predict

问题 I need to apply lm() to an enlarging subset of my dataframe dat , while making prediction for the next observation. For example, I am doing: fit model predict ---------- ------- dat[1:3, ] dat[4, ] dat[1:4, ] dat[5, ] . . . . dat[-1, ] dat[nrow(dat), ] I know what I should do for a particular subset (related to this question: predict() and newdata - How does this work?). For example to predict the last row, I do dat1 = dat[1:(nrow(dat)-1), ] dat2 = dat[nrow(dat), ] fit = lm(log(clicks) ~ log

问题 I'm having trouble fitting a loess smooth plus confidence limits to a scatterplot of residuals. My model is height ~ weight + chest circumference. To check linearity of chest circumference, I've fitted a model without chest circumference (i.e. height ~ weight), and plotted the residuals of this model against chest circumference. So far so good. I then tried to use loess() and predict() to plot a loess line, plus confidence limits. The result looks like this (in the picture I've only plotted

问题 Does anyone have a nice clean way to get predict behavior for felm models? library(lfe) model1 <- lm(data = iris, Sepal.Length ~ Sepal.Width + Species) predict(model1, newdata = data.frame(Sepal.Width = 3, Species = "virginica")) # Works model2 <- felm(data = iris, Sepal.Length ~ Sepal.Width | Species) predict(model2, newdata = data.frame(Sepal.Width = 3, Species = "virginica")) # Does not work 回答1: As a workaround, you could combine felm , getfe , and demeanlist as follows: library(lfe) lm

问题 I've got some coefficients for a logit model set by a non-r user. I'd like to import those coefficients into r and generate some goodness of fit estimates on the same dataset (ROC and confusion matrix) vs my own model. My first thought was to coerce the coefficients into an existing GLM object using something like summary(fit)$coefficients[,1] <- y or summary(fit)$coefficients <- x where y and x are matrices containing the coefficients I'm trying to use to predict and fit is a previously

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 3 years ago . I've read other postings regarding named variables and tried implementing the answers but still get too many values for my new data that I want to run my existing model on. Here is working example code: set.seed(123) mydata <- data.frame("y"=rnorm(100,mean=0, sd = 1),"x"=c(1:100)) mylm <- lm(y ~ x, data=mydata)

问题 I posted earlier today about an error I was getting with using the predict function. I was able to get that corrected, and thought I was on the right path. I have a number of observations (actuals) and I have a few data points that I want to extrapolate or predict. I used lm to create a model, then I tried to use predict with the actual value that will serve as the predictor input. This code is all repeated from my previous post, but here it is: df <- read.table(text = ' Quarter Coupon Total

问题 I would like to fit a quadratic to (Time,SkinTemp) for each id in the following data.frame df. Each id has a different number of Time,SkinTemp entries so I'm stuck with 'predict' df<-data.frame(Time=seq(65), SkinTemp=rnorm(65,37,0.5), id=rep(1:10,c(5,4,10,6,7,8,9,8,4,4))) So far I have: #Fit the model y=x^2+x+C fitted_models = df %>% group_by(id) %>% do(model = lm(SkinTemp ~ Time+I(Time^2), data = .)) So far so good. Here's where I'm stuck. How do I pass the original Time data into the

问题 I built a classification model in R using C5.0 given below: library(C50) library(caret) a = read.csv("All_SRN.csv") set.seed(123) inTrain <- createDataPartition(a$anatomy, p = .70, list = FALSE) training <- a[ inTrain,] test <- a[-inTrain,] Tree <- C5.0(anatomy ~ ., data = training, trControl = trainControl(method = "repeatedcv", repeats = 10, classProb = TRUE)) TreePred <- predict(Tree, test) The training set has features like - examcard, coil_used, anatomy_region, bodypart_anatomy and

问题 This question arose as a result of another question posted here: non-conformable arguments error from lmer when trying to extract information from the model matrix When trying to obtain predicted means from an lmer model containing a factor variable, the output varies depending on how the factor variable is specified. I have a variable agegroup, which can be specified using the groups "Children <15 years", "Adults 15-49 years", "Elderly 50+ years" or "0-15y", "15-49y", "50+y". My choice

问题 I am currently dealing witha very small data set (20 observations, I know it's terrible). But I need to somehow forecast out the values. When I simply regress time on the dependent variable I am able to get a prediction, but when I add lagged or differenced variables it does not predict more than one year into the future. Is this due to having too few observations? Here is my code for context. The two lines have have commented out result in a better fitting prediction for present data, but