我想预测一个多元回归模型。但是，使用forecast函数时出现错误：
forecast(fit, 1:24)
Error in eval(expr, envir, enclos) : object 'carReg' not found
plot(forecast(fit, 1:24))
Error in plot(forecast(fit, 1:24)) :
error in evaluating the argument 'x' in selecting a method for function 'plot': Error in eval(expr, envir, enclos) : object 'carReg' not found
您可以在下面找到使用过的代码：
require("bootstrap")
require("relaimpo")
require("DAAG")
library(bootstrap)
library(DAAG)
library(relaimpo)
library(forecast)
# Data
######
datSel <- structure(list(oenb_dependent = c(1.0227039, -5.0683144, 0.6657713,
3.3161374, -2.1586704, -0.7833623, -0.2203209, 2.416144, -1.7625406,
-0.1565037, -7.9803936, 9.4594715, -4.8104584, 8.4827107, -6.1895262,
1.4288595, 1.4896459, -0.4198522, -5.1583964, 5.2502294, 1.0567102,
-1.0923342, -1.5852298, 0.6061936, -0.3752335, 2.5008664, -1.3999729,
2.2802166, -2.1468756, -1.4890328, -0.79254376, 3.21804705, -0.94407886,
-0.27802316, -0.20753079, -1.12610048, 2.0883735, -0.7424854,
0.44203729, -1.48905938, 1.39644424, -3.8917377, 11.25665848,
-9.22884035, 3.26856762, -0.00179541, -2.39664325, 4.00455574,
-5.60891295, 4.6556348, -4.40536951, 6.64234497, -7.34787319,
7.56303006, -8.23083674, 4.43247855, 1.31090412), carReg = c(0.73435946,
0.24001161, 16.90532537, -14.60281976, 6.47603166, -8.35815849,
3.55576685, 7.10705794, -4.6955223, 10.9623709, 5.5801857, -6.4499936,
-9.46196502, 9.36289122, -8.52630424, 5.45070994, -4.5346405,
-2.26716538, 2.56870398, 0.013737, 5.7750101, -27.1060826, 1.08977179,
4.94934712, 17.55391859, -13.91160577, 10.38981128, -11.81349246,
-0.0831467, 2.79748237, 1.84865463, -1.98736934, -6.24191695,
13.33602659, -3.86527871, 0.78720993, 4.73360651, -4.1674034,
9.37426802, -5.90660464, -0.4915792, -5.84811629, 9.67648643,
-6.96872719, -7.6535767, 0.24847595, 0.18685263, -2.28766949,
1.1544631, -3.87636933, -2.4731545, 4.33876671, 1.08836339, 5.64525271,
1.90743854, -3.94709355, -0.84611324), cpi = c(1.16, -3.26, 0.22,
-3.51, 0.84, -2.81, -0.34, -4.57, -0.12, -3.95, -1.37, -2.73,
0.35, -5.38, -4.43, -3.08, 0.74, -3.03, -1.09, -2, 0.35, -1.52,
1.28, 0.2, -0.25, -4.55, -2.49, -4.24, -0.31, -2.96, -2.24, -0.46,
-0.06, -2.67, -1.27, -1.4, -0.7, -0.96, -2.18, -2.53, -0.52,
-1.74, -2.18, -1.4, -0.34, -0.09, -1.65, -1.15, -0.17, -2.01,
-1.38, -1.24, 0.09, -2.44, -1.92, -2.61, -0.34), primConstTot = c(-0.33334,
-0.93333, -0.16667, -0.33333, -0.16667, -0.86666, -0.3, -0.4,
-0.26667, -1.56667, -0.73333, 0.1, -0.23333, -0.26667, -1.5774,
-0.19284, 0.38568, -2.42423, -0.93663, 0.08265, -0.63361, 0.0551,
-0.49587, 2.39668, -1.70798, -3.36085, -2.56196, 0.16529, 0,
