我需要帮助在 TableOne 人口统计表上运行 lm（） 回归

问：

我正在尝试使用来自数据帧的信息运行下面的人口统计表的 lm（） 回归函数：

X      HL.both   NHL.B.both   NHL.W.both         HL.F      NHL.B.F      NHL.W.F         HL.M      NHL.B.M      NHL.W.M      HL.none   NHL.B.none
1                                   n           12           14          409           16           20          569           58           53         1576           50           66
2               Age (yrs) (mean (SD)) 69.06 (2.75) 69.57 (3.52) 69.67 (3.97) 71.72 (4.48) 72.52 (5.67) 70.75 (4.57) 70.61 (4.27) 69.45 (4.12) 70.63 (4.32) 72.82 (4.85) 71.62 (5.28)
3                    Sex = Female (%)     7 (58.3)     8 (57.1)   233 (57.0)     9 (56.2)    15 (75.0)   297 (52.2)    36 (62.1)    42 (79.2)  1062 (67.4)    32 (64.0)    43 (65.2)
4         Education (yrs) (mean (SD)) 16.50 (2.15) 17.57 (2.50) 16.74 (2.86) 15.25 (2.74) 15.75 (2.38) 17.03 (2.71) 16.03 (3.00) 16.08 (2.61) 16.55 (2.75) 15.52 (3.25) 16.23 (3.07)
5                         APOEgen (%)                                                                                                                                               
6                                 e2+      1 (8.3)      1 (7.1)    44 (10.8)      0 (0.0)     6 (30.0)    57 (10.0)      3 (5.2)     8 (15.1)    129 (8.2)      4 (8.0)    16 (24.2)
7                                e2e4      1 (8.3)     2 (14.3)      6 (1.5)      1 (6.2)      1 (5.0)     17 (3.0)      2 (3.4)      2 (3.8)     38 (2.4)      2 (4.0)      3 (4.5)
8                                e3e3     8 (66.7)     8 (57.1)   183 (44.7)     8 (50.0)     9 (45.0)   288 (50.6)    35 (60.3)    24 (45.3)   821 (52.1)    36 (72.0)    29 (43.9)
9                                 e4+     2 (16.7)     3 (21.4)   176 (43.0)     7 (43.8)     4 (20.0)   207 (36.4)    18 (31.0)    19 (35.8)   588 (37.3)     8 (16.0)    18 (27.3)
10               PET SUVr (mean (SD))  1.04 (0.14)  1.06 (0.10)  1.12 (0.20)  1.11 (0.17)  1.03 (0.07)  1.09 (0.18)  1.09 (0.19)  1.06 (0.15)  1.10 (0.19)  1.10 (0.20)  1.03 (0.14)
11 Amyloid eligibility = positive (%)     3 (25.0)     4 (28.6)   152 (37.2)     5 (31.2)      1 (5.0)   157 (27.6)    13 (22.4)    12 (22.6)   496 (31.5)    17 (34.0)    14 (21.2)
12                   MMSE (mean (SD)) 28.50 (1.51) 29.21 (1.12) 28.98 (1.14) 28.06 (1.98) 28.65 (1.35) 28.85 (1.17) 28.53 (1.27) 28.58 (1.49) 28.93 (1.14) 28.16 (1.36) 28.47 (1.19)
13                   PACC (mean (SD)) -0.53 (2.48) -0.14 (2.18)  0.45 (2.36) -1.74 (2.99) -1.22 (3.09)  0.11 (2.52) -0.56 (2.56) -0.61 (2.66)  0.31 (2.50) -1.33 (2.38) -1.37 (2.29)
     NHL.W.none      p test
1          1309          NA
2  72.76 (4.96) <0.001   NA
3    714 (54.5) <0.001   NA
4  16.59 (2.95) <0.001   NA
5               <0.001   NA
6    178 (13.6)          NA
7      36 (2.8)          NA
8    784 (59.9)          NA
9    311 (23.8)          NA
10  1.09 (0.20)  0.007   NA
11   371 (28.3)  0.005   NA
12 28.75 (1.22) <0.001   NA
13 -0.22 (2.51) <0.001   NA

但是每次我尝试在其上运行 lm（0 回归函数时，我都会收到错误：

Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 
  NA/NaN/Inf in 'y'
In addition: Warning message:
In storage.mode(v) <- "double" : NAs introduced by coercion
> 

