calc_contrast: Calculate contrast analysis for factorial designs

In cofad: Contrast Analyses for Factorial Designs

Description

Calculate contrast analysis for factorial designs

Usage

calc_contrast(
  dv,
  between = NULL,
  lambda_between = NULL,
  within = NULL,
  lambda_within = NULL,
  ID = NULL,
  data = NULL
)

Arguments

dv	dependent variable. Values must be numeric.
between	independent variable that divides the data into independent groups. Vector must be a factor.
lambda_between	contrast weights must be a named numeric. Names must match the levels of between. If lambda_between does not sum up to zero, this will be done automatically.
within	independent variable which divides the data into dependent groups. This must be a factor.
lambda_within	contrast must be a named numeric. Names must match the levels of between. If lambda_between does not sum up to zero, this will be done automatically.
ID	identifier for cases or subjects is needed for within- and mixed contrastanalysis.
data	optional argument for the data.frame containing dv and groups.

Details

For multi-factorial designs, the lambda weights of the factors must be connected.

Value

Calculates the significance of the contrast analysis. given.

References

Rosenthal, R., Rosnow, R.L., & Rubin, D.B. (2000). Contrasts and effect sizes in behavioral research: A correlational approach. New York: Cambridge University Press.

Examples

# Example for between-subjects design Table 3.1 from
# Rosenthal, Rosnow and Rubin (2001)

tab31 <- data.frame(
  Val = c(2, 6, 8, 4,10, 6, 8, 10, 4, 12, 8,
    16, 10, 14, 12, 12,  18, 14, 20, 16),
  Let = as.factor(rep(c("A", "B", "C", "D"), c(5, 5, 5, 5)))
  )
contr_bw <- calc_contrast(
   dv = Val,
   between = Let,
   lambda_between = c("A" = -3, "B" = -1, "C" = 1, "D" = 3),
   data = tab31)
contr_bw
summary(contr_bw)

# Example for within-subjects design Calculation 16.6 from
# Sedlmeier and Renkewitz (2018, p. 537)

sedlmeier537 <- data.frame(
   Var = c(27, 25, 30, 29, 30, 33, 31, 35,
           25, 26, 32, 29, 28, 30, 32, 34,
           21, 25, 23, 26, 27, 26, 29, 31,
           23, 24, 24, 28, 24, 26, 27, 32),
   within = as.factor(rep(1:4,c(8,8,8,8))),
   ID = as.factor(rep(1:8,4)))
contr_wi <- calc_contrast(
   dv = Var,
   within = within,
   ID = ID,
   lambda_within = c("1" = 0.25, "2" = -.75, "3" = 1.25, "4" = -.75),
   data=sedlmeier537
 )
contr_wi
summary(contr_wi, ci=.90)

# Exampel for mixed-designs Table 5.3 from
# Rosenthal, Rosnow and Rubin (2001)
tab53 <- data.frame(
   Var = c(3, 1, 4, 4, 5, 5, 6, 5, 7, 2, 2, 5,
           5, 6, 7, 6, 6, 8, 3, 1, 5, 4, 5, 6,
           7, 6, 8, 3, 2, 5, 6, 6, 7, 8, 8, 9),
           bw = as.factor(rep(rep(LETTERS[1:3], c(3, 3, 3)), 4)),
           wi = as.factor(rep(1:4, c(9, 9, 9, 9))),
           ID = as.factor(rep(1:9, 4 ))
   )
   lambda_within <- c("1" = -3, "2" = -1, "3" = 1, "4" = 3)
   lambda_between <-c("A" = -1, "B" = 0, "C" = 1)

contr_mx <- calc_contrast(dv = Var, between = bw,
              lambda_between = lambda_between,
              within = wi,
               lambda_within = lambda_within,
              ID = ID, data = tab53
              )
contr_mx
summary(contr_mx)

Example output

Contrast Analysis for between factor design

F(1,16) = 28.9; p = 6.183e-05
Contrasts:  A = -3; B = -1; C = 1; D = 3
r_effectsize = 0.797
$`F-Table`
          SS df  MS    F  p
contrast 289  1 289 28.9  0
within   160 16  10   NA NA
total    455 19  NA   NA NA

$Effects
             effects
r_effectsize   0.797
r_contrast     0.802
r_alerting     0.990


Contrast Analysis for within factor design

L-Values: Mean =  -1.625 ; SD =  2.409
t(7) = -1.908; p = 0.95096843
Contrasts:  1 = 0.25; 2 = -0.75; 3 = 1.25; 4 = -0.75
g_contrast = -0.675
$`L-Statistics`
       Mean        SE df         p  CI-lower    CI-upper
[1,] -1.625 0.8517314  7 0.9509684 -3.238672 -0.01132786

$Effects
                 [,1]
r-contrast  0.3241617
g-contrast -0.6745369


Contrast Analysis for Mixed-Design:

F(1,6) = 20.211; p = 0.004
Contrasts:  A = -1 B = 0 C = 1
r_effectsize = 0.871
$F_Table
             SS df     MS      F     p
contrast 42.667  1 42.667 20.211 0.004
within   12.667  6  2.111     NA    NA
total    56.222  8     NA     NA    NA

$Effects
             effect
r_effectsize  0.871
r_contrast    0.878
r_alerting    0.990

$Within_Groups
         M        SE
A 2.000000 0.5773503
B 4.000000 1.0000000
C 7.333333 0.8819171

cofad documentation built on March 2, 2020, 5:07 p.m.