Hello, thank you very much for your contribution and effort.

I am a beginner in using the R language. I managed to install the bruceR package with the help of various online resources. I would like to know if there are any tutorials available that can help me quickly get started with bruceR.

For example, I would like to learn how to convert certain text into word vectors or perform similarity calculations.

I'm sorry for the interruption. Best wishes to you.

I cannot install bruceR. The warning seemed like Bug #4, but the soluation of Bug#04 was not work.
I had updated the R to the latest version 4.0.2, but the error still remained.

devtools::install_github("psychbruce/bruceR")
Downloading GitHub repo psychbruce/bruceR@HEAD

checking for file 'C:\Users\THINK\AppData\Local\Temp\RtmpSoK5KO\remotes48d859227c82\psychbruce-bruceR-eded61e/DESCRIPTION' ...
√ checking for file 'C:\Users\THINK\AppData\Local\Temp\RtmpSoK5KO\remotes48d859227c82\psychbruce-bruceR-eded61e/DESCRIPTION'

• preparing 'bruceR':
  checking DESCRIPTION meta-information ...

  checking DESCRIPTION meta-information ...

√ checking DESCRIPTION meta-information

• checking for LF line-endings in source and make files and shell scripts
• checking for empty or unneeded directories
• building 'bruceR_0.5.1.tar.gz'

Installing package into ‘C:/Users/THINK/Documents/R/win-library/4.0’
(as ‘lib’ is unspecified)

• installing source package 'bruceR' ...
  ** using staged installation
  ** R
  ** data
  *** moving datasets to lazyload DB
  ** byte-compile and prepare package for lazy loading
  Error in loadNamespace(i, c(lib.loc, .libPaths()), versionCheck = vI[[i]]) :
  there is no package called 'emmeans'
  Calls: ... loadNamespace -> withRestarts -> withOneRestart -> doWithOneRestart
  停止执行
  ERROR: lazy loading failed for package 'bruceR'
• removing 'C:/Users/THINK/Documents/R/win-library/4.0/bruceR'
  错误: Failed to install 'bruceR' from GitHub:
  (由警告转换成)installation of package ‘C:/Users/THINK/AppData/Local/Temp/RtmpSoK5KO/file48d823453f6b/bruceR_0.5.1.tar.gz’ had non-zero exit status

Bruce先生您好，在PROCESS()的输出结果中的模型编号一行
PROCESS Model Code : 14 (Hayes, 2018; www.guilford.com/p/hayes3)
中的网址已经失效了，现在打开它只会跳转到Introduction to Mediation, Moderation, and Conditional Process Analysis这本书的购书页面，我不知道这个网页原本的内容是什么，所以这个模型编号就没有提供实际的参考信息。
以及，bruceR真的是一个非常实用和便捷的工具，谢谢你的贡献。

想问一个关于MANOVA()函数的问题，我的实验是一个重复测量两因素的三因素方差分析。在我处理我的宽数据的时候，我发现当我的列名是（sec_3_t1，sec_3_t6，sec_9_t1，sec_9_t6）的时候能正常返回结果，但是列名变成（sec_4_t1，sec_4_t6，sec_10_t1，sec_10_t6）的时候就不能正常运行了。报错原因是：Failed to perform MANOVA.。想问一下是否是因为列名称里出现了两位数就不能正常运行函数。
我的代码部分如下
MANOVA(data = data,
dvs=paste(c(dvs[1], dvs[4]), collapse = ":"),
dvs.pattern="sec_(.)_(t.)",
between="disease",
within=c("section", "time"),
file = file.path(wide_format_dir, basename(file)))
报错如下：
Note:
dvs="sec_4_t1:sec_10_t6" is matched to variables:
sec_4_t1, sec_4_t6, sec_10_t1, sec_10_t6

Error:
Failed to perform MANOVA.
Please follow the correct usage.
See: help(MANOVA)

数据的前五行格式如下
disease sec_4_t1 sec_4_t6 sec_10_t1 sec_10_t6
0 0.148010408 0.240479665 0.137365773 0.162679725
0 0.164722607 0.208784311 0.155697349 0.131884651
0 0.128987875 0.155202186 0.123803308 0.107702303
0 0.14838339 0.212513358 0.146451598 0.19047774
0 0.134528406 0.208767676 0.108596354 0.130336021

