search for: hmean

...yv(temp_hist, mykey) setkeyv(humid_hist, mykey) # hist<- merge(temp_hist, humid_hist, by=mykey) # hist$X.x <- NULL hist$X.y <- NULL # # Max hist_max <- hist hist_max$FIPS <- hist_max$year <- hist_max$month <- hist_max$tmin <- hist_max$tmean <- hist_max$hmin <- hist_max$hmean <- NULL # # Adding Factors hist_max$citycode <- rep(101,nrow(hist_max)) hist_max$year <- rep(2010,nrow(hist_max)) hist_max$lnincome <- rep(10.262,nrow(hist_max)) # # Predictions pred_hist_max <- predict.gam(gam_max_count_wk,hist_max) # pred_hist_max <- as.data.table(pred_hist_max)...

...substitute(x)) means <- meds <- numeric(B) for (i in 1:B) { s <- sample(x, replace = TRUE) means[i] <- mean(s) meds[i] <- median(s) } CImedian <- quantile(meds, c(0.025, 0.5, 0.975)) CImean <- quantile(means, c(0.025, 0.5, 0.975)) hmean <- hist(means, freq = FALSE, plot = FALSE) hmeds <- hist(meds, freq = FALSE, plot = FALSE) split.screen(c(1, 2)) xlims <- range(c(hmean$breaks, hmeds$breaks)) ymax <- max(c(hmean$density, hmeds$density)) screen(1) plot(hmean, freq = FALSE, col = "red",...

...substitute(x)) means <- meds <- numeric(B) for (i in 1:B) { s <- sample(x, replace = TRUE) means[i] <- mean(s) meds[i] <- median(s) } CImedian <- quantile(meds, c(0.025, 0.5, 0.975)) CImean <- quantile(means, c(0.025, 0.5, 0.975)) hmean <- hist(means, freq = FALSE, plot = FALSE) hmeds <- hist(meds, freq = FALSE, plot = FALSE) split.screen(c(1, 2)) xlims <- range(c(hmean$breaks, hmeds$breaks)) ymax <- max(c(hmean$density, hmeds$density)) screen(1) plot(hmean, freq = FALSE, col = "red",...