similar to: Factor problem

HI!!! I have a table containing qualitative and quantitative data; one of the columns contains "Level of education", and the possibilities are "none", "High school", "college"; I want to give the value 0 to "none", the value 1 to "High school", and 2 to "college", but I got the following error: In `[<-.factor`(`*tmp*`,

Hi, I have some doubt about how qualitative factors are coded in R. For instance, I consider a response y, a quantitative factor x and a qualitative factor m at 3 levels, generated as follow : y_c(6,4,2.3,5,3.5,4,1.,8.5,4.3,5.6,2.3,4.1,2.5,8.4,7.4) x_c(3,1,3,1,2,1,4,5,1,3,4,2,5,4,3) m_gl(3,5) lm(y~x+m) Coefficients: (Intercept) x m2 m3 3.96364 0.09818

All, I'm relatively new to using R, having used it thus far for some simple statistics and plotting. However, I'm not new to programming by any measure. I've been looking at the various modules available for clustering, factor analysis, etc. and find that I need advice on which modules I should be focusing on and their application. I have a data set comprised of columns of both

I'm a bit confused about the anova/aov functions. Both seem to rely on data models, where the relationship between the dependent variables and the independent variables can be expressed as a formula. In what I am trying to do, all of my independent variables are qualitative, not quantitative. For example, for each of two options, "option A" and "option B" I have

When I load a table from a data source and run summary() on it, the summary gives me basic summary statistics I'm looking for, and it also discriminates between quantitative and qualitative data and summarizes them accordingly. For example, if I do this: mydata <- read.table("data.txt") summary(mydata) I would get output like this: > summary(mydata) County

Hello, My name is Dontrece Smith. I am creating figures for my GAMs. I change my qualitative variables to 1 or 2 in my dataset, so I would be able to run my GAMs. However, R will only display plots for my quantitative variables and not my qualitative variables. Is there any way to fix this issue? I listed some of my code below: > library(mgcv) This is mgcv 1.7-13. For overview type

Hello, I'm a R newcomer and I'm wondering the kind of data structure that would best fit to my problem: my data are equities (stocks) : so I have a time serie (say 1 year of weekly data), and a bunch of qualitative + quantitative variables : the sector of the stock (biotech/finance...), the geographical region, the name, ISIN code, P/E ratios, whatever... The data.frame is perfect for the

Does anyone know of a test for stationarity of a time series, or like all ordination techniques it is a qualitative assessment of a quantitative result. Books, papers, etc. suggestions welcome. thanks Stephen -- Let's not spend our time and resources thinking about things that are so little or so large that all they really do for us is puff us up and make us feel like gods. We are

Hello, i have this dataset http://www.umass.edu/statdata/statdata/data/pharynx.txt. the variables GRADE, T_STAGE anda N_STAGE are qualitative or quantitative variables??? i only have this simple doubt...! another example: why in the dataset ovarian (library survival) the variable ecog.ps: ECOG performance status (1 is better, see reference) it is consider quantitative? Thank's for

RDQA is a package for Qualitative Data Analysis built upon R. It works both on the Windows and Linux/FreeBSD platforms. RQDA is an easy-to-use tool to assist in the analysis of textual data. At the present, it supports only plain text format data. All the information is stored in SQLite database via the R package of RSQLite. The GUI is based on RGtk2, via the aid of gWidgetsRGtk2. It includes a

RDQA is a package for Qualitative Data Analysis built upon R. It works both on the Windows and Linux/FreeBSD platforms. RQDA is an easy-to-use tool to assist in the analysis of textual data. At the present, it supports only plain text format data. All the information is stored in SQLite database via the R package of RSQLite. The GUI is based on RGtk2, via the aid of gWidgetsRGtk2. It includes a

Hello everybody! I've a file with several data six variables, three quantitative and three qualitative, I would like to select a group of data from the file to analyze then, i.e: my file is like that (but with 6 variables): Var1 Var2 2 1 5 1 8 1 7 2 3 2 8 2 I want to use only the data where var2 is "1" 2 1 5 1 8 1 Exist a

Hi! Apologies if this is not the correct place to ask. I am attempting a MFA analysis of a dataset based on wine chemical and sensory analysis, based on the STHDA tutorial [1]. (I am using this dataset here too, as an example dataset to work on without posting my actual data. I've tried this with both my data and the example data, with the exact same results.) The only issue I am having is

Dear all: My logistic regression model includes one qualitative and one quantitative predictor variable, aes <- glm(p.a ~ spp * log(light), family=binomial(link=logit)), where spp is abundance of 3 species and light is subcanopy light availability varying from 0 1. I want to test differences among levels of the quantitative variable at a value of x other than the current log(light)=0.

I am trying to use R lm() with quantitative and qualitative predictors, but am getting different results than those that I get in SAS. In the R ANOVA table documentation I see that "Type-II tests corresponds to the tests produced by SAS for analysis-of-variance models, where all of the predictors are factors, but not more generally (i.e., when there are quantitative predictors)." Is

Hello, I apologize that this is off-topic. I am seeking information on perception of graphical data, in an effort to improve the plots I produce. Would anyone point me to literature reviews in this area? (Or keywords to try on google?) Is this located somewhere near cognitive science, psychology, human factors research? For example, some specific questions I have are: I recall as a child

Hello! This is not a topic I am well versed in but required to become well versed in...I welcome any assistance! Using R, I want to create an optimal design for an experiment. I'll be analyzing the results with logistic regression or some generalized linear model. I am thinking that the algdesign package can help (but no idea where to start?). I'm presenting an example here that I have

Hi all, I want to conduct a cluster analysis with quantitative variables. More precisely, it concerns binary and non-ordered categorical variables. For such data, various similarity measures have been proposed, such as the Jaccard index or the simple matching index. So, is there a package such as mva or multiv in the case of quantitative variables? Could you indicate me reviews, papers or

Hello. I have been browsing the R- manual and not seen any direct implementation of Factor analysis. Is there anyone out there who has run Factor Analysis with R? Thanks Michael -- Michael Preminger Forsker / Research Scientist Avdeling for journalistikk, bibliotek- og informasjonsfag / Faculty of Journalism, Library and Information Science H?gskolen i Oslo / Oslo College

(Remembering to +llvm-dev this time…) There has been some progress in the direction of improving debuggability of optimized code, in the past year. There have been a number of patches to improve tracking of debug info in various passes, and some more general improvements such as work on the LiveDebugValues pass. I don't think anyone has done a specific analysis to identify passes that lose