The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
The goal of autoMFA is to fit the Mixture of Factor Analyzers (MFA) model with as few inputs from users as possible. Specifically, it aims to provide methods which can automatically determine the number of components, g, and the number of factors, q, without them needing to be specified by the user.
You can install the released version of autoMFA from CRAN with:
install.packages("autoMFA")This is a basic example which shows you how to fit an MFA model using autoMFA, and how we can access the output structure:
library(autoMFA)
RNGversion('4.0.3'); set.seed(3)
MFA.fit <- AMFA(autoMFA::MFA_testdata,3,3, nkmeans = 3, nrandom = 3, itmax = 100)
#> | | | 0% | |............ | 17% | |....................... | 33% | |................................... | 50% | |............................................... | 67% | |.......................................................... | 83% | |......................................................................| 100%
#Looking at the fitted model parameters
MFA.fit$model
#> $pivec
#> [,1] [,2] [,3]
#> [1,] 0.09444444 0.3333333 0.5722222
#>
#> $B
#> , , 1
#>
#> [,1]
#> [1,] 0.51597871
#> [2,] 0.08739884
#> [3,] -0.10294159
#>
#> , , 2
#>
#> [,1]
#> [1,] -0.5594624
#> [2,] -0.1989323
#> [3,] 0.7229942
#>
#> , , 3
#>
#> [,1]
#> [1,] 0.5599214
#> [2,] -0.1553632
#> [3,] 0.5990317
#>
#>
#> $mu
#> [,1] [,2] [,3]
#> [1,] 0.07634856 -0.006538803 2.94287575
#> [2,] -0.04333060 2.995101672 0.03491934
#> [3,] 2.96309540 0.006519381 -0.07965993
#>
#> $D
#> , , 1
#>
#> [,1] [,2] [,3]
#> [1,] 0.005 0.0000000 0.0000000
#> [2,] 0.000 0.1018841 0.0000000
#> [3,] 0.000 0.0000000 0.1134614
#>
#> , , 2
#>
#> [,1] [,2] [,3]
#> [1,] 0.1246119 0.0000000 0.0000000
#> [2,] 0.0000000 0.1124388 0.0000000
#> [3,] 0.0000000 0.0000000 0.0855889
#>
#> , , 3
#>
#> [,1] [,2] [,3]
#> [1,] 0.1002322 0.0000000 0.00000000
#> [2,] 0.0000000 0.1000401 0.00000000
#> [3,] 0.0000000 0.0000000 0.09205569
#>
#>
#> $numFactors
#> [,1] [,2] [,3]
#> [1,] 1 1 1
#How long did it take for each start to fit?
MFA.fit$diagnostics$times
#> g time status
#> 1 3 0.203135013580322 OK
#> 2 3 0.190057039260864 OK
#> 3 3 0.209331035614014 OK
#> 4 3 0.188174962997437 OK
#> 5 3 0.186160087585449 OK
#> 6 3 0.222768068313599 OK
#Lets look at the clusterings of the fitted model
MFA.fit$clustering$allocations
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,] 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
#> [1,] 3 3 3 3 3 3 3 3 3 3 3 3
#> [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38]
#> [1,] 3 3 3 3 3 3 3 3 3 3 3 3
#> [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50]
#> [1,] 3 3 3 3 3 3 3 3 3 3 3 3
#> [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62]
#> [1,] 3 3 3 3 3 3 3 3 3 3 3 3
#> [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] [,74]
#> [1,] 3 3 3 3 3 3 3 3 3 3 3 3
#> [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85] [,86]
#> [1,] 3 3 3 3 3 3 3 3 3 3 3 3
#> [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96] [,97] [,98]
#> [1,] 3 3 3 3 3 3 3 3 3 3 3 3
#> [,99] [,100] [,101] [,102] [,103] [,104] [,105] [,106] [,107] [,108]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,109] [,110] [,111] [,112] [,113] [,114] [,115] [,116] [,117] [,118]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,119] [,120] [,121] [,122] [,123] [,124] [,125] [,126] [,127] [,128]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,129] [,130] [,131] [,132] [,133] [,134] [,135] [,136] [,137] [,138]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,139] [,140] [,141] [,142] [,143] [,144] [,145] [,146] [,147] [,148]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,149] [,150] [,151] [,152] [,153] [,154] [,155] [,156] [,157] [,158]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,159] [,160] [,161] [,162] [,163] [,164] [,165] [,166] [,167] [,168]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,169] [,170] [,171] [,172] [,173] [,174] [,175] [,176] [,177] [,178]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,179] [,180] [,181] [,182] [,183] [,184] [,185] [,186] [,187] [,188]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,189] [,190] [,191] [,192] [,193] [,194] [,195] [,196] [,197] [,198]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,199] [,200] [,201] [,202] [,203] [,204] [,205] [,206] [,207] [,208]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,209] [,210] [,211] [,212] [,213] [,214] [,215] [,216] [,217] [,218]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,219] [,220] [,221] [,222] [,223] [,224] [,225] [,226] [,227] [,228]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,229] [,230] [,231] [,232] [,233] [,234] [,235] [,236] [,237] [,238]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,239] [,240] [,241] [,242] [,243] [,244] [,245] [,246] [,247] [,248]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,249] [,250] [,251] [,252] [,253] [,254] [,255] [,256] [,257] [,258]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,259] [,260] [,261] [,262] [,263] [,264] [,265] [,266] [,267] [,268]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,269] [,270] [,271] [,272] [,273] [,274] [,275] [,276] [,277] [,278]