Skip to main content
Search type Search syntax
Tags [tag]
Exact "words here"
Author user:1234
user:me (yours)
Score score:3 (3+)
score:0 (none)
Answers answers:3 (3+)
answers:0 (none)
isaccepted:yes
hasaccepted:no
inquestion:1234
Views views:250
Code code:"if (foo != bar)"
Sections title:apples
body:"apples oranges"
URL url:"*.example.com"
Saves in:saves
Status closed:yes
duplicate:no
migrated:no
wiki:no
Types is:question
is:answer
Exclude -[tag]
-apples
For more details on advanced search visit our help page
Results for fitted resid
Search options not deleted
-1 votes

R: glm function with family = "binomial" and "weight" specification

(Intercept) (Intercept) 2.333333 vcov() takes this one" View(survey:::summary.svyglm) "Shows that cov.unscaled = covmat, cov.scaled = covmat not scaled by dispersion dispersion <- svyvar(resid … (object, 'pearson'), object$survey.design, na.rm = TRUE)" svyvar(resid(Model, 'pearson'), Model$survey.design, na.rm = TRUE) " variance SE [1,] 1.5 0 is what summary(Model)$dispersion uses …
DrJerryTAO's user avatar
  • 2,423
2 votes
Accepted

Interpreting Negative Binomial residual plot

)) plot(simulateResiduals(m1)) And here it is with the offset omitted: dd2 <- simfun(seed = 101, off = FALSE) m2 <- with(dd2, MASS::glm.nb(form, data = data)) plot(simulateResiduals(m2)) Code for fitted … vs residual above: ggplot(augment(m2), aes(.fitted, .resid)) + geom_point() + geom_smooth() I looked for offset-related issues on the DHARMa issues page, but none of the hits here jumped out at me as …
Ben Bolker's user avatar
  • 47.4k
4 votes
1 answer
124 views

How is Gaussian log likelihood value calculated in weighted LM, GLM and GLS?

~1/n Coefficients: Value Std.Error t-value p-value (Intercept) 0.25 0.03535534 7.071068 0.0194 Residual standard error: 0.2236068 0.2236068 = 0.2738613 * sqrt(2/3) is underestimated resid … + residuals, mean = fitted, sd = sigma(Model))))) "4.907056 matches above" Remaining Question Should the log likelihood value of OLS models be based on not the unbiased residual variance relationship …
DrJerryTAO's user avatar
  • 2,423
0 votes
0 answers
24 views

Improving glmm accuracy - what can I do here?

colour:factor(treatment) + factor(treatment):factor(screening) + (1|id), df, family=beta_family(link="logit") ) For validating I've been using the following: sresid=resid … (m1, type="pearson") shapiro.test(sresid) hist(sresid) fits=fitted(m1) plot(sresid~fits) simulateResid <- simulateResiduals(m1) #from DHARMa package plot(simulateResid) I'm at a loss where to go after …
drsp's user avatar
  • 1
2 votes
2 answers
63 views

Resids vs fitted plot shows cyclical patterns. I have chosen 5 explanatory variables out of ...

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 …
Samuel beauregard-tousignant's user avatar
5 votes

Isn't it normal that residual plots for mixed effect models will show a trend?

(1) N <- 500 R <- 4 subjects <- rep(factor(1:N),each = R) mu <- rep(rnorm(N), each = R) y <- mu + 0.2 * rnorm(N * R) df <- data.frame(subjects, mu, y) mdl <- lme4::lmer(y ~ 1 + (1|subjects), df) plot(resid … (mdl) ~ fitted(mdl), col = "steelblue", pch = 16) abline(coef(lm(resid(mdl) ~ fitted(mdl))), col = 2, lwd = 2) One solution, provided by the DHARMa package, is to use simulated residuals conditional …
Frans Rodenburg's user avatar
0 votes
0 answers
17 views

R – Model specification for TWO TIME variables AND PAIRED design in repeated measures genera...

