R语言Bass模型进行销售预测
BASS扩散模型三个参数:#最终购买产品的总人数,m; 创新系数p; 和#系数的模仿:
BASS扩散模型
q# exampleT79<-1:10Tdelt<-(1:100)/10Sales<-c(840,1470,2110,4000,7590,10950,10530,9470,7790,5890)Cusales<-cumsum(Sales)Bass.nls<-nls(Sales~M*(((P+Q)^2/P)*exp(-(P+Q)*T79))/(1+(Q/P)*exp(-(P+Q)*T79))^2,start=list(M=60630,P=0.03,Q=0.38))summary(Bass.nls)
##
## Formula: Sales ~ M * (((P + Q)^2/P) * exp(-(P + Q) * T79))/(1 + (Q/P) *
## exp(-(P + Q) * T79))^2
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## M 6.80e+04 3.13e+03 21.74 1.1e-07 ***
## P 6.59e-03 1.43e-03 4.61 0.0025 **
## Q 6.38e-01 4.14e-02 15.41 1.2e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 727 on 7 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 7.32e-06
# get coefficient
Bcoef <- coef(Bass.nls)
m <- Bcoef[1]
p <- Bcoef[2]
q <- Bcoef[3]
#将M的起始值设置为记录的总销售额。
ngete <- exp(-(p + q) * Tdelt)
# plot pdf
Bpdf <- m * ((p + q)^2/p) * ngete/(1 + (q/p) * ngete)^2
plot(Tdelt, Bpdf, xlab = "Year from 1979", ylab = "Sales per year", type = "l")
points(T79, Sales)
# plot cdfBcdf <- m * (1 - ngete)/(1 + (q/p) * ngete)plot(Tdelt, Bcdf, xlab = "Year from 1979", ylab = "Cumulative sales", type = "l")points(T79, Cusales)
#当q = 0时,只有创新者没有模仿者。
Ipdf <- m * ((p + 0)^2/p) * exp(-(p + 0) * Tdelt)/(1 + (0/p) * exp(-(p + 0) *
Tdelt))^2# plot(Tdelt, Ipdf, xlab = 'Year from 1979',ylab = 'Isales per year',# type='l')Impdf <- Bpdf - Ipdfplot(Tdelt, Bpdf, xlab = "Year from 1979", ylab = "Sales per year", type = "l",
col = "red")lines(Tdelt, Impdf, col = "green")lines(Tdelt, Ipdf, col = "blue")
#当q = 0时
Icdf <- m * (1 - exp(-(p + 0) * Tdelt))/(1 + (0/p) * exp(-(p + 0) * Tdelt))
# plot(Tdelt, Icdf, xlab = 'Year from 1979',ylab = 'ICumulative sales',
# type='l')
Imcdf <- m * (1 - ngete)/(1 + (q/p) * ngete) - Icdf
plot(Tdelt, Imcdf, xlab = "Year from 1979", ylab = "Cumulative sales", type = "l",
col = "red")
lines(Tdelt, Bcdf, col = "green")
lines(Tdelt, Icdf, col = "blue")
可下载资源
关于作者
Kaizong Ye是拓端研究室(TRL)的研究员。在此对他对本文所作的贡献表示诚挚感谢,他在上海财经大学完成了统计学专业的硕士学位,专注人工智能领域。擅长Python.Matlab仿真、视觉处理、神经网络、数据分析。
本文借鉴了作者最近为《R语言数据分析挖掘必知必会 》课堂做的准备。
非常感谢您阅读本文,如需帮助请联系我们!