search for: 1se

...- split(dataS$P1_retained, dataS$TotalHours_R) yc <- split(dataS$x, dataS$TotalHours_R) for (i in 1:length(yc)) { fit=cv.glmnet(as.matrix(yc[[i]]), y=xc[[i]], alpha=.05, type="mse", nfolds=10, standardize=TRUE,family="binomial") c_output = c(i,fit$cvlo[fit$lambda==fit$lambda.1se],fit$cvm[fit$lambda==fit$lambda.1se], fit$cvup[fit$lambda==fit$lambda.1se]) names(c_output) = names(output_x) output_x = rbind(output_x, t(c_output)) fit1=cv.glmnet(as.matrix(yc[[i]]), y=xc[[i]], alpha=.05, type="auc", nfolds=10, standardize=TRUE,family="binomial") c_output1 =...

Dear my R friends, I want to get a number from a list using paste function. In my example, lambda.rule <- "lambda.1se" cvtest is a list (result from cv.glmnet) and cvtest$lambda.1se [1] 1.308973 I want to call the value using paste function. I used get function but there was an error. test <- get(paste("cvtest$",lambda.rule, sep="")) Error in get(paste("cvtest$", lambda.rule...

...was wondering if there is a simple way to avoid printing the multiple cross-validation automatic output to the console of recursive partitionning functions like rpart or mvpart. For example... > data(spider) > mvpart(data.matrix(spider[,1:12])~herbs+reft+moss+sand+twigs+water,spider,xv="1se",xvmult=100) *X-Val rep : 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70...

...am fairly new to R and I am trying to run mvpart and create a MRT using explanatory variables and covariables. I've been following the procedures in Numerical Ecoogy with R. The command (no covariables) which works fine - ABUNDTMRT <- mvpart(abundance ~ .,factors,margin=0.08,cp=0,xv="1se",xval=nrow(abundance),xvmult=100,which=4) where abundance is 4th root transformed fish abundance (103 species x 168 samples), and factors is the relief (high, medium, low profile, sand inundated reef, flat), benthos (coral, sessile inverts, kelp, macroalgae, seagrass, sand), depth (continuous...

...l", alpha = 1, # lasso penalty type.measure = "deviance", standardize = FALSE, intercept = FALSE, nfolds = 10, keep = FALSE) plot(fit.net) log(fit.net$lambda.1se) pred <- predict(fit.net, x, type = "response", s = "lambda.1se") all(coef(fit.net) == 0) all(pred ==0.5) Thanks in advance for your thoughts. Regards, Lars. [[alternative HTML version deleted]]

.... We recently conducted a study using TBR. In our first study, we used "regular" TBR in SPSS to model 1 dependent variable. Note we have a relatively small data-set of 100 cases. In SPSS, we used a minimum change of improvement smaller than 0.000001 as a stopping rule. Also, we chose the 1SE "rule", set the maximum tree depth to 10 levels, and the minimum number of cases was set to 5 for parent nodes and 3 for child nodes. Now we would like to proceed with fitting a multivariate tree. We only used pruning by the way, no v-fold cross validation afterwards. Using the aforementi...

...in a fitful breeze. A bigger problem with rpart is the users consistenly ignore the xerror column above, and print out (and believe) bigger trees than they should. Once the xerror bottoms out you are almost certainly looking at random noise. Since the xerror curve often has a long flat bottom the 1SE rule is better (anything within 1SE of the min is a tie, use the smallest of a set of tied models). Terry Therneau

Hi, I am writing to seek some guidance regarding using Lasso regression with the R package LARS. I have introductory statistics background but I am trying to learn more. Right now I am trying to duplicate the results in a paper for shRNA prediction "An accurate and interpretable model for siRNA efficacy prediction, Jean-Philippe Vert et. al, Bioinformatics" for a Bioinformatics project

Hello, I'm using mvpart option xv="1se" to compute a regression tree of good size with the 1-SE rule. To better understand 1-SE rule, I took a look on its coding in mvpart, which is : Let z be a rpart object , xerror <- z$cptable[, 4] xstd <- z$cptable[, 5] splt <- min(seq(along = xerror)[xerror <= min(xerror) + xvse *...

I would like to use smbmount on a SGI running IRIX 6.5.1m. The distribution of samba for IRIX does not include the executables smbmount or smbmnt. Is there a source for these executables? Regards, Fred -------------------------------------- Fred Kunzler Phone: (281) 575-5090 91-1SE-42C Pager: (800) 618-5062 10200 Bellaire Blvd. Fax: (281) 575-5473 Houston, TX 77072 E-mail: fred.kunzler@halliburton.com <mailto:fred.kunzler@halliburton.com>

...blem with create mode = 0777 and force create mode = 0777 with no success. I have no problems with other applications, including MS Excel, MS PowerPoint, Visio, Notepad or MS Project. I tried the saving a Word document from Windows 95 and had no problem. Regards, Fred Kunzler Systems Analyst 91-1SE-42C Phone: (281) 575-5090 10200 Bellaire Blvd. Pager: (800) 618-5062 Houston, TX 77072 Fax: (281) 575-5473 E-mail: fred.kunzler@halliburton.com <mailto:fred.kunzler@halliburton.com>

Hello everyone, I am currently using the mvpart package and would like to change the color scheme it uses, and was hoping someone could help me out. All of the papers I have found have used a grayscale but I can't seem to figure out how they did that! Currently, mvpart plots barplots in a repeating sequence of 3 shades of blue. So if you have 6 response variables the same shade of blue is used

Hi All,  I have been trying to use glmnet package to do LASSO linear regression. my x data is a matrix n_row by n_col and y is a vector of size n_row corresponding to the vector data. The number of n_col is much more larger than the number of n_row. I do the following: fits = glmnet(x, y, family="multinomial")I have been following this

Dear R's I'm trying to use specific rather than random cross-validation groups in mvpart. The man page says: xval Number of cross-validations or vector defining cross-validation groups. And I found this reply to the list by Terry Therneau from 2006 The rpart function allows one to give the cross-validation groups explicitly. So if the number of observations was 10, you could use

...for alive, 1 for dead) and over 230 predictor variables. I currently using LASSO to reduce the number of predictor variables. I am using the cv.glmnet function to do 10-fold cross validation on a sequence of lambda values which I let glmnet determine. I then take the optimal lambda value (lambda.1se) which I then use to predict on an independent cohort. What I am finding is that this optimal lambda value fluctuates everytime I run glmnet with LASSO. It deviates quite a bit such that each time I generate an ROC curve for my validation cohort, I get AUC values which deviate a bit. Does anyo...

Hi, I am trying to do logistic regression for data of 104 patients, which have one outcome (yes or no) and 15 variables (9 categorical factors [yes or no] and 6 continuous variables). Number of yes outcome is 25. Twenty-five events and 15 variables mean events per variable is much less than 10. Therefore, I tried to analyze the data with penalized regression method. I would like please some of the