pc8807 - 3 months ago 13

R Question

I'm trying to run

`anova()`

Here is the

`str()`

`str(mhw)`

'data.frame': 500 obs. of 5 variables:

$ r : int 1 2 3 4 5 6 7 8 9 10 ...

$ c : int 1 1 1 1 1 1 1 1 1 1 ...

$ grain: num 3.63 4.07 4.51 3.9 3.63 3.16 3.18 3.42 3.97 3.4 ...

$ straw: num 6.37 6.24 7.05 6.91 5.93 5.59 5.32 5.52 6.03 5.66 ...

$ Quad : Factor w/ 4 levels "NE","NW","SE",..: 2 2 2 2 2 2 2 2 2 2 ...

Column r is a numerical value indicating which row in the field an individual plot resides

Column c is a numerical value indicating which column an individual plot resides

Column Quad corresponds to the geographical location in the field to which each plot resides

`Quad <- ifelse(mhw$c > 13 & mhw$r < 11, "NE",ifelse(mhw$c < 13 & mhw$r < 11,"NW", ifelse(mhw$c < 13 & mhw$r >= 11, "SW","SE")))`

mhw <- cbind(mhw, Quad)

I have fit a

`lm()`

`nov.model <-lm(mhw$grain ~ mhw$straw)`

anova(nov.model)

This is an

`anova()`

My trouble is that I want to run an individual

`anova()`

perhaps a

`with()`

Answer

I think you are looking for `by`

facility in R.

```
fit <- with(mhw, by(mhw, Quad, function (dat) lm(grain ~ straw, data = dat)))
```

Since you have 4 levels in `Quad`

, you end up with 4 linear models in `fit`

, i.e., `fit`

is a "by" class object (a type of "list") of length 4.

To get coefficient for each model, you can use

```
sapply(fit, coef)
```

To produce model summary, use

```
lapply(fit, summary)
```

To export ANOVA table, use

```
lapply(fit, anova)
```

As a reproducible example, I am taking the example from `?by`

:

```
tmp <- with(warpbreaks,
by(warpbreaks, tension,
function(x) lm(breaks ~ wool, data = x)))
class(tmp)
# [1] "by"
mode(tmp)
# [1] "list"
sapply(tmp, coef)
# L M H
#(Intercept) 44.55556 24.000000 24.555556
#woolB -16.33333 4.777778 -5.777778
lapply(tmp, anova)
#$L
#Analysis of Variance Table
#
#Response: breaks
# Df Sum Sq Mean Sq F value Pr(>F)
#wool 1 1200.5 1200.50 5.6531 0.03023 *
#Residuals 16 3397.8 212.36
#---
#Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
#$M
#Analysis of Variance Table
#
#Response: breaks
# Df Sum Sq Mean Sq F value Pr(>F)
#wool 1 102.72 102.722 1.2531 0.2795
#Residuals 16 1311.56 81.972
#
#$H
#Analysis of Variance Table
#
#Response: breaks
# Df Sum Sq Mean Sq F value Pr(>F)
#wool 1 150.22 150.222 2.3205 0.1472
#Residuals 16 1035.78 64.736
```