小计对 expss 表中显着性检验的影响

Question

R/expss 专家您好！这是这个问题的后续问题 --> .

我使用出色的 expss 包将小计添加到已经很复杂的 tables 中，它适用于大多数任务（计数、比例、均值...）。然而，我发现统计测试评估在没有小计的 table 和完全相同的小计之间存在差异。 @Gregory Demin，您的知识将不胜感激:)

一个例子来说明我的话，使用数据集包中可用的推断数据集：

example <- infert %>%
  tab_significance_options(sig_level=0.2, keep="none", sig_labels=NULL, subtable_marks="greater", mode="append") %>%
  tab_cols(total(), education) %>%
  tab_cells(parity) %>%
  # block for cases
  tab_stat_cases(label="N", total_row_position="above", total_statistic="u_cases", total_label="TOTAL") %>% 
  tab_last_add_sig_labels() %>%
  # block for percent statistic - Subtable tests  
  tab_stat_cpct(label="%Col.", total_row_position="above", total_statistic="u_cpct", total_label="TOTAL") %>%
  tab_last_add_sig_labels() %>%
  tab_last_sig_cpct(label="T.1", compare_type="subtable") %>%
  # block for percent statistic - First column tests
  tab_stat_cpct(label="T.2", total_row_position="above", total_statistic="u_cpct", total_label="TOTAL") %>%
  tab_last_add_sig_labels() %>%
  tab_last_sig_cpct(compare_type="first_column", mode="replace") %>%
  tab_pivot(stat_position="inside_columns") %>%
  # converts NA to zero
  recode(as.criterion(is.numeric) & is.na ~ 0, TRUE ~ copy)
example <- example[,-c(4,5)]
print(example)

注意：sig_level 非常高 (20%) 来说明这个具体问题，不要惊慌 :) 这是起点，我对此很好。然后我们只添加小计（第 5 行）：

example2 <- infert %>%
  tab_significance_options(sig_level=0.2, keep="none", sig_labels=NULL, subtable_marks="greater", mode="append") %>%
  tab_cols(total(), education) %>%
  tab_cells(parity) %>%
  tab_subtotal_cells("#FIRST 3"=c(1,2,3),"#LAST 3"=c(4,5,6), position = "above") %>%
  # block for cases
  tab_stat_cases(label="N", total_row_position="above", total_statistic="u_cases", total_label="TOTAL") %>% 
  tab_last_add_sig_labels() %>%
  # block for percent statistic - Subtable tests  
  tab_stat_cpct(label="%Col.", total_row_position="above", total_statistic="u_cpct", total_label="TOTAL") %>%
  tab_last_add_sig_labels() %>%
  tab_last_sig_cpct(label="T.1", compare_type="subtable") %>%
  # block for percent statistic - First column tests
  tab_stat_cpct(label="T.2", total_row_position="above", total_statistic="u_cpct", total_label="TOTAL") %>%
  tab_last_add_sig_labels() %>%
  tab_last_sig_cpct(compare_type="first_column", mode="replace") %>%
  tab_pivot(stat_position="inside_columns") %>%
  # converts NA to zero
  recode(as.criterion(is.numeric) & is.na ~ 0, TRUE ~ copy)
example2 <- example2[,-c(4,5)]
print(example2)

不知道怎么回事，这次显着性检验的结果不一样。此外，我觉得在两个小计行上没有计算显着性检验。有什么见解吗？

Answer 1

对于百分比之间的显着性检验，我们需要总统计量中的个案。所以我们将用两行进行总统计。在所有具有总个案的操作行之后将被删除。 significance_cpct 使用 # 符号检测总行数。 # 在小计中导致不正确的结果。

考虑到以上所有因素：

example2 <- infert %>%
    tab_significance_options(sig_level=0.2, keep="none", sig_labels=NULL, subtable_marks="greater", mode="append") %>%
    tab_cols(total(), education) %>%
    tab_cells(parity) %>%
    tab_subtotal_cells("FIRST 3"=c(1,2,3),"LAST 3"=c(4,5,6), position = "above") %>%
    # block for cases
    tab_stat_cases(label="N", total_row_position="above", total_statistic="u_cases", total_label="TOTAL") %>% 
    tab_last_add_sig_labels() %>%
    # block for percent statistic - Subtable tests  
    # note additional total statistic
    tab_stat_cpct(label="%Col.", total_row_position="above", total_statistic= c("u_cases", "u_cpct"), 
                  total_label=c("TO DELETE", "TOTAL")) %>%
    tab_last_add_sig_labels() %>%
    tab_last_sig_cpct(label="T.1", compare_type="subtable") %>%
    # block for percent statistic - First column tests
    tab_stat_cpct(label="T.2", total_row_position="above", total_statistic= c("u_cases", "u_cpct"), 
                  total_label=c("TO DELETE", "TOTAL")) %>%
    tab_last_add_sig_labels() %>%
    tab_last_sig_cpct(compare_type="first_column", mode="replace") %>%
    tab_pivot(stat_position="inside_columns") %>%
    # drop row with TO_DELETE
    where(!grepl("TO DELETE", row_labels)) %>% 
    # converts NA to zero
    recode(as.criterion(is.numeric) & is.na ~ 0, TRUE ~ copy)
example2 <- example2[,-c(4,5)]
print(example2)

更新 net 列：

data(infert)
example2 <- infert %>%
    apply_labels(
        education = "Education"
    ) %>% 
    tab_significance_options(sig_level=0.2, keep="none", sig_labels=NULL, subtable_marks="greater", mode="append") %>%
    tab_cols(total(), net(education, "LESS THAN 12 Y.O."=levels(education)[1:2])) %>%
    tab_cells(parity) %>%
    tab_subtotal_cells("FIRST 3"=c(1,2,3),"LAST 3"=c(4,5,6), position = "above") %>%
    # block for cases
    tab_stat_cases(label="N", total_row_position="above", total_statistic="u_cases", total_label="TOTAL") %>% 
    tab_last_add_sig_labels() %>%
    # block for percent statistic - Subtable tests  
    # note additional total statistic
    tab_stat_cpct(label="%Col.", total_row_position="above", total_statistic= c("u_cases", "u_cpct"), 
                  total_label=c("TO DELETE", "TOTAL")) %>%
    tab_last_add_sig_labels() %>%
    tab_last_sig_cpct(label="T.1", compare_type="subtable") %>%
    # block for percent statistic - First column tests
    tab_stat_cpct(label="T.2", total_row_position="above", total_statistic= c("u_cases", "u_cpct"), 
                  total_label=c("TO DELETE", "TOTAL")) %>%
    tab_last_add_sig_labels() %>%
    tab_last_sig_cpct(compare_type="first_column", mode="replace") %>%
    tab_pivot(stat_position="inside_columns") %>%
    # drop row with TO_DELETE
    where(!grepl("TO DELETE", row_labels)) %>% 
    # converts NA to zero
    recode(as.criterion(is.numeric) & is.na ~ 0, TRUE ~ copy)
example2 <- example2[,-c(4,5)]
print(example2)

小计对 expss 表中显着性检验的影响

Influence of subtotals on significance tests in expss tables

r

subtotal

expss