-1.84572, -1.3774, -0.49586, -1.70798, -1.90081, -0.55096, -0.77134,
-0.16529, -0.30303, -0.17066, -0.23853, -0.64401, -1.52657, -1.57426,
-0.28623, -0.54861, -1.07336, -0.71558, 0.02385, -0.38164, -1.09721,
0, 0.14311, -0.38164, -1.02566, -0.42934, -0.35779, -0.4532),
resProp.Dwell = c(0.8, -4, -3.2, 2.7, -1.6, -1, -2.4, -0.4,
-0.8, 1, -12.1, 0.2, -5.2, 3.7, -2.7, -1.7, 1.5, 0.7, -7.9,
0.3, 0.3, 1.4, -3.3, -1, -1.6, 1.5, 0.5, 1.5, -1, -2.2, -3.5,
0.5, 0.5, -0.9, -0.4, -3.4, 0.9, 0.1, -0.2, -2.8, -0.8, -6.2,
11.3, -4.6, 1, 1.1, -1.7, 4.1, -5, 2.3, -2.3, 4.6, -6.3,
6.3, -6.9, 0, 2.4), cbre.office.primeYield = c(0, 0, 0.15,
0.15, 0.2, 0.2, 0.2, 0.25, 0.25, 0.25, 0.25, 0.2, 0.15, 0.1,
0.05, 0.15, 0.3, 0.35, 0.4, 0.3, 0.2, 0, -0.15, -0.85, -1,
-0.85, -0.75, -0.1, 0, 0, 0, 0.05, 0.05, 0.05, 0.05, 0, 0,
0, 0.2, 0.2, 0.2, 0.2, 0, 0, 0, 0, 0.25, 0.25, 0.25, 0.25,
0, 0, 0, 0, 0, 0, 0), cbre.retail.capitalValue = c(-1882.35294,
230.76923, -230.76923, -226.41509, -670.78117, -436.13707,
-222.22223, 0, -205.91233, -202.16847, 0, -393.5065, -403.91909,
-186.30647, -539.81107, -748.11463, -764.70588, -311.47541,
-301.42782, -627.09677, -480, 720, 782.6087, 645.96273, 251.42857,
1386.66667, -533.33334, -533.33333, -533.33333, 0, 0, -1024.56141,
-192.10526, 0, -730, 0, 0, 0, 0, 0, -834.28571, 0, -1450.93168,
0, 0, 0, -700.78261, 0, 0, 0, 0, 0, 0, 0, -1452, 0, 0)), .Names = c("oenb_dependent",
"carReg", "cpi", "primConstTot", "resProp.Dwell", "cbre.office.primeYield",
"cbre.retail.capitalValue"), row.names = c(NA, -57L), class = "data.frame")
fit <- lm(oenb_dependent ~ carReg + cpi + primConstTot +
resProp.Dwell + cbre.office.primeYield + cbre.retail.capitalValue , data = datSel)
summary(fit) # show results
# Other useful functions, tables and information
coefficients(fit) # model coefficients
confint(fit, level=0.95) # CIs for model parameters
fitted(fit) # predicted values
residuals(fit) # residuals
anova(fit) # anova table
vcov(fit) # covariance matrix for model parameters
influence(fit) # regression diagnostics
# diagnostic plots
layout(matrix(c(1,2,3,4),2,2)) # optional 4 graphs/page
plot(fit)
# plot
######
forecast(fit, 1:24)
plot(forecast(fit, 1:24))
我在这里得到错误：
# plot
######
forecast(fit, 1:24)
plot(forecast(fit, 1:24))
有什么建议我做错了吗？
感谢您的回复！
答案 0 :(得分：2)
你需要的是预测。见https://stackoverflow.com/a/31410788/2824732
请参阅：
new <- data.frame(carReg=-6.45, cpi=-2.73 , primConstTot=0.1,
resProp.Dwell= 0.2 ,cbre.office.primeYield=0.2,cbre.retail.capitalValue=-393)
predict(fit,new )
plot(predict(fit, new))
> predict(fit,new ) 1 1.556804