从多个线性回归模型的输出创建数据框。

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英文:

Creating a dataframe from output of several linear regression models

问题

I understand that you want the code parts to be left untranslated. Here's the translation for the non-code sections:

我正在运行5个简单的线性回归，然后进行1个包含了所有5个预测变量的多元线性回归。

我可以生成一个包含了来自这5个简单回归模型的所有beta系数的数据框，以及一个包含了多元回归模型调整后的beta系数的第二个数据框。我希望以最有效的方式将这些数据框合并在一起。我希望最终的产物看起来像这样：

  1. 系数              (简) 估计     (简) 标准误差    (多)调整估计    (多) 调整标准误差
  2. FEV1              74.1         14.1               31.255            27.041        
  3. AGE               -3.10        1.33                -3.236            1.257
  4. 等等。

以下是变量。对于所有的模型，MWT1Best 是结果变量：

  1. str(copd2)
  2.  $ 年龄         : 整数  77 79 80 56 65 67 67 83 72 75 ...
  3.  $ COPD严重程度: 字符串  "轻度" "中度" "中度" "非常严重" ...
  4.  $ MWT1Best    : 整数  120 176 201 210 210 216 237 237 237 240 ...
  5.  $ FEV1        : 数值  1.21 1.09 1.52 0.47 1.07 1.09 0.69 0.68 2.13 1.06 ...
  6.  $ 性别         : 整数  1 0 0 1 1 0 0 1 1 0 ...
  7.  $ 共病2        : 整数  1 1 1 1 1 1 1 1 1 1 ...

5个简单线性回归模型的代码：

  1. copd2$COPDSEVERITY <- recode(copd2$COPDSEVERITY, "轻度" = 0, "中度" = 1, "严重" = 2, "非常严重" = 3)
  3. f.MWT <- melt(data.frame(x = copd2$MWT1Best,
  4.                     FEV1=copd2$FEV1,
  5.                     年龄=copd2$AGE,
  6.                     性别=copd2$gender,
  7.                     严重程度=copd2$COPDSEVERITY,
  8.                     共病=copd2$共病2),
  9.                id.vars = "x")
  11. MWT.simp <- mergedf.MWT %>% group_by(变量) %>% do(tidy(lm(x ~ value, data = .)))

简单线性回归的输出：

  1.   变量      term        估计      标准误差      统计量     p值
  2. 1 FEV1   (截距)   280.       24.6         11.4   1.15e-19
  3. 2 FEV1   value          74.1      14.1          5.26  8.47e- 7
  4. 3 年龄    (截距)   616.       93.4          6.60  2.14e- 9
  5. 4 年龄    value         -3.10      1.33        -2.34  2.13e- 2
  6. 5 性别    (截距)   380.       17.7         21.5   7.77e-39
  7. 6 性别    value         30.5      22.1          1.38  1.70e- 1
  8. 7 严重程度 (截距)   459.       16.4         28.0   1.60e-48
  9. 8 严重程度 value       -50.1      11.0         -4.55  1.54e- 5
  10. 9 共病      (截距)   423.       15.6         27.0   3.13e-47
  11. 10 共病     value         -43.0      21.1        -2.04  4.43e- 2

使用 MWT.mult <- tidy(model) 的多元回归输出：

  1.   term                        估计     标准误差    统计量       p值
  2. 1 (截距)                     615.     116.        5.31  0.000000766
  3. 2 FEV1                         31.3      27.0       1.16  0.251      
  4. 3 年龄                          -3.24      1.26     -2.57  0.0117     
  5. 4 copd$gender1             29.3      24.2       1.21  0.228      
  6. 5 COPD严重程度中度          -25.9      29.0      -0.894 0.374      
  7. 6 COPD严重程度严重         -42.7      42.4      -1.01  0.317      
  8. 7 COPD严重程度非常严重   -135.      60.6      -2.22  0.0289     
  9. 8 共病1                          -45.3      18.6      -2.44  0.0167

