### Install GAMLj from GitHub

Source: https://gamlj.github.io/rpackage/index.html

Use this command to install the latest version of GAMLj directly from the GitHub repository.

```R
devtools::install_github("gamlj/gamlj")
```

```R

devtools::install_github(“gamlj/gamlj”)
```

--------------------------------

### Load GAMLj library

Source: https://gamlj.github.io/rpackage/articles/vignettes_plot.html

Initial setup to access GAMLj functions.

```R
library(GAMLj3)
```

--------------------------------

### Initialize GAMLj Environment

Source: https://gamlj.github.io/rpackage/articles/vignettes_defining_model.html

Load the GAMLj3 library and prepare the example dataset.

```R
library(GAMLj3)
data("clustermanymodels")
data<-clustermanymodels
```

--------------------------------

### Install Legacy GAMLj Version

Source: https://gamlj.github.io/rpackage/index.html

Use this command to install version 2.6.6 if compatibility with older analyses is required.

```R
devtools::install_github("gamlj/gamlj",ref="Version.2.6.6")
```

--------------------------------

### Install Legacy GAMLj Version

Source: https://gamlj.github.io/rpackage/index.html

Install version 2.6.6 of the GAMLj module for compatibility with older analyses.

```R
devtools::install_github("gamlj/gamlj",ref="Version.2.6.6")
```

```R


devtools::install_github(“gamlj/gamlj”,ref=“Version.2.6.6”)
```

--------------------------------

### Install GAMLj from Source

Source: https://gamlj.github.io/rpackage/index.html

Compile the module from a local repository clone using the jmvtools package.

```R

library(jmvtools)
jmvtools::install()
```

--------------------------------

### Install GAMLj in R

Source: https://gamlj.github.io/index.html

Use the devtools package to install the GAMLj library directly from the GitHub repository.

```R
devtools::install_github("gamlj/gamlj")
```

--------------------------------

### Install GAMLj from source

Source: https://gamlj.github.io/rpackage/index.html

Compile the module from a local clone of the repository using the jmvtools package.

```R
library(jmvtools)

jmvtools::install()
```

--------------------------------

### Interactive Terminal Startup Notice

Source: https://gamlj.github.io/rpackage/LICENSE.html

Short license notice to display when a program starts in interactive mode.

```text
<program>  Copyright (C) <year>  <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type 'show c' for details.
```

--------------------------------

### Generating plots with GAMLj

Source: https://gamlj.github.io/rpackage/reference/jnplot.html

Examples showing how to generate plots from a GAMLj linear model object.

```R
data(qsport)
gmod <- GAMLj3::gamlj_lm(
    formula = performance ~ hours,
    data = qsport
)

plot(gmod, plot_x = "hours")

plot(gmod, formula = ~hours)
#> [[1]]

#> 
```

--------------------------------

### Example usage of GAMLj linear model

Source: https://gamlj.github.io/rpackage/reference/s3methods.html

Demonstrates fitting a linear model using GAMLj3 and displaying the summary output.

```r
data(fivegroups)
fivegroups$Group <- factor(fivegroups$Group)
gmod <- GAMLj3::gamlj_lm(
    formula = Score ~ Group,
    data = fivegroups
)

summary(gmod)
```

--------------------------------

### Display contrast matrix output

Source: https://gamlj.github.io/rosetta_contrasts.html

Example output generated by the difference.emmc function.

```text
##   2 vs previous 3 vs previous 4 vs previous
## 1            -1          -0.5        -0.333
## 2             1          -0.5        -0.333
## 3             0           1.0        -0.333
## 4             0           0.0         1.000
```

--------------------------------

### GAMLj model estimation example

Source: https://gamlj.github.io/rpackage/reference/test_contrasts.html

Example of fitting a linear model with GAMLj and calculating estimated marginal means.

```R
data(fivegroups)
fivegroups$Group <- factor(fivegroups$Group)
gmod <- GAMLj3::gamlj_lm(
    formula = Score ~ Group,
    data = fivegroups
)

em_means(gmod, formula = ~Group)
```

