lm

Why I have the exact same model, but run predictions on different grid size (by 0.001 vs by 0.01) getting different predictions? set.seed(0) n_data=2000 x=runif(n_data)-0.5 y=0.1*sin(x*30)/x+runif(n_data) plot(x,y) poly_df=5 x_exp=as.data.frame(cbind(y,poly(x, poly_df))) fit=lm(y~.,data=x_exp) x_plt1=seq(-1,1,0.001) x_plt_exp1=as.data.frame(poly(x_plt1,poly_df)) lines(x_plt1,predict(fit,x_plt_exp1),lwd=3,col=2) x_plt2=seq(-1,1,0.01) x_plt_exp2=as.data.frame(poly(x_plt2,poly_df)) lines(x_plt2,predict(fit,x_plt_exp2),lwd=3,col=3) 李哲源 This is a coding / programming problem as on my quick run I

The standard way of doing a linear regression is something like this: l <- lm(Sepal.Width ~ Petal.Length + Petal.Width, data=iris) and then use predict(l, new_data) to make predictions, where new_data is a dataframe with columns matching the formula. But lm() returns an lm object, which is a list that contains crap-loads of stuff that is mostly irrelevant in most situations. This includes a copy of the original data, and a bunch of named vectors and arrays the length/size of the data: R> str(l) List of 12 $ coefficients : Named num [1:3] 3.587 -0.257 0.364 ..- attr(*, "names")= chr [1:3] "

I wonder if it is possible to use lm() within mutate() of dplyr package. Currently I have a dataframe of "date", "company", "return" and "market.ret" reproducible as below: library(dplyr) n.dates <- 60 n.stocks <- 2 date <- seq(as.Date("2011-07-01"), by=1, len=n.dates) symbol <- replicate(n.stocks, paste0(sample(LETTERS, 5), collapse = "")) x <- expand.grid(date, symbol) x$return <- rnorm(n.dates*n.stocks, 0, sd = 0.05) names(x) <- c("date", "company", "return") x <- group_by(x, date) x <- mutate(x, market.ret = mean(x$return, na.rm = TRUE)) Now for each company I would like to fit "return" by

After reviewing other stackoverflow posts, I am attempting to add a regression line to my scatter plot with: plot(subdata2$PeakToGone, subdata2$NO3_AVG, xlim = c(0, 70)) abline(lm(PeakToGone~NO3_AVG, data = subdata2)) However, it is not showing the line. I would also like to add the R^2, RMSE, and p-value from lm as text on the plot. How can I add the regression line to the plot, along with these goodness-of-fit stats? lukeA By default, plot regards the 1st param as x and the 2nd as y . Try plot(y = subdata2$PeakToGone, x = subdata2$NO3_AVG, xlim = c(0, 70)) abline(lm(PeakToGone~NO3_AVG, data

I have a data set called data which has 481 092 rows. I split data into two equal halves: The first halve (row 1: 240 546) is called train and was used for the glm() ; the second halve (row 240 547 : 481 092) is called test and should be used to validate the model; Then I started the regression: testreg <- glm(train$returnShipment ~ train$size + train$color + train$price + train$manufacturerID + train$salutation + train$state + train$age + train$deliverytime, family=binomial(link="logit"), data=train) Now the prediction: prediction <- predict.glm(testreg, newdata=test, type="response") gives

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(v1) + log(v12), data=dat1) predict.fit = predict(fit, newdata=dat2, se.fit=TRUE) How can I do this