contrast.lm | R Documentation |
This function computes one or more contrasts of the estimated regression coefficients in a fit from one of the functions in Design, along with standard errors, confidence limits, t or Z statistics, P-values.
## S3 method for class 'lm' contrast(fit, ...) ## S3 method for class 'gls' contrast(fit, ...) ## S3 method for class 'lme' contrast(fit, ...) ## S3 method for class 'geese' contrast(fit, ...) contrast_calc( fit, a, b, cnames = NULL, type = c("individual", "average"), weights = "equal", conf.int = 0.95, fcType = "simple", fcFunc = I, covType = NULL, ..., env = parent.frame(2) )
fit |
A fit of class |
... |
For |
a, b |
Lists containing conditions for all predictors in the model that
will be contrasted to form the hypothesis |
cnames |
A vector of character strings naming the contrasts when
|
type |
A character string. Set |
weights |
A numeric vector, used when |
conf.int |
The confidence level for confidence intervals for the contrasts. |
fcType |
A character string: "simple", "log" or "signed". |
fcFunc |
A function to transform the numerator and denominator of fold changes. |
covType |
A string matching the method for estimating the covariance
matrix. The default value produces the typical estimate. See
|
env |
An environment in which evaluate fit. |
These functions mirror rms::contrast.rms()
but have fewer options.
There are some between-package inconsistencies regarding degrees of freedom in some models. See the package vignette for more details.
Fold changes are calculated for each hypothesis. When fcType ="simple"
, the
ratio of the a
group predictions over the b
group predictions are used. When fcType = "signed"
, the ratio is used
if it is greater than 1; otherwise the negative inverse (e.g.,
-1/ratio
) is returned.
a list of class contrast.Design
containing the elements
Contrast
, SE
, Z
, var
, df.residual
Lower
, Upper
, Pvalue
, X
,
cnames
, and foldChange
, which denote the contrast estimates, standard
errors, Z or t-statistics, variance matrix, residual degrees of freedom
(this is NULL
if the model was not ols
), lower and upper confidence
limits, 2-sided P-value, design matrix, and contrast names (or NULL
).
rms::contrast.rms()
, sandwich::vcovHC()
library(nlme) Orthodont2 <- Orthodont Orthodont2$newAge <- Orthodont$age - 11 fm1Orth.lme2 <- lme(distance ~ Sex * newAge, data = Orthodont2, random = ~ newAge | Subject ) summary(fm1Orth.lme2) contrast(fm1Orth.lme2, a = list(Sex = levels(Orthodont2$Sex), newAge = 8 - 11), b = list(Sex = levels(Orthodont2$Sex), newAge = 10 - 11) ) # --------------------------------------------------------------------------- anova_model <- lm(expression ~ diet * group, data = two_factor_crossed) anova(anova_model) library(ggplot2) theme_set(theme_bw() + theme(legend.position = "top")) ggplot(two_factor_crossed) + aes(x = diet, y = expression, col = group, shape = group) + geom_point() + geom_smooth(aes(group = group), method = lm, se = FALSE) int_model <- lm(expression ~ diet * group, data = two_factor_crossed) main_effects <- lm(expression ~ diet + group, data = two_factor_crossed) # Interaction effect is probably real: anova(main_effects, int_model) # Test treatment in low fat diet: veh_group <- list(diet = "low fat", group = "vehicle") trt_group <- list(diet = "low fat", group = "treatment") contrast(int_model, veh_group, trt_group) # --------------------------------------------------------------------------- car_mod <- lm(mpg ~ am + wt, data = mtcars) print(summary(car_mod), digits = 5) mean_wt <- mean(mtcars$wt) manual_trans <- list(am = 0, wt = mean_wt) auto_trans <- list(am = 1, wt = mean_wt) print(contrast(car_mod, manual_trans, auto_trans), digits = 5)
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