**summary**() function is a generic function used to produce result **summaries** of the results of various **model** fitting functions. The function invokes particular methods which depend on the.

To fix an outdated citation hyperlink: Take the alphanumeric code at end of the broken hyperlink and add to the end of the link. To find a specific citation by accession number: Take the accession number and add to the end of the link below.. On this page, youโll learn how to apply **summary** statistics like the mean or median to the columns of a data.**table** in **R**. The post will consist of these topics: 1) Example Data & Packages 2) Example 1: Calculate Mean Values for Groups 3) Example 2: Create new Column with **Summary** Statistic: Mean values 4) Example 3: Show Several Statistics. **model**.**tables**: Compute **Tables** of Results from an Aov **Model** Fit Description Computes **summary** **tables** for **model** fits, especially complex aov fits. Usage **model**.**tables** (x, ) # S3 method for aov **model**.**tables** (x, type = "effects", se = FALSE, cterms, ) # S3 method for aovlist **model**.**tables** (x, type = "effects", se = FALSE, ) Arguments x. All: Include all columns of a dataframe. AllObs: Display all observations in a **table**. Arguments: 'Arguments' pseudo-function coef_rename: Rename **model** terms colLabels: Retrieve or modify the row or column labels. datasummary: **Summary** **tables** using 2-sided formulae: crosstabs,... datasummary_balance: Balance **table**: **Summary** statistics for different subsets of.

. **R**-sq. (adj) = 0.556 Deviance explained = 87.8% UBRE = 1.4389 Scale est. = 1 n = 73 GAM .pdf 62.67 KB Vegetation Generalized Linear Models P Value Grassland Generalized Additive **Model** Advanced.... **Summary** of 2014-2015 Chrysler, Ram, Dodge, Jeep & Fiat Vehicles Estimated Depreciation and Section 179 Expense Allowance BRAND **MODEL** MSRP * Estimated Section 179 Depreciation Estimated Depreciation Total Estimated First Year Depreciation Note 1 2 = 1 + 2 ~'15 2015 Chrysler 200 $21,700 $0 $3,160 $3,160 A,D.

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**modelsummary** includes a powerful set of utilities to customize the information displayed in your** model summary tables.** You can easily rename, reorder, subset or omit parameter estimates; choose the set of goodness-of-fit statistics to display; display various โrobustโ standard errors or confidence intervals; add titles, footnotes, or source notes; insert stars or custom characters to indicate levels of statistical significance; or add rows with supplemental information about your models.. Sep 06, 2020 ยท Because I am working in an **R** -> Word workflow, I need to know what options I have for creating nice **tables** of my models. So, I am taking a break from my first assignment to compare the default output of the most popular **R** **model** **summary** packages, look at some extended features, and make a cross-package comparison for my own future reference..

**modelsummary**: **Summary** **Tables** and Plots for Statistical **Models** and Data: Beautiful, Customizable, and Publication-Ready Create beautiful and customizable **tables** to summarize several statistical **models** side-by-side. Draw coefficient plots, multi-level cross-tabs, dataset summaries, balance **tables** (a.k.a. "**Table** 1s"), and correlation matrices. .

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As you can see from the output that the **summary**() of a vector returns descriptive statistics such as the minimum, the 1st quantile, the median, the mean, the 3rd quantile, and the maximum value of our input data. Applying **summary**() function on the List. To get the **summary** of the list in **R**, use the **summary**() function. To define a list, use the.

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Jan 25, 2019 · Specify your own statistics and classes Prerequisite Install the stable version from CRAN: install.packages ( "skimr") Load the package: library (skimr) **Summarize** a whole dataset skim (iris) Variable type: factor Variable type: numeric Select specific columns to **summarize** skim (iris, Sepal.Length, Petal.Length) Variable type: numeric. "/>. **summary**() function is a generic function used to produce result **summaries** of the results of various **model** fitting functions. The function invokes particular methods which depend on the class of the first argument. ... A **summary table** is a new spreadsheet that instead of having all of the data, has new data that has statistics computed from the.

