我正在尝试通过运行以下代码来调整标准错误：
#################################################################################
# Metaregression -- Academic Model
#################################################################################
# save list of moderators to include
terms_1 <- c("Targeted_c",
"MOOSES_Rating_5_c", "Middle_c","High_c")
# Student_report_c is reference variable
# format moderators into formula (an R-specifc type)
formula_academic <- reformulate(termlabels = c(terms_1))
formula_academic
# estimate a covariance matrix
V_list_academic <- impute_covariance_matrix(vi = full_academic$variance, #known correlation vector
cluster = full_academic$Study_ID, #study ID
r = 0.80) #assumed correlation
MVfull_academic <- rma.mv(yi=ES_adjusted, #effect size
V = V_list_academic, #variance (ThIS IS WHAt CHANGES FROM HEmodel)
mods = formula_academic, #ADD COVS HERE
random = ~1 | Study_ID/ES_ID, #nesting structure
test= "t", #use t-tests
data=full_academic, #define data
method="REML") #estimate variances using REML
MVfull_academic
#t-tests of each covariate #
MVfull.coef_academic <- coef_test(MVfull_academic,#estimation model above
cluster=full_academic$Study_ID, #define cluster IDs
vcov = "CR2") #estimation method (CR2 is best)
MVfull.coef_academic
这是返回错误的部分：
MVfull_academic
#t-tests of each covariate #
MVfull.coef_academic <- coef_test(MVfull_academic,#estimation model above
cluster=full_academic$Study_ID, #define cluster IDs
vcov = "CR2") #estimation method (CR2 is best)
MVfull.coef_academic
错误如下：
Error in x[fac == f, fac == f, drop = FALSE] :
(subscript) logical subscript too long
听起来好像某些数据不适合我，但我不确定它可能是什么。看起来数据集中的所有内容都一样长。如何解决此错误？
这是我的数据：
structure(list(APA = structure(c("Barr et al. (2015)", "Blair & Ravor (2014)",
"Bos et al. (2019)", "Bos et al. (2019)", "Conduct Problems Prevention Research Group (1999)",
"Conduct Problems Prevention Research Group (1999)"), label = "APA", format.stata = "%215s"),
Intervention = structure(c("Facing History and Ourselves",
"Tools of the Mind", "BARR", "BARR", "Fast Track (Selective)",
"Fast Track (Selective)"), label = "Intervention", format.stata = "%74s"),
TxCluster = structure(c(32, 16, 1, 1, 27, 27), label = "Tx.\nCluster", format.stata = "%10.0g"),
ControlCluster = structure(c(30, 13, 1, 1, 27, 27), label = "Control.\nCluster", format.stata = "%10.0g"),
UnitofCluster = structure(c("schools", "schools", "", "",
"schools", "schools"), label = "Unit of Cluster", format.stata = "%10s"),
TxN = structure(c(587, 408, 1467, 1466, 419, 275), label = "Tx.N", format.stata = "%10.0g"),
ControlN = structure(c(700, 282, 1916, 1910, 418, 276), label = "Control.N", format.stata = "%10.0g"),
Total_N = structure(c(1287, 690, 3383, 3376, 837, 551), label = "Total_N", format.stata = "%10.0g"),
WebsiteCategoryacademicemot = structure(c("Academic", "Academic",
"Academic", "Academic", "Academic", "Academic"), label = "Website Category (academic, emotion, relations, problem behavior)", format.stata = "%20s"),
MOOSES = structure(c(4, 5, 5, 5, 5, 5), label = "MOOSES rating\n1= cognitive/lower level skills (e.g. emotional recog.; pencil tap", format.stata = "%10.0g"),
ES = structure(c(0.14, 0.13, 0.31, 0.11, -0.01, 0.17), label = "ES", format.stata = "%10.0g"),
TypeofMeasure = structure(c("student self-report", "Standardized assessment",
"school record", "school record", "official report", "standardized assessment"
), label = "Type of Measure", format.stata = "%23s"), ES_ID = structure(c(22,
41, 58, 59, 135, 138), format.stata = "%9.0g"), Study_ID = structure(c(5,
9, 11, 11, 19, 19), label = "group(APA)", format.stata = "%9.0g"),
Targeted = structure(c(0, 0, 0, 0, 0, 0), format.stata = "%9.0g"),
Primary = structure(c(0, 1, 0, 0, 1, 1), format.stata = "%9.0g"),
Middle = structure(c(0, 0, 0, 0, 0, 0), format.stata = "%9.0g"),
High = structure(c(1, 0, 1, 1, 0, 0), format.stata = "%9.0g"),
Significant = structure(c(1, 1, 1, 1, 1, 1), format.stata = "%9.0g"),
MOOSES_Rating_4 = structure(c(1, 0, 0, 0, 0, 0), format.stata = "%9.0g"),
MOOSES_Rating_5 = structure(c(0, 1, 1, 1, 1, 1), format.stata = "%9.0g"),
MOOSES_Rating_4_c = structure(c(0.295774638652802, -0.704225361347198,
-0.704225361347198, -0.704225361347198, -0.704225361347198,
-0.704225361347198), format.stata = "%9.0g"), MOOSES_Rating_5_c = structure(c(-0.253521114587784,
0.746478855609894, 0.746478855609894, 0.746478855609894,
0.746478855609894, 0.746478855609894), format.stata = "%9.0g"),
Targeted_c = structure(c(-0.239436626434326, -0.239436626434326,
-0.239436626434326, -0.239436626434326, -0.239436626434326,
-0.239436626434326), format.stata = "%9.0g"), Primary_c = structure(c(-0.718309879302979,
0.281690150499344, -0.718309879302979, -0.718309879302979,
0.281690150499344, 0.281690150499344), format.stata = "%9.0g"),
Middle_c = structure(c(-0.126760557293892, -0.126760557293892,
-0.126760557293892, -0.126760557293892, -0.126760557293892,
-0.126760557293892), format.stata = "%9.0g"), High_c = structure(c(0.845070421695709,
-0.154929578304291, 0.845070421695709, 0.845070421695709,
-0.154929578304291, -0.154929578304291), format.stata = "%9.0g"),
Full_Sample = structure(c(1287, 690, 3383, 3376, 837, 551
), format.stata = "%9.0g"), Clusters_Total = structure(c(62,
29, 2, 2, 54, 54), format.stata = "%9.0g"), ES_adjusted = structure(c(0.12521980702877,
0.116275534033775, 0.277272433042526, 0.0983869880437851,
-0.00894427206367254, 0.152052626013756), format.stata = "%9.0g"),
SE = structure(c(0.05644915625453, 0.0780460089445114, 0.0353467278182507,
0.0349567793309689, 0.0690869837999344, 0.0861022993922234
), format.stata = "%9.0g"), variance = structure(c(0.0439638122916222,
0.0306105446070433, 0.00127180037088692, 0.001214295392856,
0.02976069226861, 0.100570656359196), format.stata = "%9.0g")), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame"))
答案 0 :(得分：0)
我刚刚在数据中找到了NA，我想可能是这样！