When I run the following code,

lm1=lm(Temp ~ Month + Day, data=airquality)
lm2=lm(Temp ~ Month + Day + Wind + Solar.R, data=airquality)
model_summary(lm1)
model_summary(lm2)
model_summary(list(lm1, lm2))
model_summary(list(lm1, lm2), std=TRUE, digits=2)
model_summary(list(lm1, lm2), file="OLS Models.doc")

I received the following error
Error in output %>% stringr::str_replace_all(" ", "") %>% stringr::str_replace_all("-", :
object 'magrittr_pipe' not found

Can you please help?

We are about to release dplyr 1.0.8 and this currently make this package fail with:

# bruceR

<details>

* Version: 0.7.3
* GitHub: https://github.com/psychbruce/bruceR
* Source code: https://github.com/cran/bruceR
* Date/Publication: 2021-11-05 17:40:01 UTC
* Number of recursive dependencies: 202

Run `cloud_details(, "bruceR")` for more info

</details>

## Newly broken

*   checking examples ... ERROR
    ```
    Running examples in ‘bruceR-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: Alpha
    > ### Title: Reliability analysis (Cronbach's alpha and McDonald's omega).
    > ### Aliases: Alpha
    > 
    > ### ** Examples
    > 
    > # ?psych::bfi
    > Alpha(bfi, "E", 1:5)  # "E1" & "E2" should be reverse scored
    Warning: replacing previous import ‘ggplot2::enquo’ by ‘jmvcore::enquo’ when loading ‘jmv’
    Error in jmvcore::enquo(vars) : object 'rlang_enquo' not found
    Calls: Alpha -> <Anonymous> -> <Anonymous> -> <Anonymous>
    Execution halted
    ```

This is due to this problem in jmvcore: jamovi/jmvcore#19 possibly fixed by jamovi/jmvcore#20

is this package can only be installed in R version>4.0.0?

Hello Han-Wu-Shuang Bao,

first let me say that I am amazed at how Your package works. It is exactly what I was searching for for my masters thesis.

I was trying to do a moderated multilevel mediation (1-1-1), where the moderator had an effect on all paths:
PROCESS( data_long_labs, y = "HO_sat", x = "HO_isol", meds = "HO_self", clusters = "ID", mods = "HO_cont_sup", mod.path = "all", nsim = 10)

The first part is being computed without problems, but when it starts to work on the simulations, it ends with the message:
Error in .rowNamesDF<-(x, value = value) : invalid 'row.names' length

I tried it with a normal mediation at a single point in time, the multilevel mediation without the moderation and the moderation having an influence only on special paths + combinations of mod.path types. All of that worked perfectly fine, only the computation of the effect on all paths doesn't, also when supplied by c("x-y", "x-m", "m-y"), or even c("m-y", "x-y)".
I updated my R, RStudio and all packages and closed the R-Session multiple times.

I hope, that You can solve this problem. I will be happy to help You if I can.
Greetings from Germany!
Richard

The Traceback returned:
11.stop("invalid 'row.names' length")
10..rowNamesDF<-(x, value = value)
9.row.names<-.data.frame(*tmp*, value = gsub(":", " x ", row.names(dp1)))
8.row.names<-(*tmp*, value = gsub(":", " x ", row.names(dp1)))
7.interaction_F_test(model, data = data, data.name = data.name)
6.interaction_test(model, data = data.c, data.name = "data.c")
5.process_mod(model.y0, model.y, data.c, x, y, mod1, mod2, mod1.val,
mod2.val, mod.type, x.label = "X", y.label = "Y", eff.tag = "(Conditional Direct Effects [c'] of X on Y)",
nsmall, file = file) at #1
4.eval(parse(text = text), envir = parent.frame())
3.eval(parse(text = text), envir = parent.frame())
2.Run(run.process.mod.xy(eff.tag = "(Conditional Direct Effects [c'] of X on Y)"))
1.PROCESS(data_long_labs, y = "HO_sat", x = "HO_isol", meds = "HO_self",
clusters = "ID", mods = "HO_cont_sup", mod.path = c("m-y",
"x-y"), nsim = 5)

Will the bruceR support the Covariance analysis (ANCOVA) ？

请问做完PROCESS函数的中介分析，尤其是多中介变量的中介分析，如何进行可视化，用哪个函数可以做？

Hi,

By allowing for additional arguments (the "..." syntax in functions), one can use robust standard errors for the test.