--------------------------------

### Extracting ANOVA results

Source: https://gamlj.github.io/rpackage/reference/anova.html

Example showing how to fit a GAMLj linear model and extract the ANOVA table.

```R
data(fivegroups)
fivegroups$Group <- factor(fivegroups$Group)
gmod <- GAMLj3::gamlj_lm(
    formula = Score ~ Group,
    data = fivegroups
)
#> 
#> Attaching package: ‘jmvcore’
#> The following objects are masked from ‘package:base’:
#> 
#>     endsWith, format, startsWith

anova(gmod)
#> [[1]]
#> 
#>  Model Fit                                                             
#>  ───────────────────────────────────────────────────────────────────── 
#>    R²           Adj. R²      df    df (res)    F           p           
#>  ───────────────────────────────────────────────────────────────────── 
#>    0.1472811    0.1318705     3         166    9.557137    0.0000074   
#>  ───────────────────────────────────────────────────────────────────── 
#> 
#> 
#> [[2]]
#> 
#>  ANOVA Omnibus tests                                                       
#>  ───────────────────────────────────────────────────────────────────────── 
#>                 SS            df     F            p            η²p         
#>  ───────────────────────────────────────────────────────────────────────── 
#>    Model         7.4831734      3    9.5571370    0.0000074    0.1472811   
#>    Group         7.4831734      3    9.5571370    0.0000074    0.1472811   
#>    Residuals    43.3256242    166                                          
#>    Total        50.8087976    169                                          
#>  ───────────────────────────────────────────────────────────────────────── 
#> 
#> 
```

--------------------------------

### Perform post-hoc tests on a GAMLj model

Source: https://gamlj.github.io/rpackage/reference/posthoc.html

Example of running a linear model with GAMLj and performing post-hoc tests on the 'Group' factor.

```R
data(fivegroups)
fivegroups$Group <- factor(fivegroups$Group)
gmod <- GAMLj3::gamlj_lm(
    formula = Score ~ Group,
    data = fivegroups
)

posthoc(gmod, formula = ~Group)
```

--------------------------------

### Extracting estimated marginal means from a GAMLj model

Source: https://gamlj.github.io/rpackage/reference/em_means.html

Example showing how to fit a linear model using GAMLj and extract the estimated marginal means for a specific factor.

```R
data(fivegroups)
fivegroups$Group <- factor(fivegroups$Group)
gmod <- GAMLj3::gamlj_lm(
    formula = Score ~ Group,
    data = fivegroups
)

em_means(gmod, formula = ~Group)
#> 
#>  ESTIMATED MARGINAL MEANS
#> 
#>  Estimate Marginal Means - Group                                        
#>  ────────────────────────────────────────────────────────────────────── 
#>    Group    Mean          SE           df     Lower         Upper       
#>  ────────────────────────────────────────────────────────────────────── 
#>    1         0.0095455    0.0770180    166    -0.1425156    0.1616065   
#>    2        -0.0905556    0.0851466    166    -0.2586653    0.0775542   
#>    3         0.2531111    0.0761574    166     0.1027492    0.4034731   
#>    4         0.4526667    0.0761574    166     0.3023047    0.6030286   
#>  ────────────────────────────────────────────────────────────────────── 
#>
```

--------------------------------

### Fit a logistic regression model

Source: https://gamlj.github.io/rpackage/reference/gamlj_glm.html

Example of fitting a logistic regression model using the GAMLj3 package with the neuralgia dataset.

```R
data <- emmeans::neuralgia
GAMLj3::gamlj_glm(
    formula = Pain ~ Duration,
    data = data,
    model_type = "logistic"
)
```

--------------------------------

### Get Residuals

Source: https://gamlj.github.io/rpackage/articles/vignettes_gamlj_methods.html

Extract residuals from the model.

```R
res<-residuals(mod1)
head(res)
```

--------------------------------

### Get Predicted Values

Source: https://gamlj.github.io/rpackage/articles/vignettes_gamlj_methods.html

Calculate predicted values for the model.

```R
preds<-predict(mod1)
head(preds)
```

