similar to: array extraction

*Problem-1* CASE-I---------(works fine) > var1<-"tom" > var1 [1"tom" > var1<-as.character(var1) > var1 [1] "tom" > var2<-c("tom","harry","kate") > logc<-(var1 %in% var2) > logc [1] TRUE > typeof(var1) [1] "character" > typeof(var2) [1] "character"

I have dataframe with 17factors variables (for example every factor have 3levels) I have maybe 5000 observation. And i need to do table where is in every raw 1 of possible combination of this factors and the numbur how many time is this combination in my dataset. I wrote one code, but this is very slow and dumb. it looks like this: i<-0 for(i1 in levels(hivdat$pohl)){

Hi all, I am trying to merge 2 big data.frame. The problem is merge is memory intensive so R is going out of memory error: cannot allocate vector of size 360.1 Mb. To overcome this, I am exploring option of using data.table package. But its not helping in term of memory as merge in data.table is fast but not memory efficient. Similar error is coming. My inputs are inp1 V1 V2 1 a i1 2 a i2 3 a

Hi all, Can anyone suggest me how to insert rows in between data frames and also keep the ordering of row numbers correct? Estimate Std. Error t value Pr(>|t|) recmeanC2 9.275880e-17 6.322780e-17 1.467057e+00 0.14349903 recmeanC3 1.283534e-17 2.080644e-17 6.168929e-01 0.53781390

How to NATURAL sort a vector or data frame* by row* , in ascending order ? V1 V2 V3 V4 i1 5.000000e-01 1.036197e-17 4.825338e+16 0.00000000 i10 4.001692e-18 1.365740e-17 2.930053e-01 0.76973827 i12 -1.052843e-17 1.324484e-17 -7.949081e-01 0.42735000 i13 2.571236e-17 1.357336e-17 1.894325e+00 0.05922715 i2

I am having problems with comparing individual means in a within-subjects ANOVA. From my understanding, TukeyHSD is not appropriate in this context. So I am trying to compute contrasts, as follows: seven subjects participated in each of 6 conditions (intervals). > subject = factor(rep(c(1:7), each = 6)) > interval = factor(rep(c(1:6), 7)) and here is the dependent variable: > dv

I'm looking for guidance on how to implement forward stepwise regression using lmStepAIC in Caret. The stepwise "direction" appears to default to "backward". When I try to use "scope" to provide a lower and upper model, Caret still seems to default to "backward". Any thoughts on how I can make this work? Here is what I tried: itemonly <-

Hello: When I use SPSS I execute the AGGREGATE DATA comand for the next data: 2112141123212213212213334 3143244113442312121213344 2114141123112214212113344 2112211122212413421213221 3114444123442414343413344 2312231223212222323223322 2143241123212313131213234 2113241113212313222213333 2113141123112214212113344 2114141123412111114413344 2113211122342314222313234 2114141123112414212113344

Hi, I am doing a hierarchical CFA using the sem package. I have 20 items, and I have 2 factors (F3 and F4), and also F1 and F2 are nested within F3. Here is the code that I have, but it is giving me an error message "Warning message: In eval(expr, envir, enclos) : Negative parameter variances. Model may be underidentified." and a further error "Error in summary.objectiveML(cfa,

* * Hi , Can anyone help me please with this problem?* * *CASE-I* all_raw_data_NAomitted is my data frame.It has columns with names i1 ,i2, i3,i4…, till i15.It has 291 rows actually ,couldn’t show here. The data frame looks like this:-- i1 i2 i3 i4 i5 i6 i7 i8 i9 i10 i11 i12 i13 i14 i15 2 2 2 2 2 2 2 2 2 2 2 1 2 2 3 2 3 2 2 2 2 3 2 2 3 3

Hello, I've been working with "nnet" and now I'd like to use the weigths, from the fitted model, to iterpret some of variables impornatce. I used the following command: mts <- nnet(y=Y,x=X,size =4, rang = 0.1, decay = 5e-4, maxit = 5000,linout=TRUE) X is (m x n) Y is (m x 1) And then I get the coeficients by: Wts<-coef(mts) b->h1 i1->h1

Hi All, ? I am trying to use the lordif package in R and can't figure out the reason for the above error when I try to run the calctheta() function. ################## # read data using foreign package - must be Stata 11 or earlier racedata<- read.dta("race.dta")??? # item responses resp.data<-racedata[,-c(1:2)] # grouping variable is in the second column

Hi, Suppose I've got a matrix, and the first few elements look like "x1 + x3 + x4 + x5 + x1:x3 + x1:x4" "x1 + x2 + x3 + x5 + x1:x2 + x1:x5" "x1 + x3 + x4 + x5 + x1:x3 + x1:x5" and so on (have got terms from x1 ~ x14). If I want to replace all the x1 with i7, all x2 with i14, all x3 with i13, for example. Is there an easy way? I tried to put what I want

Can any one help it will be very kind, loop statements I have this table and some more records, I want to reshape it V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 TP53 Dis1 Dis2 Dis3 Dis4 Dis5 Dis6 DCI New1 New2 New3 New4 FDI Hi2 H3 H4 GHD I1 I3 I4 I5 I6 I7 I8 I want my new table or matrix to be some thing like this V1 V2 V3 Tp53 Dis1 Dis2 Tp53 Dis1 Dis3 Tp53 Dis1 Dis4 Tp53 Dis1 Dis5 Tp53 Dis1 Dis6 Tp53 Dis2

Hi All , Can anybody tell me if there's any way to get the summarized anova values.Now i will explain what i mean , when i say "*summarized*". Below you can see the anova table of recmeanC1 with rest* all* i.e from recmeanC2 to i15(predictors),as shown in table. Df Sum Squares Mean Square F value Significance [Pr(>F)] recmeanC2 1 89.272 89.272

Dear useRs from the physiology department, Does someone know of an implementation of the SF-36 questionnaire scoring in R? I only found SAS and STATA versions, e.g http://gim.med.ucla.edu/FacultyPages/Hays/util.htm Dieter

I am exploring neural networks (adding non-linearities) to see if I can get more predictive power than a linear regression model I built. I am using the function nnet and following the example of Venables and Ripley, in Modern Applied Statistics with S, on pages 246 to 249. I have standardized variables (z-scores) such as assets, age and tenure. I have other variables that are binary (0 or 1). In

Hi All, Can someone help me?The way to do this may be very easy but i do not know. *Question1:----* factanal() function produces the results in this way:-- *RESULTS:--* *>fact1<- factanal(data_withNA,factors=1,rotation="none") >fact1$"loadings"* Loadings: Factor1 i1 0.784 i2 0.874 i3 0.786 i4 0.839 i5 0.778 i6 0.859 i7 0.850 i8 0.763 i9 0.810 i10 0.575

Hello all, I have just started using fitting the PCM (Partial Credit Model) using eRm and have 2 problems which I cannot solve, I have checked everywhere on the net, but no joy: Firstly, I have fit a PCM model to 10 variables with differing response categories (3 for the first 6 items, 4 for the following 2 and 2 for the last two items). mat1 <- matrix(c(rasch_bart$bart_bowel,

As a follow-up to my email below: The input data frame to nnet() has dimensions: > dim(coreaff.trn.nn) [1] 5088 8 And the predictions from the neural network (35 records are dropped - see email below for more details) has dimensions: > pred <- predict(coreaff.nn1) > dim(pred) [1] 5053 1 So, the following line of R code does not work as the dimensions are