This can be done by including ", ..." to the 'granger_test()' function on line 1 and on line 24.
Example code attached below.

Best regards

Tomas

library(sandwich)
library(lmtest)
library(bruceR)

original function

?granger_test

Prepare HAC robust version of the test : start

granger_test_HAC <- function (formula, data, lags = 1:5, test.reverse = TRUE, file = NULL, ...) # changed here
{
installed("lmtest")
res = data.frame(Lag = lags, D1 = "", D2 = "", D12 = "")
names(res)[2:4] = c("Hypothesized Direction", "Reverse Direction",
"Hypothesized (vs. Reverse)")
if (test.reverse) {
formula.rev = as.formula(paste(formula[3], formula[1],
formula[2]))
formulas = list(formula, formula.rev)
}
else {
formulas = list(formula)
}
Print("\n \n\n <<cyan Granger Causality Test (Bivariate)>>\n\n Hypothesized direction:\n <<blue {formula[2]} ~ {formula[2]}[1:Lags] + <<green {formula[3]}[1:Lags]>>>>\n ")
for (f in formulas) {
rev = FALSE
if (test.reverse & f != formulas[[1]]) {
rev = TRUE
Print("\n \n\n Reverse direction:\n <<blue {formula[3]} ~ {formula[3]}[1:Lags] + <<green {formula[2]}[1:Lags]>>>>\n ")
}
for (lag in lags) {
gt = lmtest::grangertest(formula = f, data = data,
order = lag, na.action = na.omit, ...) # changed here
Fval = gt[2, "F"]
df1 = -gt[2, "Df"]
df2 = gt[1, "Res.Df"]
sig = str_trim(sig.trans(p.f(Fval, df1, df2)))
result = bruceR::p(f = Fval, df1 = df1, df2 = df2)
result.simple = formatF(Fval, 2) %^% ifelse(sig ==
"", "", "^{" %^% sig %^% "}")
Print("Lags = {lag}:\t{result}")
res[which(res$Lag == lag), ifelse(rev, 3, 2)] = p.plain(f = Fval,
df1 = df1, df2 = df2)
res[which(res$Lag == lag), 4] = ifelse(rev, res[[which(res$Lag ==
lag), 4]] %^% " (vs. " %^% result.simple %^%
")", result.simple)
}
}
cat("\n")
if (!is.null(file)) {
RES = res
RES[[2]] = str_replace(str_replace(RES[[2]], "p", "p"),
"F", "F")
RES[[3]] = str_replace(str_replace(RES[[3]], "p", "p"),
"F", "F")
if (test.reverse == FALSE)
RES = RES[1:2]
print_table(RES, row.names = FALSE, digits = 0, file.align.head = "left",
file.align.text = "left", title = "Table. Granger Causality Test (Bivariate).",
note = "Note. * p < .05. ** p < .01. *** p < .001.",
file = file)
}
}

environment(granger_test_HAC) <- asNamespace('bruceR')

Prepare HAC robust version of the test : end

granger_test(chicken ~ egg, data=lmtest::ChickEgg)
granger_test_HAC(chicken ~ egg, data=lmtest::ChickEgg, vcov=vcovHAC) # HAC

Platform: Mac latest
R Version: 4.0.2 (2020-06-22)
bruceR Version: latest (installed via install_github())

First of all, thanks for developing this useful and powerful package!
Here is my code:
`

install packages

library(carData)
library(car)
library(haven)
library("lme4")
library (haven)
library(tidyverse)
library(RColorBrewer)
library(lmerTest)
library (dplyr)
library(ggplot2)
library(readxl)
library("ggpubr")
library(compareGroups)
library(lmerTest)
library(RLRsim)
library(mice)
library(dplyr)
library(lattice)
library(grid)
library(DMwR)
library(bootstrap)
library(interactions)
library(jtools)
library(devtools)
library(usethis)
library(rJava)
library(reghelper)
library(pacman)
library(interactions)
library(bbmle)
library(xlsx)
library(bruceR)

One model to illustrate this problem

lmm.fit2=lmer(trust ~(1|countrycode)+gini+gender+education+logGDP+Scarcity+social_class+age,data=gini_hlmanalysis1,REML=F)
HLM_summary(lmm.fit2)
`

Here is the studio feedback:

Thanks for your kindly reply!

