Comments (1)
Not sure if the code has changed, but all your three examples work fine for me:
data <- structure(
list(
ID = c(1482L, 1482L, 1482L, 1483L, 1483L, 1483L, 1516L, 1516L, 1516L, 1532L, 1532L, 1532L, 1545L, 1545L, 1545L),
status = c("D", "D", "D", "D", "D", "D", "B", "B", "B", "B", "B", "B", "B", "B", "B"),
x = c(0.107674842, 0.096711338, 0.104180187, 0.17978836, 0.144056182, 0.12804244, 0.240996261, 0.238791261, 0.274007305, 0.750598233, 0.757686569, 0.704884029, 0.468086496, 0.411304874, 0.348525367),
`x:y` = c(0.107674842, 0.096711338, 0.104180187, 0.17978836, 0.144056182, 0.12804244, 0.240996261, 0.238791261, 0.274007305, 0.750598233, 0.757686569, 0.704884029, 0.468086496, 0.411304874, 0.348525367)
),
class = "data.frame",
row.names = c(NA, -15L)
)
model_1 <- lme4::lmer(x ~ status + (1 | ID), data = data)
model_2 <- lme4::lmer(`x:y` ~ status + (1 | ID), data = data)
model_3 <- lm(`x:y` ~ status, data = data)
plot(ggeffects::ggpredict(model = model_1, terms = "status"))
plot(ggeffects::ggpredict(model = model_2, terms = "status"))
plot(ggeffects::ggpredict(model = model_3, terms = "status"))
Created on 2024-02-29 with reprex v2.1.0
Can you please update ggeffects (and possibly other packages, in particular insight) and try again?
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Related Issues (20)
- reference issue in "Introduction_randomeffects.Rmd"
- plot add data=TRUE not making sense HOT 6
- Error: Objects of class `ggeffects` are not yet supported. HOT 5
- "Could not find model object to extract residuals." fails with a pipe HOT 4
- Curvilinear interaction is significant, then how do I get the significances of the linear term and quadratic term of the focal variable at the +1SD, mean, -SD points of moderator? HOT 7
- Problems with predictions from "robustbase" package ("hypothesis_test") HOT 1
- predict_response() vs ggeffect() HOT 3
- test_predictions() for zero inflated model HOT 1
- Clustered SEs with margin=marginalmeans HOT 7
- Population-level predictions and GAMLSS HOT 3
- How to use ggpredict to get the corresponding standard errors of the 25th and 75th percentile predicted values HOT 15
- Adjusted predictions different between log(Y) ~NO and Y~LOGNO HOT 5
- back_transform doesn't change results HOT 2
- Predictions for survival analysis based on median time HOT 2
- Using an offset with method='empirical' HOT 1
- clearer table of contents HOT 3
- Control color based on group in a clm() or cumulative model when using plot() HOT 1
- print_html() and embed-resources | polyfill.io warning
- Breaking change in `marginaleffects` 0.22.0 HOT 2
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