I have been trying to run the following code, to analyse the simple main effect :

em <- emmeans(data, "var1", by = "var2") %>%
as.data.frame() %>%
pairs() %>%
update(by = NULL, adjust = "holm")

I keep getting the follwing error:

Error in h(simpleError(msg, call)) :
error in evaluating the argument 'object' in selecting a method for function 'update': Can't handle an object of class “tbl_df”
Use help("models", package = "emmeans") for information on supported models.

The type of my data is "tbl_df". I am for some reason also unable to change it into 'data.frame'.

I am still quite new to R, would appreciate any help, thank you!

Hi @julkakulka,
Welcome to the Posit/RStudio Community Forum.

Here are some changes to your code that may (or may not) achieve what you require (see the inline comments):

library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(emmeans)
# Fit a very simple model since we don't have your data.
my_data <- lm(mpg ~ disp + cyl, data=mtcars)
my_data
#>
#> Call:
#> lm(formula = mpg ~ disp + cyl, data = mtcars)
#>
#> Coefficients:
#> (Intercept) disp cyl
#> 34.66099 -0.02058 -1.58728
summary(my_data)
#>
#> Call:
#> lm(formula = mpg ~ disp + cyl, data = mtcars)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -4.4213 -2.1722 -0.6362 1.1899 7.0516
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 34.66099 2.54700 13.609 4.02e-14 ***
#> disp -0.02058 0.01026 -2.007 0.0542 .
#> cyl -1.58728 0.71184 -2.230 0.0337 *
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 3.055 on 29 degrees of freedom
#> Multiple R-squared: 0.7596, Adjusted R-squared: 0.743
#> F-statistic: 45.81 on 2 and 29 DF, p-value: 1.058e-09
# This works with pairs() graphics function commented out, although
# in this example update() is achieving nothing.
em1 <- emmeans(my_data, "disp", by = "cyl") %>%
as.data.frame() %>%
#pairs() %>%
update(by = NULL, adjust = "holm")
em1
#> disp cyl emmean SE df lower.CL upper.CL
#> 231 6.19 20.1 0.54 29 19 21.2
#>
#> Confidence level used: 0.95
# This produces a non-sensical graph of the fitted model components
# and the output object is empty
em2 <- emmeans(my_data, "disp", by = "cyl") %>%
as.data.frame() %>%
update(by = NULL, adjust = "holm") %>%
pairs()

em2
#> NULL
# Maybe what you really want is to look at your variables before any model fitting?
mtcars %>%
select(1:3) %>% # Include only the first three variables
pairs()

^{Created on 2022-12-26 with reprex v2.0.2}

If you post again you will be more likely to get help promptly if you include a Reproducible Example (reprex) - see the Posting Guide for more info.

Hi @technocrat, thank you for your answer, I quite don't understand what you mean exactly, but as I answered in antoher comment - in the end I used a different approach. Thanks!