问题1：我通过将 COPD 严重程度 编码为简单数据框中的整数，丧失了一些beta系数。是否有一种方式可以让简单模型的所有3个beta系数显示在我使用的代码创建的简单数据框中？我想象中的替代方式可能是单独运行简单回归，然后手动合并产生的输出。

问题2：是否有一个包可以创建结合了简单和多元线性回归输出的工具？为了合并这些数据框，我做了以下操作：

  1. MWT.simp <- filter(MWT.simp, term=="
  3. <details>
  4. <summary>英文:</summary>
  6. I am running 5 simple linear regressions, then 1 multiple linear regression with all 5 predictors.
  8. I can produce a dataframe with all betas from the 5 simple regression models, and a second dataframe with the adjusted betas from the multiple regression model. I would like to combine these dataframes in the most efficient way possible. I want the final product to look something like this:

Coefficient (Simp) Est. (Simp) Std. Error (Mult)Adj. Est. (Mult) Adj. Std. Error
FEV1 74.1 14.1 31.255 27.041.
AGE -3.10 1.33 -3.236 1.257
etc.

  2. Here are the variables. For all models, MWT1Best is the outcome variable:

str(copd2)
$ AGE : int 77 79 80 56 65 67 67 83 72 75 ...
$ COPDSEVERITY: chr "SEVERE" "MODERATE" "MODERATE" "VERY SEVERE" ...
$ MWT1Best : int 120 176 201 210 210 216 237 237 237 240 ...
$ FEV1 : num 1.21 1.09 1.52 0.47 1.07 1.09 0.69 0.68 2.13 1.06 ...
$ gender : int 1 0 0 1 1 0 0 1 1 0 ...
$ comorbid2 : int 1 1 1 1 1 1 1 1 1 1 ...

  2. 5 Simple linear regression models code:

copd2$COPDSEVERITY <- recode(copd2$COPDSEVERITY, "MILD" = 0, "MODERATE" = 1, "SEVERE" = 2, "VERY SEVERE" = 3)

f.MWT <- melt(data.frame(x = copd2$MWT1Best,
FEV1=copd2$FEV1,
AGE=copd2$AGE,
Gender=copd2$gender
Severity=copd2$COPDSEVERITY,
Comorbid=copd2$comorbid2),
id.vars = "x")

MWT.simp - mergedf.MWT %>% group_by(variable) %>% do(tidy(lm(x ~ value, data = .)))

  2. Simple linear regression output:

variable term estimate std.error statistic p.value
<fct> <chr> <dbl> <dbl> <dbl> <dbl>
1 FEV1 (Intercept) 280. 24.6 11.4 1.15e-19
2 FEV1 value 74.1 14.1 5.26 8.47e- 7
3 AGE (Intercept) 616. 93.4 6.60 2.14e- 9
4 AGE value -3.10 1.33 -2.34 2.13e- 2
5 Gender (Intercept) 380. 17.7 21.5 7.77e-39
6 Gender value 30.5 22.1 1.38 1.70e- 1
7 Severity (Intercept) 459. 16.4 28.0 1.60e-48
8 Severity value -50.1 11.0 -4.55 1.54e- 5
9 Comorbid (Intercept) 423. 15.6 27.0 3.13e-47
10 Comorbid value -43.0 21.1 -2.04 4.43e- 2

  2. Multiple regression output using MWT.mult &lt;- tidy(model):

term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 615. 116. 5.31 0.000000766
2 FEV1 31.3 27.0 1.16 0.251
3 AGE -3.24 1.26 -2.57 0.0117
4 copd$gender1 29.3 24.2 1.21 0.228
5 COPDSEVERITYMODERATE -25.9 29.0 -0.894 0.374
6 COPDSEVERITYSEVERE -42.7 42.4 -1.01 0.317
7 COPDSEVERITYVERY SEVERE -135. 60.6 -2.22 0.0289
8 comorbid1 -45.3 18.6 -2.44 0.0167