--------------------------------

### R Deviation Contrast Implementation

Source: https://gamlj.github.io/rosetta_contrasts.html

Demonstrates setting up deviation contrasts in R by reversing the default contr.sum behavior and fitting a linear model.

```R
file<-"../data/rosetta.contrasts.csv"
dat<-read.csv2(file,stringsAsFactors = F)
dat$Group<-factor(dat$Group)
dat$Score<-as.numeric(dat$Score)
### reverse contrast sum ###
contrasts(dat$Group)<-matrix(rev(contr.sum(4)),ncol=3)
contrasts(dat$Group)
```

```R
##   [,1] [,2] [,3]
## 1   -1   -1   -1
## 2    1    0    0
## 3    0    1    0
## 4    0    0    1
```

```R
model<-lm(Score~Group,data=dat)
summary(model)
```

```R
## 
## Call:
## lm(formula = Score ~ Group, data = dat)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.06311 -0.27955 -0.00111  0.28531  1.65733 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.15619    0.03936   3.969 0.000107 ***
## Group1      -0.24675    0.07193  -3.430 0.000760 ***
## Group2       0.09692    0.06670   1.453 0.148092    
## Group3       0.29647    0.06670   4.445 0.000016 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5109 on 166 degrees of freedom
## Multiple R-squared:  0.1473, Adjusted R-squared:  0.1319 
## F-statistic: 9.557 on 3 and 166 DF,  p-value: 0.000007399
```

```R
## means
(mm<-tapply(dat$Score, dat$Group, mean))
```

```R
##            1            2            3            4 
##  0.009545455 -0.090555556  0.253111111  0.452666667
```

--------------------------------

### Source File License Header

Source: https://gamlj.github.io/rpackage/LICENSE.html

Standard copyright and license notice to be placed at the beginning of each source file.

```text
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year>  <name of author>

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
```

--------------------------------

### Load qsport dataset

Source: https://gamlj.github.io/rpackage/reference/qsport.html

Loads the simulated qsport dataset into the R environment.

```R
data(qsport)
```

--------------------------------

### Predicting values from a linear model

Source: https://gamlj.github.io/rpackage/reference/predict.html

Example usage of the predict function on a GAMLj linear model object.

```R
data("qsport")
gmod <- GAMLj3::gamlj_lm(
    formula = performance ~ hours,
    data = qsport
)
preds <- predict(gmod)
```

--------------------------------

### Implement Post-hoc Tests with emmeans

Source: https://gamlj.github.io/posthoc_specs.html

This snippet demonstrates the internal logic for generating post-hoc comparisons using the emmeans package with different adjustment methods.

```R
      formula <- as.formula(paste('~', term))
      referenceGrid <- emmeans::emmeans(private$.model, formula)
      bonferroni <- summary(pairs(referenceGrid, adjust='bonferroni'))
      holm <- summary(pairs(referenceGrid, adjust='holm'))
      tukey<-summary(pairs(referenceGrid, adjust='tukey'))
      scheffe<-summary(pairs(referenceGrid, adjust='scheffe'))
      sidak<-summary(pairs(referenceGrid, adjust='sidak'))
```

--------------------------------

### Perform Post-Hoc Comparisons with emmeans

Source: https://gamlj.github.io/gmixed_specs.html

This snippet demonstrates how to generate a reference grid and compute pairwise comparisons with various adjustment methods for GZLM models.

```R
          referenceGrid <- emmeans::emmeans(model, formula)
          none <- summary(pairs(referenceGrid, adjust='none'))
          tukey <- summary(pairs(referenceGrid, adjust='tukey'))
          scheffe <- summary(pairs(referenceGrid, adjust='scheffe'))
          bonferroni <- summary(pairs(referenceGrid, adjust='bonferroni'))
          holm <- summary(pairs(referenceGrid, adjust='holm'))
```

