search for: scoring

Hi, I have two model, challenge(embeds_many :tasks) and task(embedded_in :challenge), finally database structue is like this { "_id" : ObjectId("4db8b0524f0b495c3a7dbba4"), "title" : "Testing1", "created" : ISODate("2011-12-12T00:00:00Z"), "tasks" : [ { "name" : "task no 1",

Dear Professor Revelle and R-helpers, This is a two-part question: the first part is general, and the second is specific to the psych package. First question: In the past I've constructed composite variables from questionnaire data by using rowMeans(), and then correlating items with the scale using cor() as an informal check for "bad" items. Over the weekend I decided to take a

List: Thank you for the replies to my post yesterday. Gabor and Phil also gave useful replies on how to improve the function by relying on mapply rather than the explicit for loop. In general, I try and use the family of apply functions rather than the looping constructs such as for, while etc as a matter of practice. However, it seems the mapply function in this case is slower (in terms of CPU

Dear List! I am trying to calculate the distance between original data points and their position in the PLS model. In order to do this, I tried to predict the scores using the predict.mvr function and calculate the corresponding positions in variable space. The prediction of scores works perfectly: ------ data(trees) # build model t<-plsr(Volume~.,data=trees) # predict scores for training

Hola Tengo un problema con Sweave y xtable, concretamente quiero cambiar el color según un criterio, creo que es más fácil explicar con los siguientes ejemplos, uno puede correr directamente porque es R, al siguiente código hay que guardarlo como Rnw, y luego puede ser corrido en R, donde seguramente al correr este se darán cuenta de mis dos problemas porque saltan al abrir el pdf que se obtiene

Hi everyone, I am trying to get the factor score for each individual case from a principal component analysis, as I understand, both princomp() and prcomp() can not produce this factor score, the principal() in psych package has this option: scores=T, but after running the code, I could not figure out how to show the factor score results. Here is my code, could anyone give me some advice please?

Hi, First I should note I am relatively new to R so I would appreciate answers that take this into account. I am trying to perform an MDS ordination using the function ?metaMDS? of the ?vegan? package. I want to ordinate species according to a set of functional traits. ?Species? here refers to ?sites? in traditional vegetation analyses while ?traits? here correspond to ?species? in such

Hi, I'm new to R so please fogive if I write someting silly ... I need to recode a series of responses from a number of questionnaires. The data is read via ODBC from a database where all responses are coded as tables of the form (id, question, score). After dealing with recoding of missing values, I need to "invert" the scores of some questionnaire's item in the form x <-

Hi, I want to produce a pie chart with titles. I used the following code. So it created a pie chart for the percentage of the score for ID="002". ID <- c("001","001","002","002","002","003","003","003","004","004","005","005","006") test <-

Hi, I have a data frame with 3 columns: ID, year and score. How can I select for each unique ID, the year that has the max score? For example, for data frame ID, year, score tom, 1995, 88 rick, 1994, 90 mary, 2000, 97 tom, 1998, 60 mary, 1998,100 I shall have ID, year, score tom, 1995, 88 rick, 1994, 90 mary, 1998,100 Thanks, Richard [[alternative HTML version deleted]]

Dear all I'm unable to find an example of extracting the rotated scores of a principal components analysis. I can do this easily for the un-rotated version. data(mtcars) .PC <- princomp(~am+carb+cyl+disp+drat+gear+hp+mpg, cor=TRUE, data=mtcars) unclass(loadings(.PC)) # component loadings summary(.PC) # proportions of variance mtcars$PC1 <- .PC$scores[,1] # extract un-rotated scores of

..., how can I identify which items or loadings are being used to create each of the columns in the $scores output? I have used generalised linear models which have shown that some of the scores are significant predictors of treatment outcome, but I can't work out which of the 11 factors they are scoring? when I export to csv the $loadings I get the following columns in this order: MR4, MR3, MR2, MR6, MR7, MR5, MR1, MR8, MR10, MR9, MR11 When I export to csv the $scores I get the following columns in this order: V1, V2, V3, V4, V5, V6, V7, V8, V9, V10,V11 Are the scores in the column V1 derive...

Dear R experts: I have following data structure: student id, exam time and score. I want to create three new columes: 1 st before, 2nd before and 3rd before. For example, for student 1's 4th exam score is assumed to be influenced by his previous three cloest scores, which are 6, 9, and 10 in that order. For student 2's 5th exam score is assumed to be inflenced by her previous cloest

The function factor.scores is used with package ltm and others to estimate IRT type scores for various models. It inherits objects of class grm, gpcm and a few others. What I would like to do is to use the factor.scores function, but feed it my own item parameters (from a bifactor model where the 2PL parameters are adjusted for the bifactor structure). Does anybody have an idea of how this might

I'm working with a data collected through complex survey design. My goal is to conduct a factor analysis to extract two a priori, known factors, and to get factor scores for these factors. Unfortunately, the "svyfactanal" procedure from the Survey package does not allow for the calculation of either Thompson regression scores or Bartlett scores. So, I found several sources that say

Dear all, I am trying to calculate a score for a string sequence consisting of the following four letters: ACGT. I have got a matrix giving the scores for each pair of letters. So for example the string ACCT has got the pairs: AC, CC and CT. The matrix has got the following form: names<-c("A","C","G","T"); mscore<-matrix(0,4,4);

Hello, I''m trying to define my own analyzer by doing something like: #----------------------------------------------------- require ''ferret'' include Ferret class MyAnalyzer < Analysis::Analyzer def token_stream(field, str) # Display results of analysis puts ''Analyzing: field:%s str:%s'' % [field, str] t =

I am sure there is a simple way to do the following, but i haven't been able to find it. I am hoping a merciful soul on R-help could point me in the right direction. I am doing a factor analysis on survey data with missing values. to do this, I run: FA1<-factanal(na.omit(DATA), factors = X, rotation = 'oblimin', scores = 'regression') Now that I have my factors and

Hi, I have a model tests_user and score which are associated with each other by has_one and belongs_to association. class TestsUser < ActiveRecord::Base has_one :score, :foreign_key => :reg_no end class TestsUser < ActiveRecord::Base belongs_to :tests_user, :foreign_key => :reg_no end where reg_no is a unique field in both the table! score = Score.find(:first) gives me

Hello, I need help in performing a Van_der_Waerden normal scores test in R. I have two arrays of scores(final on therapy scores from drug and placebo) and want to use the normal scores procdeure to test for significance. (observations are unequal in number - due to dropouts). Could you please help me out with the coding or let me know if there is a package that can be used (for example,