Oct 20, 2022 ยท modelplot: **Model** **Summary** Plots with Estimates and Confidence Intervals; modelsummary: **Model** **Summary** **Tables**; modelsummary_wide: Superseded function; msummary: 'msummary()' is a shortcut to 'modelsummary()' Multicolumn: Use a variable as a factor to give rows in a **table**. N: datasummary statistic shortcut; Ncol: datasummary statistic shortcut. modelsummary {modelsummary} **R** Documentation **Model** **Summary** **Tables** Description Create beautiful and customizable **tables** to summarize several statistical models side-by-side. This function supports dozens of statistical models, and it can produce **tables** in HTML, LaTeX, Word, Markdown, PDF, PowerPoint, Excel, RTF, JPG, or PNG.. Title **Summary** **Tables** and Plots for Statistical Models and Data: Beautiful, Customizable, and Publi-cation-Ready Description Create beautiful and customizable **tables** to summarize several statistical models side-by-side. Draw coefficient plots, multi-level cross-tabs, dataset summaries, balance **tables** (a.k.a. ``**Table** 1s''), and correlation matrices.. gtsummary. The {gtsummary} package provides an elegant and flexible way to create publication-ready analytical and **summary tables** using the **R** programming language. The {gtsummary}. Title **Summary** **Tables** and Plots for Statistical Models and Data: Beautiful, Customizable, and Publi-cation-Ready Description Create beautiful and customizable **tables** to summarize several statistical models side-by-side. Draw coefficient plots, multi-level cross-tabs, dataset summaries, balance **tables** (a.k.a. ``**Table** 1s''), and correlation matrices.. **modelsummary** is very easy to use. This simple call often suffices: library ( **modelsummary** ) mod <- lm ( y ~ x, dat ) **modelsummary** ( mod) The command above will automatically display a **summary** **table** in the Rstudio Viewer or in a web browser. All you need is one word to change the output format. R2 is the percentage of variation in the response that is explained by the **model**. It is calculated as 1 minus the ratio of the error sum of squares (which is the variation that is not explained by **model**) to the total sum of squares (which is the total variation in the **model**). Interpretation Use R2 to determine how well the **model** fits your data.

event sent when a column is added, removed or moved type :INSERT :DELETE :MOVE in the case of add query first col, for removed query second. Create beautiful and customizable **tables** to summarize several statistical models side-by-side. Draw coefficient plots, multi-level cross-tabs, dataset summaries, balance **tables** (a.k.a. "**Table** 1s"), and correlation matrices. This package supports dozens of statistical models, and it can produce **tables** in HTML, LaTeX, Word, Markdown, PDF, PowerPoint, Excel, RTF, JPG, or PNG. **Tables** can easily be ....

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Compared with the SVR **model**, both PSO-SVR and GA-SVR enabled some improvement in their prediction accuracy, but the PSO-SVR **model** ran faster at 4.85 s, whereas the GA-SVR **model** had a higher prediction accuracy with a prediction set correlation coefficient ( **R** P) of 0.9996 and a root mean square error (RMSEP) of 0.011.

Figure 1. **Model** **summary** The **model** **summary** **table** reports the strength of the relationship between the **model** and the dependent variable. **R**, the multiple correlation coefficient, is the linear correlation between Its large value indicates a strong relationship. **R** Square, the coefficient of determination, is the squared value. R2 is the percentage of variation in the response that is explained by the **model**. It is calculated as 1 minus the ratio of the error sum of squares (which is the variation that is not explained by **model**) to the total sum of squares (which is the total variation in the **model**). Interpretation Use R2 to determine how well the **model** fits your data..

