Description Usage Arguments Details Value See Also Examples

Computes joint and stratified effects of the SNP and another variable based on a fitted model.

1 | ```
snp.effects(fit, var, var.levels=c(0, 1), method=NULL)
``` |

`fit` |
Return object from |

`var` |
Name of the second variable to compute the effects for. This variable can be
a dummy variable, continuous variable, or a factor. Note that if this variable enters the model
as both a main effect and interaction, then it must enter the model the same way as a main effect
and interaction for the effects to be computed correctly.
For example, if |

`var.levels` |
(For continuous |

`method` |
Vector of values from "UML", "CML", "EB" or "CCL", "HCL", "CLR". The default is NULL. |

The joint and stratified effects are computed for each method in `fit`

.
The stratified effects are the sub-group effect of the SNP stratified by
`var`

and the sub-group effect of `var`

stratified by the SNP.

**Definition of joint and stratified effects:**

Consider the model:

*logit(P(y=1)) = alpha + beta*SNP + gamma*X + delta*SNP*X.*

Let 0 be the baseline for SNP and *x_0* the baseline for X. Then the joint effect
for SNP = s and X = x relative to SNP = 0 and X = *x_0* is

*exp(alpha + beta*s + gamma*x + delta*s*x)/exp(alpha + gamma*x_0)*

The stratified effect of the SNP relative to SNP = 0 given X = x is

*exp(alpha + beta*s + gamma*x + delta*s*x)/exp(alpha + gamma*x)*

The stratified effect of `var`

relative to X = x given SNP = s is

*exp(alpha + beta*s + gamma*x + delta*s*x)/exp(alpha + beta*s)*

A convenient way to print the returned object to view the effects tables is with the function `printEffects`

.

If `fit`

is of class `snp.logistic`

, then the return object is a list of with names "UML", "CML", and "EB".
If `fit`

is of class `snp.matched`

, then the return object is a list of with names "CLR", "CCL", and "HCL".
Each sublist contains joint effects, stratified effects, standard errors and confidence intervals.
The sub-group effect of the SNP stratified by `var`

is in the list "StratEffects", and the
sub-group effect of `var`

stratified by the SNP is in the list "StratEffects.2".

1 2 3 4 5 6 7 8 9 10 11 | ```
# Use the ovarian cancer data
data(Xdata, package="CGEN")
# Fit using a stratification variable
fit <- snp.logistic(Xdata, "case.control", "BRCA.status",
main.vars=c("oral.years", "n.children"),
int.vars=c("oral.years", "n.children"),
strata.var="ethnic.group")
# Compute the effects
effects <- snp.effects(fit, "oral.years", var.levels=0:5)
``` |

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