anova

问题 I'm simultaneously trying to understand R's predict() function and the 'effects' package effect() function. Essentially, I'm running a regression to test the interaction of two dichotomous IVs on a DV while controlling for two continuous covariates. In my actual dataset, the interaction is significant and so now I would like to plot the interaction. Because I have covariates in my model, I should plot the means after controlling for these other variables (i.e. estimated marginal means in SPSS

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 8 years ago . Improve this question I want to model the treatment effect over time on biological data using lmer() to take into account the individual effect. The procedure usually used is to build several models by deleting step by step fixed effects and interactions terms and then find the best models using anova(model1

问题 I have a data frame similar to the one created by the code below. In this example, measurements of 5 variables are taken on are 30 individuals represented by ID . The individuals can be separated by any of three grouping variables: GroupVar1,GroupVar2,GroupVar3 . For each of the grouping variables, I need to conduct an ANOVA for each of the 5 variables, and return the results of each (possibly onto a pdf or separate document?). How can I write a function, or use iteration, to handle this

问题 I have a very large data set that requires individual one-way ANOVAs for multiple species on 4 different treatments with several measurements. Usually I just make separate excel spread sheets for each species and run ANOVAs on each, looping through each measurement column, but this is very time consuming. Is it possible to use a single spread sheet and run ANOVAs on the melted data? Or perhaps there is another reshape option I could use? Data: structure(list(Species = c("A", "A", "A", "A", "A

问题 I'm having trouble generating a compact letter display for my results. I've run an ANOVA followed by Tukey's HSD to generate the p values for each pair, but I do not know how (or if it is possible?) to assign letters to these p values to show which pairs are significant from each other. csa.anova<-aov(rate~temp*light,data=csa.per.chl) summary(csa.anova) TukeyHSD(csa.anova) This runs the tests I need, but I don't know how to assign letters to each p value to show which pairs are significant.

问题 I found the statsmodels implementation of the anova testing for linear models to be very useful (http://www.statsmodels.org/dev/generated/statsmodels.stats.anova.anova_lm.html#statsmodels.stats.anova.anova_lm) but I was wondering, since it's not present in the library, how one could approach building the equivalent version for the logistic regression part. formulas : from statsmodels.formula.api import ols, logit import statsmodels.api as sm ols(formula_str, data=data_on_which_to_perform

问题 I am trying to run a post hoc analysis on an unbalanced two way anova using the anova_test funciton in the rstatix package. I need to run this post hoc test iteratively, as I have ~26 response (y) variables. My first step is to create models of all my y variables with relation to group and treatment . I have successfully managed to do this, creating a single list with 26 models: models <- map(data[,y1:y26], ~(lm(.x ~data$group*data$treatment))) Now comes the part I'm stuck on. Referring to

问题 I am trying to follow the tutorial by Datanovia for Two-way repeated measures ANOVA. A quick overview of my dataset: I have measured the number of different bacterial species in 12 samplingsunits over time. I have 16 time points and 2 groups. I have organised my data as a tibble called "richness"; # A tibble: 190 x 4 id selection.group Day value <fct> <fct> <fct> <dbl> 1 KRH1 KR 2 111. 2 KRH2 KR 2 141. 3 KRH3 KR 2 110. 4 KRH1 KR 4 126 5 KRH2 KR 4 144 6 KRH3 KR 4 135. 7 KRH1 KR 6 115. 8 KRH2

问题 I want to do the shapiro-wilk test for my regressions. Here is my code library(data.table) Regresiones_shapirotest_1<- data.table(tabla.Enero)[, .(Lm = lapply(.SD, function(x) resid(lm(tabla.Enero$PPNA ~ x)))), .SDcols = 15:93] I can get the test values only in the console Regresiones_shapirotest_1 [, lapply(Lm, shapiro.test)] [2] write.xlsx (Regresiones_shapirotest_1 , file = "shapirotest.xlsx", sheetName = "Hoja1", Col.names = TRUE, row.names = TRUE, append = FALSE) But when I try to export

问题 I want to do the shapiro-wilk test for my regressions. Here is my code library(data.table) Regresiones_shapirotest_1<- data.table(tabla.Enero)[, .(Lm = lapply(.SD, function(x) resid(lm(tabla.Enero$PPNA ~ x)))), .SDcols = 15:93] I can get the test values only in the console Regresiones_shapirotest_1 [, lapply(Lm, shapiro.test)] [2] write.xlsx (Regresiones_shapirotest_1 , file = "shapirotest.xlsx", sheetName = "Hoja1", Col.names = TRUE, row.names = TRUE, append = FALSE) But when I try to export