R2 is the percentage of variation in the response that is explained by the **model**. It is calculated as 1 minus the ratio of the error sum of squares (which is the variation that is not explained by **model**) to the total sum of squares (which is the total variation in the **model**). Interpretation Use R2 to determine how well the **model** fits your data.. Sep 09, 2020 ยท When we find the **summary** statistics of a data frame then the output is returned as a **table** and each of the column records the minimum, first quartile, median, median, third quartile, and maximum with their names. If we want to save this **summary** as a data frame then it is better to calculate it with apply function and store it as data.frame. Example. Oct 23, 2013 ยท community oriented multistake hold **r** **model**. The IDN TOD programme and the variant part, IDN variant TOD programme is quite a good case study in the development and deployment of the multistakeholder **model** and multistakeholder system. To start with, various languages and scripts -- script systems that were selected for study, analysis and. Create Descriptive **Summary** Statistics **Tables** in **R** with table1 The next **summary** statistics package which creates a beautiful **table** is table1. In the code below, we are first relabelling our.

Bayesian **models summaries** as HTML **table**. For Bayesian regression **models**, some of the differences to the **table** output from simple **models** or mixed **models** of tab_**models**() are the.

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Bayesian **models summaries** as HTML **table**. For Bayesian regression **models**, some of the differences to the **table** output from simple **models** or mixed **models** of tab_**models**() are the. **R** Documentation **Compute Tables of Results from** an Aov **Model** Fit Description Computes **summary** **tables** for **model** fits, especially complex aov fits. Usage **model**.**tables** (x, ...) ## S3 method for class 'aov' **model**.**tables** (x, type = "effects", se = FALSE, cterms, ...) ## S3 method for class 'aovlist' **model**.**tables** (x, type = "effects", se = FALSE, ...). R2 is the percentage of variation in the response that is explained by the **model**. It is calculated as 1 minus the ratio of the error sum of squares (which is the variation that is not explained by **model**) to the total sum of squares (which is the total variation in the **model**). Interpretation Use R2 to determine how well the **model** fits your data. **Summary** of 2014-2015 Chrysler, Ram, Dodge, Jeep & Fiat Vehicles Estimated Depreciation and Section 179 Expense Allowance BRAND **MODEL** MSRP * Estimated Section 179 Depreciation Estimated Depreciation Total Estimated First Year Depreciation Note 1 2 = 1 + 2 ~'15 2015 Chrysler 200 $21,700 $0 $3,160 $3,160 A,D. The use of the **summary**_**table** use to define a **summary**, that is, a list-of-lists of formulas for **summarizing** the data.frame. The inner lists are named formula e defining the wanted. Create Descriptive **Summary** Statistics **Tables** in **R** with table1. The next **summary** statistics package which creates a beautiful **table** is table1. In the code below, we are first relabelling our columns for aesthetics. Then we are creating the **table** with only one line of code. We again created a **table** by groupings. Nov 23, 2022 ยท **Summary**. We have introduced ways to join **tables** in **R**. You can use the merge () function or the functions in the dplyr package to join **tables** in **R**. Choose the most suitable method for efficient data analysis using the **R** language. mnwr. Full Name: Manh Ha..

The simplest way of producing the **table** output is by passing the fitted **models** as parameter. By default, estimates ( B ), confidence intervals ( CI) and p-values ( p) are reported. The **models** are named **Model** 1 and **Model** 2. sjt.lm (fit1, fit2) Custom labels You can specify the **'model'** label via labelDependentVariables parameter:. **R** Documentation **Compute Tables of Results from** an Aov **Model** Fit Description Computes **summary** **tables** for **model** fits, especially complex aov fits. Usage **model**.**tables** (x, ...) ## S3 method for class 'aov' **model**.**tables** (x, type = "effects", se = FALSE, cterms, ...) ## S3 method for class 'aovlist' **model**.**tables** (x, type = "effects", se = FALSE, ...). The residual **summary** statistics give information about the symmetry of the residual distribution. The median should be close to as the mean of the residuals is , and symmetric distributions have median=mean. Further, the 3Q and 1Q should be close to each other in magnitude. They would be equal under a symmetric mean distribution.

Description. Create beautiful and customizable **tables** to summarize several statistical models side-by-side. This function supports dozens of statistical models, and it can produce **tables** in HTML, LaTeX, Word, Markdown, PDF, PowerPoint, Excel, RTF, JPG, or PNG.. I have made tons of dataset in **R** studio like you could see in the right part of the image below and I would like to make a simple **table** like a **summary** which contains only "name, number of observation and number of variables" of all of each dataset. (I want the **table** to be made just like the RIGHT PART OF THE IMAGE below).

