killono.blogg.se - Package to calculate standard error in r

That is, the function tapply() applies FUN on X grouped by factors in INDEX. The function tapply(X, INDEX,FUN) split the data of X into subgroups based on the levels of INDEX variable, then apply the function FUN to each subgroup of the data. simplify: If FALSE, tapply returns an array of mode list.INDEX: list of one or more factor each of same length as X.X: an atomic object, typically a vector.The general syntax of tapply() function is tapply(X, INDEX,FUN=NULL.,simplify=TRUE) That is tapply() function allows us to create a group summaries based on factor levels. The tapply() function is very useful to aggregate the data. tapply() function is available in base R package. In this tutorial, we will discuss about tapply() function in R with some examples. 2.5 Example 5: tapply() Function with multiple factors.2.4 Example 4: Simplified result using tapply() Function.2.3 Example 3: tapply() Function with user-defined function.

2.2 Example 2 : quantiles using tapply() function on data frame.

2.1 Example 1: tapply() function on data frame.

If you fit a different model (using, say, nlme::gls() that allows for nonhomogeneous error variances, then you will get unequal SEs in both the model summary and the emmeans results. That is a consequence of the fact that the design is balanced (equal numers of observations in each cell) and the model assumption of a constant error SD. Notice that within each set of results above, the standard errors stay constant within a table. > contrast(emmeans(warp.lm, ~ wool*tension), interaction = "pairwise") For (1), note that the first result below matches the intercept, in both the estimate and the standard error: > emmeans(warp.lm, ~ wool * tension) We can observe these results in the output from emmeans() and its relatives.

The interaction coefficients are estimates of certain interaction contrasts (namely, differences of differences).

In particular, they are comparisons of cell means where one factor is held constant while the other one changes.

The main-effect coefficients (for a model with interaction) are estimates of certain comparisons between cell means.

The intercept is an estimate of the cell mean when each factor is at its first level.With the default contrast coding ( "eatment"), This model has an underlying assumption that the error SD is homogeneous, and its estimated value is 10.94. Residual standard error: 10.94 on 48 degrees of freedom First, the model itself: > getOption("contrasts")