从均值、标准差和 n 中获取 P-VALUE

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如何解决从均值、标准差和 n 中获取 P-VALUE？

我有这些数据，包括每个组 (n=4) 的平均值、标准差和计数。所以我有不同的组，我需要得到 P 值和方差分析 (ANOVA) 以及每行的 F 检验。我尝试了一些函数，例如 ind.oneway.second 和 t.test.from.sumMary，但出现错误。你能给我建议一种方法，我可以为组中的每一行获得 p 值和 f 检验。@H_772_3@ @H_404_2@structure(List(VAR = c("AGEFY19","Onset_age","FEMALE"),mean_1 = c(41.947791,30.830435,0.196787),count_1 = c(249,249,249),std_1 = c(10.167612,10.848377,0.398371),conf_1 = c(1.262919,1.402029,0.049482 ),conf_hi_1 = c(43.21071,32.232464,0.246269),conf_lo_1 = c(40.684873,29.428406,0.147306),mean_2 = c(41.313953,31.797468,0.151163 ),count_2 = c(86,86,86),std_2 = c(8.109924,8.03738,0.360308 ),conf_2 = c(1.71405,1.772381,0.076152),conf_hi_2 = c(43.028004,33.569849,0.227315),conf_lo_2 = c(39.599903,30.025088,0.075011 ),mean_3 = c(39.379032,28.577586,0.25),count_3 = c(124,124,124),std_3 = c(8.240878,8.716951,0.434769),conf_3 = c(1.450503,1.586323,0.076525),conf_hi_3 = c(40.829536,30.163909,0.326525 ),conf_lo_3 = c(37.928529,26.991263,0.173475),mean_4 = c(40.5,30.181818,0.242857),count_4 = c(70,70,70),std_4 = c(8.07169,7.302074,0.431906),conf_4 = c(1.890913,1.761693,0.10118),conf_hi_4 = c(42.390913,31.943512,0.344037),conf_lo_4 = c(38.609087,28.420125,0.141677)),row.names = c(NA,3L),class = "data.frame")

解决方法

library(dplyr)
library(rpsychi)

# get row 1 from your df
row1 <- df %>%  
  slice(1)

# SELEct mean and return a vector
row1.mean <- row1 %>% 
  SELEct(contains("mean")) %>% 
  slice(1) %>% unlist(use.names = falSE)

# SELEct sd and return a vector
row1.sd <- row1 %>% 
  SELEct(contains("std")) %>% 
  slice(1) %>% unlist(use.names = falSE)

# SELEct count and return a vector
row1.n <- row1 %>% 
  SELEct(contains("count")) %>% 
  slice(1) %>% unlist(use.names = falSE)

# do the anov on row1
row1.anova <- ind.oneway.second(row1.mean,row1.sd,row1.n)

# show results
row1.anova

$anova.table
               SS  df      MS     F
between (A)   574   3 191.364 2.279
Within      44078 525  83.957      
@R_733_10586@l       44652 528              

$omnibus.es
      etasq etasq.lower etasq.upper 
      0.013       0.000       0.033 

$raw.contrasts
    mean.diff  lower upper   std
1-2     0.634 -1.618 2.885 1.146
1-3     2.569  0.590 4.547 1.007
1-4     1.448 -0.987 3.883 1.240
2-3     1.935 -0.591 4.461 1.286
2-4     0.814 -2.084 3.712 1.475
3-4    -1.121 -3.812 1.570 1.370

$standardized.contrasts
        es  lower upper   std
1-2  0.069 -0.177 0.315 0.125
1-3  0.280  0.064 0.496 0.110
1-4  0.158 -0.108 0.424 0.135
2-3  0.211 -0.065 0.487 0.140
2-4  0.089 -0.227 0.405 0.161
3-4 -0.122 -0.416 0.171 0.149

$power
 small medium  large 
 0.469  0.999  1.000 

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