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Provides actions for building, training, and scoring reinforcement learning **models** Syntax Examples Details rlExportModel Action Saves a reinforcement learning **model** as an ASTORE.. CASL Syntax **Summary**: Input and Output **Tables** Parameter Descriptions CASL Syntax reinforcementLearn.rlExportModel < result = results > < status = rc > / * **model** = {. This **model** is designed to handle up to three types of enterprise customers that are completely configurable. Final outputs include an Executive **Summary** (annual), monthly and annual pro forma details, DCF Analysis, and plenty of visualizations. The most difficult piece of logic for this **model** was correctly **modeling** out the input for contract length.

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of-health service delivery (Adjusted **R** Square = 0.513). Therefore, the remaining 49% is explained by other Monitoring and Evaluation principles not considered in the study. Second, the ANOVA output was examined to check whether the proposed **model** was viable.

The Packages. The most popular packages that can take a **model** object and produce a neat **table summary** include modelsummary, gtsummary, huxtable, and sjPlot.I will generate default **tables** with each of these packages. Aug 06, 2019 ยท model1 <- glm (weight ~ Var1 + Var2 + Var3 + Var4 + Var5, data=cg) write.csv (**summary** (model1) ['coefficients'],file='GZLM_CG_M1_result.csv') I think this is as far as I can see Share Follow edited Aug 6, 2019 at 12:17 answered Aug 6, 2019 at 11:31 Amit 1,948 1 7 11 Thank you - but no, this is not what I want to.. Added a cap **table** and capex with depreciation logic as well. This **model** is designed to handle up to three types of enterprise customers that are completely configurable. Final outputs include an Executive **Summary** (annual), monthly and annual pro forma details, DCF Analysis, and plenty of. He stated that SMART has come to mean different things to different people, as shown below. To make sure your goals are clear and reachable, each one should be: Specific (simple, sensible, significant). Measurable (meaningful, motivating). Achievable (agreed, attainable). Relevant (reasonable, realistic and resourced, results-based). Itโs important to use the Anova function rather than the **summary**.aov function in base **R** because Anova allows you to control the type of sums of squares you want to calculate, whereas **summary**.aov only uses Type 1 ( generally not what you want, especially if you have an unblanced design and/or any missing data )..

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Description. Create beautiful and customizable **tables** to summarize several statistical models side-by-side. This function supports dozens of statistical models, and it can produce **tables** in HTML, LaTeX, Word, Markdown, PDF, PowerPoint, Excel, RTF, JPG, or PNG..

Create Descriptive **Summary** Statistics **Tables** in **R** with table1 The next **summary** statistics package which creates a beautiful **table** is table1. In the code below, we are first relabelling our. Given that the P-value is a little less than 0.05 at 95 percent level of confidence for the four variables (0.03, 0.02, 0.01 and 0.03) in **Table** 4.10, the **model** is statistically significant in explaining the influence of policy content, stakeholder involvement, funding, and monitoring on disaster management policy implementation.. In this post, you learned first to examine the general information, review the coefficients for significance, understand how to determine if our results meet the **model** assumptions, and then compare various **models**. Contents Understanding **ARIMA** Results 1. Review General Information 2. Determine Term Significance 3. Review Assumptions Ljung-Box.

