R 想要使用gls获得系数s.e.和t-stat，但它们没有显示在输出中

我很难让回归的系数显示出来。它们在应该出现的部分中似乎是空白的

以下是我正在运行的代码：

gls <- gls(prevm_adh ~ factor(alter_relation) + factor(group) + factor(sa.y) + factor(visno) + factor(female),
           corr = corCompSymm(form = ~ 1 | EgoID),
           data = ego_alter_data_regressions, # compound symmetry
           method = "ML",
           na.action = na.omit,
           control = list(singular.ok = TRUE))
summary(gls)

---

显示了系数的标题，它们通常应该列在那里，但它们不是。我不知道这是怎么回事。

我无法重现您的错误。看起来您的数据中的某些异常正在导致问题。你能试着调整这个例子，让它引起你看到的问题吗

library(nlme)
set.seed(436456)
ego_alter_data_regressions <- data.frame(
    alter_relation = factor(rep(c("Friend", "Other"), each = 12)), 
    group = factor(rep(1:3, times = 8)), 
    ego_id = factor(rep(1:12, times = 2)), 
    female = factor(0:1), 
    prevm_adh = rpois(24, 2))

gls1 <- gls(prevm_adh ~ factor(alter_relation) + factor(group) + factor(female),
    corr = corCompSymm(form = ~ 1 | ego_id),
    data = ego_alter_data_regressions, # compound symmetry
    method = "ML",
    na.action = na.omit,
    control = list(singular.ok = TRUE))
sg1 <- summary(gls1)
sg1
# Generalized least squares fit by maximum likelihood
# Model: prevm_adh ~ factor(alter_relation) + factor(group) + factor(female)
# Data: ego_alter_data_regressions 
# AIC      BIC    logLik
# 114.7237 122.9701 -50.36187
# 
# Correlation Structure: Compound symmetry
# Formula: ~1 | ego_id 
# Parameter estimate(s):
#     Rho 
# 0.1456311 
# 
# Coefficients:
#     Value Std.Error    t-value p-value
# (Intercept)                  2.1250000 1.0610047  2.0028186  0.0597
# factor(alter_relation)Other  0.3333333 0.8411910  0.3962635  0.6963
# factor(group)2               0.0000000 1.1929986  0.0000000  1.0000
# factor(group)3              -0.1250000 1.1929986 -0.1047780  0.9177
# factor(female)1              0.1666667 0.9740793  0.1711017  0.8660
# 
# Correlation: 
#     (Intr) fc(_)O fct()2 fct()3
# factor(alter_relation)Other -0.396                     
# factor(group)2              -0.562  0.000              
# factor(group)3              -0.562  0.000  0.500       
# factor(female)1             -0.459  0.000  0.000  0.000
# 
# Standardized residuals:
#     Min          Q1         Med          Q3         Max 
# -1.32345932 -0.62496690 -0.07352552  0.35712394  2.85699155 
# 
# Residual standard error: 1.983438 
# Degrees of freedom: 24 total; 19 residual

---

感谢您尝试重新创建错误@csjcampbell。我想知道我显示结果的方式是否有错误。在控制台中，系数不会显示（就像上面为您显示的那样），但如果我向下滚动代码下方，它们会显示在数据帧中。我是R的新手，所以我只是学习了一些。与AIC、BIC相同-它们显示在“我的代码”下面的数据框中，但不显示在控制台输出中。@AliData您可以使用

$

尝试从摘要对象提取系数值

library(nlme)
set.seed(436456)
ego_alter_data_regressions <- data.frame(
    alter_relation = factor(rep(c("Friend", "Other"), each = 12)), 
    group = factor(rep(1:3, times = 8)), 
    ego_id = factor(rep(1:12, times = 2)), 
    female = factor(0:1), 
    prevm_adh = rpois(24, 2))

gls1 <- gls(prevm_adh ~ factor(alter_relation) + factor(group) + factor(female),
    corr = corCompSymm(form = ~ 1 | ego_id),
    data = ego_alter_data_regressions, # compound symmetry
    method = "ML",
    na.action = na.omit,
    control = list(singular.ok = TRUE))
sg1 <- summary(gls1)
sg1
# Generalized least squares fit by maximum likelihood
# Model: prevm_adh ~ factor(alter_relation) + factor(group) + factor(female)
# Data: ego_alter_data_regressions 
# AIC      BIC    logLik
# 114.7237 122.9701 -50.36187
# 
# Correlation Structure: Compound symmetry
# Formula: ~1 | ego_id 
# Parameter estimate(s):
#     Rho 
# 0.1456311 
# 
# Coefficients:
#     Value Std.Error    t-value p-value
# (Intercept)                  2.1250000 1.0610047  2.0028186  0.0597
# factor(alter_relation)Other  0.3333333 0.8411910  0.3962635  0.6963
# factor(group)2               0.0000000 1.1929986  0.0000000  1.0000
# factor(group)3              -0.1250000 1.1929986 -0.1047780  0.9177
# factor(female)1              0.1666667 0.9740793  0.1711017  0.8660
# 
# Correlation: 
#     (Intr) fc(_)O fct()2 fct()3
# factor(alter_relation)Other -0.396                     
# factor(group)2              -0.562  0.000              
# factor(group)3              -0.562  0.000  0.500       
# factor(female)1             -0.459  0.000  0.000  0.000
# 
# Standardized residuals:
#     Min          Q1         Med          Q3         Max 
# -1.32345932 -0.62496690 -0.07352552  0.35712394  2.85699155 
# 
# Residual standard error: 1.983438 
# Degrees of freedom: 24 total; 19 residual

names(sg1)
#  [1] "modelStruct"  "dims"        
#  [3] "contrasts"    "coefficients"
#  [5] "varBeta"      "sigma"       
#  [7] "apVar"        "logLik"      
#  [9] "numIter"      "groups"      
# [11] "call"         "method"      
# [13] "fitted"       "residuals"   
# [15] "parAssign"    "na.action"   
# [17] "corBeta"      "tTable"      
# [19] "BIC"          "AIC" 

sg1$coefficients
#                 (Intercept) 
#                2.125000e+00 
# factor(alter_relation)Other 
#                3.333333e-01 
#              factor(group)2 
#                3.896296e-16 
#              factor(group)3 
#               -1.250000e-01 
#             factor(female)1 
#                1.666667e-01