terms.formula:'；中存在错误'；在公式中，无'；数据'；线性回归论元_R_Regression_Linear Regression_Lm

terms.formula:'；中存在错误'；在公式中，无'；数据'；线性回归论元

terms.formula:'；中存在错误'；在公式中，无'；数据'；线性回归论元,r,regression,linear-regression,lm,R,Regression,Linear Regression,Lm,我正在为R中的多元线性回归编写一个简单的代码。代码如下 dataset$State = factor (dataset$State, levels = c ('New York','California','Florida'), labels = c ('1','2','3') ) #Splitting the dataset library(caTools) set.seed(123)

我正在为R中的多元线性回归编写一个简单的代码。代码如下

dataset$State = factor (dataset$State, 
                        levels = c ('New York','California','Florida'), 
                        labels = c ('1','2','3') ) 
#Splitting the dataset 
library(caTools) 
set.seed(123) 
split = sample.split(dataset$Profit, SplitRatio = 0.8) 
training_set = subset(dataset$Profit, split == TRUE)
test_set = subset(dataset$Profit, split == FALSE) 

#Fitting Multiple Linear Regression to the Training set 
regressor = lm(formula = Profit ~ ., data = training_set)

但是我在运行时遇到了这个错误

术语中存在错误。公式（公式，数据=数据）中的“.”和没有“数据”参数

为什么会出现这样的错误

数据集

位于

感谢您使此数据可复制

dataset <- 
structure(list(R.D.Spend = c(165349.2, 162597.7, 153441.51, 144372.41, 
142107.34, 131876.9, 134615.46, 130298.13, 120542.52, 123334.88, 
101913.08, 100671.96, 93863.75, 91992.39, 119943.24, 114523.61, 
78013.11, 94657.16, 91749.16, 86419.7, 76253.86, 78389.47, 73994.56, 
67532.53, 77044.01, 64664.71, 75328.87, 72107.6, 66051.52, 65605.48, 
61994.48, 61136.38, 63408.86, 55493.95, 46426.07, 46014.02, 28663.76, 
44069.95, 20229.59, 38558.51, 28754.33, 27892.92, 23640.93, 15505.73, 
22177.74, 1000.23, 1315.46, 0, 542.05, 0), Administration = c(136897.8, 
151377.59, 101145.55, 118671.85, 91391.77, 99814.71, 147198.87, 
145530.06, 148718.95, 108679.17, 110594.11, 91790.61, 127320.38, 
135495.07, 156547.42, 122616.84, 121597.55, 145077.58, 114175.79, 
153514.11, 113867.3, 153773.43, 122782.75, 105751.03, 99281.34, 
139553.16, 144135.98, 127864.55, 182645.56, 153032.06, 115641.28, 
152701.92, 129219.61, 103057.49, 157693.92, 85047.44, 127056.21, 
51283.14, 65947.93, 82982.09, 118546.05, 84710.77, 96189.63, 
127382.3, 154806.14, 124153.04, 115816.21, 135426.92, 51743.15, 
116983.8), Marketing.Spend = c(471784.1, 443898.53, 407934.54, 
383199.62, 366168.42, 362861.36, 127716.82, 323876.68, 311613.29, 
304981.62, 229160.95, 249744.55, 249839.44, 252664.93, 256512.92, 
261776.23, 264346.06, 282574.31, 294919.57, 0, 298664.47, 299737.29, 
303319.26, 304768.73, 140574.81, 137962.62, 134050.07, 353183.81, 
118148.2, 107138.38, 91131.24, 88218.23, 46085.25, 214634.81, 
210797.67, 205517.64, 201126.82, 197029.42, 185265.1, 174999.3, 
172795.67, 164470.71, 148001.11, 35534.17, 28334.72, 1903.93, 
297114.46, 0, 0, 45173.06), State = structure(c(1L, 2L, 3L, 1L, 
3L, 1L, 2L, 3L, 1L, 2L, 3L, 2L, 3L, 2L, 3L, 1L, 2L, 1L, 3L, 1L, 
2L, 1L, 3L, 3L, 1L, 2L, 3L, 1L, 3L, 1L, 3L, 1L, 2L, 3L, 2L, 1L, 
3L, 2L, 1L, 2L, 2L, 3L, 2L, 1L, 2L, 1L, 3L, 2L, 1L, 2L), .Label = c("1", 
"2", "3"), class = "factor"), Profit = c(192261.83, 191792.06, 
191050.39, 182901.99, 166187.94, 156991.12, 156122.51, 155752.6, 
152211.77, 149759.96, 146121.95, 144259.4, 141585.52, 134307.35, 
132602.65, 129917.04, 126992.93, 125370.37, 124266.9, 122776.86, 
118474.03, 111313.02, 110352.25, 108733.99, 108552.04, 107404.34, 
105733.54, 105008.31, 103282.38, 101004.64, 99937.59, 97483.56, 
97427.84, 96778.92, 96712.8, 96479.51, 90708.19, 89949.14, 81229.06, 
81005.76, 78239.91, 77798.83, 71498.49, 69758.98, 65200.33, 64926.08, 
49490.75, 42559.73, 35673.41, 14681.4)), .Names = c("R.D.Spend", 
"Administration", "Marketing.Spend", "State", "Profit"), row.names = c(NA, 
-50L), class = "data.frame")

与

lm（formula=mpg~，data=mtcars）

是否抛出类似错误？lm（formula=mpg~，data=mtcars）没有返回错误。#编码分类数据集$State=factor（数据集$State，levels=c（'New York'，'California'，'Florida'），labels=c（'1'，'2'，'3'）））#拆分数据集库（caTools）集。种子（123）拆分=样本。拆分（数据集$PROPERT，拆分比率=0.8）训练集=子集（数据集$PROPERT，拆分==TRUE）测试集=子集（数据集$PROPERT，拆分==FALSE）#将多元线性回归拟合到训练集回归器=lm（formula=Profit~，data=training\u set）@Nide；如果您在问题下方单击，您可以将信息添加到问题中。您也可以发布str（training\u set）的结果。可以复制错误：

x=1:10；lm（x~）

所以我猜您创建

培训数据的代码是不正确的
training_set = subset(dataset$Profit, split == TRUE)
test_set = subset(dataset$Profit, split == FALSE)

training_set = subset(dataset, subset = split)
test_set = subset(dataset, subset = !split)

lm(formula = Profit ~ ., data = training_set)

#Call:
#lm(formula = Profit ~ ., data = training_set)
#
#Coefficients:
#    (Intercept)        R.D.Spend   Administration  Marketing.Spend  
#      4.965e+04        7.986e-01       -2.942e-02        3.268e-02  
#         State2           State3  
#      1.213e+02        2.376e+02