(globalmodel, type="pearson") fits<-fitted(globalmodel) infl_model <- influence(globalmodel,obs=T) cd_model <- cooks.distance(infl_model) cd_thres <- 4/df_n devresid<-resid(globalmodel … (model, type="pearson") hist(sresid) qqnorm(sresid); qqline(sresid, col="red") fits<-fitted(model) plot(sresid~fits) boxplot(mydata$Response) cooksd <- cooks.distance(model) %>% as.data.frame …
kris sales's user avatar
0 votes
0 answers
44 views

Constructing nomogram from approximate model in Regression Modelling Strategies

hypothetical data f <- areg.boot(response ~ I(age) + monotone(blood.pressure) + race) # use I(response) to not transform the response variable plot(f, conf.int=.9) # Check distribution of residuals plot(fitted … (f), resid(f)) qqnorm(resid(f)) # Refit this model using ols so that we can draw a nomogram of it. # The nomogram will show the linear predictor, median, mean. # The last two are smearing estimators. …
ScapeProf's user avatar
  • 101
0 votes
0 answers
11 views

How might I go about analyzing the affect that the number of attempts of something has on th...

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 …
peeezy's user avatar
  • 1
3 votes
Accepted

Linear Regression with Only Categorical Features: Evaluating the Model

To test heteroscedasticity, I recommend White’s general test, implemented as the fitted-value approach of a Breusch-Pagan test, lmtest::bptest(lm(), varformula = ~ fitted + I(fitted^2) + I(fitted^3)) after … saving fitted values into the data frame as fitted. …
DrJerryTAO's user avatar
  • 2,423
3 votes
Accepted

lavaan's estimated residuals output different than manually estimated residuals

The fitted function and residual function are both standardized, just as Jeremy pointed out. … Try resid_lavaan = resid(fit)$cov resid_manually = lavInspect(fit, what = "sampstat")$cov - fitted(fit)$cov You will find out that the resid_lavaan matches resid_manually. …
Sola Cong Mou's user avatar
3 votes

From overdispersion to underdispersion: comparing linear regression models with DHARMa

The most common overdispersion tests in the literature — comparing (resid deviance)/(resid df) to 1, or (sum of [Pearson resids]^2)/(resid df) to 1, or computing the one-tailed p-value of resid deviance … In contrast, the DHARMa test is based on simulations from the fitted model — it is computationally intensive, but should be much more reliable. …
Ben Bolker's user avatar
  • 47.4k
4 votes
2 answers
128 views

How to check linearity of a variable without plots/graphs in R?

(cpg1_gamm_cases$mer) cpg1_r <- resid(cpg1_gamm_cases$mer) plot(x = cpg1_f, y = cpg1_r, xlab = "Fitted values", ylab = "Residuals", cex = 0.3) abline(lm(resid(cpg1_gamm_cases$mer) ~ … fitted(cpg1_gamm_cases$mer))) result 1: result 2: The above plots show there is no clear linear relationship between cpg_1 and time. …
bmr's user avatar
  • 43
8 votes
1 answer
208 views

Is my intuition behind the weight matrix correct for quantile regression?

rnorm(200) y <- (.40 * x) + rnorm(200) plot(x,y) #### Fit Q25 Regression #### qu <- .25 fit <- rq( y ~ x, tau = qu ) summary(fit) #### Plot #### broom::augment(fit) %>% mutate(weight = ifelse(.resid … x <- rnorm(200) y <- (.40 * x) + rnorm(200) #### Fit Q001 Regression #### qu <- .001 fit <- rq( y ~ x, tau = qu ) summary(fit) #### Plot #### broom::augment(fit) %>% mutate(weight = ifelse(.resid
Shawn Hemelstrand's user avatar
3 votes
1 answer
58 views

Model diagnosis in GLMM model of binary outcome variable

I got the following results or plots. scatter.smooth(fitted(twoRIM), sqrt(abs(resid(twoRIM))), col=6) qqline(resid(twoRIM)) plot(twoRIM) qqnorm(resid(twoRIM),main="Residual normal plot",col=4,adj … (twoRIM),resid(twoRIM),col=4) qqnorm(resid(twoRIM)) The plots I got — the residual and other related plots — are quite different from other forms of model diagnosis I knew before, and I faced a bit …
Sofonias Derso's user avatar

1
2 3 4 5
12
15 30 50 per page