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. **R** Documentation **Model Summary** Plots with Estimates and Confidence Intervals Description Dot-Whisker plot of coefficient estimates with confidence intervals. For more. Example 4: Using **summary**() with Regression **Model**. Here we can also calculate **summary**() for linear regression **model**. We can create an linear regression **model** for. On this page, youโll learn how to apply **summary** statistics like the mean or median to the columns of a data.**table** in **R**. The post will consist of these topics: 1) Example Data &. See full list on **r**-bloggers.com. Provides actions for building, training, and scoring reinforcement learning **models** Syntax Examples Details rlExportModel Action Saves a reinforcement learning **model** as an ASTORE.. CASL Syntax **Summary**: Input and Output **Tables** Parameter Descriptions CASL Syntax reinforcementLearn.rlExportModel < result = results > < status = rc > / * **model** = {. Oct 20, 2022 ยท modelsummary **R** Documentation **Model** **Summary** **Tables** Description Create beautiful and customizable **tables** to summarize several statistical models side-by-side. This function supports dozens of statistical models, and it can produce **tables** in HTML, LaTeX, Word, Markdown, PDF, PowerPoint, Excel, RTF, JPG, or PNG.. Sep 06, 2020 ยท Because I am working in an **R** -> Word workflow, I need to know what options I have for creating nice **tables** of my models. So, I am taking a break from my first assignment to compare the default output of the most popular **R** **model** **summary** packages, look at some extended features, and make a cross-package comparison for my own future reference.. event sent when a column is added, removed or moved type :INSERT :DELETE :MOVE in the case of add query first col, for removed query second.

Nov 23, 2022 ยท The first **table** contains data on the number of cylinders and fuel consumption (Miles/gallon (US)), and the second **table** contains data on cylinder displacement (cu.in.) and total horsepower. These two **tables** have one column in common, the car name. Code: **R** 19 1 2 cat("Fuel consumption (Miles/gallon (US)) and number of cylinders ") 3 merc1 <- 4.

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Create Descriptive **Summary Statistics Tables in R** with table1 The next **summary** statistics package which creates a beautiful **table** is table1. In the code below, we are first relabelling our columns for aesthetics. Then we are creating the **table** with only one line of code. We again created a **table** by groupings..

To fix an outdated citation hyperlink: Take the alphanumeric code at end of the broken hyperlink and add to the end of the link. To find a specific citation by accession number: Take the accession number and add to the end of the link below..

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**modelsummary**: **Summary** **Tables** and Plots for Statistical Models and Data: Beautiful, Customizable, and Publication-Ready Create beautiful and customizable **tables** to summarize several statistical models side-by-side. Draw coefficient plots, multi-level cross-tabs, dataset summaries, balance **tables** (a.k.a. "**Table** 1s"), and correlation matrices.. Any statistic reported in a gtsummary **table** can be extracted and reported in-line in a **R** Markdown document with the inline_text () function. inline_text (tbl_reg_1, variable = trt, level = "Drug B") 1.13 (95% CI 0.60, 2.13; p=0.7) The pattern of what is reported can be modified with the pattern = argument.

In this post, you learned first to examine the general information, review the coefficients for significance, understand how to determine if our results meet the **model** assumptions, and then compare various **models**. Contents Understanding **ARIMA** Results 1. Review General Information 2. Determine Term Significance 3. Review Assumptions Ljung-Box. 47Table 4.1 **Model** summaryModel **R** **R** Square Adjusted RSquare Std. Error oftheEstimate1 .788 .653 .636 .0634a. Predictors: (Constant), policy content, stakeholder involvement, funding and monitoringThe **R**-Square (coefficient of determination) of 65.3 percent was obtained from the analysis.. . See full list on **r**-bloggers.com.

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The first **model** yields an **R** 2 of more than 50%. The second **model** adds cooling rate to the **model**. Adjusted **R** 2 increases, which indicates that cooling rate improves the **model**. The third **model**, which adds cooking temperature, increases the **R** 2 but not the adjusted **R** 2. These results indicate that cooking temperature does not improve the **model**. A diagram is a partial graphic representation of a system's **model**. The set of diagrams need not completely cover the **model** and deleting a diagram does not change the **model**. The **model** may also contain documentation that drives the **model** elements and diagrams (such as. As you can see from the output that the **summary**() of a vector returns descriptive statistics such as the minimum, the 1st quantile, the median, the mean, the 3rd quantile, and the maximum value of our input data. Applying **summary**() function on the List. To get the **summary** of the list in **R**, use the **summary**() function. To define a list, use the. of-health service delivery (Adjusted **R** Square = 0.513). Therefore, the remaining 49% is explained by other Monitoring and Evaluation principles not considered in the study. Second, the ANOVA. The first **model** yields an **R** 2 of more than 50%. The second **model** adds cooling rate to the **model**. Adjusted **R** 2 increases, which indicates that cooling rate improves the **model**. The third **model**, which adds cooking temperature, increases the **R** 2 but not the adjusted **R** 2. These results indicate that cooking temperature does not improve the **model**. R2 is the percentage of variation in the response that is explained by the **model**. It is calculated as 1 minus the ratio of the error sum of squares (which is the variation that is not explained by **model**) to the total sum of squares (which is the total variation in the **model**). Interpretation Use R2 to determine how well the **model** fits your data..

