R 中的預測 function 和零膨脹負二項式 model 錯誤

Question

我得到了一個Rdata文件，其中包含來自回歸 model 的大量輸入和輸出。 我已經能夠提取 model 分析的數據並重現參數估計。 但是，當我嘗試使用原始predict語句時，我收到一個錯誤，即使predict語句在應用於存儲在Rdata文件中的 model 時沒有返回錯誤。

我希望下面提供了足夠的信息，即使我沒有提供功能可重現的示例，也有人可以告訴我如何更正我的預測語句my.probs 。 我認為，這是我第一次在這里發布問題而沒有提供這樣的例子。 該數據集包含 > 100,000 個觀察值，有點敏感，我不確定如何重現Rdata文件。

library(msm)
library(MASS)
library(pscl)

# model output returned when extracting the model name from the `Rdata` file
original.model
# Call:
# zeroinfl(formula = AA ~ log(BB) + CC + DD + CC:DD | log(BB) + DD, data = original.data, 
#     offset = log(EE), dist = "negbin")
# 
# Count model coefficients (negbin with log link):
#           (Intercept)  log(BB)      CC3      CC4      CC5 DDPrivate CC3:DDPrivate CC4:DDPrivate CC5:DDPrivate  
#              -2.05317  0.31178 -0.41402 -0.71208 -0.92290   0.17878      -0.18476      -0.18674       0.07307  
# Theta = 0.8551 
# 
# Zero-inflation model coefficients (binomial with logit link):
#           (Intercept)  log(BB)        DDPrivate  
#                1.6724 -0.5022            0.9742  
#  
# Warning message:
# In deparse(x$call, width.cutoff = floor(getOption("width") * 0.85)) :
#   invalid 'cutoff' value for 'deparse', using default

# data for new observation for use in the predict statement
new.data
#         DD      EE   CC               BB
# 1  Private       1    4         1118.948

str(new.data)
#'data.frame': 1 obs. of  4 variables:
# $ DD       : Factor w/ 2 levels "Public","Private": 2
# $ EE       : num 1
# $ CC       : Factor w/ 4 levels "2","3","4","5": 3
# $ BB: num 1119

original.probs <- predict(original.model, new.data, type='prob')
original.probs
# truncated probabilities returned by the predict statement.  These sum to one if vector not truncated
c(0.7534319,    0.1552296,    0.05681916,   0.02133936,   0.008116065,  0.003110019,  0.001197667)

# reproduce the original model
my.version <- zeroinfl(formula = AA ~ log(BB) + CC + DD + CC:DD | log(BB) + DD, offset = log(EE), dist = "negbin")

# Error returned by the predict statement
my.probs <- predict(my.version, new.data, type='prob')
my.probs
# Error in exp(X %*% object$coefficients$count + offsetx)[, 1] : 
#   incorrect number of dimensions
# In addition: Warning message:
# In X %*% object$coefficients$count + offsetx :
#   Recycling array of length 1 in array-vector arithmetic is deprecated.
#   Use c() or as.vector() instead.

Answer 1

在我將輸入變量分組到data.frame並將data選項包含在zeroinfl model 語句中之后， predict function 起作用：

my.data <- data.frame(AA = AA,
                      BB = BB,
                      CC = CC,
                      DD = DD,
                      EE = EE)

my.version <- zeroinfl(formula = AA ~ log(BB) + CC + DD + CC:DD | log(BB) + DD, 
                               offset = log(EE), dist = "negbin", data = my.data)

summary(my.version)

my.probs <- predict(my.version, new.data, type='prob')
my.probs