nlgm (version 1.0)

Non-linear growth models: Non-linear growth models

Description

Non-linear growth models.

Usage

nlgm(y, ti, type, ini, ti.ahead = NULL)

Value

A list including:

mod

The object as returned by the function nls.

param

A vector with the parameters of the chosen model.

fit

The fitted values.

pred

The future predictions if values for the argument "ti.ahead" were given, otherwise this is NULL.

Arguments

y

A vector with the cumulative number of cases.

ti

A vector with the time, e.g. days.

ini

A vector with the initial values. The length varies according to the type of growth model.

type

The type of the growth model. "richards", "3logistic", "4logistic", "5logistic", "gompertz" or "weibull". See Reddy et al. (2021) for more information.

ti.ahead

If you want to make predictions for a number of days ahead, provide the number of days, a single number.

Author

Michail Tsagris and Nikolaos Pandis.

R implementation and documentation: Michail Tsagris and Nikolaos Pandis .

Details

Non-linear growth curves are fitted using least squares.

References

Reddy T., Shkedy Z., van Rensburg C. J., Mwambi H., Debba P., Zuma K. and Manda, S. (2021). Short-term real-time prediction of total number of reported COVID-19 cases and deaths in South Africa: a data driven approach. BMC medical research methodology, 21(1), 1-11.

See Also

boot.pred, fit.plot

Examples

## Data on Covid 19 for the first 96 days of Belgium
y <- c( 19, 38, 72, 125, 206, 316, 343, 407, 501, 600, 774, 1024, 1362, 1541, 1755, 2142,
        2564, 3098, 3811, 4473, 4942, 5428, 6756, 7951, 9150, 10513, 12031, 12875, 13558,
        15296, 16977, 18493, 19971, 21665, 22587, 23252, 25186, 26701, 28299, 30538, 32874,
        33903, 34427, 34964, 36524, 38157, 39831, 41225, 41947, 42390, 43666, 44936, 45713,
        46689, 47500, 47888, 48093, 48848, 49417, 49939, 50525, 50762, 51048, 51188, 51858,
        52404, 52956, 53398, 53881, 54121, 54239, 54715, 55110, 55431, 55736, 56082, 56229,
        56310, 56627, 56919, 57304, 57374, 57622, 57745, 57820, 58134, 58336, 58518, 58689,
        58854, 58915, 58964, 59023, 59204, 59363, 59535 )
ti <- 1:96
## Apply the 4-parameter logistic model
mod1 <- nlgm(y, ti, type = "4logistic", ini = c(60000, 1, 1, 35) )
## predict the cases 10 days ahead
mod2 <- nlgm(y, ti, type = "4logistic", ini = c(60000, 1, 1, 35), ti.ahead = 10 )