Lin

近期使用三因素混合设计的SPSS结果和bruceR的结果不一样，其中group是组间，task和con是组内。task因素有3个，做简单效应分析的时候，df2 应该是N-K，N一共是20个，那应该是17个。但是bruceR结果df2是18。不知道是不是我操作错误。过程和测试数据均已上传。
SPSS 语法：
GLM task1con1 task2con1 task3con1 task1con2 task2con2 task3con2 BY group
/WSFACTOR=con 2 Polynomial task 3 Polynomial
/METHOD=SSTYPE(3)
/POSTHOC=group(LSD BONFERRONI)
/PLOT=PROFILE(taskgroupcon) TYPE=BAR ERRORBAR=CI MEANREFERENCE=YES
/EMMEANS=TABLES(OVERALL)
/EMMEANS=TABLES(contaskgroup) COMPAR(task) ADJ(BONFERRONI)
/PRINT=DESCRIPTIVE ETASQ PARAMETER HOMOGENEITY
/CRITERIA=ALPHA(.05)
/WSDESIGN=con task con*task
/DESIGN=group.
结果：

R 语法：
library(bruceR)
set.wd()
datas <- import(file ='test.sav')
Result <- MANOVA(datas,dvs='task1con1:task3con2',dvs.pattern = 'task(.)con(.)',
between='group',within=c('task','con'),file='dpd_face.doc') %>%
#EMMEANS(effect=c('task','con'),by='group')
EMMEANS(effect='task',by=c('group','con'),model.type = "multivariate")
结果：

测试数据：
test.zip

Dear users,

Thanks for your use of the bruceR package. This package has passed all code checks performed by CRAN, see CRAN Package Check Results for Package bruceR. If there were any errors in the check results, then the CRAN would notify the developer immediately. However, any error appearing only in YOUR operating system but not in the CRAN check results is highly likely due to your installed (old) versions of R and/or R packages, or due to some other unknown reasons not related to bruceR!

Although it is possible that bruceR may still have some bugs not detected by CRAN and not noticed by me, I strongly suggest you following these steps before you decide to open/submit a new issue here:

1. Update R to its latest version (https://www.r-project.org).
2. Update all R packages in your library to their latest versions.
3. Restart R/RStudio if you have updated R and/or any R packages.
4. Read the help page of the R function you don't know how to use (using help(function) or ?function).

I hope all issues you came across would be automatically fixed if you follow these suggestions.

If you are sure that the bugs are relevant to the functionality of bruceR and you have already read the help pages, then open and submit a new issue here.

Best regards,
Han-Wu-Shuang Bao

您好，很感谢您的付出，bruceR真的是一个非常方便的工具。我最近在使用中遇到一个问题，我有一个两因素被试内的数据，但是其中一个因素的不同水平之间的试次数量是不一样的，这导致我将两个因素都纳入方差分析中时，进行事后检验使用的均值是三个组的均值的均值，和仅计算单个因素的均值是不一样的，得出来的检验结果应该是不准确的。

I think this is a good job. But I have some questions. For example,this package can not installed。

install.packages("bruceR")

Warning in install.packages :
package ‘bruceR’ is not available for this version of R

A version of this package for your version of R might be available elsewhere,
see the ideas at
https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packages

Platform: Windows x64
R Version: 4.1.1 (x64)
bruceR Version: latest (installed via install_github())

When I tried to generate tidy report of an object returned from function lmer(), the following error occurred:

HLM_summary(model.hlm3)

