Preparatory:
- replace
-
with_
in variable names - replace
-
withminus
and+
withplus
for last variable - convert all variables to factors
- Review
mjca
function signature for expected argument
ob j A response pattern matrix (data frame containing factors), or a frequency table(a “table” object) or an integer array
suppressPackageStartupMessages({
library(ca)
})
input <- as.data.frame(structure(list(
dens_ville = c(
"moderate", "very_low", "moderate",
"low", "very_low", "moderate", "moderate", "moderate", "moderate",
"moderate", "moderate", "moderate", "moderate", "low", "low",
"moderate", "moderate", "moderate", "moderate", "low"
), climat = c(
"cold_semi_arid",
"cold_semi_arid", "cold_semi_arid", "cold_semi_arid", "cold_semi_arid",
"hot_semi_arid", "hot_semi_arid", "continental", "continental",
"continental", "continental", "continental", "continental", "continental",
"continental", "continental", "continental", "continental", "continental",
"continental"
), prov = c(
"Instagram", "direct", "Instagram",
"Facebook", "Facebook", "Facebook", "Instagram", "Facebook",
"direct", "Instagram", "direct", "Instagram", "Facebook", "direct",
"direct", "Instagram", "Instagram", "Facebook", "Instagram",
"Instagram"
), promo = c(
"N", "N", "O", "N", "N", "N", "N", "N",
"N", "N", "N", "N", "N", "O", "N", "O", "O", "N", "N", "N"
),
sub_mail = c(
"O", "O", "O", "O", "O", "O", "O", "O", "N",
"N", "N", "O", "N", "O", "O", "O", "O", "N", "N", "N"
), GDP_ca = c(
"60k",
"40k", "60k", "60k", "60k", "60k", "60k", "50k", "50k", "50k",
"50k", "50k", "50k", "60k", "50k", "50k", "50k", "60k", "60k",
"50K"
), benef_quali = c(
"plus", "plus", "minus", "plus",
"plus", "plus", "minus", "plus", "plus", "plus", "plus",
"minus", "minus", "minus", "plus", "plus", "plus", "minus",
"minus", "minus"
)
), class = c(
"spec_tbl_df", "tbl_df", "tbl",
"data.frame"
), row.names = c(NA, -20L), spec = structure(list(
cols = list(dens_ville = structure(list(), class = c(
"collector_character",
"collector"
)), climat = structure(list(), class = c(
"collector_character",
"collector"
)), prov = structure(list(), class = c(
"collector_character",
"collector"
)), promo = structure(list(), class = c(
"collector_character",
"collector"
)), sub_mail = structure(list(), class = c(
"collector_character",
"collector"
)), GDP_ca = structure(list(), class = c(
"collector_character",
"collector"
)), benef_quali = structure(list(), class = c(
"collector_character",
"collector"
))), default = structure(list(), class = c(
"collector_guess",
"collector"
)), skip = 1L
), class = "col_spec")))
mjca(input)
#>
#> Eigenvalues:
#> 1 2 3 4 5
#> Value 0.079674 0.025836 0.01188 0.008318 0.00043
#> Percentage 45.01% 14.59% 6.71% 4.7% 0.24%
#>
#>
#> Columns:
#> dens_ville:low dens_ville:moderate dens_ville:very_low
#> Mass 0.028571 0.100000 0.014286
#> ChiDist 0.887074 0.326054 1.640732
#> Inertia 0.022483 0.010631 0.038457
#> Dim. 1 0.203258 0.508740 -3.967700
#> Dim. 2 0.521807 -0.313550 1.151234
#> climat:cold_semi_arid climat:continental climat:hot_semi_arid
#> Mass 0.035714 0.092857 0.014286
#> ChiDist 0.926191 0.406944 1.302186
#> Inertia 0.030637 0.015377 0.024224
#> Dim. 1 -2.184247 0.884040 -0.285640
#> Dim. 2 -0.612577 0.750688 -3.348028
#> prov:direct prov:Facebook prov:Instagram promo:N promo:O sub_mail:N
#> Mass 0.035714 0.042857 0.064286 0.114286 0.028571 0.050000
#> ChiDist 0.796396 0.654224 0.500775 0.220038 0.880154 0.680510
#> Inertia 0.022652 0.018343 0.016121 0.005533 0.022133 0.023155
#> Dim. 1 -0.611919 -0.527934 0.691910 -0.078394 0.313577 1.246810
#> Dim. 2 2.270584 -0.831213 -0.707294 0.131480 -0.525919 0.899438
#> sub_mail:O GDP_ca:40k GDP_ca:50k GDP_ca:50K GDP_ca:60k
#> Mass 0.092857 0.007143 0.064286 0.007143 0.064286
#> ChiDist 0.366428 2.250560 0.557181 2.013094 0.535171
#> Inertia 0.012468 0.036179 0.019958 0.028947 0.018412
#> Dim. 1 -0.671359 -4.783702 0.752008 2.261258 -0.471736
#> Dim. 2 -0.484313 3.164784 1.211729 0.728415 -1.644307
#> benef_quali:minus benef_quali:plus
#> Mass 0.057143 0.085714
#> ChiDist 0.569845 0.379897
#> Inertia 0.018556 0.012370
#> Dim. 1 0.882021 -0.588014
#> Dim. 2 -0.893626 0.595750
Created on 2020-12-04 by the reprex package (v0.3.0.9001)