我尝试过转置表并以这种方式调用变量，但它仍然不起作用。

     variable        e2.     e2e4       e3e3        e4.          age Amyloid.eligibility...positive.... Education..yrs...mean..SD.. MMSE..mean..SD..    n PACC..mean..SD..
1     HL.both    1 (8.3)  1 (8.3)   8 (66.7)   2 (16.7) 69.06 (2.75)                           3 (25.0)                16.50 (2.15)     28.50 (1.51)   12     -0.53 (2.48)
2  NHL.B.both    1 (7.1) 2 (14.3)   8 (57.1)   3 (21.4) 69.57 (3.52)                           4 (28.6)                17.57 (2.50)     29.21 (1.12)   14     -0.14 (2.18)
3  NHL.W.both  44 (10.8)  6 (1.5) 183 (44.7) 176 (43.0) 69.67 (3.97)                         152 (37.2)                16.74 (2.86)     28.98 (1.14)  409      0.45 (2.36)
4        HL.F    0 (0.0)  1 (6.2)   8 (50.0)   7 (43.8) 71.72 (4.48)                           5 (31.2)                15.25 (2.74)     28.06 (1.98)   16     -1.74 (2.99)
5     NHL.B.F   6 (30.0)  1 (5.0)   9 (45.0)   4 (20.0) 72.52 (5.67)                            1 (5.0)                15.75 (2.38)     28.65 (1.35)   20     -1.22 (3.09)
6     NHL.W.F  57 (10.0) 17 (3.0) 288 (50.6) 207 (36.4) 70.75 (4.57)                         157 (27.6)                17.03 (2.71)     28.85 (1.17)  569      0.11 (2.52)
7        HL.M    3 (5.2)  2 (3.4)  35 (60.3)  18 (31.0) 70.61 (4.27)                          13 (22.4)                16.03 (3.00)     28.53 (1.27)   58     -0.56 (2.56)
8     NHL.B.M   8 (15.1)  2 (3.8)  24 (45.3)  19 (35.8) 69.45 (4.12)                          12 (22.6)                16.08 (2.61)     28.58 (1.49)   53     -0.61 (2.66)
9     NHL.W.M  129 (8.2) 38 (2.4) 821 (52.1) 588 (37.3) 70.63 (4.32)                         496 (31.5)                16.55 (2.75)     28.93 (1.14) 1576      0.31 (2.50)
10    HL.none    4 (8.0)  2 (4.0)  36 (72.0)   8 (16.0) 72.82 (4.85)                          17 (34.0)                15.52 (3.25)     28.16 (1.36)   50     -1.33 (2.38)
11 NHL.B.none  16 (24.2)  3 (4.5)  29 (43.9)  18 (27.3) 71.62 (5.28)                          14 (21.2)                16.23 (3.07)     28.47 (1.19)   66     -1.37 (2.29)
12 NHL.W.none 178 (13.6) 36 (2.8) 784 (59.9) 311 (23.8) 72.76 (4.96)                         371 (28.3)                16.59 (2.95)     28.75 (1.22) 1309     -0.22 (2.51)
13          p                                                 <0.001                               0.01                      <0.001           <0.001   NA           <0.001
   PET.SUVr..mean..SD.. Sex...Female....
1           1.04 (0.14)         7 (58.3)
2           1.06 (0.10)         8 (57.1)
3           1.12 (0.20)       233 (57.0)
4           1.11 (0.17)         9 (56.2)
5           1.03 (0.07)        15 (75.0)
6           1.09 (0.18)       297 (52.2)
7           1.09 (0.19)        36 (62.1)
8           1.06 (0.15)        42 (79.2)
9           1.10 (0.19)      1062 (67.4)
10          1.10 (0.20)        32 (64.0)
11          1.03 (0.14)        43 (65.2)
12          1.09 (0.20)       714 (54.5)
13                 0.01           <0.001

但这仍然返回相同的错误。我也试过：

#Attempt 1: Trying to transpose the demog table to switch put the variable i want to compare across the x axis and the race categories on the y axis
require(data.table)

#transpose the demog table to be readbale by lm function
ERxFH_df_t <- dcast(melt(ERxFH_df, id.vars = "X"), variable ~ X)

colnames(ERxFH_df_t)

#cols.num <- c("HL.both","NHL.B.both","NHL.W.both","HL.F","NHL.B.F","NHL.W.F","HL.M","NHL.B.M N","HL.W.M","HL.none","NHL.B.none","NHL.W.none","p" )

#ERxFH_df[cols.num] <- sapply(ERxFH_df[cols.num],as.numeric)

#sapply(ERxFH_df, class)
#ERxFH_df %>% mutate_if(is.character,as.numeric) #ruined table :(

#rename columns: 
names(ERxFH_df_t)[names(ERxFH_df_t) == "Age (yrs) (mean (SD))" ] <- "age"
names(ERxFH_df_t)[names(ERxFH_df_t) == "APOEgen (%)"] <- "APOEgen"

names(ERxFH_df_t)[names(ERxFH_df_t) == "PET.SUVr..mean..SD.."] <- "AB"
ERxFH_df_t

colnames(ERxFH_df_t)

ERxFH_df_t = ERxFH_df_t[-14,]

ERxFH_df_t

write.csv(ERxFH_df_t, "/Users/phe/Library/CloudStorage/OneDrive-UCLAITServices/UCLA/Deters lab/Family history and cog decline/ERxFH_df_t.csv", row.names=FALSE)

sapply(ERxFH_df_t, class)

#change to numbers in excel
ERxFH_df_t <- read.csv("/Users/phe/Library/CloudStorage/OneDrive-UCLAITServices/UCLA/Deters lab/Family history and cog decline/ERxFH_df_t_nopara.csv")

#ERxFH_df_t %>% mutate(across(where(is.character), as.numeric))
#^^ changed all my values to NA

ERxFH_df_t

#removing APOEgen column since it's empty
ERxFH_df_t <- subset(ERxFH_df_t, select = -APOEgen)

ERxFH_df_t <- na.omit(ERxFH_df_t)

ERxFH_df_t.lm <- lm(formula = age ~ AB,data = ERxFH_df_t)

#still doesnt work
ERxFH_df_t

我仍然遇到同样的错误： lm.fit（x， y， offset = offset， singular.ok = singular.ok， ...） 中的错误： “y”中的 NA/NaN/Inf 此外：警告消息： 在 storage.mode（v） <- “double” 中：强制引入的 NA

我不确定接下来该尝试什么！