# Error in formula[[2]] : object of type 'symbol' is not subsettable

我留意道CFA估计器似乎只有默认的ML估计器，然而其实一些入WLS等估计器也是很常用的，当然还有一些稳健的估计器如MLR等。在lavaan中可以通过输入estimator来更换估计器，我知道CFA是对lavaan结果得封装。这在brucer包中似乎上可以实现。

可能需要添加一些提示告知用户目前使用的是何种估计器。

我现在希望能将简单效应分析和多重比较的结果也以文本的形式输出。但是现在只在MANOVA()函数里找到了file关键字，输出出来的内容也只包含了方差分析的部分，帮助文档里面的EMMEANS()这个函数没有相关的关键词。
因为课题要处理的数据比较多，所以如果能整合到一个文档里输出会节省很多功夫，因为没有找到这个功能所以打扰一下作者，不知道有没有这个功能？

bruce你好，我一直使用你的包进行一些因子分析和描述性统计。也向这个包提了一些issues，感谢你热心的解决了这些问题。

我之前做测量不变性的模型一直要使用lavaan语法来定义不同的模型，这显得很繁琐。我留意到semtools曾有一个测量不变性的函数，不过目前已经废弃被更底层的函数取代了。目前R中缺少能简单的测量不变性的函数，即使有也并不好用（如有一个包psycmodel中有一个测量不变性的函数，然而却没法定义估计器以及测量的模型阶数较少）。

我想你是否有加入这一函数的想法。测量不变性也是因子分析中的重要内容，我觉得你如果加入这个函数的话可以加入一些是否通过不变性测试的描述。你可以参考一下psycmodel中的实现https://jasonmoy28.github.io/psycModel/reference/measurement_invariance.html。

再一次感谢你为大家带来如此有用的工具。

just like the title.

After updated my R packages, when I use MANOVA function, I get the following error:

MSE = Mean Square Error (an estimate of the population variance σ²)

ANOVA Effect Size:
Error in `$<-.data.frame`(`*tmp*`, "p.value", value = character(0)) : 
  replacement has 0 rows, data has 3

But this error was not occurred before, how to solve this error.

R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] cowplot_1.1.1     performance_0.6.1 data.table_1.13.6 rio_0.5.16       
 [5] bruceR_0.5.4      ggstatsplot_0.6.6 psych_2.0.12      lsr_0.5          
 [9] forcats_0.5.0     stringr_1.4.0     dplyr_1.0.2       purrr_0.3.4      
[13] readr_1.4.0       tidyr_1.1.2       tibble_3.0.4      ggplot2_3.3.3    
[17] tidyverse_1.3.0  

Hi Bruce, thanks for your effort to make this package! It is terrific and really makes ANOVA easy.

Would there be any way to save APA-style ANOVA output as a word document? I did know an r package named apaTables (https://github.com/dstanley4/apaTables) can achieve this goal, but it can not provide effect size and its CI.

It will be great to see how bruceR become better and better!

您好！我在使用bruceR时，用MANOVA计算单因素重复测量方差分析的结果，数据输入的是长数据，计算一切正常。但是当我把model输入到EMMEANS，并指定by为被试内因素时，会报错提示：“There are no factors to test”。请问是因为我输入数据为长数据的原因吗？谢谢您！

Dear bruce,
I am using PROCESS in bruceR for modelling, however I have found a problem with the display. When I run model 7, the values of the covariates are incorrectly substituting the values of the direct effects. You can see this in the code below,I have used the example dataset data from your help file for this example

As you can see in the section Direct Effect: "parent_edu" (X) ==> "score" (Y) it shows a coefficient of -0.444, which should be the coefficient of partjob. Not the coefficient of the direct effect, which is 3.581 instead.

> PROCESS(data, y="score", x="parent_edu",
+         meds=c("family_inc"),
+         mod.path = c("x-m"),
+         mods="gender",
+         covs=c("partjob"))

************ PART 1. Regression Model Summary ************

PROCESS Model Code : 7 (Hayes, 2018; www.guilford.com/p/hayes3)
PROCESS Model Type : Moderated Mediation
-      Outcome (Y) : score
-    Predictor (X) : parent_edu
-    Mediators (M) : family_inc
-   Moderators (W) : gender
-   Covariates (C) : partjob
- Level-2 Clusters : -
All numeric predictors have been mean-centered.