--------------------------------

### Load HSB Demo Dataset

Source: https://gamlj.github.io/rpackage/reference/hsbdemo.html

Loads the hsbdemo dataset into the R environment.

```R
data(hsbdemo)
```

--------------------------------

### R Omnibus test output

Source: https://gamlj.github.io/rosetta_gzlmmixed1.html

Example output from the car::Anova command showing Wald chi-square tests.

```text
Analysis of Deviance Table (Type III Wald chisquare tests)

Response: remission
               Chisq Df Pr(>Chisq)    
(Intercept)   14.918  1  0.0001123 ***
IL6           24.293  1  8.273e-07 ***
CRP            4.420  1  0.0355197 *  
CancerStage  256.523  3  < 2.2e-16 ***
LengthofStay  12.982  1  0.0003145 ***
Experience    19.135  1  1.218e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
```

--------------------------------

### Inverting and Transposing a Model Matrix

Source: https://gamlj.github.io/rosetta_contrasts.html

Demonstrates how to derive a contrast matrix by taking the inverse of the transpose of a model matrix.

```R
### take the inverse ####
ilmat<-ginv(lmat)
### transpose it ###
round(t(ilmat),digits = 3)
```

--------------------------------

### Implement Simple Contrast in R

Source: https://gamlj.github.io/rosetta_contrasts.html

Configures simple contrasts for a factor in R using contr.treatment.

```R
#### contrast=simple #######
dat$Group<-factor(dat$Group)
k<-4 # number of levels
contrasts(dat$Group)<-contr.treatment(k)-(1/k)
contrasts(dat$Group)
```

--------------------------------

### Get coefficients covariances for a GAMLj model

Source: https://gamlj.github.io/rpackage/reference/vcov.gamlj.html

Use this method to extract the variance-covariance matrix from a fitted GAMLj model object.

```R
# S3 method for class 'gamlj'
vcov(object, ...)
```

--------------------------------

### Fit a linear mixed model

Source: https://gamlj.github.io/rpackage/reference/gamlj_mixed.html

Example of fitting a linear mixed model using the gamlj_mixed function with a specified formula and dataset.

```R
data(clustermanymodels)
GAMLj3::gamlj_mixed(
    formula = ycont ~ 1 + x + (1 | cluster),
    data = clustermanymodels
)
```

--------------------------------

### Load the beers_bars dataset

Source: https://gamlj.github.io/rpackage/reference/beers_bars.html

Load the simulated dataset into the R environment.

```R
data(beers_bars)
```

--------------------------------

### Define and test custom contrasts

Source: https://gamlj.github.io/rpackage/reference/test_contrasts.html

Basic syntax for the test_contrasts function.

```R
test_contrasts(object, contrasts, ...)
```

--------------------------------

### print(x, ...)

Source: https://gamlj.github.io/rpackage/reference/s3methods.html

Prints GAMLj3 objects of class 'jmvrtable', 'jmvrobj', or 'gamlj_list'.

```APIDOC
## print(x, ...)

### Description
Prints the content of GAMLj3 tables or objects in R style.

### Parameters
- **x** (jmvrtable/jmvrobj/gamlj_list) - Required - The object to print.
- **...** (dots) - Optional - Additional arguments passed to methods.
```

--------------------------------

### Calculate Simple Effects for a GAMLj Model

Source: https://gamlj.github.io/rpackage/reference/simple_effects.html

Demonstrates how to fit a mixed model using GAMLj and subsequently calculate simple effects for the interaction between time and group.

```R
data(wicksell)
wicksell$time <- factor(wicksell$time)
wicksell$group <- factor(wicksell$group)
wicksell$subj <- factor(wicksell$subj)
gmod<-GAMLj3::gamlj_mixed(
   formula = dv ~ 1 +group+ time:group+ time+( 1 | subj ),
   data = wicksell)

simple_effects(gmod,formula =~time:group)
```

--------------------------------

### Create Basic Scatterplot

Source: https://gamlj.github.io/rosetta_moderation.html

Generate a simple scatterplot to visualize the interaction.

```SPSS
GRAPH
  /SCATTERPLOT(BIVAR)=x WITH y BY z.
```

