library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.2.1 v purrr 0.3.3
## v tibble 2.1.3 v dplyr 0.8.3
## v tidyr 1.0.0 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(readxl)
covid <- read_xlsx("C:/Dropbox/DRPH/P9489 Application of Epi Research Methods II/Data/COVID-2019.xlsx")
covid <- covid %>% mutate(dno = row_number())
covid
ggplot(data = covid, aes(x = Date, y = RoW_Confirmed)) + geom_line(color = "#00AFBB", size = 0.5)
ds <- length(covid$Confirmed) # all observations to date
d2 <- ds-10 # look back 10 days in the model
lastday <- 101 # 04/30/2020
x <- c(1:ds) #as.numeric(covid$Date)
y <- covid$RoW_Confirmed[1:ds]
plot(x, y)
fit <- lm(y ~ x + I(x^3) + I(x^2)-1)
points(x, predict(fit), type="l")
summary(fit)
##
## Call:
## lm(formula = y ~ x + I(x^3) + I(x^2) - 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -645.83 -308.28 -89.04 231.08 1166.45
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## x 127.45866 24.73376 5.153 8.22e-06 ***
## I(x^3) 0.32592 0.03294 9.895 4.57e-12 ***
## I(x^2) -12.90055 1.84756 -6.982 2.60e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 381.4 on 38 degrees of freedom
## Multiple R-squared: 0.964, Adjusted R-squared: 0.9612
## F-statistic: 339.5 on 3 and 38 DF, p-value: < 2.2e-16
curve(fit$coefficients[1]*x + fit$coefficients[3]*x^2 + fit$coefficients[2]*x^3, d2, (lastday))
fit$coefficients[1]*(lastday) + fit$coefficients[3]*(lastday)^2 + fit$coefficients[2]*(lastday)^3
## x
## 217068.1
x <- c(d2:ds) #as.numeric(covid$Date)
y <- covid$RoW_Confirmed[d2:ds]
plot(x, y)
fit <- lm(y ~ x + I(x^3) + I(x^2)-1)
points(x, predict(fit), type="l")
summary(fit)
##
## Call:
## lm(formula = y ~ x + I(x^3) + I(x^2) - 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -175.25 -99.61 21.11 85.86 179.25
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## x 1580.2161 167.0141 9.462 1.28e-05 ***
## I(x^3) 1.5440 0.1279 12.073 2.05e-06 ***
## I(x^2) -97.5965 9.2706 -10.528 5.78e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 134 on 8 degrees of freedom
## Multiple R-squared: 0.999, Adjusted R-squared: 0.9987
## F-statistic: 2772 on 3 and 8 DF, p-value: 2.099e-12
fit$coefficients[1:3]
## x I(x^3) I(x^2)
## 1580.216087 1.543963 -97.596461
curve(fit$coefficients[1]*x + fit$coefficients[3]*x^2 + fit$coefficients[2]*x^3, d2, (lastday))
fit$coefficients[1]*(lastday) + fit$coefficients[3]*(lastday)^2 + fit$coefficients[2]*(lastday)^3
## x
## 754766.7