CALM

Producing Common Contrast Codes

CALM can take a set of linear contrasts to be tested and encode them into coding schemes. The examples below take some common contrasts (treatment, control, deviation, etc.) and produce the coding scheme associated with it.

The package takes linear contrast weights (rows represented the weights for the groups, columns represent the different groups) and provides the coding pattern (where columns represent vector codes, rows represent the groups).

Treatment (First) Contrasts

For this set of hypotheses, the intercept is equivalent to mean of the first group. The additional parameters represent the difference between each group and the first group.

M1 <- c(1,0,0)
H1 <- c(-1,1,0)
H2 <- c(-1,0,1)
ex_treatment.first <- rbind(M1,H1,H2)
calm.encode(ex_treatment.first)

##         M1 H1 H2
## GROUP 1  1  0  0
## GROUP 2  1  1  0
## GROUP 3  1  0  1

Treatment (Last) Contrasts

For this set of hypotheses, the intercept is equivalent to mean of the last group. The additional parameters represent the difference between each group and the last group.

M3 <- c(0,0,1)
H1 <- c(1,0,-1)
H2 <- c(0,1,-1)
ex_treatment.last <- rbind(M3,H1,H2)
calm.encode(ex_treatment.last)

##         M3 H1 H2
## GROUP 1  1  1  0
## GROUP 2  1  0  1
## GROUP 3  1  0  0

Control (First) Contrasts

For this set of hypotheses, the intercept is equivalent to mean of the group means. The additional parameters represent the difference between each group and the first group.

GM <- c(1/3,1/3,1/3)
H1 <- c(-1,1,0)
H2 <- c(-1,0,1)
ex_control.first <- rbind(GM,H1,H2)
calm.encode(ex_control.first)

##         GM      H1      H2
## GROUP 1  1 -0.3333 -0.3333
## GROUP 2  1  0.6667 -0.3333
## GROUP 3  1 -0.3333  0.6667

Control (Last) Contrasts

For this set of hypotheses, the intercept is equivalent to mean of the group means. The additional parameters represent the difference between each group and the last group.

GM <- c(1/3,1/3,1/3)
H1 <- c(1,0,-1)
H2 <- c(0,1,-1)
ex_control.last <- rbind(GM,H1,H2)
calm.encode(ex_control.last)

##         GM      H1      H2
## GROUP 1  1  0.6667 -0.3333
## GROUP 2  1 -0.3333  0.6667
## GROUP 3  1 -0.3333 -0.3333

Deviation (First) Contrasts

For this set of hypotheses, the intercept is equivalent to mean of the group means. The additional parameters represent the difference between each group and the grand mean. The first group is not compared.

GM <- c(1/3,1/3,1/3)
H1 <- c(-1/3,2/3,-1/3)
H2 <- c(-1/3,-1/3,2/3)
ex_deviation.first <- rbind(GM,H1,H2)
calm.encode(ex_deviation.first)

##         GM H1 H2
## GROUP 1  1 -1 -1
## GROUP 2  1  1  0
## GROUP 3  1  0  1

Deviation (Last) Contrasts

For this set of hypotheses, the intercept is equivalent to mean of the group means. The additional parameters represent the difference between each group and the grand mean. The last group is not compared.

GM <- c(1/3,1/3,1/3)
H1 <- c(2/3,-1/3,-1/3)
H2 <- c(-1/3,2/3,-1/3)
ex_deviation.last <- rbind(GM,H1,H2)
calm.encode(ex_deviation.last)

##         GM H1 H2
## GROUP 1  1  1  0
## GROUP 2  1  0  1
## GROUP 3  1 -1 -1

Helmert (Forward) Contrasts

For this set of hypotheses, the intercept is equivalent to mean of the group means. The additional parameters represent the difference between each group and the mean of the subsequent groups.

GM <- c(1/3,1/3,1/3)
H1 <- c(1,-1/2,-1/2)
H2 <- c(0,1,-1)
ex_helmert.forward <- rbind(GM,H1,H2)
calm.encode(ex_helmert.forward)

##         GM      H1   H2
## GROUP 1  1  0.6667  0.0
## GROUP 2  1 -0.3333  0.5
## GROUP 3  1 -0.3333 -0.5

Helmert (Reverse) Contrasts

For this set of hypotheses, the intercept is equivalent to mean of the group means. The additional parameters represent the difference between each group and the mean of the previous groups.

GM <- c(1/3,1/3,1/3)
H1 <- c(-1,1,0)
H2 <- c(-1/2,-1/2,1)
ex_helmert.reverse <- rbind(GM,H1,H2)
calm.encode(ex_helmert.reverse)

##         GM   H1      H2
## GROUP 1  1 -0.5 -0.3333
## GROUP 2  1  0.5 -0.3333
## GROUP 3  1  0.0  0.6667

Repeated (Forward) Contrasts

For this set of hypotheses, the intercept is equivalent to mean of the first group. The additional parameters represent the difference between each group and the subsequent group.

M1 <- c(1,0,0)
H1 <- c(1,-1,0)
H2 <- c(0,1,-1)
ex_repeated.forward <- rbind(M1,H1,H2)
calm.encode(ex_repeated.forward)

##         M1 H1 H2
## GROUP 1  1  0  0
## GROUP 2  1 -1  0
## GROUP 3  1 -1 -1

Repeated (Reverse) Contrasts

For this set of hypotheses, the intercept is equivalent to mean of the first group. The additional parameters represent the difference between each group and the previous group.

M1 <- c(1,0,0)
H1 <- c(-1,1,0)
H2 <- c(0,-1,1)
ex_repeated.reverse <- rbind(M1,H1,H2)
calm.encode(ex_repeated.reverse)

##         M1 H1 H2
## GROUP 1  1  0  0
## GROUP 2  1  1  0
## GROUP 3  1  1  1

Difference (Forward) Contrasts

For this set of hypotheses, the intercept is equivalent to mean of the group means. The additional parameters represent the difference between each group and the subsequent group.

GM <- c(1/3,1/3,1/3)
H1 <- c(1,-1,0)
H2 <- c(0,1,-1)
ex_difference.forward <- rbind(GM,H1,H2)
calm.encode(ex_difference.forward)

##         GM      H1      H2
## GROUP 1  1  0.6667  0.3333
## GROUP 2  1 -0.3333  0.3333
## GROUP 3  1 -0.3333 -0.6667

Difference (Reverse) Contrasts

For this set of hypotheses, the intercept is equivalent to mean of the group means. The additional parameters represent the difference between each group and the previous group.

GM <- c(1/3,1/3,1/3)
H1 <- c(-1,1,0)
H2 <- c(0,-1,1)
ex_difference.reverse <- rbind(GM,H1,H2)
calm.encode(ex_difference.reverse)

##         GM      H1      H2
## GROUP 1  1 -0.6667 -0.3333
## GROUP 2  1  0.3333 -0.3333
## GROUP 3  1  0.3333  0.6667
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