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Summarize the sums of squares attributable to each factor in a full factorial experimental design

Source: R/factorial_summary.R
factorial_summary.Rd

Computes an analysis of variance for a set of factors in a full factorial experimental design. For each term order within the design (i.e., main effects, two-way interactions, three-way interactions), the total sum of squares attributable to each factor is computed. For terms beyond order

  1. Optionally, also computes the total sum of squares for each order (across all factors) and the total sum of squares attributable to each factor.

Usage

factorial_summary(
  data,
  y,
  factors,
  sum_orders = TRUE,
  include_total = TRUE,
  check_balance = TRUE
)

Arguments

data

data.frame or tibble containing simulation results. Each row should correspond to a unique set of parameter values.

y

character string corresponding to the outcome variable in data.

factors

character vector containing the names of two or more variables in data that correspond to factors in the experimental design.

sum_orders

logical indicating whether to compute the total sum of squares attributable to each factor, with a default of TRUE.

include_total

logical indicating whether to compute the total sum of squares for each term order (across all factors), with a default of TRUE.

check_balance

logical indicating whether to check that the experimental design is balanced, with a default of TRUE.

Value

A data.frame

Examples


data("Chen_Pusto")
dat <- subset(Chen_Pusto, method == "PET-PEESE")

factorial_summary(dat, "bias", c("k","mu","tau","cor_mu","wts"))
#>   factor d.f.      Order 1     Order 2     Order 3     Order 4     Order 5
#> 1      k    3 0.1389360239 0.095743716 0.062559309 0.007415054 0.003567425
#> 2     mu    3 0.8150849247 0.929389444 0.231529831 0.008377466 0.003567425
#> 3    tau    3 0.9466150845 1.111121964 0.215655348 0.007856071 0.003567425
#> 4 cor_mu    2 0.0004559467 0.003466843 0.004117043 0.005308448 0.003567425
#> 5    wts    5 0.7150778461 1.547949574 0.209064274 0.008403422 0.003567425
#> 6  Total   NA 2.6161698260 1.843835770 0.240975268 0.009340115 0.003567425
#>          Sum
#> 1 0.30822153
#> 2 1.98794909
#> 3 2.28481589
#> 4 0.01691571
#> 5 2.48406254
#> 6 4.71388840
factorial_summary(dat, "bias", c("k","mu","tau","wts"), include_total = FALSE)
#>      factor d.f.    Order 1   Order 2    Order 3     Order 4        Sum
#> 1         k    3 0.13893602 0.0956514 0.06103523 0.004031667 0.29965433
#> 2        mu    3 0.81508492 0.9285014 0.22907022 0.004031667 1.97668817
#> 3       tau    3 0.94661508 1.1104292 0.21368031 0.004031667 2.27475626
#> 4       wts    5 0.71507785 1.5461559 0.20678891 0.004031667 2.47205432
#> 5 Residuals  768 0.01691571 0.0000000 0.00000000 0.000000000 0.01691571