--------------------------------

### Calculate Simple Contrast with emmeans

Source: https://gamlj.github.io/rosetta_contrasts.html

Uses the dunnett method in emmeans to perform simple contrasts.

```R
model<-lm(Score~Group,data=dat)
emm<-emmeans(model,~Group)
contrast(emm,"dunnett",adjust = "none")
```

--------------------------------

### Run a General Linear Model

Source: https://gamlj.github.io/rpackage/reference/gamlj_lm.html

Executes a linear model analysis using the ToothGrowth dataset.

```R
data("ToothGrowth")
GAMLj3::gamlj_lm(formula = len ~ supp, data = ToothGrowth)
```

--------------------------------

### Summarize Model Results

Source: https://gamlj.github.io/rpackage/articles/vignettes_gamlj_lm.html

Render the entire results object into simple tables using the summary command.

```R
summary(mod1)
```

--------------------------------

### S3 methods for GAMLj3 classes

Source: https://gamlj.github.io/rpackage/reference/s3methods.html

Methods for interacting with GAMLj3 result objects, including summary, print, coefficient extraction, and model fit.

```R
# S3 method for class 'ResultsElement'
summary(object, ...)

# S3 method for class 'gamlj_list'
summary(object, ...)

# S3 method for class 'jmvrtable'
print(x, ...)

# S3 method for class 'jmvrobj'
print(x, ...)

# S3 method for class 'gamlj_list'
print(x, ...)

# S3 method for class 'gamlj'
coef(object, ...)

fit(x, ...)

# S3 method for class 'gamlj'
fit(x, ...)
```

--------------------------------

### Configure simple effects table labels

Source: https://gamlj.github.io/rpackage/articles/vignettes_gamlj_lm.html

Computes simple effects with percentile conditioning and custom labels for the moderator values.

```R
se<-simple_effects(mod3,simple_x = "xage",simple_mods = "zexer",covs_conditioning="percent",covs_scale_labels="values_labels")
```

```R
se
```

--------------------------------

### Apply and Scale Helmert Contrasts in R

Source: https://gamlj.github.io/rosetta_contrasts.html

Demonstrates how to reverse and rescale R's default contr.helmert to match jamovi/SPSS results.

```R
dat$Group<-factor(dat$Group)
contrasts(dat$Group)<-matrix(rev(contr.helmert(4)),ncol=3)
con_weights<-contrasts(dat$Group)
#### not scaled contrast weights ######
con_weights
```

```R
model<-lm(Score~Group,data=dat)
summary(model)
```

```R
cv<-apply(con_weights,2,max)+1
cv
```

```R
coef(model)[c(2:4)]*cv
```

```R
### make a diagonal matrix with cv in the diagonal ##########
diag_cv<-matrix(rep(cv,each=4),ncol=3)
########  divide the contrast weights ##########
helm<-con_weights/diag_cv

### scaled contrast weights ############
helm
```

```R
contrasts(dat$Group)<-helm
model<-lm(Score~Group,data=dat)
summary(model)
```

--------------------------------

### Estimate model and generate initial plot

Source: https://gamlj.github.io/rpackage/articles/vignettes_plot.html

Estimate a linear model using gamlj_lm and extract a plot for the main effect of Sepal.Width.

```R
mod<-gamlj_lm(formula=Sepal.Length~Sepal.Width*Petal.Length*Petal.Width, 
              data=iris,
              plot_x = "Sepal.Width"
              )
```

```R
plot(mod)
```

--------------------------------

### Defining Custom Contrasts in emmeans

Source: https://gamlj.github.io/rosetta_contrasts.html

Shows how to define custom contrast coding by calculating the inverse of the transpose of a model matrix.

```R
(rcodes<-(-1)*contr.sdif(4))
```

```R
cont<-contrast(emm,"consec",referse=T,adjust = "none")
(weigths<-coef(cont)[,2:4])
```

```R
#### model matrix ######
rcodes
```

```R
#### transpose the R model matrix ######
tcodes<-t(rcodes)
#### invert it #####
round(ginv(tcodes),digits = 3)
```

