# [BioC] (Limma) LmFit and fit object

Beatriz ramos.beatriz at gmail.com
Mon Dec 19 18:11:39 CET 2005

```Hello to everybody!

I want to know everything about Linear Models.
I have read "limma: Linear Models for Microarray Data User's Guide" and
"Bioinformatics and Computational Biology Solutions Using R and
my statistical knowledge is... poor (I'm sorry).

I'm calculating the standard deviation and another statistical
parameters with basic functions in R (like sd) because I don't know  the
meaning of each fit "parameter" (\$stdev.unscaled, \$sigma, \$lods...). I
have read something about them in R help but I don't know how to
interpret it

Any book or webpage?
could anybody explain it me?

thank you

BEATRIZ

***********************************************
> fit2
An object of class "MArrayLM"
\$coefficients
1007_s_at     1053_at      117_at      121_at   1255_g_at
0.06480576 -0.05789401  0.24983909  0.10716122  0.04500985
54670 more elements ...

\$stdev.unscaled
1007_s_at   1053_at    117_at    121_at 1255_g_at
0.4472136 0.4472136 0.4472136 0.4472136 0.4472136
54670 more elements ...

\$sigma
[1] 0.2250619 0.3603931 0.4365034 0.5544353 0.1156594
54670 more elements ...

\$df.residual
[1] 4 4 4 4 4
54670 more elements ...

\$cov.coefficients
[,1]
[1,]  0.2

\$pivot
[1] 1

\$method
[1] "ls"

\$design
Vertical
55T_56T        1
20T_19T        1
40T_11T        1
26T_10T        1
25T_13T        1

\$Amean
1007_s_at   1053_at    117_at    121_at 1255_g_at
9.074874  5.801200  5.357923  7.232856  3.274542
54670 more elements ...

\$df.prior
[1] 1.930101

\$s2.prior
[1] 0.06249642

\$var.prior
[1] 0.2326472

\$proportion
[1] 0.01

\$s2.post
[1] 0.05450763 0.10795045 0.14886179 0.22768891 0.02936421
54670 more elements ...

\$t
1007_s_at    1053_at     117_at     121_at  1255_g_at
0.6206831 -0.3940092  1.4479507  0.5021711  0.5873317
54670 more elements ...

\$p.value
1007_s_at   1053_at    117_at    121_at 1255_g_at
0.5578930 0.7073516 0.1983561 0.6336441 0.5786410
54670 more elements ...

\$lods
[1] -4.865354 -4.933060 -4.456481 -4.904074 -4.876949
54670 more elements ...

\$F
[1] 0.3852475 0.1552433 2.0965613 0.2521758 0.3449585
54670 more elements ...

\$F.p.value
1007_s_at   1053_at    117_at    121_at 1255_g_at
0.5578930 0.7073516 0.1983561 0.6336441 0.5786410
54670 more rows ...

```