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ask this because the geepack does not "depend" or import from the stats package according to https://cran.r-project.org/web/packages/geepack/geepack.pdf. the stats package has the function residuals(fitted … object) and resid(fitted object). residuals {stats} R Documentation Extract Model Residuals Description residuals is a generic function which extracts model residuals from objects returned by modeling …

I am using the same code to build the model for two different species but getting very different looking residual vs fitted plots and I am wondering why. …

The problem is: now my residuals plot (resid vs. fitted) has a clear pattern. If I delete that new "observation level random term" the plot looks good again. Why? …

I found a similar question (Residual vs Fitted) but (unless I am overthinking it) it does not apply to my issue since my data is not discrete data. … This is the residual vs fit graph I get: And the QQ Plot: From my understanding the Resid vs Fitted violates the assumptions of linear regression. From there I do now know what to do. …

However, When I finaly fitted a model with FUll variable . qqPlot(resid(Model)) tell me I got the right things but DHARMa's result told me I'm wrong. Shall I believe DHARMa Pakacages? … par(mfrow = c(1,2)) qqPlot(resid(M)) plot(resid(M) ~ fitted(M)); abline(h=0) And pardon me I can't upload my dataset for your convinience because it will exceed the number of character. …

Here is the resids vs fitted and qqplot of the model (obtained using 'plot(mod_och, type = 'residuals', series = 3)', the tails are heavy and residuals not normally distributed I assumed the red line …

The results agree with the suspicions, but clear groups are seen on the residual plot (resid vs. fitted) - please see the fig1. …

However, when I checked the diagnostic plots for the best model, they look as follows: Should I be concerned about the fitted vs. resid plot? What could be causing that kind of dispersion? …

hist(resid(M3)) qqnorm(resid(M3)) qqline(resid(M3)) Independence checks look good (?), for example here for facesex: plot(data$facesex,resid(M3)) Heterogeneity check looks... well, hum. … plot(fitted(M3),resid(M3)) abline(h=0,col="grey") lines(lowess(fitted(M3)[is.finite(fitted(M3))],resid(M3)[is.finite(fitted(M3))]),col="red") The pattern is absent in M1 and M2. …

With plot(fitted(model), resid(model)) I get the plot on the left below. … I get the following from model <- lm(time ~ gender + age + runs + friends, mydata) and plot(fitted(model), resid(model)): So, the lm() seems okay in this regard. …

I am familiar with the idea of comparing alternative linear regression models using anova(model1,model2), for models fitted using lm() in R. … . df Resid. …

.fitted, y = .resid)) + geom_point() + geom_smooth(se = FALSE, col = "red") + #Adds the line of fit geom_hline(yintercept = 0, linetype = 2) + #Adds the horizontal dashed line labs(x ="Fitted … Values", y ="Residuals", title = "Residual vs Fitted") + theme_bw() ##Residuals vs Petal.Length (what my prof wants) ggplot(data = d, aes(x = Petal.Length, y = .resid)) + geom_point() + geom_smooth …

Passing this into a linear model with a log-log transform, the qqplot and histogram looked pretty normal but the resid vs fitted was definitely off, but I don't know exactly what causes the behavior (pictured …

It appears to shows residuals that are correlated with the fitted values: library(ggplot2) df_lm_longitude <- ggplot2::fortify(lm_longitude) ggplot(df_lm_longitude, aes(.fitted, .resid)) + geom_point … () + stat_smooth() But change the scale of the y axis, and residuals vs fitted values plot looks perfect: ggplot(df_lm_longitude, aes(.fitted, .resid)) + geom_point() + stat_smooth() + ylim(-0.01, …

I ran this Poisson regression: library(ggplot2) glm_diamonds <- glm(price ~ carat, family = "poisson", data=diamonds) I then saved the Pearsons residuals and fitted values from the model: resid <- … resid(glm_diamonds, type = "pearson") fitted <- fitted(glm_diamonds) df <- data.frame(resid, fitted) I then plotted the Pearsons residuals against fitted values: ggplot(df, aes(fitted, resid)) + geom_point …