--------------------------------

### Load and summarize exercise data

Source: https://gamlj.github.io/rpackage/articles/vignettes_gamlj_lm.html

Reads the exercise dataset from a remote URL and displays summary statistics for the relevant variables.

```R
data<-read.csv("https://raw.githubusercontent.com/mcfanda/gamlj_docs/master/data/exercise.csv")
summary(data[,c("xage","zexer","yendu")])
```

--------------------------------

### Initialize a Linear Model

Source: https://gamlj.github.io/rpackage/articles/vignettes_gamlj_lm.html

Specify the model formula and data frame to create a linear model object.

```R
mod1<-gamlj_lm(formula = yendu~xage+zexer, data=data)
```

--------------------------------

### Replicate ANOVA with R Linear Model

Source: https://gamlj.github.io/rosetta_simpleint.html

Sets up a linear model using lm() and performs Type II ANOVA tests via the car package.

```R
data<-read.csv("../data/keppel_3way_pg466.csv",stringsAsFactors=T)
data$Feedback <- car::recode(data$Feedback, "'none'='None'; 'pos'='Positive'; 'neg'='Negative'")
data$Feedback<-factor(data$Feedback,levels=c("None","Positive","Negative"))
data$WordType <- factor(data$WordType,
                    levels=c("LF_LE","HF_LE","HF_HE"))                  
contrasts(data$Grade)<-c(0,1)
model<-lm(numrecall~Feedback*WordType*Grade,data=data)
car::Anova(model)
```

--------------------------------

### Load fivegroups dataset

Source: https://gamlj.github.io/rpackage/reference/fivegroups.html

Loads the fivegroups dataset into the R environment.

```R
data(fivegroups)
```

--------------------------------

### Load Wicksell dataset

Source: https://gamlj.github.io/rpackage/reference/wicksell.html

Loads the wicksell dataset into the R environment.

```R
data(wicksell)
```

--------------------------------

### Load subjects_on_stimuli dataset

Source: https://gamlj.github.io/rpackage/reference/subjects_on_stimuli.html

Loads the simulated dataset into the R environment.

```R
data(subjects_on_stimuli)
```

--------------------------------

### Extract residuals from a linear model

Source: https://gamlj.github.io/rpackage/reference/residuals.html

Demonstrates how to fit a linear model using GAMLj and extract the residuals.

```R
data("qsport")
gmod <- GAMLj3::gamlj_lm(
    formula = performance ~ hours,
    data = qsport
)
preds <- residuals(gmod)
```

--------------------------------

### Post-hoc Analysis

Source: https://gamlj.github.io/rpackage/articles/vignettes_gamlj_methods.html

Attempt to retrieve post-hoc tests or perform them by passing options to the command.

```R
ph<-posthoc(mod1)
```

```R
ph
```

```R
posthoc(mod1,posthoc= ~cat3)
```

--------------------------------

### Define Models via Terms

Source: https://gamlj.github.io/rpackage/articles/vignettes_defining_model.html

Specify models using the dep argument and model_terms as either a formula or a list.

```R
gamlj_lm(dep=ycont,model_terms=~x*z , data=data)
```

```R
gamlj_lm(dep=ycont,model_terms=list("x","z",c("x","z")) , data=data)
```

--------------------------------

### Load manymodels dataset

Source: https://gamlj.github.io/rpackage/reference/manymodels.html

Load the manymodels dataset into the R environment.

```R
data(manymodels)
```

--------------------------------

### Load Poisson Acts Dataset

Source: https://gamlj.github.io/rpackage/reference/poissonacts.html

Loads the poissonacts dataset into the R environment.

```R
data(poissonacts)
```

--------------------------------

### posthoc()

Source: https://gamlj.github.io/rpackage/reference

Performs post-hoc tests on GAMLj results.

```APIDOC
## posthoc()

### Description
Calculates post-hoc tests for GAMLj model results.
```