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,279] [,280] [,281] [,282] [,283] [,284] [,285] [,286] [,287] [,288]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,289] [,290] [,291] [,292] [,293] [,294] [,295] [,296] [,297] [,298]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,299] [,300] [,301] [,302] [,303] [,304] [,305] [,306] [,307] [,308]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,309] [,310] [,311] [,312] [,313] [,314] [,315] [,316] [,317] [,318]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,319] [,320] [,321] [,322] [,323] [,324] [,325] [,326] [,327] [,328]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,329] [,330] [,331] [,332] [,333] [,334] [,335] [,336] [,337] [,338]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,339] [,340] [,341] [,342] [,343] [,344] [,345] [,346] [,347] [,348]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,349] [,350] [,351] [,352] [,353] [,354] [,355] [,356] [,357] [,358]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,359] [,360] [,361] [,362] [,363] [,364] [,365] [,366] [,367] [,368]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,369] [,370] [,371] [,372] [,373] [,374] [,375] [,376] [,377] [,378]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,379] [,380] [,381] [,382] [,383] [,384] [,385] [,386] [,387] [,388]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,389] [,390] [,391] [,392] [,393] [,394] [,395] [,396] [,397] [,398]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,399] [,400] [,401] [,402] [,403] [,404] [,405] [,406] [,407] [,408]
#> [1,] 3 3 3 3 3 3 3 3 3 3
#> [,409] [,410] [,411] [,412] [,413] [,414] [,415] [,416] [,417] [,418]
#> [1,] 3 3 3 3 2 2 2 2 2 2
#> [,419] [,420] [,421] [,422] [,423] [,424] [,425] [,426] [,427] [,428]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,429] [,430] [,431] [,432] [,433] [,434] [,435] [,436] [,437] [,438]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,439] [,440] [,441] [,442] [,443] [,444] [,445] [,446] [,447] [,448]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,449] [,450] [,451] [,452] [,453] [,454] [,455] [,456] [,457] [,458]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,459] [,460] [,461] [,462] [,463] [,464] [,465] [,466] [,467] [,468]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,469] [,470] [,471] [,472] [,473] [,474] [,475] [,476] [,477] [,478]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,479] [,480] [,481] [,482] [,483] [,484] [,485] [,486] [,487] [,488]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,489] [,490] [,491] [,492] [,493] [,494] [,495] [,496] [,497] [,498]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,499] [,500] [,501] [,502] [,503] [,504] [,505] [,506] [,507] [,508]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,509] [,510] [,511] [,512] [,513] [,514] [,515] [,516] [,517] [,518]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,519] [,520] [,521] [,522] [,523] [,524] [,525] [,526] [,527] [,528]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,529] [,530] [,531] [,532] [,533] [,534] [,535] [,536] [,537] [,538]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,539] [,540] [,541] [,542] [,543] [,544] [,545] [,546] [,547] [,548]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,549] [,550] [,551] [,552] [,553] [,554] [,555] [,556] [,557] [,558]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,559] [,560] [,561] [,562] [,563] [,564] [,565] [,566] [,567] [,568]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,569] [,570] [,571] [,572] [,573] [,574] [,575] [,576] [,577] [,578]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,579] [,580] [,581] [,582] [,583] [,584] [,585] [,586] [,587] [,588]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,589] [,590] [,591] [,592] [,593] [,594] [,595] [,596] [,597] [,598]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,599] [,600] [,601] [,602] [,603] [,604] [,605] [,606] [,607] [,608]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,609] [,610] [,611] [,612] [,613] [,614] [,615] [,616] [,617] [,618]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,619] [,620] [,621] [,622] [,623] [,624] [,625] [,626] [,627] [,628]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,629] [,630] [,631] [,632] [,633] [,634] [,635] [,636] [,637] [,638]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,639] [,640] [,641] [,642] [,643] [,644] [,645] [,646] [,647] [,648]
#> [1,] 2 2 2 2 2 2 2 2 2 2
#> [,649] [,650] [,651] [,652] [,653] [,654] [,655] [,656] [,657] [,658]
#> [1,] 2 2 2 2 1 1 1 1 1 1
#> [,659] [,660] [,661] [,662] [,663] [,664] [,665] [,666] [,667] [,668]
#> [1,] 1 1 1 1 1 1 1 1 1 1
#> [,669] [,670] [,671] [,672] [,673] [,674] [,675] [,676] [,677] [,678]
#> [1,] 1 1 1 1 1 1 1 1 1 1
#> [,679] [,680] [,681] [,682] [,683] [,684] [,685] [,686] [,687] [,688]
#> [1,] 1 1 1 1 1 1 1 1 1 1
#> [,689] [,690] [,691] [,692] [,693] [,694] [,695] [,696] [,697] [,698]
#> [1,] 1 1 1 1 1 1 1 1 1 1
#> [,699] [,700] [,701] [,702] [,703] [,704] [,705] [,706] [,707] [,708]
#> [1,] 1 1 1 1 1 1 1 1 1 1
#> [,709] [,710] [,711] [,712] [,713] [,714] [,715] [,716] [,717] [,718]
#> [1,] 1 1 1 1 1 1 1 1 1 1
#> [,719] [,720]
#> [1,] 1 1These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.