lmerTest (version 2.0-32)

carrots: Consumer preference mapping of carrots

Description

In a consumer study 103 consumers scored their preference of 12 danish carrot types on a scale from 1 to 7. Moreover the consumers scored the degree of sweetness, bitterness and crispiness in the products. The carrots were harvested in autumn 1996 and tested in march 1997. In addition to the consumer survey, the carrot products were evaluated by a trained panel of tasters, the sensory panel, with respect to a number of sensory (taste, odour and texture) properties. Since usually a high number of (correlated) properties(variables) are used, in this case 14, it is a common procedure to use a few, often 2, combined variables that contain as much of the information in the sensory variables as possible. This is achieved by extracting the first two principal components in a principal components analysis(PCA) on the product-by-property panel average data matrix. In this data set the variables for the first two principal components are named (sens1 and sens2).

Usage

carrots

Format

Consumer

factor with 103 levels: numbering identifying consumers

Frequency

factor with 5 levels; "How often do you eat carrots?" 1: once a week or more, 2: once every two weeks, 3: once every three weeks, 4: at least once month, 5: less than once a month

Gender

factor with 2 levels. 1: male, 2:female

Age

factor with 4 levels. 1: less than 25 years, 2: 26-40 years, 3: 41-60 years, 4 more than 61 years

Homesize

factor with two levels. Number of persons in the household. 1: 1 or 2 persons, 2: 3 or more persons

Work

factor with 7 levels. different types of employment. 1: unskilled worker(no education), 2: skilled worker(with education), 3: office worker, 4: housewife (or man), 5: independent businessman/ self-employment, 6: student, 7: retired

Income

factor with 4 levels. 1: <150000, 2:="" 150000-300000,="" 3:="" 300000-500000,="" 4:="">500000

Source

Per Bruun Brockhoff, The Royal Veterinary and Agricultural University, Denmark.

Examples

#import lme4 package and lmerTest package
library(lmerTest)

m.carrots <- lmer(Preference ~ sens2 + Homesize
+(1+sens2|Consumer), data=carrots)

# only elimination of the random part is required. 
#approximation of ddf is Satterthwaite
step(m.carrots, reduce.random = FALSE)