. For example: >library(gmodels) >data(infert, package = "datasets") > CrossTable(infert$education, infert$induced)->aa # the function prints everything ok on the screen > aa # works

This may be a begining question. If so, please bear with me. If I have some data that based on the historgram and other plots it "looks" like a beta distribution. Is there a function or functions within R to help me determine the model parameters for such a distirbution? Similarily for other "common" distirbutions, Poisson(lambda), Chi-Square(degrees of freedom, chi-square

Hi, I am using the CrossTable function from library(gmodels). x=unique(data[,c("L1","L1_Revenues","RIC")]) L1_Classification=CrossTable(x$L1,x$L1_Revenues,exclude = c("NA","","0%","0"),prop.r=FALSE,prop.c=FALSE,prop.t=FALSE,prop.chisq=FA LSE,dnn=c("L1_Classification","Revenue")) What I would like

Hello R-users, I have a data frame with a factor of ages in 5 year increments, and various count data for each age group. I only have this summary information in R at the moment. I want to create a new factor that aggregates the age factors if the existing factors have insufficient counts. Then I can use aggregate to build a new data set. I figured out I can get the cut values I want using cut2

Dear all, I would like to aggregate a data frame (consisting of 2 columns - one for the bins, say factors, and one for the values) along bins and quantiles within the bins. I have tried aggregate(data.frame$values, list(bin = data.frame $bin,Quantile=cut2(data.frame$bin,g=10)),sum) but then the quantiles apply to the population as a whole and not the individual bins. Upon this

Hello, I am seeking advice regarding how I might add the prefix " kc$ " to variables in a series of commands. The complication is that there is a large number of variables with different commands. Examples of the variables in typical commands follow. It is simple to use search & replace for common variables such as "group" but I would appreciate advice about whether