predict

I am doing a tobit analysis on a dataset where the dependent variable (lets call it y) is left censored at 0. So this is what I do: library(AER) fit <- tobit(data=mydata,formula=y ~ a + b + c) This is fine. Now I want to run the "predict" function to get the fitted values. Ideally I am interested in the predicted values of the unobserved latent variable "y*" and the observed censored variable "y" [See Reference 1]. I checked the documentation for predict.survreg [Reference 2] and I don't think I understood which option gives me the predicted censored variables (or the latent variable). Most

I am trying to use predict function in R using a model saved earlier. The model was created and saved using the following code: lrModel1 <- glm(response ~ .,data = modelData,family = binomial,model = TRUE) save(lrModel1,file = "lrModel100.rda") When I load the model for later use as follows and try to use the predict function on it as follows: bar <- load("lrModel100.rda") predicted <- predict(bar,validationData,type = "response") I get the following error: Error in UseMethod("predict") : no applicable method for 'predict' applied to an object of class "character" Is there a way to get the

I split Train data set and Test data set. I used a package rpart for CART (classification tree) in R (only train set). And I want to carry out a ROC analysis using the ROCR package. Variable is `n. use' (response varible... 1=yes, 0=no): > Pred2 = prediction(Pred.cart, Test$n.use) Error in prediction(Pred.cart, Test$n.use) : **Format of predictions is invalid.** This is my code. What is problem? And what is right type ( "class" or "prob" ? library(rpart) train.cart = rpart(n.use~., data=Train, method="class") Pred.cart = predict(train.cart, newdata = Test, type = "class") Pred2 = prediction

Has anyone had any success in predicting or profiling DXL applications for memory and CPU usage? I have a script that opens, closes, and modifies modules, possibly hundreds or even thousands of modules. I want to know, or least have a rough idea, how many modules I can run through the script before running out of memory. Cross posts: http://smartdxl.com/forum/viewtopic.php?f=1&t=602 https://www.ibm.com/developerworks/community/forums/html/topic?id=c5d4cc33-9986-463e-a73b-36523e6add7a&ps=25 来源： https://stackoverflow.com/questions/19422567/predicting-dxl-memory-and-cpu-usage

I was thinking about how in the probably distant future many people think that we wont rely on physical input (i.e. keyboard) as much because the technology that reads brain waves (which already exists to some extent) will be available. Kinda scares me....anyway, I while I was daydreaming about this, the idea came to me that: what if a programmer could implement logic in their code to accurately predict the users intentions and then carry out the intended operation with no need for human interaction. I am not looking for anything specific, I'm just a little curious as to what anyone's thoughts

import cv2 import numpy as np faceDetect=cv2.CascadeClassifier('haarcascade_frontalface_default.xml'); cam=cv2.VideoCapture(0); rec=cv2.face.LBPHFaceRecognizer_create(); rec.load("recognizerr\\trainingData.yml") id=0 fontface=cv2.FONT_HERSHEY_SIMPLEX while(True): ret,img=cam.read(); gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) faces=faceDetect.detectMultiScale(gray,1.3,5); for(x,y,w,h) in faces: cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2) id,conf=rec.predict(gray[y:y+h,x:x+w]) if(id==1): id="Name" else: id="Unknown" cv2.putText(img,str(id),(x,y+h),fontface,2,(255,0,0),3); cv2.imshow("Face",img