search for: listwise

Hello, I have many hundred variables in my longitudinal dataset and lots of missings. In order to plot data I need to remove missings. If I do > data <- na.omit(data) that will reduce my dataset to 2% of its original size ;) So I only need to listwise delete missings on 3 variables (the ones I am plotting). data$variable1 <-na.omit(data$variable1) does not work. Thank you [[alternative HTML version deleted]]

Hello all, This is in reference to "Learning to Rank" Project Idea. [I know, i made the entry a bit late, but hope you are still in interest to help out] I am looking for suggestions to help me narrowing down the choices of algorithms. I had been readily surveying on the referred algorithms for the purpose of choosing the right one. I am mentioning here some of my doubts to discuss and

Hello, I am Mudit Raj Gupta, fourth year student of M.S. (Hons.) Chemistry and B.E. (Hons.) Electronics and Instrumentation at BITS-Pilani ( http://www.bits-pilani.ac.in/). I am interested in *Machine Learning and Computaional Intelligence*. I have an interest in implementing various existing algorithms and developing modified/new algorithms related to machine learning and computational

Dear Rusers, The default behavior in R when performing a regression model with missing values is to exclude any case that contains a missing value? How could i set the bahavior that R deal with missing values? e.g.: exclude cases listwise exclude cases pairwise replace with mean Thanks very much! -- Kind Regards, Zhi Jie,Zhang ,PHD Department of Epidemiology School of Public Health Fudan University Tel:86-21-54237149 [[alternative HTML version deleted]]

Why do we get Partial correlation values greater than 1? I have used the default function pcor.mat :-- I have manipulated the default pcor.mat function a bit so ignore tha variables corr_type,element1_in_no,element2_in_no,P.Please ignore the ?pairwise? section and have a look at athe ?listwise ? part i.e else part. *pcor.mat <- function(x,y,z,method="p",na.rm=T,corr_type,element1_in_no,element2_in_no,P){ * * * * print("pcor.mat")* * x <- c(x)* * y <- c(y)* * z <- as.data.frame...

Dear all I'm trying to run a cluster analysis with R Here are the commands: mydata <- na.omit(matrix) # listwise deletion of missing mydata <- scale(matrix) # standardize variables fit <- kmeans(mydata, 8) # 8 cluster solution # get cluster means aggregate(mydata,by=list(fit$cluster),FUN=mean) # append cluster assignment mydata <- data.frame(mydata, fit$cluster) library(cluster) clusplot(m...

...ert -999 to NA for (k in 1:dim(D)[2]) { I = which(D[,k]==-999) if (length(I) > 0) { D[I,k] = NA } } The dataset has many missing values. I am running several regressions on this dataset, and want to ensure every regression has the same subjects. Thus I want to drop subjects listwise for dependent variables y1-y9 and covariates x1-x5 (if data is missing on ANY of these variables, drop subject). How would I do this after running the syntax above? Thank you [[alternative HTML version deleted]]

Dear Sir, I am Pankaj Singhal from Jaipur, India. I am very much interested and strongly looking forward in getting involved in this project Learning-to-Rank. My previous experience in this field is good. Last semester I did a similar job of ranking the URLs of the given huge dataset based on their attribute values. The dataset consisted hundreds of thousands of URLs and each url

...mple.nobs=300, std.lv = T,estimator="ML", ordered=c("var1","var2" and so on for the other 23 variables)). This however does not produce thresholds suggesting what I have tried is nonsense but is there a way to do this? Final question is I have a lot of missing data - listwise deletion leaves 90 subjects. Is there a way to calculate estimates using pairwise deletion (this is another reason why I tried using the correlation matrix as the input). I have tried the analysis using John Fox's SEM package / command. I calculate the correlation matrix with smoothing my....