The easiest way to create **summary** **tables** in **R** is to use the describe () and describeBy () functions from the psych library. library(psych) #create **summary** **table** describe (df) #create **summary** **table**, grouped by a specific variable describeBy (df, group=df$var_name) The following examples show how to use these functions in practice.

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Sep 06, 2020 ยท Because I am working in an **R** -> Word workflow, I need to know what options I have for creating nice **tables** of my models. So, I am taking a break from my first assignment to compare the default output of the most popular **R** **model** **summary** packages, look at some extended features, and make a cross-package comparison for my own future reference.. Visual explanation on how to read the **Model** **Summary** **table** generated by SPSS. Includes step by step explanation of each calculated value. Includes explanati. In this post, you learned first to examine the general information, review the coefficients for significance, understand how to determine if our results meet the **model** assumptions, and then compare various **models**. Contents Understanding **ARIMA** Results 1. Review General Information 2. Determine Term Significance 3. Review Assumptions Ljung-Box. Itโs important to use the Anova function rather than the **summary**.aov function in base **R** because Anova allows you to control the type of sums of squares you want to calculate, whereas **summary**.aov only uses Type 1 ( generally not what you want, especially if you have an unblanced design and/or any missing data )..

Visual explanation on how to read the **Model** **Summary** **table** generated by SPSS. Includes step by step explanation of each calculated value. Includes explanati. Value. A tbl_regression object . Methods. The default method for tbl_regression() **model summary** uses broom::tidy(x) to perform the initial tidying of the **model** object. There are,. Mar 18, 2021 ยท The gtsummary package is for making beautiful **summary** **tables** with **R**, in **R** Markdown documents. 1 Like statistishdan March 25, 2021, 7:51pm #5 Hey @AndyZ, It's possible to get the results in a data frame using the gtsummary package. You will however need to dev version of the package. I've included an example below..

The simplest way of producing the **table** output is by passing the fitted **models** as parameter. By default, estimates ( B ), confidence intervals ( CI) and p-values ( p) are reported. The **models** are named **Model** 1 and **Model** 2. sjt.lm (fit1, fit2) Custom labels You can specify the **'model'** label via labelDependentVariables parameter:. May 09, 2022 · Kusto Query Language is a powerful tool to explore your data and discover patterns, identify anomalies and outliers, create statistical **modeling**, and more. The query uses schema entities that are organized in a hierarchy similar to SQL's: databases, **tables**, and column s . //**summarize** -- Produces a **table** that aggregates the content of the input **table**.

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modelsummary is very easy to use. This simple call often suffices: library ( modelsummary) mod <- lm (y ~ x, dat) modelsummary (mod) The command above will automatically display a **summary** **table** in the Rstudio Viewer or in a web browser. All you need is one word to change the output format.. R2 is the percentage of variation in the response that is explained by the **model**. It is calculated as 1 minus the ratio of the error sum of squares (which is the variation that is not explained by **model**) to the total sum of squares (which is the total variation in the **model**). Interpretation Use R2 to determine how well the **model** fits your data..

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summarystatistics like the mean or median to the columns of a data.tableinR. The post will consist of these topics: 1) Example Data & Packages. 2) Example 1: Calculate Mean Values for Groups. 3) Example 2: Create new Column withSummaryStatistic: Mean values. 4) Example 3: Show Several Statistics.. tabularsummaryfor lm object Source:R/as_flextable.R. as_flextable.lm.Rd. produce a flextable describing a linearmodelproduced by function lm. # S3 method for lm as_flextable (x, ...).