I'm trying to identify the impact of how meltout day and temperature affects the species i.e colonization and extinction. My model output shows the positive relationship between these two factors (note I'm using OLM Method):When analyzing the data for colonization and extinction, the output result the value for median temperature and median meltout day is significant but median meltout shows a slight negative correlation. Here the median line is slightly downward. Can anyone help me for this in visualization?
Call:
lm(formula = log_ratio_index ~ median_temperature + median_meltoutday,
data = final_data)
Residuals:
Min 1Q Median 3Q Max
-2.8735 -0.6936 -0.2344 0.7888 2.4716
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -9.78362 4.27628 -2.288 0.02485 *
median_temperature 0.50633 0.16239 3.118 0.00255 **
median_meltoutday 0.04440 0.02003 2.216 0.02958 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.277 on 78 degrees of freedom
Multiple R-squared: 0.1137, Adjusted R-squared: 0.09094
F-statistic: 5.002 on 2 and 78 DF, p-value: 0.009042
I'm trying to identify the impact of how meltout day and temperature affects the species i.e colonization and extinction. My model output shows the positive relationship between these two factors (note I'm using OLM Method):When analyzing the data for colonization and extinction, the output result the value for median temperature and median meltout day is significant but median meltout shows a slight negative correlation. Here the median line is slightly downward. Can anyone help me for this in visualization?
Call:
lm(formula = log_ratio_index ~ median_temperature + median_meltoutday,
data = final_data)
Residuals:
Min 1Q Median 3Q Max
-2.8735 -0.6936 -0.2344 0.7888 2.4716
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -9.78362 4.27628 -2.288 0.02485 *
median_temperature 0.50633 0.16239 3.118 0.00255 **
median_meltoutday 0.04440 0.02003 2.216 0.02958 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.277 on 78 degrees of freedom
Multiple R-squared: 0.1137, Adjusted R-squared: 0.09094
F-statistic: 5.002 on 2 and 78 DF, p-value: 0.009042
1 Answer
Reset to default 0There are lots of ways to do this but one of the easiest is with the effects package.
m1 <- lm(mpg ~ hp + disp, data = mtcars)
library(effects)
plot(allEffects(m1))
You can also do this with the sjPlot package, the ggeffects package, the marginaleffects package, or the emmeans package: see also How do I plot a single numerical covariate using emmeans (or other package) from a model? ...