  2. Problem 1: I lost some betas by coding COPDSEVERITY as an integer in the simple dataframe. Is there a way to have all 3 betas from the simple model show up on the simple dataframe that I created with the code that I used? I imagine the alternative would be to run the simple regression separately and manually merge the resulting output.
  4. Output for lm(MWB1.Best ~ COPDSEVERITY)
  1.         (Intercept)    COPDSEVERITYMODERATE      COPDSEVERITYSEVERE 
  2.           458.08696               -51.08696               -89.42029

COPDSEVERITYVERY SEVERE
-167.21196

  2. Problem 2: Is there a package that creates combined simple and multiple linear regression outputs? To combine these dataframes I did the following:

MWT.simp <- filter(MWT.simp, term=="value") #remove all intercepts
MWT.simp <- MWT.simp %>% select(variable, estimate, std.error) #select appropriate columns
MWT.mult <- MWT.mult %>% select(term, estimate, std.error) #select appropriate columns
MWT.mult <- MWT.mult %>% rename("variable" = "term") #rename to prepare for merge
MWT.compare <- merge(x = MWT.simp, y = MWT.mult, by = "variable", all.x = TRUE)

  2. Output:

variable estimate.x std.error.x estimate.y std.error.y
1 FEV1 74.110667 14.089604 31.254582 27.041458
2 AGE -3.104007 1.326155 -3.235664 1.257062
3 Gender 30.510417 22.097009 NA NA
4 Severity -50.130769 11.017792 NA NA
5 Comorbid -42.951515 21.084591 NA NA

  2. Upon viewing my output I realize that the variables Gender and Comorbid also need to be renamed across the two datasets, and that I didn&#39;t address the COPDSEVERITY issue. Before I go further I thought there must be a better way of doing this, as this is such a common way of presenting data in journals.
  4. Thanks!
  6. </details>
  9. # 答案1
  10. **得分**: 2
  12. I try to reduce your question and address to your core problems. Do 5 models and compare them. There are good packages that do the job.
  14. # 5 linear regression models
  16. ols1 <- lm(mpg ~ vs, data=mtcars)
  17. ols2 <- lm(mpg ~ drat, data=mtcars)
  18. ols3 <- lm(mpg ~ cyl, data=mtcars)
  19. ols4 <- lm(mpg ~ disp, data=mtcars)
  20. ols5 <- lm(mpg ~ vs + drat + cyl + disp, data=mtcars)
  22. # model comparison
  24. library(modelsummary)
  25. modelsummary(list("simple 1" = ols1,
  26.                   "simple 2" = ols2, 
  27.                   "simple 3" = ols3, 
  28.                   "simple 4" = ols4,
  29.                   "multiple" = ols5))
  31. <details>
  32. <summary>英文:</summary>
  34. I try to reduce your question and adress to your core problems. Do 5 models and compare them. There are good packages that do the job.
  36.     # 5 linear regression models 
  37.     
  38.     ols1 &lt;- lm(mpg ~ vs, data=mtcars)
  39.     ols2 &lt;- lm(mpg ~ drat, data=mtcars)
  40.     ols3 &lt;- lm(mpg ~ cyl, data=mtcars)
  41.     ols4 &lt;- lm(mpg ~ disp, data=mtcars)
  42.     ols5 &lt;- lm(mpg ~ vs + drat + cyl + disp, data=mtcars)
  43.     
  44.     # model comparison 
  45.     
  46.     library(modelsummary)
  47.     modelsummary(list(&quot;simple 1&quot; = ols1,
  48.                       &quot;simple 2&quot; = ols2, 
  49.                       &quot;simple 3&quot; = ols3, 
  50.                       &quot;simple 4&quot; = ols4,
  51.                       &quot;multiple&quot; = ols5)) 
  53. [![enter image description here][1]][1]
  56.   [1]: https://i.stack.imgur.com/e5kD4.png
  58. </details>

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  • 本文由 发表于 2023年4月17日 04:41:37
  • 转载请务必保留本文链接：https://go.coder-hub.com/76030231.html
  • dplyr
  • linear-regression
  • r
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