Hi, I recently tried to estimate a linear unconditional latent growth curve on 7 repeated measures using lavaan (most recent version): modspec=' alpha =~ 1*read_g0 + 1*read_g1 + 1*read_g2 + 1*read_g3 + 1*read_g4 + 1*read_g5 + 1*read_g6 beta =~ 0*read_g0 + 1*read_g1 + 2*read_g2 + 3*read_g3 + 4*read_g4 + 5*read_g5 + 6*read_g6 ' gmod=lavaan(modspec, data=math, meanstructure=T,

....377 0.268 -0.428 0.158 0.143 0.746 <snip SS loadings> >plot(DV.pca) # This is exactly the same as the SPSS scree-plot. SPSS Analysis ============= FACTOR /VARIABLES webeval1 webeval2 webeval3 webeval4 webeval5 webeval6 webeval7 webeval8 /MISSING LISTWISE /ANALYSIS webeval1 webeval2 webeval3 webeval4 webeval5 webeval6 webeval7 webeval8 /PRINT INITIAL EXTRACTION /PLOT EIGEN /CRITERIA FACTORS(8) ITERATE(25) /EXTRACTION PC /ROTATION NOROTATE /METHOD=CORRELATION . As mentioned the proportions of varience explained and the scree...

...sentiment classification. Combining the above two points, I suggest an approach which uses features learnt from data in an unsupervised fashion "*in addition to*" the commonly used features. *Please note:* all this is in addition to the traditional features and finally we would be using *listwise/pairwise approaches*[ListMLE, et cetera] to train our models on the new set of features. Please let me know if this sounds good. *Doubt2:* *Rank Aggregation:* Now that Xappian will have >1 Learning to rank algorithms, we should look into some kind of rank aggregation as well: combining outputs...

Does anyone know if someone is developing full-information maximum likelihood (FIML) estimation algorithms for basic regression functions, like glm()? I think that having FIML options for workhorse functions that typically use ML would give R an edge over other statistical software, given how well FIML performs in missing data situations compared to ML. While my current level of programming

...join + conflictXleave + conflictXalways ' mod1 = sem(pathmod, data=sampledat, missing="fiml", se="robust") At the time, the model ran fine. Now, using version 0.4-14, the model returns all 0's for coefficients. This does not happen, however, when I run the model using listwise deletion for missing data. Any idea what is happening, or how I can fix it? For those wishing to reproduce the problem, you can download a sample code file and data frame from the following two links. https://fds.duke.edu/db/aas/Sociology/grad/aam34/files/problem%20code.R https://fds.duke.edu/db...

...Li (MSR) > *Subject:* Doubt regarding Feature selection for 'Learning to Rank' > algorithms**** > > ** ** > > Hello sir,**** > > ** ** > > I attended your talk on Learning to Rank at MLSS 2011 at NUS Singapore > last year in June. I was going through various Listwise approaches for > ranking and **** > > the various features used to represent the documents.**** > > ** ** > > Recently Deep architectures have been used to learn feature > representations in an unsupervised manner and have outperformed the > state-of-the-art algorithms fo...

...0.143 $rotmat [,1] [,2] [1,] 0.7585207 0.6516489 [2,] -0.6516489 0.7585207 This is the output generated by SPSS Call: FACTOR /VARIABLES ds140_01 ds140_02 ds140_03 ds140_04 ds140_05 ds140_06 ds140_07 ds140_08 ds140_09 ds140_10 ds140_11 ds140_12 ds140_13 ds140_14 /MISSING LISTWISE /ANALYSIS ds140_01 ds140_02 ds140_03 ds140_04 ds140_05 ds140_06 ds140_07 ds140_08 ds140_09 ds140_10 ds140_11 ds140_12 ds140_13 ds140_14 /PRINT INITIAL EXTRACTION ROTATION /FORMAT BLANK(.10) /CRITERIA MINEIGEN(1) ITERATE(25) /EXTRACTION PC /CRITERIA ITERATE(25) /ROTATION VARIMAX /MET...

Am I trying to perform multiple linear regressions on each 'VARIABLE2'. I figured out that there are different ways, using the following code: (data is given at the end of this message) reg <- lapply(split(TRY, VARIABLE2), function(X){lm(X2 ~ X3, data=X)}) lapply(reg, summary) Which produces the following: $`1` Call: lm(formula = X2 ~ X3, data = X) Residuals: Min

Dear R-List, (I am not sure whether this list is the right place for my question...) I have a dataframe df.cfa