如何编写R中系数趋于0的函数？

Question

I would like to write a function that smooths the coefficient of growth rate to 0 in 60 days. So far I managed to write the following code:

corona <- data.frame(Cases = c(3, 16, 79, 157, 229, 322, 400, 650, 888, 1128, 1694, 2036, 2502, 3089, 3858), Date = seq(as.Date("2020/02/20"), as.Date("2020/03/05"), by = "days"))

library(dplyr)
corona_entire <- corona %>% mutate(Growth = (Cases - lag(Cases))/lag(Cases)*100)  

mean(corona_entire$Growth[12:15])

ff = function(x) x*(1.2285823)^60

ff(3858)

However, in my function the growth rate (0.2285823) is constant over 60 periods. I would like to tell R to make that growth rate tend to 0 as we get closer and closer to 60. I need to write a convergence function for the growth rate basically.

Any idea how can I code it?

Thanks!

Answer 1

Further to my comment above, it's not clear to me what you're trying to do. If you want to model the Growth rate you need to fit some form of model.

For a start, how about an exponential model of the form y = y0 * exp(k * time) ?

In that case we can linearise the model (and data) by taking the log, and then use lm to estimate the model coefficients log(y0) and k .

df <- corona_entire %>% mutate(Time = as.integer(Date - min(Date)))
fit <- lm(log(Growth) ~ Time, weights = df$Growth, data = df)

Here I have used a weighted least squares approach by weighting every point by its Growth rate.

We can then plot the points and best fit curve:

f <- function(x, fit) exp(coef(fit)[1])*exp(coef(fit)[2] * x)
ggplot(df, aes(Time, Growth)) +
    geom_point() +
    stat_function(fun = f, args = list(fit = fit)) +
    labs(x = sprintf("Days since %s", min(df$Date)))

Not a good fit but this should give you some ideas. You probably want to fit a more suitable non-linear growth-rate model, and estimate parameters using nls .

---

Update

Since you really want to predict Cases , let's re-formulate our model.

We start again with an exponential model of the form Cases ~ y0 * exp(k * Time)

ggplot(df, aes(Time, Cases)) +
    geom_point()
fit1 <- lm(log(Cases) ~ Time, data = df)
f1 <- function(x, fit) exp(coef(fit)[1])*exp(coef(fit)[2] * x)
ggplot(df, aes(Time, Cases)) +
    geom_point() +
    stat_function(fun = f1, args = list(fit = fit1)) +
    labs(x = sprintf("Days since %s", min(df$Date)))

Not a good fit! Results seem to suggest sub-exponential growth. A simple model for sub-exponential growth in epidemiology is a model of the form Cases ~ (r / m * Time + A)^m , see eg Chowell et al., Phys. Life Rev. 18, 66 (2016) .

So let's fit the model, this time using the non-linear least-squares routine nls .

fit2 <- nls(
    Cases ~ (r / m * Time + A)^m,
    data = df,
    start = list(r = 4, m = 3, A = 1))
f2 <- function(x, r, m, A) (r / m * x + A)^m
ggplot(df, aes(Time, Cases)) +
    geom_point() +
    stat_function(
        fun = f2, 
        args = list(
            r = coef(fit2)[1],
            m = coef(fit2)[2],
            A = coef(fit2)[3])) +
    labs(x = sprintf("Days since %s", min(df$Date)))

Looks like a decent fit. You can inspect the quality of the fit and the non-linear least-squares estimates for the coefficients if you type summary(fit2)

summary(fit2)
#
#Formula: Cases ~ (r/m * Time + A)^m
#
#Parameters:
#  Estimate Std. Error t value Pr(>|t|)
#r   2.3308     0.6543   3.562  0.00391 **
#m   3.3316     0.4202   7.929 4.12e-06 ***
#A   2.1101     0.3126   6.750 2.04e-05 ***
#---
#Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
#Residual standard error: 51.41 on 12 degrees of freedom
#
#Number of iterations to convergence: 6
#Achieved convergence tolerance: 6.514e-07
#

Answer 2

If you just want a linear fall in growth rate towards 1 over 60 days you can do this:

ff = function(initial_n, initial_rate = 1.2285823, days = 60, time_to_stasis = 60)
{
  daily_rate <- seq(initial_rate, 1, length.out = time_to_stasis)
  result <- numeric(days)
  result[1] <- initial_n
  for(i in seq(days - 1)) result[i + 1] <- floor(daily_rate[i] * result[i])
  return(result)
}

So you get a daily number like this:

ff(3858)
#>  [1]    3858    4739    5803    7084    8620   10456   12643   15239   18309   21926
#> [11]   26173   31141   36932   43656   51436   60403   70699   82477   95897  111129
#> [21]  128350  147743  169494  193790  220818  250760  283791  320073  359754  402961
#> [31]  449796  500332  554607  612621  674331  739644  808418  880454  955498 1033237
#> [41] 1113297 1195248 1278600 1362812 1447290 1531398 1614460 1695773 1774611 1850239
#> [51] 1921922 1988936 2050581 2106192 2155151 2196899 2230944 2256873 2274360 2283171

and you can adjust the parameters to whatever you like.

You could use it to plot projections like this:

plot(1:60, ff(3858))

I'm not sure how biologically plausible this is though.

Answer 3

Looking at the data, it looks like a quadratic curve is the better option to model Cases as a function of days

corona$days = as.numeric(corona$Date - corona$Date[1], "days") + 1
mod = lm(Cases ~ poly(days, 2, raw = TRUE), corona)
summary(mod)

#Call:
#lm(formula = Cases ~ poly(days, 2, raw = TRUE), data = corona)

#Residuals:
#    Min      1Q  Median      3Q     Max 
#-140.48  -50.63  -24.30   65.89  148.04 

#Coefficients:
#                           Estimate Std. Error t value Pr(>|t|)    
#(Intercept)                 264.912     84.071   3.151  0.00836 ** 
#poly(days, 2, raw = TRUE)1 -158.269     24.179  -6.546 2.75e-05 ***
#poly(days, 2, raw = TRUE)2   25.863      1.469  17.600 6.17e-10 ***
#---
#Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

#Residual standard error: 94.38 on 12 degrees of freedom
#Multiple R-squared:  0.9949,   Adjusted R-squared:  0.9941 
#F-statistic:  1181 on 2 and 12 DF,  p-value: 1.668e-14


plot(corona$days, corona$Cases)
lines(predict(mod, data.frame(days = corona$days)))

# Growth Rate 
d = predict(mod, data.frame(days = 59:60))
diff(d)/d[1]
#         2 
#0.03606188