Formula of Mediator:
-    family_inc ~ partjob + parent_edu*gender
Formula of Outcome:
-    score ~ partjob + parent_edu + gender + family_inc

Model Summary

─────────────────────────────────────────────────────────────────
                       (1) score     (2) family_inc  (3) score   
─────────────────────────────────────────────────────────────────
(Intercept)              51.912 ***     9.207 ***      51.261 ***
                         (0.094)       (0.030)         (0.126)   
partjob                  -0.298         0.132 **       -0.444 *  
                         (0.194)       (0.045)         (0.187)   
parent_edu                5.546 ***     1.786 ***       3.581 ***
                         (0.190)       (0.061)         (0.199)   
genderMale                              0.107 *         1.358 ***
                                       (0.044)         (0.182)   
parent_edu:genderMale                   0.021                    
                                       (0.089)                   
family_inc                                              1.086 ***
                                                       (0.042)   
─────────────────────────────────────────────────────────────────
R^2                       0.081         0.146           0.145    
Adj. R^2                  0.081         0.146           0.145    
Num. obs.              9679          9679            9679        
─────────────────────────────────────────────────────────────────
Note. * p < .05, ** p < .01, *** p < .001.

************ PART 2. Mediation/Moderation Effect Estimate ************

Package Use : ‘mediation’ (v4.5.0), ‘interactions’ (v1.1.5)
Effect Type : Moderated Mediation (Model 7)
Sample Size : 9679
Random Seed : set.seed()
Simulations : 100 (Bootstrap)

Warning: nsim=1000 (or larger) is suggested!

Direct Effect: "parent_edu" (X) ==> "score" (Y)
────────────────────────────────────────────────────────────────────
               Effect     S.E.       t      p               [95% CI]
────────────────────────────────────────────────────────────────────
Direct (c')    -0.444  (0.187)  -2.372   .018  *    [-0.811, -0.077]
────────────────────────────────────────────────────────────────────

Interaction Effect on "family_inc" (M)
──────────────────────────────────────────────────
                          F  df1   df2      p     
──────────────────────────────────────────────────
parent_edu x gender    0.05    1  9674   .815     
──────────────────────────────────────────────────

Simple Slopes: "parent_edu" (X) ==> "family_inc" (M)
(Conditional Effects [a] of X on M)
───────────────────────────────────────────────────────────────
  "gender"  Effect     S.E.       t      p             [95% CI]
───────────────────────────────────────────────────────────────
  Female     1.786  (0.061)  29.104  <.001  ***  [1.665, 1.906]
  Male       1.806  (0.064)  28.294  <.001  ***  [1.681, 1.932]
───────────────────────────────────────────────────────────────

Running 100 * 2 simulations...
Indirect Path: "parent_edu" (X) ==> "family_inc" (M) ==> "score" (Y)
(Conditional Indirect Effects [ab] of X through M on Y)
───────────────────────────────────────────────────────────────
  "gender"  Effect     S.E.       z      p        [Boot 95% CI]
───────────────────────────────────────────────────────────────
  Female     1.940  (0.110)  17.650  <.001  ***  [1.725, 2.109]
  Male       1.962  (0.085)  23.009  <.001  ***  [1.825, 2.149]
───────────────────────────────────────────────────────────────
Percentile Bootstrap Confidence Interval
(SE and CI are estimated based on 100 Bootstrap samples.)

Note. The results based on bootstrapping or other random processes
are unlikely identical to other statistical software (e.g., SPSS).
To make results reproducible, you need to set a seed (any number).
Please see the help page for details: help(PROCESS)
Ignore this note if you have already set a seed. :)

Hopeing this will help you.

Based on the documentation, I think the Alpha function required the item to be in the format of common var name + unique var name (e.g., a1 , a2, a3 should be written as var = a, items = 1:3). However, in practice, that's not always the case to have a common var name follow by a unique var name. For example, I want to use the item Q67, Q88, Q89a, Q89b, Q89c, and it is not supported by the Alpha function (at least I don't know how to do it by reading the documentation)

I think it would be much more useful if the function supports subsetting using the dplyr::select() syntax and the helpers (e.g., everything(), starts_with()).