英文:
linear regression using dataset with missing values
问题
我有14个变量（var1-var14）的效应大小数据。每个值代表某种处理对特定变量的效应大小。缺失值是因为某些文章没有考虑特定变量。正值表示该处理对变量的促进作用，负值表示其对变量的抑制作用。我想要：（1）进行一对一的线性回归，涵盖每个变量，比较变量之间是否存在关联，（2）将var1视为因变量，var2-var14都视为自变量，找到最佳拟合模型（可能使用glmulti包？）并展示哪些变量对var1的变化最重要。
以下是示例数据：
set.seed(123)# 创建带有效应大小和缺失值的数据集mydata <- data.frame(Var1 = sample(c(-20:14, NA), 64, replace = TRUE),Var2 = sample(c(-20:14, NA), 64, replace = TRUE),Var3 = sample(c(-20:14, NA), 64, replace = TRUE),Var4 = sample(c(-20:14, NA), 64, replace = TRUE),Var5 = sample(c(-20:14, NA), 64, replace = TRUE),Var6 = sample(c(-20:14, NA), 64, replace = TRUE),Var7 = sample(c(-20:14, NA), 64, replace = TRUE),Var8 = sample(c(-20:14, NA), 64, replace = TRUE),Var9 = sample(c(-20:14, NA), 64, replace = TRUE),Var10 = sample(c(-20:14, NA), 64, replace = TRUE),Var11 = sample(c(-20:14, NA), 64, replace = TRUE),Var12 = sample(c(-20:14, NA), 64, replace = TRUE),Var13 = sample(c(-20:14, NA), 64, replace = TRUE),Var14 = sample(c(-20:14, NA), 64, replace = TRUE))# 在每列中设置超过50%的缺失值for (col in 1:14) {missing_indices <- sample(1:64, size = 32)mydata[missing_indices, col] <- NA}
使用这种数据集（即带有缺失值）是否可能执行所有这些操作？谢谢！
英文:
I have data on the effect sizes for 14 variables (var1-var14). Each value is the effect size of a specific treatment on a certain variable. Missing values are due to that some articles did not consider certain variables. A positive value show promoting while a negative value shows the inhibiting effect of that treatment on the variable. I want (1) to do a pairwise linear regression that runs through each and every variable and compare if there is an association between variables, (2) consider var1 as the dependent variable and var2-var14 all as independent variables to find the best-fit model (maybe using glmulti package?) and show changes in which variables are most important for change in var1.
Here is a sample data:
set.seed(123)**# Create the dataset with effect sizes and missing values**mydata <- data.frame(Var1 = sample(c(-20:14, NA), 64, replace = TRUE),Var2 = sample(c(-20:14, NA), 64, replace = TRUE),Var3 = sample(c(-20:14, NA), 64, replace = TRUE),Var4 = sample(c(-20:14, NA), 64, replace = TRUE),Var5 = sample(c(-20:14, NA), 64, replace = TRUE),Var6 = sample(c(-20:14, NA), 64, replace = TRUE),Var7 = sample(c(-20:14, NA), 64, replace = TRUE),Var8 = sample(c(-20:14, NA), 64, replace = TRUE),Var9 = sample(c(-20:14, NA), 64, replace = TRUE),Var10 = sample(c(-20:14, NA), 64, replace = TRUE),Var11 = sample(c(-20:14, NA), 64, replace = TRUE),Var12 = sample(c(-20:14, NA), 64, replace = TRUE),Var13 = sample(c(-20:14, NA), 64, replace = TRUE),Var14 = sample(c(-20:14, NA), 64, replace = TRUE))**# Set more than 50% missing values in each column**for (col in 1:14) {missing_indices <- sample(1:64, size = 32)mydata[missing_indices, col] <- NA}
Is it possible to do all this with such dataset (i.e., missing values)? Thanks!
答案1
得分: 1
Here is the translated code:
d <-paste0('Var_', 1:14) |>Map(f = \(.) sample(c(-20:14, NA),size = 64,prob = c(rep(.49/35, 35), .51),replace = TRUE)) |>as.data.frame()# To get the pairwise associations in terms of the correlation matrix:correlation_matrix <- d |> cor(use = 'pairwise.complete.obs')# For basic column-wise imputation (replacing NA with the mean value):d_imputed <- d |>apply(2, \(var) replace(var, is.na(var), mean(var, na.rm = TRUE)))# To obtain the regression coefficients of the predictors (columns) for each column:coefficients <- d_imputed |>apply(2, FUN = \(var) coef(lm(var ~ ., as.data.frame(d_imputed))))
Note: The code has been translated, and only the code portions have been provided without additional content.
英文:
d being your example data:
d <-paste0('Var_', 1:14) |>Map(f = \(.) sample(c(-20:14, NA),size = 64,prob = c(rep(.49/35, 35), .51),replace = TRUE)) |>as.data.frame()
... you get the pairwise associations in terms of the correlation matrix like so:
d |> cor(use = 'pairwise.complete.obs')
... and a basic column-wise imputation (replacing NA with the mean value) this way:
d_imputed <- d |>apply(2, \(var) replace(var, is.na(var), mean(var, na.rm = TRUE)))
Finally you can obtain the regression coefficients of the predictors (columns) for each column like so:
d_imputed |>apply(2, FUN = \(var) coef(lm(var ~ ., as.data.frame(d_imputed))))
A word of caution: above is just a technical answer to your literal question. For a statistically sound solution, I'd recommend researching over at Cross Validated about imputation, dimensionality reduction, predictor selection and such (see Ben Bolker's comment).
通过集体智慧和协作来改善编程学习和解决问题的方式。致力于成为全球开发者共同参与的知识库，让每个人都能够通过互相帮助和分享经验来进步。
评论